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Boubaker, Sahbi, Souad Kamel, Nejib Ghazouani, and Adel Mellit. "Assessment of Machine and Deep Learning Approaches for Fault Diagnosis in Photovoltaic Systems Using Infrared Thermography." Remote Sensing 15, no. 6 (2023): 1686. http://dx.doi.org/10.3390/rs15061686.
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Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The first sub-dataset contained normal and faulty IRT images, while the second one comprised only faulty IRT images. The first sub-dataset was used to develop fault detection models referred to as binary classification, for which an image was classified as representing a faulty PV panel or a normal one. The second one was used to design fault diagnosis models, referred to as multi-classification, where four classes (Fault1, Fault2, Fault3 and Fault4) were examined. The investigated faults were, respectively, failure bypass diode, shading effect, short-circuited PV module and soil accumulated on the PV module. To evaluate the efficiency of the investigated models, convolution matrix including precision, recall, F1-score and accuracy were used. The results showed that the methods based on deep learning exhibited better accuracy for both binary and multiclass classification while solving the fault detection and diagnosis problem in PV modules/arrays. In fact, deep learning techniques were found to be efficient for the detection and classification of different kinds of defects with good accuracy (98.71%). Through a comparative study, it was confirmed that the DL-based approaches have outperformed those based on ML-based algorithms.
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Basnet, Barun, Hyunjun Chun, and Junho Bang. "An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems." Journal of Sensors 2020 (June 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/6960328.
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Effective fault diagnosis in a PV system requires understanding the behavior of the current/voltage (I/V) parameters in different environmental conditions. Especially during the winter season, I/V characters of certain faulty states in a PV system closely resemble that of a normal state. Therefore, a normal fault detection model can falsely predict a well-operating PV system as a faulty state and vice versa. In this paper, an intelligent fault diagnosis model is proposed for the fault detection and classification in PV systems. For the experimental verification, various fault state and normal state datasets are collected during the winter season under wide environmental conditions. The collected datasets are normalized and preprocessed using several data-mining techniques and then fed into a probabilistic neural network (PNN). The PNN model will be trained with the historical data to predict and classify faults when new data is fetched in it. The trained model showed better performance in prediction accuracy when compared with other classification methods in machine learning.
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Muhammad, N., H. Zainuddin, E. Jaaper, and Z. Idrus. "An early fault detection approach in grid-connected photovoltaic (GCPV) system." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 2 (2020): 671. http://dx.doi.org/10.11591/ijeecs.v17.i2.pp671-679.
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<span>Faults in any components of PV system shall lead to performance degradation and if prolonged, it can leads to fire hazard. This paper presents an approach of early fault detection via acquired historical data sets of grid-connected PV (GCPV) systems. The approach is a developed algorithm comprises of failure detection on AC power by using Acceptance Ratio (AR) determination. Specifically, the implemented failure detection stage was based on the algorithm that detected differences between the actual and predicted AC power of PV system. Furthermore, the identified alarm of system failure was a decision stage which performed a process based on developed logic and decision trees. The results obtained by comparing two types of GCPV system (polycrystalline and monocrystalline silicon PV system), showed that the developed algorithm could perceive the early faults upon their occurrence. Finally, when applying AR to the PV systems, the faulty PV system demonstrated 93.38 % of AR below 0.9, while the fault free PV system showed only 31.4 % of AR below 0.9.</span>
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Lipták, Róbert, and István Bodnár. "Simulation of fault detection in photovoltaic arrays." Analecta Technica Szegedinensia 15, no. 2 (2021): 31–40. http://dx.doi.org/10.14232/analecta.2021.2.31-40.
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In solar systems, faults in the module and inverter occur in proportion to increased operating time. The identification of fault types and their effects is important information not only for manufacturers but also for investors, solar operators and researchers. Monitoring and diagnosing the condition of photovoltaic (PV) systems is becoming essential to maximize electric power generation, increase the reliability and lifetime of PV power plants. Any faults in the PV modules cause negative economic and safety impacts, reducing the performance of the system and making unwanted electric connections that can be dangerous for the user. In this paper have been classified all possible faults that happen in the PV system, and is presented to detect common PV array faults, such as open-circuit fault, line-to-line fault, ground fault, shading condition, degradation fault and bypass diode fault. In this studies examines the equivalent circuits of PV arrays with different topological configurations and fault conditions to evaluate the effects of these faults on the performance of a solar system, taking into account the influence of temperature and solar radiation. This work presents the validation of a simulated solar network by measuring the output curves of a low-power photovoltaic array system under real outdoor conditions. This method can be useful in future solar systems.
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Benmouiza, Khalil. "Grid Connected PV Systems Fault Detection using K-Means Clustering Algorithm." International Journal of Emerging Technology and Advanced Engineering 13, no. 5 (2023): 73–83. http://dx.doi.org/10.46338/ijetae0523_07.
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—Efficiency in photovoltaic (PV) energy production is significantly influenced by various electrical, environmental, and manufacturing-related factors. These variables often lead to a range of PV generator faults, compromising the system's performance and the overall grid's safety. The current fault detection methods can be complex and resource-intensive. In this paper, we propose a novel and efficient grid-connected PV system fault detection mechanism using the k-means clustering algorithm. Our approach categorizes the possible faults based on clustering the output PV and grid powers under healthy and faulty conditions. A comparison between centroid locations of both conditions leads to fault categorization. The findings demonstrate the efficacy of the proposed technique for addressing localized faults in grid-tied PV systems without the need for complicated calculations. The technique is both cost-effective and accurate, with a straightforward application that can be easily adopted by all stakeholders. This method enables users to safeguard their PV system's health and ensure the more comprehensive grid's safety.
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Amiri, Ahmed Faris, Sofiane Kichou, Houcine Oudira, Aissa Chouder, and Santiago Silvestre. "Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)." Sustainability 16, no. 3 (2024): 1012. http://dx.doi.org/10.3390/su16031012.
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The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.
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Al-Katheri, Ahmed A., Essam A. Al-Ammar, Majed A. Alotaibi, Wonsuk Ko, Sisam Park, and Hyeong-Jin Choi. "Application of Artificial Intelligence in PV Fault Detection." Sustainability 14, no. 21 (2022): 13815. http://dx.doi.org/10.3390/su142113815.
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The rapid revolution in the solar industry over the last several years has increased the significance of photovoltaic (PV) systems. Power photovoltaic generation systems work in various outdoor climate conditions; therefore, faults may occur within the PV arrays in the power system. Fault detection is a fundamental task needed to improve the reliability, efficiency, and safety of PV systems, and, if not detected, the cost associated with the loss of power generated from PV modules will be quite high. Moreover, maintenance staff will take more time and effort to fix undetermined faults. Due to the current-limiting nature and nonlinear output characteristics of PV arrays, fault detection is not that easy and the application of artificial intelligence is proposed for the sake of fault detection in PV systems. The idea behind this approach is to compare the faulty PV module with its accurate model (factory fingerprint) by checking every PV array’s I–V and P–V curves using the Artificial Neural Network (ANN) logarithm as a subsection of the Artificial Intelligence’s (AI) techniques. This proposed approach achieves a high performance of fault detection and gives the advantage of determining what type of fault has occurred. The results confirm that the proposed logarithm performance becomes better as the number of distinguishing points extend, providing great value to the Solar PV industry.
