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Journal articles on the topic 'Mppt; neural network'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Khanam, Jobeda J., and Simon Y. Foo. "Modeling of a photovoltaic array in MATLAB simulink and maximum power point tracking using neural network." Electrical & Electronic Technology Open Access Journal 2, no. 2 (2018): 40–46. http://dx.doi.org/10.15406/eetoaj.2018.02.00019.

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In this paper, we present our work on Maximum Power Point Tracking (MPPT) using neural network. The MATLAB/Simulink is used to establish a model of photovoltaic array. The Simulink model is tested with different temperature and irradiation and resultant I-V and P-V characteristics proved the validation of Simulink model of PV array. We collected a set of data from the Simulink model of PV array after simulated under a range of irradiation and temperature. The data collected from the system is used to train the neural network. When we tested the neural network with different irradiance and temp

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2

Harrag, Abdelghani, Hamza Bahri, and Sabir Messalti. "Steady state oscillations reduction using neural network IC-based variable step Size MPPT." Journal of Renewable Energies 19, no. 3 (2023): 487–95. http://dx.doi.org/10.54966/jreen.v19i3.588.

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This paper deals with the development of neural network IC-based variable step size MPPT controller. The proposed neural network MPPT controller is firstly, developed in offline mode required for testing different set of neural network parameters and architectures, and used secondly in the online mode to track the output power of the PV system composed of Solarex MSX 60W PV module fed by a DC-DC boost converter drived using the proposed ANN MPPT controller. The proposed neural network MPPT controller is tested and validated using Matlab/Simulink environments. Simulation results and analysis ar

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3

Chang, Shuhao, Qiancheng Wang, Haihua Hu, Zijian Ding, and Hansen Guo. "An NNwC MPPT-Based Energy Supply Solution for Sensor Nodes in Buildings and Its Feasibility Study." Energies 12, no. 1 (2018): 101. http://dx.doi.org/10.3390/en12010101.

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Sensors for data collecting are vital in the development of IoT and intelligent systems. High power consuming current and voltage monitors are indispensable in conducting maximum power point tracking (MPPT) in traditional PV energy wireless sensor nodes. This paper presents a sensor node system based on Neural Network MPPT with cloud method (NNwC) which utilizes information sharing process that is specific to sensor networks. NNwC uses a few sample sensor nodes to collect environmental parameter data such as light intensity (L) and temperature (T) to build the MPPT regression model by Neural N

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4

Wen, Liong Han, and Mohd Rezal Mohamed. "Softplus function trained artificial neural network based maximum power point tracking." International Journal of Power Electronics and Drive Systems (IJPEDS) 16, no. 2 (2025): 1174. https://doi.org/10.11591/ijpeds.v16.i2.pp1174-1183.

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To optimize the electrical output of a photovoltaic system, maximum power point tracking (MPPT) methods are commonly employed. These techniques work by operating the photovoltaic system at its maximum power point (MPP), which varies based on environmental factors like solar irradiance and ambient temperature, thereby ensuring optimal power transfer between the photovoltaic system and the load. In this paper, an artificial neural network (ANN) is selected as an MPPT technique. The main contribution of the work is to introduce a softplus function trained artificial neural network-based maximum p

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5

BENCHIKH, Salma, Tarik JAROU, Mohamed Khalifa BOUTAHIR, Elmehdi NASRI, and Roa ELAMRANI. "Improving Photovoltaic System Performance with Artificial Neural Network Control." Data and Metadata 2 (December 30, 2023): 144. http://dx.doi.org/10.56294/dm2023144.

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Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels

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Myint, Thuzar, and Hnin Moh Moh Aung Cho. "Artificial Neural Network for Solar Photovoltaic System Modeling and Simulation." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2110–14. https://doi.org/10.5281/zenodo.3591117.

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This paper presented neural network based maximum power point tracking on the design of photovoltaic power input to a DC DC boot converter to the load. Simulink model of photovoltaic array tested the neural network with different temperature and irradiance for maximum power point of a photovoltaic system. DC DC boot converter is used in load when an average output voltage is stable required which can be lower than the input voltage. At the end, the different temperature and irradiance of the data collected from the photovoltaic array system is used to train the neutral network and output effic

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Subramanian, Balakumar, Samuel Kefale, Mesfin Godato, Yalisho Girma, and Zerihun Zegeye. "Design and simulation of zeta solar charge controller with artificial neural network MPPT." Multidisciplinary Science Journal 7, no. 2 (2024): 2025079. http://dx.doi.org/10.31893/multiscience.2025079.

