Bibliografie tematiche / PV system fault detection

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Letteratura scientifica selezionata sul tema "PV system fault detection"

Autore: Grafiati
Pubblicato: 13 agosto 2024
Ultima modifica: 31 luglio 2025

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Articoli di riviste sul tema "PV system fault detection"

1

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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Abstract (sommario):
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 f
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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
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N., Muhammad, Zainuddin H., Jaaper E., and Idrus Z. "An early fault detection approach in grid-connected photovoltaic (GCPV) system." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 2 (2020): 671–79. https://doi.org/10.11591/ijeecs.v17.i2.pp671-679.

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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 gridconnected 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 de
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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 connectio
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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 fai
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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 he
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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, f
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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
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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
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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. F
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Tesi sul tema "PV system fault detection"

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García-Gutiérrez, Luis Antonio. "Développement d'un contrôle actif tolérant aux défaillances appliqué aux systèmes PV." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30071.

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Cette thèse de doctorat aborde la problématique de la réalisation d'un système de contrôle actif de détection de défaut et diagnosis (FDD) pour un système de conversion photovoltaïque. Ce type de système de production d'énergie électrique est composé de panneaux solaires, d'un dispositif MPPT, d'un convertisseur de courant DC-DC, d'un onduleur DC-AC et d'une charge. Le système de contrôle actif à tolérance de pannes qui a été développé dans cette thèse est composé de deux étages : * Un étage assurant la fonction de diagnostic et comprenant les fonctions de détection de défauts, la fonction d'i
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Mahajan, Vijyant. "PV Module and system fault analysis." Thesis, Mahajan, Vijyant (2014) PV Module and system fault analysis. Other thesis, Murdoch University, 2014. https://researchrepository.murdoch.edu.au/id/eprint/25561/.

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Abstract (sommario):
In the recent years, there is a noticeable escalation in the number of Photovoltaic module systems installed on the rooftops for the residential and small level commercial purposes. Lower consumer prices, government grants and increase in the awareness of environmental issues are some of the basic causes for this increase. Increase in the renewable energy production is a long term solution to the problems faced due to the fossil fuels energy production methods including the availability and cost of the fossil fuels and environmental pollution. To keep the positive slope of the trend of acc
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Chen, Yi-Ching. "Co-design of Fault-Tolerant Systems with Imperfect Fault Detection." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104942.

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In recent decades, transient faults have become a critical issue in modernelectronic devices. Therefore, many fault-tolerant techniques have been proposedto increase system reliability, such as active redundancy, which can beimplemented in both space and time dimensions. The main challenge of activeredundancy is to introduce the minimal overhead of redundancy and to schedulethe tasks. In many pervious works, perfect fault detectors are assumed to simplifythe problem. However, the induced resource and time overheads of suchfault detectors make them impractical to be implemented. In order to tac
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Dicharry, Jeff. "Power System Fault Detection Using Conductor Dynamics." ScholarWorks@UNO, 2005. http://scholarworks.uno.edu/td/289.

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Power system fault detection is conventionally achieved using current and potential measurements. An alternate and unconventional form of protective relaying is feasible using rigid bus conductor motion as the means of detection. The research presented focuses on the detection of power system faults using visual displacement of conductor spans. Substation rigid bus conductor motion is modeled using dual spring-mass systems for accurate representation of conductor response to electromagnetic forces generated during system faults. Bundled rigid conductors have advantages including detec
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Koubli, Eleni. "Impact of data quality on photovoltaic (PV) performance assessment." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/27508.

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In this work, data quality control and mitigation tools have been developed for improving the accuracy of photovoltaic (PV) system performance assessment. These tools allow to demonstrate the impact of ignoring erroneous or lost data on performance evaluation and fault detection. The work mainly focuses on residential PV systems where monitoring is limited to recording total generation and the lack of meteorological data makes quality control in that area truly challenging. Main quality issues addressed in this work are with regards to wrong system description and missing electrical and/or met
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Choi, Sang-Sung. "Fault detection algorithm for Global Positioning System receivers." Ohio : Ohio University, 1991. http://www.ohiolink.edu/etd/view.cgi?ohiou1183661191.

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Vinsonneau, Jocelyn A. F. "Fault detection and modelling for an automotive system." Thesis, Coventry University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399534.

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McMichael, D. W. "On-line fault detection, a system-nonspecific approach." Thesis, University of Oxford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232802.

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Luo, Dapeng. "SYSTEM IDENTIFICATION AND FAULT DETECTION OF COMPLEX SYSTEMS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3583.

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The proposed research is devoted to devising system identification and fault detection approaches and algorithms for a system characterized by nonlinear dynamics. Mathematical models of dynamical systems and fault models are built based on observed data from systems. In particular, we will focus on statistical subspace instrumental variable methods which allow the consideration of an appealing mathematical model in many control applications consisting of a nonlinear feedback system with nonlinearities at both inputs and outputs. Different solutions within the proposed framework are presented t
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Tian, Ninghan. "ETFIDS: Efficient Transient Fault Injection and Detection System." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1544716635499045.