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Zaki, Sayed A., Honglu Zhu, and Jianxi Yao. "Fault detection and diagnosis of photovoltaic system using fuzzy logic control." E3S Web of Conferences 107 (2019): 02001. http://dx.doi.org/10.1051/e3sconf/201910702001.
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Among several renewable energy resources, Solar has great potential to solve the world’s energy problems. With the rapid expansion and installation of PV system worldwide, fault detection and diagnosis has become the most significant issue in order to raise the system efficiency and reduce the maintenance cost as well as repair time. This paper presented a method for monitoring, identifying, and detecting different faults in PV array. This method is built based on comparing the measured electrical parameters with its theoretical parameters in case of normal and faulty conditions of PV array. For this purpose, three ratios of open circuit voltage, current, and voltage are obtained with their associated limits in order to detect eight different faults. Moreover, the fuzzy logic control FLC method is performed for studying the failure configuration and categorizing correctly the different faults occurred. The outcomes obtained by performing the different faults representing permanent and temporary faults demonstrated that the FLC was equipped to precisely identify the faults upon their occurring. Different simulated and experimental tests are conducted to demonstrate the performance of the proposed method.
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Osmani, Khaled, Ahmad Haddad, Thierry Lemenand, Bruno Castanier, and Mohamad Ramadan. "Material Based Fault Detection Methods for PV Systems." Key Engineering Materials 865 (September 2020): 111–15. http://dx.doi.org/10.4028/www.scientific.net/kem.865.111.
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The overall efficiency of a PV system is strongly affected by the PV cell raw materials. Since a reliable renewable energy source is expected to produce maximum power with longest lifetime and minimum errors, a critical aspect to bear in mind is the occurrence of PV faults according to raw material types. The different failure scenarios occurring in PV system, decrease its output power, reduce its life expectancy and ban the system from meeting load demands, yielding to severe consecutive blackouts. This paper aims first to present different core materials types, material based fault occurring on the PV cell level and consequently the fault detection techniques corresponding to each fault type.
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Hussain, Imran, Ihsan Ullah Khalil, Aqsa Islam, et al. "Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line." Energies 15, no. 14 (2022): 5106. http://dx.doi.org/10.3390/en15145106.
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Solar photovoltaic PV plants worldwide are continuously monitored and carefully protected to ensure safe and reliable operation through detecting and isolating faults. Faults are very common in modern solar PV systems which interrupt normal system operation adversely affecting the performance of the PV systems. When undetected, faults not only cause significant reduction in the efficiency and life span of the PV system, but also result in damage and fire hazards compromising their reliability. Therefore, early fault detection and diagnosis of photovoltaic plants is a necessity for safe and reliable operation required for growing solar PV systems. Unfortunately, several recent fire incidents have been reported recently caused by undetected faults in solar PV systems. Motivated by this challenge, this paper, utilizing a proposed fuzzy logic algorithm, presents a novel technique for detecting and classifying faults in solar PV systems. Furthermore, the proposed method introduces fault indexing as a performance indicator that measures the degree of deviation from the normal operating conditions of the photovoltaic system. Various signatures of each fault scenario are identified in the shape of corresponding current-voltage trajectories and their extracted parameters. The effectiveness of the proposed technique is evaluated both in simulation and experimentally using a 5 kW grid connected solar array. It is demonstrated that the proposed technique is capable of diagnosing the occurrence of different faults with more than 98% accuracy.
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Pei, Tingting, and Xiaohong Hao. "A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation." Energies 12, no. 9 (2019): 1712. http://dx.doi.org/10.3390/en12091712.
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Photovoltaic (PV) power generation systems work chronically in various climatic outdoor conditions, therefore, faults may occur within the PV arrays in PV systems. Online fault detection for the PV arrays are important to improve the system’s reliability, safety and efficiency. In view of this, a fault-detection method based on voltage and current observation and evaluation is presented in this paper to detect common PV array faults, such as open-circuit, short-circuit, degradation and shading faults. In order to develop this detection method, fault characteristic quantities (e.g., the open-circuit voltage, short-circuit current, voltage and current at the maximum power point (MPP) of the PV array) are identified first to define the voltage and current indicators; then, the fault-detection thresholds are defined by utilizing voltage and current indicators according to the characteristic information of various faults; finally, voltage and current indicators evaluated at real-time voltage and current data are compared with the corresponding thresholds to detect potential faults and fault types. The performances of the proposed method are simulated verifying by setting eight different fault patterns in the PV array. Simulation experimental results show the effectiveness of the proposed method, especially the capacities of distinguishing the degradation faults, partial shading faults and variable shading faults.
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Lazzaretti, André Eugênio, Clayton Hilgemberg da Costa, Marcelo Paludetto Rodrigues, et al. "A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants." Sensors 20, no. 17 (2020): 4688. http://dx.doi.org/10.3390/s20174688.
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Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.
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Toche Tchio, Guy M., Joseph Kenfack, Joseph Voufo, Yves Abessolo Mindzie, Blaise Fouedjou Njoya, and Sanoussi S. Ouro-Djobo. "Diagnosing faults in a photovoltaic system using the Extra Trees ensemble algorithm." AIMS Energy 12, no. 4 (2024): 727–50. http://dx.doi.org/10.3934/energy.2024034.
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The application of machine learning techniques for monitoring and diagnosing faults in photovoltaic (PV) systems has been shown to enhance the reliability of PV power generation. This research introduced a novel machine learning classifier for fault diagnosis in PV systems, utilizing an ensemble algorithm known as extra trees (ETC). The study initially proposed a system with two PV modules and developed a low-cost Arduino-based data logger to gather data from the PV system in free-fault and faulty conditions. Subsequently, the study evaluated six other advanced classifiers for fault diagnosis in PV systems, namely logistic regression (LR), k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), AdaBoost, and random forest (RF) models using the collected data from the proposed PV system. The assessment of the various models' performance indicated that the extra trees model exhibits superior classification capabilities for partial shading (PS), open circuit (OCF), partial shading with bypass diode disconnected (PSBD), and combined partial shading with bypass diode disconnected plus open circuit (PSBDOC) faults. The results demonstrated that the new ETC classifier achieves an accuracy of 92%, surpassing the 91%, 87%, 7%, and 59% accuracy of the RF, DT, kNN, and LR classifiers, respectively. This highlights the effectiveness of the extra trees model in enhancing fault detection and classification by distinguishing between open circuits and twin faults. Consequently, these results can be utilized to develop advanced diagnostic tools for photovoltaic systems, thereby improving the reliability of solar technology and accelerating the rate of installation.
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Joseph, Easter, Pradeep Menon Vijaya Kumar, Balbir Singh Mahinder Singh, and Dennis Ling Chuan Ching. "Performance Monitoring Algorithm for Detection of Encapsulation Failures and Cell Corrosion in PV Modules." Energies 16, no. 8 (2023): 3391. http://dx.doi.org/10.3390/en16083391.