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Fossil fuel are non-renewable energy sources with constrained reserves. This advocates renewable energy as a viable alternative for electricity generation. PV is a renewable energy source that harnesses solar energy. The current issue with PV is its low efficiency and elevated cost. Photovoltaic control devices and Maximum Power Point Tracking (MPPT) mimic human neural networks in processing multiple conditions and providing solutions from current reference data to optimise photovoltaic power. This technique is known as Artifical neural network (ANN). This method uses a zeta converter to adjus

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8

Khalin, Khalin, Sutedjo Sutedjo, and Dimas Okky Anggriawan. "Identifikasi Gangguan Open Circuit Dan Short Circuit Pada Instalasi Photovoltaic Array Dengan MPPT Berbasis Artificial Neural Network." ENERGI & KELISTRIKAN 14, no. 1 (2022): 34–44. http://dx.doi.org/10.33322/energi.v14i1.1554.

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In the field of photovoltaic, the last few years have been very hotly discussed and researched as a new renewable source to produce electricity that cannot be exhausted. In the development effort there must be some problems arising from the existence of a new system. As with open circuit and short circuit interference. Therefore, The Identification of Open Circuit and Short Circuit Interference in Photovoltaic Array Installation with MPPT Based Artificial Neural Network is present to solve the problem. For identification of the location of the disruption is carried out on each photovoltaic str

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9

HABIBI, MUHAMMAD NIZAR, DIMAS NUR PRAKOSO, NOVIE AYUB WINDARKO, and ANANG TJAHJONO. "Perbaikan MPPT Incremental Conductance menggunakan ANN pada Berbayang Sebagian dengan Hubungan Paralel." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 8, no. 3 (2020): 546. http://dx.doi.org/10.26760/elkomika.v8i3.546.

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ABSTRAKAlgoritma IncrementaL Conductance (IC) adalah algoritma yang bisa diimplementasikan pada sistem Maximum Power Point Tracking (MPPT) untuk mendapatkan daya maksimum dari panel surya. Akan tetapi algoritma MPPT IC tidak bisa bekerja dikondisi berbayang sebagian, karena menimbulkan daya maksimum lebih dari satu. Artificial Neural Network (ANN) bisa mengidentifikasi kurva karakteristik pada kondisi berbayang sebagian dan dapat mengetahui posisi daya maksimum yang sebenarnya. Masukan dari ANN merupakan nilai arus hubung singkat serta tegangan buka dari panel surya, dan keluaran dari ANN adal

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10

Allahyari, S. A., Nasser Taheri, M. Zadehbagheri, and Z. Rahimkhani. "A Novel Adaptive Neural MPPT Algorithm for Photovoltaic System." International Journal of Automotive and Mechanical Engineering 15, no. 3 (2018): 5421–34. http://dx.doi.org/10.15282/ijame.15.3.2018.2.0417.

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This paper presents a novel adaptive neural network (ANN) for maximum power point tracking (MPPT) in photovoltaic (PV) systems under variable working conditions. The ANN-based MPPT model includes two separate NNs for PV system identification and control. NNs are trained by using of a novel back propagation algorithm in pre/post control phases. Because of online optimal performance of NNs, the proposed method, not only overcome the common drawbacks of the conventional MPPT methods, but also gives a simple and a robust MPPT scheme. Simulation results, which carried on MATLAB, show that proposed

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11

Dakhil Atiya, Hayder, Mohamed Boukattaya, and Fatma Ben Salem. "Integration of Fuzzy Logic and Neural Networks for Enhanced MPPT in PV Systems Under Partial Shading Conditions." Iraqi Journal for Electrical and Electronic Engineering 21, no. 1 (2024): 1–15. http://dx.doi.org/10.37917/ijeee.21.1.1.