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Libri sul tema "PV system fault detection"

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European Workshop on Fault Diagnostics, Reliability, and Related Knowledge-Based Approaches (2nd 1987 University of Manchester Institute of Science and Technology). Fault detection & reliability: Knowledge based & other approaches. Pergamon Press, 1987.

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Wang, Dong. Robust Filtering and Fault Detection of Switched Delay Systems. Springer Berlin Heidelberg, 2013.

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Gertler, Janos. Fault detection and diagnosis in engineering systems. Marcel Dekker, 1998.

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Meskin, Nader. Fault Detection and Isolation: Multi-Vehicle Unmanned Systems. Springer Science+Business Media, LLC, 2011.

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Caglayan, A. User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program. National Aeronautics and Space Administration, Langley Research Center, 1988.

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C, Merrill Walter, Duyar Ahmet, and United States. National Aeronautics and Space Administration., eds. A distributed fault-detection and diagnosis system using on-line parameter estimation. National Aeronautics and Space Administration, 1991.

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C, Merrill Walter, Duyar Ahmet, and United States. National Aeronautics and Space Administration., eds. A distributed fault-detection and diagnosis system using on-line parameter estimation. National Aeronautics and Space Administration, 1991.

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United States. National Aeronautics and Space Administration., ed. Advanced power system protection and incipient fault detection and protection of spaceborne power systems. [Washington, DC, 1989.

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Judith, Crow, SRI International, and Langley Research Center, eds. Evaluation of an expert system for fault detection, isolation, and recovery in the manned maneuvering unit. SRI International, 1990.

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Sohlberg, Björn. Supervision and Control for Industrial Processes: Using Grey Box Models, Predictive Control and Fault Detection Methods. Springer London, 1998.

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Capitoli di libri sul tema "PV system fault detection"

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Mashiloane, Kabelo, Peet F. Le Roux, and Coneth G. Richards. "Simulation and Fault Diagnostics Using I–V and P–V Curve Tracing." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4581-4_9.

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AbstractLocalization of problems continues to be very difficult, especially in large-scale photovoltaic (PV) systems. Especially for small-scale PV plants, the layout of PV systems significantly impacts the efficiency of detection systems. Due to faults occurring within PV arrays, this paper aims to highlight the value of fault detection in PV systems through I–V curve features. This is achieved by simulating models using MATLAB/Simulink of normal and faulty operations. Investigating faults in solar PV arrays is critical in improving PV systems’ dependability, effectiveness, and safety. A quic
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Braun, Henry, Santoshi T. Buddha, Venkatachalam Krishnan, et al. "Monitoring of PV Systems." In Signal Processing for Solar Array Monitoring, Fault Detection, and Optimization. Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-02497-9_6.

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Srinivasa Murthy, G., and Suryanarayana Gangolu. "Fault Detection in Floating PV System Using DC Leakage Current." In Control and Measurement Applications for Smart Grid. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7664-2_15.

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Yamada, Luciana, Priscila Rampazzo, Felipe Yamada, Luís Guimarães, Armando Leitão, and Flávia Barbosa. "Multiobjective Evolutionary Clustering to Enhance Fault Detection in a PV System." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46439-3_16.

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Ciampi, Anna, Annalisa Appice, Donato Malerba, and Angelo Muolo. "An Intelligent System for Real Time Fault Detection in PV Plants." In Sustainability in Energy and Buildings. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27509-8_19.

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Mohanapriya, V., B. Sharmila, and V. Manimegalai. "Classification and Detection Techniques of Fault in Solar PV System: A Review." In Springer Proceedings in Materials. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8319-3_115.

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Saliha, Sebbane, El Akchioui Nabil, and Fahim Mohamed. "Intelligent PV Fault Detection and Categorization Based on Metaheuristic Algorithm and Feedforward Neural Network." In Advances in Electrical Systems and Innovative Renewable Energy Techniques. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49772-8_11.

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Pichler, Kurt. "Early Fault Detection in Reciprocating Compressor Valves by Means of Vibration and pV Diagram Analysis." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_6.

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Al-Rifai, Yehya, Adriana Aguilera-Gonzalez, and Ionel Vechiu. "Fault Detection and Diagnosis of PV Systems Using Kalman-Filter Algorithm Based on Multi-zone Polynomial Regression." In Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27540-1_4.

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Arezki, S., A. Aissaoui, and M. Boudour. "Development of a Hybrid DLDH Fault Detection and Localization Algorithm for Two Types of PV Technologies with Experimental Validation." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60629-8_38.