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This research work aims to develop a fault detection and performance monitoring system for a photovoltaic (PV) system that can detect and communicate errors to the user. The proposed system uses real-time data from various sensors to identify performance problems and faults in the PV system, particularly for encapsulation failure and module corrosion. The system incorporates a user interface that operates on a micro-computer utilizing Python software to show the detected errors from the PV miniature scale system. Fault detection is achieved by comparing the One-diode model with a controlled state retrieved through field testing. A database is generated by the system based on acceptable training data and it serves as a reference point for detecting faults. The user is notified of any deviations based on the threshold value from the training data as an indication of an error by the system. The system offers real-time monitoring, easy-to-understand error messages, and remote access capability, making it an efficient and effective tool for both users and maintenance personnel to manage and maintain the PV system.
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Emamian, Masoud, Aref Eskandari, Mohammadreza Aghaei, Amir Nedaei, Amirmohammad Moradi Sizkouhi, and Jafar Milimonfared. "Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques." Energies 15, no. 9 (2022): 3014. http://dx.doi.org/10.3390/en15093014.
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This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction.
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Qasim Obaidi, Marwah, and Nabil Derbel. "IoT-based monitoring and shading faults detection for a PV water pumping system using deep learning approach." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 2673–81. http://dx.doi.org/10.11591/eei.v12i5.4496.
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One of the major challenges facing photovoltaic (PV) systems is fault detection. Artificial intelligence (AI) is one of the main popular techniques used in error detection due to its ability to extract signal and image features. In this paper, a deep learning approach based on convolutional neural network (CNN) and internet of things (IoT) technology are used to detect and locate shading faults for a PV water pumping system. The current and voltage signals generated by the PV panels as well as temperature and radiation were used to convert them into 3D images and then upload to a deep learning algorithm. The PV system and fault detection algorithms were simulated by MATLAB. The obtained results indicate that the performance of the proposed deep learning approach to detect and locate faults is better than the traditional statistical methods and other machine learning methods.
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Alam, Zaheer, Malak Adnan Khan, Zain Ahmad Khan, et al. "Fault Diagnosis Strategy for a Standalone Photovoltaic System: A Residual Formation Approach." Electronics 12, no. 2 (2023): 282. http://dx.doi.org/10.3390/electronics12020282.
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The search for sustainability and green energy, in electricity production, has lead many researchers to study and improve photovoltaic (PV) systems. The PV systems, being a combination of power electronic modules and PV array, have high tendency of faults in sensors, switches, and passive devices. Thus, a reliable fault diagnosis (FD) scheme plays a significant role in protecting PV systems. In this article, a sliding mode observer (SMO)-based FD scheme is presented to figure out the sensor faults in a standalone PV system. The proposed FD scheme makes use of residual formation which in turn helps in detection of faults on the basis of a defined threshold. In addition to the functionality of fault detection, the SMO provides the benefit of reduction in number of sensors required in the PV system. This feature can be utilized as software redundancy in fault-tolerant control (FTC). The test bench, standalone PV system, is equipped with a buck–boost converter for maximum power transfer (MPT) to the connected load. Moreover, the FD scheme is backed by a back-stepping (BS) analogy-based control scheme for extraction of maximum power from the PV panel. The simulations are performed in the MATLAB/Simulink platform under varying environmental conditions (temperature and irradiance) and resistive load. These simulations, subjected to varying operating conditions, confirm the efficacy, in terms of robustness, chattering (oscillations about the reference), and steady-state error, of the proposed scheme.
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Yang, Nien-Che, and Harun Ismail. "Voting-Based Ensemble Learning Algorithm for Fault Detection in Photovoltaic Systems under Different Weather Conditions." Mathematics 10, no. 2 (2022): 285. http://dx.doi.org/10.3390/math10020285.
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A photovoltaic (PV) system is one of the renewable energy resources that can help in meeting the ever-increasing energy demand. However, installation of PV systems is prone to faults that can occur unpredictably and remain challenging to detect. Major PV faults that can occur are line-line and open circuits faults, and if they are not addressed appropriately and timely, they may lead to serious problems in the PV system. To solve this problem, this study proposes a voting-based ensemble learning algorithm with linear regression, decision tree, and support vector machine (EL-VLR-DT-SVM) for PV fault detection and diagnosis. The data acquisition is performed for different weather conditions to trigger the nonlinear nature of the PV system characteristics. The voltage-current characteristics are used as input data. The dataset is studied for a deeper understanding, and pre-processing before feeding it to the EL-VLR-DT-SVM. In the pre-processing step, data are normalized to obtain more feature space, making it easy for the proposed algorithm to discriminate between healthy and faulty conditions. To verify the proposed method, it is compared with other algorithms in terms of accuracy, precision, recall, and F-1 score. The results show that the proposed EL-VLR-DT-SVM algorithm outperforms the other algorithms.
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Et-taleby, Abdelilah, Yassine Chaibi, Mohamed Benslimane, and Mohammed Boussetta. "Applications of Machine Learning Algorithms for Photovoltaic Fault Detection: a Review." Statistics, Optimization & Information Computing 11, no. 1 (2023): 168–77. http://dx.doi.org/10.19139/soic-2310-5070-1537.
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Over the years, the boom of technology has caused the accumulation of a large amount of data, famously known as big data, in every field of life. Traditional methods have failed to analyse such a huge pile of data due to outdated techniques. In recent times, the use of photovoltaic systems has risen worldwide. The arena Photovoltaic (PV) system has witnessed the same unprecedented expansion of data owing to the associated monitoring systems. However, the faults created within the PV system cannot be detected, classified, or predicted by using conventional techniques. This necessitates the use of modern techniques such as Machine Learning. Its powerful algorithms, such as artificial neural networks (ANN), help in the accurate detection and classification of faults in the PV system. This review paper introduces and evaluates the applications of Machine Learning (ML) algorithms in PV fault detection. It provides a brief overview of Machine Learning and its concepts along with various widely used ML algorithms. This review various peer-reviewed studies to investigate various models of ML algorithms in the PV system with the main focus on its fault detection accuracy and efficiency.
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Hichri, Amal, Mansour Hajji, Majdi Mansouri, et al. "Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems." Sustainability 14, no. 17 (2022): 10518. http://dx.doi.org/10.3390/su141710518.
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Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) systems is discussed in this work. Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. Only the most important features are selected to be supplied to the ANN classifier. The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. A thorough analysis of 16 faults applied on the module is performed. In general terms, the faults observed in the system are classified under three categories: simple, multiple, and mixed. The obtained results confirm the feasibility and effectiveness with a low computation time of the proposed approach for fault diagnosis.
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Raeisi, H. A., and S. M. Sadeghzadeh. "A Novel Experimental and Approach of Diagnosis, Partial Shading, and Fault Detection for Domestic Purposes Photovoltaic System Using Data Exchange of Adjacent Panels." International Journal of Photoenergy 2021 (September 17, 2021): 1–19. http://dx.doi.org/10.1155/2021/9956433.