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Efficient energy collection from photovoltaic (PV) systems in environments that change is still a challenge, especially when partial shading conditions (PSC) come into play. This research shows a new method called Maximum Power Point Tracking (MPPT) that uses fuzzy logic and neural networks to make PV systems more flexible and accurate when they are exposed to PSC. Our method uses a fuzzy logic controller (FLC) that is specifically made to deal with uncertainty and imprecision. This is different from other MPPT methods that have trouble with the nonlinearity and transient dynamics of PSC. At t

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12

Chen, Wen Ying, Yong Jun Lin, Wei Liang Liu, and Shuang Sai Liu. "Research on MPPT of PV Systems Based on BP Neural Network." Advanced Materials Research 466-467 (February 2012): 930–34. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.930.

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In order to obtain more output power of photovoltaic (PV) array, which depends on solar irradiation and ambient temperature, maximum power point tracking (MPPT) techniques are employed. Among all the MPPT strategies, the Perturb and Observe (P&O) algorithm is more attractive due to the simple control structure. Nevertheless, steady-state oscillations always appear due to the perturbation. In this paper, a new MPPT method based on BP Neural Networks and P&O is proposed for searching maximum power point (MPP) fast and exactly, and its effectiveness is validated by experimental results us

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13

Maarouf, Saliha, Abdelhamid Ksentini, El Bahi Azzag, Rachida Kebbache, and Ghania Boukerche. "Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems." Scientific Bulletin of Electrical Engineering Faculty 22, no. 2 (2022): 26–31. http://dx.doi.org/10.2478/sbeef-2022-0016.

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Abstract Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak power (MPP). This article suggests an adaptive method for maximizing power point tracking that makes use of artificial neural network (ANN) techniques (MPPT). A step-up converter powered by a separate solar generator is under the control of an ANN controller built on a neural network training database (PVS). The results show that ANN-MPPT has good control performance and is near to the maximum power point of PVS when compared to

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14

Idrissi, Yassine El Aidi, Khalid Assalaou, Lahoussine Elmahni, and Elmostafa Aitiaz. "New improved MPPT based on artificial neural network and PI controller for photovoltaic applications." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (2022): 1791. http://dx.doi.org/10.11591/ijpeds.v13.i3.pp1791-1801.

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This paper details an maximum power point tracking (MPPT) approach based on artificial neural network (ANN) to track the maximum power produced by a PV panel. This approach is rapid and accurate for following the maximum power point (MPP) during changes in weather conditions such as solar irradiation and temperature. A PV system structure including an MPPT controller is studied, designed, and simulated in this work. The aim of this paper is to use the artificial neural network (ANN) technique to develop a MPPT controller for PV applications. To increase the performance of the ANN-MPPT controll

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15

Yassine, El Aidi Idriss, Assalaou Khalid, Elmahni Lahoussine, and Aitiaz Elmostafa. "New improved MPPT based on artificial neural network and PI controller for photovoltaic applications." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (2022): 1791–801. https://doi.org/10.11591/ijpeds.v13.i3.pp1791-1801.

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This paper details an maximum power point tracking (MPPT) approach based on artificial neural network (ANN) to track the maximum power produced by a PV panel. This approach is rapid and accurate for following the maximum power point (MPP) during changes in weather conditions such as solar irradiation and temperature. A PV system structure including an MPPT controller is studied, designed, and simulated in this work. The aim of this paper is to use the artificial neural network (ANN) technique to develop a MPPT controller for PV applications. To increase the performance of the ANN-MPPT controll

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16

Zečević, Žarko, and Maja Rolevski. "Neural Network Approach to MPPT Control and Irradiance Estimation." Applied Sciences 10, no. 15 (2020): 5051. http://dx.doi.org/10.3390/app10155051.

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Photovoltaic (PV) modules require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. In this paper, we propose a low-complexity MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the maximal power point (MPP) voltage and irradiance estimation. In this way, the computational complexity is reduced compared to the other NN-based MPPT methods, in which the optimal voltage is pred

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17

Liu, Kun, Tong Zhou Ji, Xian Yun Li, Xiong Feng He, and Mei Zhang. "Study of Maximum Power Point Tracking Control Based on GRNN Neural Network." Advanced Materials Research 989-994 (July 2014): 3427–32. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3427.

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detailed analysis of mathematical model, expounded maximum power point tracking (MPPT) control principle, then a improved constant voltage tracking of maximum power point tracking control based on GRNN neural network is proposed, this control strategy uses predicted voltage of GRNN neural network to instead of constant voltage, has be simulated in Matlab /Simulink, and result is this MPPT method can be more accurate compared to traditional control strategy.