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Atti di convegni sul tema "PV system fault detection"

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Savithiri, V., and S. Gomathi. "Fault Detection Algorithms for Improving Accuracy in Solar PV System." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859624.

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Gacshe, Abdirisak A., Hossam A. Abd El-Ghany, and Amr S. Zalhaf. "Enhanced Online Fault Detection and Classification Method for PV Systems." In 2024 25th International Middle East Power System Conference (MEPCON). IEEE, 2024. https://doi.org/10.1109/mepcon63025.2024.10850249.

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Mitra, Souvik, and K. A. Chinmaya. "Fault Detection in PV Grid Integrated System via Machine Learning Technology." In 2025 IEEE 1st International Conference on Smart and Sustainable Developments in Electrical Engineering (SSDEE). IEEE, 2025. https://doi.org/10.1109/ssdee64538.2025.10967651.

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Pujara, Deep, David Ramirez, Cihan Tepedelenlioglu, Devarajan Srinivasan, and Andreas Spanias. "Real-time PV Fault Detection using Embedded Machine Learning." In 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS). IEEE, 2024. http://dx.doi.org/10.1109/icps59941.2024.10640018.

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Limpaporn, Poompat, and Surin Khomfoi. "Fault Detection and Predictive Maintenance Paradigm for Solar PV Systems." In 2024 International Conference on Materials and Energy: Energy in Electrical Engineering (ICOME-EE). IEEE, 2024. https://doi.org/10.1109/icome-ee64119.2024.10845651.

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Thomas, Ansu, and Dolly Mary Abraham. "CHALLENGES FACED BY CONVENTIONAL PROTECTION DEVICES IN PV ARRAY FAULT DETECTION." In 2024 International Conference on Advancement in Renewable Energy and Intelligent Systems (AREIS). IEEE, 2024. https://doi.org/10.1109/areis62559.2024.10893633.

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Alam, Sameer, Shivam Kaushik, Shaikh Mohd Shaique, and Nidal Rafiuddin. "PV Fault Detection Using CNN For Enhancing Reliability Of Solar Power Plants." In 2024 IEEE Third International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). IEEE, 2024. http://dx.doi.org/10.1109/icpeices62430.2024.10719330.

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Xiang, Goh Yu, Alicia Loh I-Ling, Kishore Bingi, and Rosdiazli Ibrahim. "Automated Inspection and Fault Detection in Rooftop Solar PV System Using YOLO-Driven Tello Quadrotor Drone." In 2025 IEEE International Conference on Robotics and Technologies for Industrial Automation (ROBOTHIA). IEEE, 2025. https://doi.org/10.1109/robothia63806.2025.10986536.

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Kumar, Naresh, J. Raji, S. Sridevi, Md Mujahid Irfan, R. Rajeshwari, and Abinaya Inbamani. "A PSO Tuned CNN Approach for Accurate Fault Detection in PV Grid Systems." In 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2025. https://doi.org/10.1109/csnt64827.2025.10968346.

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Safari, Ashkan, Hamed Kharrati, and Afshin Rahimi. "FaultyVoltaMan: Ensemble Learning Model for Accurate Fault Detection and Classification of PV-Integrated Systems." In 2024 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2024. http://dx.doi.org/10.1109/icphm61352.2024.10626794.

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Rapporti di organizzazioni sul tema "PV system fault detection"

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Lavrova, Olga, Jack David Flicker, and Jay Johnson. PV Systems Reliability Final Technical Report: Ground Fault Detection. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1234818.

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McCalmont, S. Low Cost Arc Fault Detection and Protection for PV Systems: January 30, 2012 - September 30, 2013. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1110454.

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King, Bruce Hardison, and Christian Birk Jones. Final Technical Report: PV Fault Detection Tool. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1233822.

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El Khatib, Mohamed, Javier Hernandez Alvidrez, and Abraham Ellis. Fault Analysis and Detection in Microgrids with High PV Penetration. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1367437.

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Klise, Geoffrey Taylor, Olga Lavrova, and Renee Lynne Gooding. PV System Component Fault and Failure Compilation and Analysis. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1424887.

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Li, Yanfei, Jian Sun, Teja Kuruganti, et al. Connected Loads – Grid Connected Appliances: Commercial Refrigeration System Fault Detection and Diagnostics. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1905423.

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Farrington, R. Reliability testing of active SDHW components. Part III. Development of a fault detection system. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/6002810.

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Abstract (sommario):
Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, contro
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9

Jiang, yilin, Kevwe Ejenakevwe, Junke Wang, et al. Development and validation of home comfort system for total performance deficiency/fault detection and optimal comfort control. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2352250.

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Schein, Jeffery, and Steven Bushby. A simulation study of a hierarchical, rule-based method for system-level fault detection and diagnostics in HVAC systems. National Institute of Standards and Technology, 2005. http://dx.doi.org/10.6028/nist.ir.7216.

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