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This paper presents a new detection method of fault and partial shading condition (PSC) in a photovoltaic (PV) domestic network, considering maximum power point tracking (MPPT). The MPPT has been executed by employing a boost converter using particle swarm optimization (PSO) technique. The system is composed of two photovoltaic arrays. Each PV array contains three panels connected in series, including distinct MPPT. The PSC detection exploits the neighboring PV system data. This suggested innovative algorithm is proficient in detecting these subjects: (a) fault, (b) partial shading condition, (c) solar panel (d) panel’s relevant bypass diode failure, (d) converter failure alongside specifying the failed semiconductor, and (e) PV disconnection failure. The simulation process has been implemented using MATLAB/Simulink software. To this end, the proposed method was investigated experimentally using two 250 W PV solar set under various PSCs and faults. A data exchange link is used to implement an integrated management system. The Zigbee protocol was also chosen to data exchange of converters. The results validated the applicability and practicality of this algorithm in domestic PV systems.
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Salman Zamzeer, Ali, Mansour S. Farhan, and Haider TH ALRikabi. "Fault Detection System of Photovoltaic Based on Artificial Neural Network." Wasit Journal of Engineering Sciences 11, no. 1 (2023): 93–104. http://dx.doi.org/10.31185/ejuow.vol11.iss1.399.
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Using PV systems, solar energy may be used to create electricity. Every year, the proportion of solar energy in the electric system increases significantly. On the other hand, photovoltaic cells are susceptible to malfunctions that diminish their efficiency and profitability. Due to the severity of the defects, fault detection and diagnosis (FDD) in the PV system have become difficult. Thus, the primary objective of the proposed study is to detect and diagnose particular types of PV system problems using an artificial neural network (ANN). This early operation is more effective for avoiding errors during PV installation and minimizing PV system power losses. A new solar cell model is created in the MATLAB/SIMULINK environment and is used to identify the fault dataset. The solar cell consists of three parallel strings and three series modules, with each module containing 20 series-connected photovoltaic cells. This model determines four parameters (V-load in Volts, I-load in Amps, Irradiance in W/m2, and Temperature in Celsius) under varying situations (five temperature values, three irradiance values, and three-time events), which are repeated for all faults evaluated. In the training and testing phases of the proposed ANN, these four parameters are utilized as effective features. Additionally, this network possesses eight outputs, one for each defect. Using implementations with three different numbers of hidden layers and an identical fault dataset, the performance of the proposed network is tested. The findings of the simulation indicate a considerable proportion of fault detection and diagnosis accuracy, ranging between 95% and 98%. The computed RMSE reveals that the training period yields 0.193% improvement, whereas the testing phase yields 0.365%.
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IBK, Sugirianta, IGNA Dwijaya_S, M Purbhawa, GK Sri Budarsa, and Ketut Ta. "Short and Open Circuit Fault Detection in On-Grid Photovoltaic Systems 1MWP Bangli Based on Current and Voltage Observation." Journal of Computer Science and Technology Studies 4, no. 2 (2022): 105–17. http://dx.doi.org/10.32996/jcsts.2022.4.2.13.
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Photovoltaic (PV) systems as clean and green electrical energy generators have increased sharply in the last 10 years. The installation of a PV system in an open area is one of the causes of frequent faults/damage to the PV system. Fault in the PV system causes a decrease in efficiency, weak reliability, and disruption of the continuity of the electrical power distribution, which in turn causes the low performance of the system. This research does on the on-grid PV system 1MWp Bangli which consists of 278 PV arrays and 5004 monocrystalline solar modules 200Wp, which started operation in 2013. This research aims to find open circuit and short circuit faults that occur in the PV system Bangli. The method used in this research is the current and voltage observation method. Current and output voltage measurements from the PV array are carried out as well as measurements of working module temperature and solar irradiance. To calculate the output PV array's current (RI) and voltage (RV) indicators; Calculation of the current (RIM) and voltage (RVM) indicators under fault-free conditions, and the short circuit (TIO) and open voltage (TVS) threshold are calculated. As a result, this study succeeded in determining short and open circuit faults that occur in PV systems on grid 1MWp Bangli.
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Lebreton, Carole, Fabrice Kbidi, Alexandre Graillet, et al. "PV System Failures Diagnosis Based on Multiscale Dispersion Entropy." Entropy 24, no. 9 (2022): 1311. http://dx.doi.org/10.3390/e24091311.
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Photovoltaic (PV) system diagnosis is a growing research domain likewise solar energy’s ongoing significant expansion. Indeed, efficient Fault Detection and Diagnosis (FDD) tools are crucial to guarantee reliability, avoid premature aging and improve the profitability of PV plants. In this paper, an on-line diagnosis method using the PV plant electrical output is presented. This entirely signal-based method combines variational mode decomposition (VMD) and multiscale dispersion entropy (MDE) for the purpose of detecting and isolating faults in a real grid-connected PV plant. The present method seeks a low-cost design, an ease of implementation and a low computation cost. Taking into account the innovation of applying these techniques to PV FDD, the VMD and MDE procedures as well as parameters identification are carefully detailed. The proposed FFD approach performance is assessed on a real rooftop PV plant with experimentally induced faults, and the first results reveal the MDE approach has good suitability for PV plants diagnosis.
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Rivai, Ahmad, Nasrudin Abd Rahim, Mohamad Fathi Mohamad Elias, and Jafferi Jamaludin. "Analysis of Photovoltaic String Failure and Health Monitoring with Module Fault Identification." Energies 13, no. 1 (2019): 100. http://dx.doi.org/10.3390/en13010100.
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In this paper, photovoltaic (PV) string failure analysis and health monitoring of PV modules based on a low-cost self-powered wireless sensor network (WSN) are presented. Simple and effective fault detection and diagnosis method based on the real-time operating voltage of PV modules is proposed. The proposed method is verified using the developed health monitoring system which is installed in a grid-connected PV system. Each of the PV modules is monitored via WSN to detect any individual faulty module. The analysis of PV string failure includes several electrical fault scenarios and their impact on the PV string characteristics. The results show that a degraded or faulty module exhibits low operating voltage as compared to the normal module. The developed health monitoring system also includes a graphical user interface (GUI) program which graphically displays the real-time operating voltage of each module with colors and thus helping users to identify the faulty modules easily. The faulty modules identification approach is further validated using the PV module electroluminescence (EL) imaging system.
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D., Balakrishnan, Raja J., Manikandan Rajagopal, Sudhakar K., and Janani K. "An IoT-Based System for Fault Detection and Diagnosis in Solar PV Panels." E3S Web of Conferences 387 (2023): 05009. http://dx.doi.org/10.1051/e3sconf/202338705009.
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This abstract describes an IoT-based system for fault detection and diagnosis in solar PV panels. The proposed Fuzzy logic-based fault detection algorithms aims to improve the performance and reliability of solar PV panels, which can be affected by various faults such as shading, soiling, degradation, and electrical faults. The system includes wireless sensor nodes that are deployed on the panels to collect data on their electrical parameters and environmental conditions, such as temperature, irradiance, and humidity. The collected data is then transmitted to a central server for processing and analysis using machine learning algorithms. The system can detect and diagnose faults in real-time, and provide alerts and recommendations to maintenance personnel to take appropriate actions to prevent further damage or downtime. The system has several advantages over traditional manual inspection and maintenance methods, including reduced downtime, lower maintenance costs, and improved energy efficiency. The proposed system has been validated through experimental tests, and the results show that it can accurately detect and diagnose faults in solar PV panels with high reliability and efficiency.