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Roshani, D. Borkar, and P.Thakare A. "A High Voltage Gain Interleaved Boost Converter for Fuel Cell Based Electric Vehicle Applications Using MATLAB." Recent Trends in Control and Converter 5, no. 2 (2022): 1–11. https://doi.org/10.5281/zenodo.6855164.

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<em>Because of the strict regulations of coal and gas emissions, the financial system, electric motors, fuel elements (FCEV), is becoming more popular inside the car enterprise. In this case, the neural network represents the majority of electrical place-checking controller (MPPT) power of 1.26 kW, which modifies the surface membranes of a gas-powered vehicle (PEMFC), which provide the power plant of an electric vehicle, using a DC-to-DC power conversion device. Proposal MPPT radial Basis Community Management neural network (RBFN), use of PEMFC, maximum power point tracking algorithm (MPPT). H

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Jarmouni, Ezzitouni, Ahmed Mouhsen, Mohamed Lamhamedi, Hicham Ouldzira, and Ilias En-naoui. "Integration of an optimized neural network in a photovoltaic system to improve maximum power point tracking efficiency." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1276. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1276-1285.

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Due to the variability of weather conditions and equipment properties the maximum power point tracking (MPPT) performance is influenced. MPPT controllers are widely used to improve photovoltaic (PV) efficiency because MPPT can produce maximum power under various weather conditions. Among the most used techniques and representing a satisfactory efficiency are those based on artificial intelligence. Since the use of neural networks requires resources at the implementation level, the optimization of these systems is an important phase. This work represents an optimized system for tracking the max

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Jarmouni, Ezzitouni, Ahmed Mouhsen, Mohamed Lamhamedi, Hicham Ouldzira, and Ilias En-Naoui. "Integration of an optimized neural network in a photovoltaic system to improve maximum power point tracking efficiency." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1276–85. https://doi.org/10.11591/ijeecs.v28.i3.pp1276-1285.

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Due to the variability of weather conditions and equipment properties the maximum power point tracking (MPPT) performance is influenced. MPPT controllers are widely used to improve photovoltaic (PV) efficiency because MPPT can produce maximum power under various weather conditions. Among the most used techniques and representing a satisfactory efficiency are those based on artificial intelligence. Since the use of neural networks requires resources at the implementation level, the optimization of these systems is an important phase. This work represents an optimized system for tracking the max

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21

Kohata, Yasushi, Koichiro Yamauchi, and Masahito Kurihara. "High-Speed Maximum Power Point Tracker for Photovoltaic Systems Using Online Learning Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 6 (2010): 677–82. http://dx.doi.org/10.20965/jaciii.2010.p0677.

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Photo Voltaic (PV) devices have a Maximum Power Point (MPP) at which they generate maximum power. Because the MPP depends on solar radiation and PV panel temperature, it is not constant over time. A Maximum Power Point Tracker (MPPT) is widely used to continuously obtain maximum power, but if the solar radiation changes rapidly, the efficiency of most classic MPPT (e.g., the Perturbation and Observation (P&O) method) reduces. MPPT controllers using neural network respond quickly to rapidly changing solar radiation but must usually undergo prelearning using PV-specific data, so we propose M

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P. Balakrishnan, Gopinath Singaram, S. Senthil Kumar, E. Vani,. "PV Wind Battery Based DC Microgrid with Neural Network MPPT." Journal of Electrical Systems 20, no. 5s (2024): 430–37. http://dx.doi.org/10.52783/jes.2055.

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This Research presents a novel approach to enhancing the performance and efficiency of a PV-wind-battery-based DC microgrid through the integration of a neural network maximum power point tracking (MPPT) system. The proposed system aims to optimize energy harvesting from photovoltaic (PV) panels and wind turbines while efficiently managing energy storage in batteries. By employing neural network algorithms, the MPPT system adapts to varying environmental conditions and load demands, thereby maximizing energy extraction and system stability. Two important RE (Renewable Energy) power sources—pho

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23

Bouzidi, Mohammed, Harrouz Abdelkader, Smail Mansouri, and Virgil Dumbrava. "Modeling of a Photovoltaic Array with Maximum Power Point Tracking Using Neural Networks." Applied Mechanics and Materials 905 (February 15, 2022): 53–64. http://dx.doi.org/10.4028/p-ndl3bi.