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Park, Sunme, Soyeong Park, Myungsun Kim, and Euiseok Hwang. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems." Energies 13, no. 3 (2020): 737. http://dx.doi.org/10.3390/en13030737.
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This work proposes a fault detection and imputation scheme for a fleet of small-scale photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site meteorological information, such as solar irradiance, is helpful for monitoring PV systems. However, collecting this type of weather data at every station is not feasible for a fleet owing to the limitation of installation costs. In this study, to monitor a PV fleet efficiently, neighboring PV generation profiles were utilized for fault detection and imputation, as well as solar irradiance. For fault detection from unlabeled raw PV data, K-means clustering was employed to detect abnormal patterns based on customized input features, which were extracted from the fleet PVs and weather data. When a profile was determined to have an abnormal pattern, imputation for the corresponding data was implemented using the subset of neighboring PV data clustered as normal. For evaluation, the effectiveness of neighboring PV information was investigated using the actual rooftop PV power generation data measured at several locations in the Gwangju Institute of Science and Technology (GIST) campus. The results indicate that neighboring PV profiles improve the fault detection capability and the imputation accuracy. For fault detection, clustering-based schemes provided error rates of 0.0126 and 0.0223, respectively, with and without neighboring PV data, whereas the conventional prediction-based approach showed an error rate of 0.0753. For imputation, estimation accuracy was significantly improved by leveraging the labels of fault detection in the proposed scheme, as much as 18.32% reduction in normalized root mean square error (NRMSE) compared with the conventional scheme without fault consideration.
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Natsheh, Emad, and Sufyan Samara. "Tree Search Fuzzy NARX Neural Network Fault Detection Technique for PV Systems with IoT Support." Electronics 9, no. 7 (2020): 1087. http://dx.doi.org/10.3390/electronics9071087.
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The photovoltaic (PV) panel’s output energy depends on many factors. As they are becoming the leading alternative energy source, it is essential to get the best out of them. Although the main factor for maximizing energy production is proportional to the amount of solar radiation reaching the photovoltaic panel surface, other factors, such as temperature and shading, influence them negatively. Moreover, being installed in a dynamic and frequently harsh environment causes a set of reasons for faults, defects, and irregular operations. Any irregular operation should be recognized and classified into faults that need attention and, therefore, maintenance or as being a regular operation due to changes in some surrounding factors, such as temperature or solar radiation. Besides, in case of faults, it would be helpful to identify the source and the cause of the problem. Hence, this study presented a novel methodology that modeled a PV system in a tree-like hierarchy, which allowed the use of a fuzzy nonlinear autoregressive network with exogenous inputs (NARX) to detect and classify faults in a PV system with customizable granularity. Moreover, the used methodology enabled the identification of the exact source of fault(s) in a fully automated way. The study was done on a string of eight PV panels; however, the paper discussed using the algorithm on a more extensive PV system. The used fuzzy NARX algorithm in this study was able to classify the faults that appeared in up to five out of the eight PV panels and to identify the faulty PV panels with high accuracy. The used hardware could be controlled and monitored through a Wi-Fi connection, which added support for Internet of Things applications.
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Jenitha, P., and A. Immanuel Selvakumar. "Fault detection in PV systems." Applied Solar Energy 53, no. 3 (2017): 229–37. http://dx.doi.org/10.3103/s0003701x17030069.
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Wang, Lina, Ehtisham Lodhi, Pu Yang, et al. "Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems." Energies 15, no. 10 (2022): 3608. http://dx.doi.org/10.3390/en15103608.
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DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected early to provide reliable and safe power delivery while preventing fire hazards. However, it is challenging to detect DC series arc faults using conventional overcurrent and current differential methods because these faults produce only minor current variations. Furthermore, it is hard to define their characteristics for detection due to the randomness of DC arc faults and other arc-like transients. This paper focuses on investigating a novel method to extract arc characteristics for reliably detecting DC series arc faults in PV systems. This methodology first uses an adaptive local mean decomposition (ALMD) algorithm to decompose the current samples into production functions (PFs) representing information from different frequency bands, then selects the PFs that best characterize the arc fault, and then calculates its multiscale fuzzy entropies (MFEs). Eventually, MFE values are inputted to the trained SVM algorithm to identify the series arc fault accurately. Furthermore, the proposed technique is compared to the logistic regression algorithm and naive Bayes algorithm in terms of several metrics assessing algorithms’ validity for detecting arc faults in PV systems. Arc fault data acquired from a PV arc-generating experiment platform are utilized to authenticate the effectiveness and feasibility of the proposed method. The experimental results indicated that the proposed technique could efficiently classify the arc fault data and normal data and detect the DC series arc faults in less than 1 ms with an accuracy rate of 98.75%.
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Navid, Qamar, Ahmed Hassan, Abbas Ahmad Fardoun, and Rashad Ramzan. "An Online Novel Two-Layered Photovoltaic Fault Monitoring Technique Based Upon the Thermal Signatures." Sustainability 12, no. 22 (2020): 9607. http://dx.doi.org/10.3390/su12229607.
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The share of photovoltaic (PV) power generation in the energy mix is increasing at a rapid pace with dramatically increasing capacity addition through utility-scale PV power plants globally. As PV plants are forecasted to be a major energy generator in the future, their reliable operation remains of primary concern due to a possibility of faults in a tremendously huge number of PV panels involved in power generation in larger plants. The precise detection of nature and the location of the faults along with a prompt remedial mechanism is deemed crucial for smoother power plant operation. The existing fault diagnostic methodologies based on thermal imaging of the panels as well as electrical parameters through inverter possess certain limitations. The current article deals with a novel fault diagnostic technique based on PV panel electrical parameters and junction temperatures that can precisely locate and categorize the faults. The proposed scheme has been tested on a 1.6 kW photovoltaic system for short circuit, open circuit, grounding, and partial shading faults. The proposed method showed improved accuracy compared to thermal imaging on panel scale fault detection, offering a possibility to adapt to the PV plant scale.
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Pang, Ruiwen, and Wenfang Ding. "Series Arc Fault Characteristics and Detection Method of a Photovoltaic System." Energies 16, no. 24 (2023): 8016. http://dx.doi.org/10.3390/en16248016.
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The DC arc is the main cause of fire in photovoltaic (PV) systems. This is due to the fact that the DC arc has no zero-crossing point and is prone to stable combustion. Failure to detect it in a timely manner can seriously endanger the PV system. This study analyzes the influences of the series arc and the maximum power point tracking (MPPT) algorithm on the PV output characteristics based on the PV equivalent circuit module. The PV voltage and current variation characteristics are obtained when the series arc occurs. The findings indicate that the input voltage of the converter remains unchanged due to the MPPT algorithm before and after the series arc occurs. Furthermore, the PV faulty string output current will drastically decrease when the series arc fault occurs. On this basis, a series arc detection method based on the current change is proposed, suppressing the combustion of the series arc by increasing the target voltage of the MPPT algorithm. The experimental results show that the proposed method can effectively detect and extinguish the series arc in the PV system within 0.6 s. Compared to the other methods, the proposed method can be integrated into the PV system without additional hardware.