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In this paper, we present a modeling of the photovoltaic array in order to tracking the maximum power point (MPPT) using a soft computing approach based on artificial neural network, The maximum power point tracking MPPT play a crucial role in photovoltaic systems for their ability to maximize the power output under varying conditions; The photovoltaic array modeled and implemented in matlab simulink environnement using the conventional perturb and observe algorithm for multiple ranges under varying temperatures and irradiances levels, a feed forward neural network collect the training data fr

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Dahmane, Kaoutar, El Mahfoud Boulaoutaq, Brahim Bouachrine, et al. "Hybrid MPPT Control: P&O and Neural Network for Wind Energy Conversion System." Journal of Robotics and Control (JRC) 4, no. 1 (2023): 1–11. http://dx.doi.org/10.18196/jrc.v4i1.16770.

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In the field of wind turbine performance optimization, many techniques are employed to track the maximum power point (MPPT), one of the most commonly used MPPT algorithms is the perturb and observe technique (PO) because of its ease of implementation. However, the main disadvantage of this method is the lack of accuracy due to fluctuations around the maximum power point. In contrast, MPPT control employing neural networks proved to be an effective solution, in terms of accuracy. The contribution of this work is to propose a hybrid maximum power point tracking control using two types of MPPT co

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Mujammal, Mujammal Ahmed Hasan, Abdelhafidh Moualdia, Saleh Boulkhrachef, Djamel Boudana, Patrice Wira, and Mohammed Abdulelah Albasheri. "Next-Generation MPPT: neural network-driven optimization for superior solar performance." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e7338. http://dx.doi.org/10.54021/seesv5n2-150.

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This paper presents a novel smart Maximum Power Point Tracking (MPPT) method based on a Neural Network Controller (NNC) to enhance solar power efficiency and mitigate significant oscillations. Traditional MPPT techniques, including perturbation and observation, fixed and variable incremental conductance methods, and fractional open circuit voltage, often suffer from slow response to sudden environmental changes (temperature and irradiation), inaccurate tracking, lack of prediction abilities, and complex structures. These drawbacks lead to power loss, reduced effectiveness of photovoltaic (PV)

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Prathaban, Arumbu Venkadasamy, and Dhandapani Karthikeyan. "Grey wolf optimization-recurrent neural network based maximum power point tracking for photovoltaic application." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 629. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp629-638.

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To increase the photovoltaic (PV) power-generation conversion, MPPT is the primary concern. This works explains about the grey wolf optimization (GWO - RNN)-based hybrid maximum power point tracking (MPPT) method to get quick and maximum photovoltaic (PV) power with zero oscillation tracking. The GWO – RNN based MPPT method doesn’t need additional sensor for measuring irradiance and temperature variables. The NLT is used for the multi-level inverter (MLI) control strategy to achieve less harmonics distraction and less switching losses with better voltage and current profile. This employed meth

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Srivastava, Spandan, Charu Lata, Prateek Lohan, and Rinchin W. Mosobi. "Comparative Analysis of Particle Swarm Optimization and Artificial Neural Network Based MPPT with Variable Irradiance and Load." International Journal of Electrical and Electronics Research 10, no. 3 (2022): 460–65. http://dx.doi.org/10.37391/ijeer.100309.

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The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT appr

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Hameed, Waleed I., Ameer L. Saleh, Baha A. Sawadi, Yasir I. A. Al-Yasir, and Raed A. Abd-Alhameed. "Maximum Power Point Tracking for Photovoltaic System by Using Fuzzy Neural Network." Inventions 4, no. 3 (2019): 33. http://dx.doi.org/10.3390/inventions4030033.

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The electrical energy from the sun can be extracted using solar photovoltaic (PV) modules. This energy can be maximized if the connected load resistance matches that of the PV panel. In search of the optimum matching between the PV and the load resistance, the maximum power point tracking (MPPT) technique offers considerable potential. This paper aims to show how the modelling process of an efficient PV system with a DC load can be achieved using a fuzzy neural network (FNN) controller. This is applied via an innovative methodology, which senses the irradiance and temperature of the PV panel a

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Prathaban, Arumbu Venkadasamy, and Dhandapani Karthikeyan. "Grey wolf optimization-recurrent neural network based maximum power point tracking for photovoltaic application." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 629–38. https://doi.org/10.11591/ijeecs.v26.i2.pp629-638.