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Suliman, Fouad, Fatih Anayi, and Michael Packianather. "Electrical Faults Analysis and Detection in Photovoltaic Arrays Based on Machine Learning Classifiers." Sustainability 16, no. 3 (2024): 1102. http://dx.doi.org/10.3390/su16031102.
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Solar photovoltaic energy generation has garnered substantial interest owing to its inherent advantages, such as zero pollution, flexibility, sustainability, and high reliability. Ensuring the efficient functioning of PV power facilities hinges on precise fault detection. This not only bolsters their reliability and safety but also optimizes profits and avoids costly maintenance. However, the detection and classification of faults on the Direct Current (DC) side of the PV system using common protection devices present significant challenges. This research delves into the exploration and analysis of complex faults within photovoltaic (PV) arrays, particularly those exhibiting similar I-V curves, a significant challenge in PV fault diagnosis not adequately addressed in previous research. This paper explores the design and implementation of Support Vector Machines (SVMs) and Extreme Gradient Boosting (XGBoost), focusing on their capacity to effectively discern various fault states in small PV arrays. The research broadens its focus to incorporate the use of optimization algorithms, specifically the Bees Algorithm (BA) and Particle Swarm Optimization (PSO), with the goal of improving the performance of basic SVM and XGBoost classifiers. The optimization process involves refining the hyperparameters of the Machine Learning models to achieve superior accuracy in fault classification. The findings put forth a persuasive case for the Bees Algorithm’s resilience and efficiency. When employed to optimize SVM and XGBoost classifiers for the detection of complex faults in PV arrays, the Bees Algorithm showcased remarkable accuracy. In contrast, classifiers fine-tuned with the PSO algorithm exhibited comparatively lower performances. The findings underscore the Bees Algorithm’s potential to enhance the accuracy of classifiers in the context of fault detection in photovoltaic systems.
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Hojabri, Mojgan, Samuel Kellerhals, Govinda Upadhyay, and Benjamin Bowler. "IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods." Energies 15, no. 6 (2022): 2097. http://dx.doi.org/10.3390/en15062097.
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Faults on individual modules within a photovoltaic (PV) array can have a significant detrimental effect on the power efficiency and reliability of the entire PV system. In addition, PV module faults can create risks to personnel safety and fire hazards if they are not detected quickly. As IoT hardware capabilities increase and machine learning frameworks mature, better fault detection performance may be possible using low-cost sensors running machine learning (ML) models that monitor electrical and thermal parameters at an individual module level. In this paper, to evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation. The faults are emulated and applied to a real PV system, allowing the collection and labelling of panel-level measurement data. Then, different ML methods are used to classify these faults in comparison to the normal condition. Results confirm that NN obtain 93% classification accuracy for seven selected classes.
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Wang, Yao, Cuiyan Bai, Xiaopeng Qian, Wanting Liu, Chen Zhu, and Leijiao Ge. "A DC Series Arc Fault Detection Method Based on a Lightweight Convolutional Neural Network Used in Photovoltaic System." Energies 15, no. 8 (2022): 2877. http://dx.doi.org/10.3390/en15082877.
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Although photovoltaic (PV) systems play an essential role in distributed generation systems, they also suffer from serious safety concerns due to DC series arc faults. This paper proposes a lightweight convolutional neural network-based method for detecting DC series arc fault in PV systems to solve this issue. An experimental platform according to UL1699B is built, and current data ranging from 3 A to 25 A is collected. Moreover, test conditions, including PV inverter startup and irradiance mutation, are also considered to evaluate the robustness of the proposed method. Before fault detection, the current data is preprocessed with power spectrum estimation. The lightweight convolutional neural network has a lower computational burden for its fewer parameters, which can be ready for embedded microprocessor-based edge applications. Compared to similar lightweight convolutional network models such as Efficientnet-B0, B2, and B3, the Efficientnet-B1 model shows the highest accuracy of 96.16% for arc fault detection. Furthermore, an attention mechanism is combined with the Efficientnet-B1 to make the algorithm more focused on arc features, which can help the algorithm reduce unnecessary computation. The test results show that the detection accuracy of the proposed method can be up to 98.81% under all test conditions, which is higher than that of general networks.
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Eskandari, Aref, Jafar Milimonfared, Mohammadreza Aghaei, and Angèle H. M. E. Reinders. "Autonomous Monitoring of Line-to-Line Faults in Photovoltaic Systems by Feature Selection and Parameter Optimization of Support Vector Machine Using Genetic Algorithms." Applied Sciences 10, no. 16 (2020): 5527. http://dx.doi.org/10.3390/app10165527.
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Photovoltaic (PV) monitoring and fault detection are very crucial to enhance the service life and reliability of PV systems. It is difficult to detect and classify the faults at the Direct Current (DC) side of PV arrays by common protection devices, especially Line-to-Line (LL) faults, because such faults are not detectable under high impedance fault and low mismatch conditions. If these faults are not diagnosed, they may significantly reduce the output power of PV systems and even cause fire catastrophe. Recently, many efforts have been devoted to detecting and classifying LL faults. However, these methods could not efficiently detect and classify the LL faults under high impedance and low mismatch. This paper proposes a novel fault diagnostic scheme in accordance with the two main stages. First, the key features are extracted via analyzing Current–Voltage (I–V) characteristics under various LL fault events and normal operation. Second, a genetic algorithm (GA) is used for parameter optimization of the kernel functions used in the Support Vector Machine (SVM) classifier and feature selection in order to obtain higher performance in diagnosing the faults in PV systems. In contrast to previous studies, this method requires only a small dataset for the learning process and it has a higher accuracy in detecting and classifying the LL fault events under high impedance and low mismatch levels. The simulation results verify the validity and effectiveness of the proposed method in detecting and classifying of LL faults in PV arrays even under complex conditions. The proposed method detects and classifies the LL faults under any condition with an average accuracy of 96% and 97.5%, respectively.
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Lu, Shiue-Der, Meng-Hui Wang, Shao-En Wei, Hwa-Dong Liu, and Chia-Chun Wu. "Photovoltaic Module Fault Detection Based on a Convolutional Neural Network." Processes 9, no. 9 (2021): 1635. http://dx.doi.org/10.3390/pr9091635.
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With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN.
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Yao, Siya, Qi Kang, Mengchu Zhou, Abdullah Abusorrah, and Yusuf Al-Turki. "Intelligent and Data-Driven Fault Detection of Photovoltaic Plants." Processes 9, no. 10 (2021): 1711. http://dx.doi.org/10.3390/pr9101711.
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Most photovoltaic (PV) plants conduct operation and maintenance (O&M) by periodical inspection and cleaning. Such O&M is costly and inefficient. It fails to detect system faults in time, thus causing heavy loss. To ensure their operations are at an ideal state, this work proposes an unsupervised method for intelligent performance evaluation and data-driven fault detection, which enables engineers to check PV panels in time and implement timely maintenance. It classifies monitoring data into three subsets: ideal period A, transition period S, and downturn period B. Based on A and B datasets, we build two non-continuous regression prediction models, which are based on a tree ensemble algorithm and then modified to fit the non-continuous characteristic of PV data. We compare real-time measured power with both upper and lower reference baselines derived from two predictive models. By calculating their threshold ranges, the proposed method achieves the instantaneous performance monitoring of PV power generation and provides failure identification and O&M suggestions to engineers. It has been assessed on a 6.95 MW PV plant. Its evaluation results indicate that it is able to accurately determine different functioning states and detect both direct and indirect faults in a PV system, thereby achieving intelligent data-driven maintenance.
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Yang, Cheng, Fuhao Sun, Yujie Zou, et al. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods." Energies 17, no. 4 (2024): 837. http://dx.doi.org/10.3390/en17040837.
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Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.
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Cardinale-Villalobos, Leonardo, Carlos Meza, Abel Méndez-Porras, and Luis D. Murillo-Soto. "Quantitative Comparison of Infrared Thermography, Visual Inspection, and Electrical Analysis Techniques on Photovoltaic Modules: A Case Study." Energies 15, no. 5 (2022): 1841. http://dx.doi.org/10.3390/en15051841.
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This paper compares multiple techniques to detect suboptimal conditions in the PV system. Detection of suboptimal conditions in the PV system is required to achieve optimal photovoltaic (PV) systems. Therefore, maintenance managers need to choose the most suitable techniques objectively. However, there is a lack of objective information comparing the effectiveness of the methods. This article calculates and compares the effectiveness of Infrared thermography (IRT), visual inspection (VI), and electrical analysis (EA) in detecting soiling, partial shadows, and electrical faults experimentally. The results showed that the VI was the best at detecting soiling and partial shading with 100% of effectiveness. IRT and EA had an effectiveness of 78% and 73%, respectively, detecting the three types of conditions under study. It was not possible to achieve maximum detection using only one of the techniques, but that VI must be combined with IR or EA. This research represents a significant contribution by achieving an objective comparison between techniques for detecting suboptimal conditions, being very useful to guide PV system maintainers and designers of fault detection techniques.
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Emre Coşgun, Atıl, and Yunus Uzun. "THERMAL FAULT DETECTION SYSTEM FOR PV SOLAR MODULES." Electrical and Electronics Engineering: An International Journal 06, no. 03 (2017): 09–15. http://dx.doi.org/10.14810/elelij.2017.6302.
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Lim, Hee-Won, Il-Kwon Kim, Ji-Hyeon Kim, and U.-Cheul Shin. "Simulation-Based Fault Detection Remote Monitoring System for Small-Scale Photovoltaic Systems." Energies 15, no. 24 (2022): 9422. http://dx.doi.org/10.3390/en15249422.
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A small-scale grid-connected PV system that is easy to install and is inexpensive as a remote monitoring system may cause economic losses if its failure is not found and it is left unattended for a long time. Thus, in this study, we developed a low-cost fault detection remote monitoring system for small-scale grid-connected PV systems. This active monitoring system equipped with a simulation-based fault detection algorithm accurately predicts AC power under normal operating conditions and notifies its failure when the measured power is abnormally low. In order to lower the cost, we used a single board computer (SBC) with edge computing as a data server and designed a monitoring system using openHAB, an open-source software. Additionally, we used the Shewhart control chart as a fault detection criterion and the ratio between the measured and predicted ac power for the normal operation data as an observation. As a result of the verification test for the actual grid-connected PV system, it was confirmed that the developed remote monitoring system was able to accurately identify the system failures in real-time, such as open circuit, short circuit, partial shading, etc.
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Wang, Meng-Hui, Chun-Chun Hung, Shiue-Der Lu, Zong-Han Lin, and Cheng-Chien Kuo. "Fault Diagnosis for PV Modules Based on AlexNet and Symmetrized Dot Pattern." Energies 16, no. 22 (2023): 7563. http://dx.doi.org/10.3390/en16227563.
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Faults in solar photovoltaic (PV) modules often result from component damage, leading to voltage fluctuations and decreased stability in the power system. In this study, the original voltage signals of different PV modules show little variation. Therefore, a solution that combines symmetrized dot pattern (SDP) and AlexNet for fault detection in PV modules was proposed. This solution investigates three common faults: poor welding, cracking, and bypass diode failure, which can be applied to fault-free modules. First, a high-frequency signal was input into the PV module, and the raw signal was captured using an NI PXI-5105 high-speed data acquisition card. Next, we used SDP to process the signal and create images with specific snowflake-like features. These images were used as a basis for fault diagnosis. Finally, deep-learning algorithms were used to perform status detection on the PV module. This research also used 3200 training samples and 800 test samples (200 for each type) to evaluate a new method for diagnosing faults in PV modules. The results show that the accuracy of the new method reached 99.8%, surpassing traditional convolutional neural networks (CNN) and extension neural networks (ENN), whose accuracies were 99.5% and 91.75%, respectively. Furthermore, this study compares the proposed method with more traditional numerical fault diagnosis methods. SDP effectively extracts fault signals and presents them as images. With AlexNet used for fault identification, the method excels in accuracy, training time, and testing time, thereby enhancing the stability and reliability of future energy systems.
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Chao, Kuei-Hsiang, Jen-Hsiang Tsai, and Ying-Hao Chen. "Development of a Low-Cost Fault Detector for Photovoltaic Module Array." Electronics 8, no. 2 (2019): 255. http://dx.doi.org/10.3390/electronics8020255.
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This study proposes a binary search-based fault detection system for photovoltaic (PV) modules to ameliorate the deficiencies in the existing fault detectors for PV module arrays. The proposed system applies a single-chip microcontroller to execute the binary search algorithm. Moreover, to overcome multi-node voltage detection and reduce the number of integrated circuit components, an analog switch is used to perform detection channel switching; the detection results are displayed on a software platform developed using Visual C#. The proposed system does not require learning to execute the fault diagnosis of PV module arrays and has advantages including high accuracy and low construction costs. Finally, to verify the feasibility of the proposed system, this study simulated the abnormal situations of actual modules and applied the binary search algorithm for maximum power point tracking to detect malfunctions of the PV module arrays.
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BADOUD, Abd Essalam. "Bond Graph Model for Fault Detection of Partial Shaded PV Array Considering Different Module Connection Schemes and Effects of Bypass Diodes." Algerian Journal of Renewable Energy and Sustainable Development 01, no. 01 (2019): 41–59. http://dx.doi.org/10.46657/ajresd.2019.1.1.5.
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Fault detection in solar photovoltaic (PV) arrays is a fundamental task to protect PV modules from damage and to eliminate risks of safety hazards. In this work, we show a new methodology for automatic supervision and fault detection of PV Systems, based mainly on optimal placement of sensors. This supposes the possibility to build a dynamic model of the system by using the bond graph tool, and the existence of a degradation model in order to predict its future health state. The choice of bond graph is motivated by the fact that it is well suited for modeling physical systems where several types of energies are involved. Fault behavior of PV arrays is highly related to the fault location, fault impedance, irradiance level, and use of blocking diodes. In this work, PV array is connected using series parallel (SP) and Total Cross Tied (TCT) configurations including sensors to measure voltage and currents. The simulation results show the importance of the approach applied for the detection and diagnosis of fault in PV system. These results have been contrasted with real measured data from a measurement campaign plant carried on electrical engineering laboratory of Grenoble using various interconnection schemes are presented.
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Ghazali, Siti Nor Azlina Mohd, and Muhamad Zahim Sujod. "A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique." International Journal of Applied Power Engineering (IJAPE) 11, no. 2 (2022): 134. http://dx.doi.org/10.11591/ijape.v11.i2.pp134-144.
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The output generated by photovoltaic arrays is influenced mainly by the irradiance, which has non-uniform distribution in a day. This has resulted in the current-limiting nature and nonlinear output characteristics, and conventional protection devices cannot detect and clean faults appropriately. This paper proposes a low-cost model for a multi-scale dual-stage photovoltaic fault detection, classification, and monitoring technique developed through MATLAB/Simulink. The main contribution of this paper is that it can detect multiple common faults, be applied on multi-scale photovoltaic arrays regardless of environmental conditions, and be beneficial for photovoltaic system maintenance work. The experimental results show that the developed algorithm using supervised learning algorithms mutual with k-fold cross-validation has produced good performances in identifying six common faults of photovoltaic arrays, achieved 100% accuracy in fault detection, and achieved good accuracy in fault classification. Challenges and suggestions for future research direction are also suggested in this paper. Overall, this study shall provide researchers and policymakers with a valuable reference for developing photovoltaic system fault detection and monitoring techniques for better feasibility, safety, and energy sustainability.
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Ramaprasanna Dalai, Et al. "Protection Scheme based on Artificial Neural Network for Fault Detection and Classification in Low Voltage PV-Based DC Microgrid." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 1960–70. http://dx.doi.org/10.17762/ijritcc.v11i9.9193.
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With the expansion of the DC distribution market, protection, and operational concerns for Direct Current (DC) Microgrids have increased. Different systems have been investigated for detecting, finding, and isolating defects utilising a variety of protective mechanisms. It might be difficult to locate high-resistance faults and shorted DC faults on low-voltage DC (LVDC) microgrids. Therefore, in this study, a Field Transform Technique like Short-Time Fourier Transform (STFT) is proposed for detecting the Fault Current (FC). This method detects the faults Pole-ground (PG), pole-pole (PP), and Arc fault are the major fault types in the DC network with PG fault as the most common and less severe. One of the difficulties the DC system faces in the incidence of a malfunction is the protection of essential converters. During this fault, the diodes, being the most vulnerable component of the system, may encounter a substantial surge in current, which can potentially cause damage if the current surpasses double their specified capacity for withstanding. After the Fault detection (FD), a Taguchi-based ANN is presented to classify the detected faults. This method effectively classifies PV-based faults. Then, to safeguard the FC, the Improved Self-Adaptive Solid State Circuit Breaker (I-SSCB) is introduced. It safeguards the FC in the low-voltage PV-based DC microgrid (DCMG) and restricts FC in the DCMG. The suggested approach is evaluated using the Matlab software and the proposed method produces 400A current and 100 KW power during the PV temperature of 25°C. The output current of the ANN is then 1A for a duration of 0.3 to 0.4 seconds. The fault voltage and FC produced in this proposed work are 1900V and 1950A. Therefore, the proposed work's current and voltage values are 21 KV and 0.35 I. Therefore, the proposed method produces more power and limits the FC in the LV-DCMG. In future studies, the improved or modified neural network or machine learning (ML)-based techniques can be utilized which may improve the protection scheme of the work.
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Platon, Radu, Jacques Martel, Norris Woodruff, and Tak Y. Chau. "Online Fault Detection in PV Systems." IEEE Transactions on Sustainable Energy 6, no. 4 (2015): 1200–1207. http://dx.doi.org/10.1109/tste.2015.2421447.
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Wang, Yao, Xiang Li, Yunsheng Ban, et al. "A DC Arc Fault Detection Method Based on AR Model for Photovoltaic Systems." Applied Sciences 12, no. 20 (2022): 10379. http://dx.doi.org/10.3390/app122010379.
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DC arc faults are dangerous to photovoltaic (PV) systems and can cause serious electric fire hazards and property damage. Because the PV inverter works in a high−frequency pulse width modulation (PWM) control mode, the arc fault detection is prone to nuisance tripping due to PV inverter noises. An arc fault detection method based on the autoregressive (AR) model is proposed. A test platform collects the database of this research according to the UL1699B standard, in which three different types of PV inverters are taken into consideration to make it more generalized. The arc current can be considered a nonstationary random signal while the noise of the PV inverter is not. According to the difference in randomness features between an arc and the noise, a detection method based on the AR model is proposed. The Burg algorithm is used to determine model coefficients, while the Akaike Information Criterion (AIC) is applied to explore the best order of the proposed model. The correlation coefficient difference of the model coefficients plays a role as a criterion to identify if there is an arc fault. Moreover, a prototype circuit based on the TMS320F28033 MCU is built for algorithm verification. Test results show that the proposed algorithm can identify an arc fault without a false positive under different PV inverter conditions. The fault clearing time is between 60 ms to 80 ms, which can meet the requirement of 200 ms specified by the standard.
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Alsafasfeh, Moath, Ikhlas Abdel-Qader, Bradley Bazuin, Qais Alsafasfeh, and Wencong Su. "Unsupervised Fault Detection and Analysis for Large Photovoltaic Systems Using Drones and Machine Vision." Energies 11, no. 9 (2018): 2252. http://dx.doi.org/10.3390/en11092252.
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One of the most important sources of clean energy in the future is expected to be solar energy which is considered a real time source. Research efforts to optimize solar energy utilization are mainly concentrated on the components of solar energy systems. Photovoltaic (PV) modules are considered the main components of solar energy systems and PVs’ operations typically occur without any supervisory mechanisms, which means many external and/or internal obstacles can occur and hinder a system’s efficiency. To avoid these problems, the paper presents a system to address and detect the faults in a PV system by providing a safer and more time efficient inspection system in real time. In this paper, we proposing a real time inspection and fault detection system for PV modules. The system has two cameras, a thermal and a Charge-Coupled Device CCD. They are mounted on a drone and they used to capture the scene of the PV modules simultaneously while the drone is flying over the solar garden. A mobile PV system has been constructed primarily to validate our real time proposed system and for the proposed method in the Digital Image and Signal Processing Laboratory (DISPLAY) at Western Michigan University (WMU). Defects have been detected accurately in the PV modules using the proposed real time system. As a result, the proposed drone mounted system is capable of analyzing thermal and CCD videos in order to detect different faults in PV systems, and give location information in terms of panel location by longitude and latitude.