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To increase the photovoltaic (PV) power-generation conversion, maximum power point tracking (MPPT) is the primary concern. This works explains about the grey wolf optimization (GWO-RNN)-based hybrid MPPT method to get quick and maximum photovoltaic (PV) power with zero oscillation tracking. The GWO–RNN based MPPT method doesn’t need additional sensor for measuring irradiance and temperature variables. The NLT is used for the multi-level inverter (MLI) control strategy to achieve less harmonics distraction and less switching losses with better voltage and current profile. This emplo

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Kacimi, Nora, Said Grouni, Abdelhakim Idir, and Mohamed Seghir Boucherit. "New improved hybrid MPPT based on neural network-model predictive control-Kalman filter for photovoltaic system." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1230–41. https://doi.org/10.11591/ijeecs.v20.i3.pp1230-1241.

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In this paper, new hybrid maximum power point tracking (MPPT) strategy for Photovoltaic Systems has been proposed. The proposed technique for MPPT control based on a novel combination of an artificial neural network (ANN) with an improved model predictive control using kalman filter (NN-MPC-KF). In this paper the Kalman filter is used to estimate the converter state vector for minimized the cost function then predict the future value to track the maximum power point (MPP) with fast changing weather parameters. The proposed control technique can track the MPP in fast changing irradiance conditi

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Rangari, Aniket. "Review On Solar Panel Based Boost Converter with Neural Network for High Voltage Gain Applications." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40677.

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Solar panels with their low output voltage often face challenges in meeting the energy requirements of high-voltage gain applications. This project tackles these issues by developing a boost converter for solar panels, combined with a Maximum Power Point Tracking (MPPT) controller and a neural network-based control system. The neural network adjusts the converter's parameters dynamically, responding to variations in solar irradiation and load conditions to optimize system performance. This innovative approach achieves higher voltage gain, enhanced efficiency, and greater stability compared to

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Kawale, Praniali Surendra. "High Voltage Gain Interleaved Boost Converter with Neural Network Based MPPT Controller for Fuel Cell Based Electric Vehicle Applications." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4728–32. http://dx.doi.org/10.22214/ijraset.2021.35499.

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As a result of the strict regulations on carbon emissions and the fuel economy, fuel cell electric vehicles (FCEV) vehicles are becoming increasingly popular in the automotive industry. This paper provides the Neural Network Maximum Power Point Tracking (MPPT) controller of the 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC), which provides electric vehicle powertrain using DC-DC power converters. The proposed neural network controls the MPPT Radial Basis Function Network (RBFN) using the PEMFC Maximum PowerPoint (MPP) tracking algorithm. High frequency switching and high DC-DC converting p

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Ye, Song-Pei, Yi-Hua Liu, Chun-Yu Liu, Kun-Che Ho, and Yi-Feng Luo. "Artificial Neural Network Assisted Variable Step Size Incremental Conductance MPPT Method with Adaptive Scaling Factor." Electronics 11, no. 1 (2021): 43. http://dx.doi.org/10.3390/electronics11010043.

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In conventional adaptive variable step size (VSS) maximum power point tracking (MPPT) algorithms, a scaling factor is utilized to determine the required perturbation step. However, the performance of the adaptive VSS MPPT algorithm is essentially decided by the choice of scaling factor. In this paper, a neural network assisted variable step size (VSS) incremental conductance (IncCond) MPPT method is proposed. The proposed method utilizes a neural network to obtain an optimal scaling factor that should be used in current irradiance level for the VSS IncCond MPPT method. Only two operating point

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Harrag, Abdelghani. "Novel Neural network single sensor MPPT for Proton Exchange Membrane Fuel Cell." Journal of New Materials for Electrochemical Systems 24, no. 1 (2021): 43–48. http://dx.doi.org/10.14447/jnmes.v24i1.a08.

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This paper presents a new neural network single sensor maximum power point tracking algorithm controlling the DC-DC boost converter to guarantee the transfer of the proton exchange membrane fuel cell maximum generated power to the load. The implemented neural network single sensor controller has been developed and trained firstly in offline mode using single sensor maximum power point tracking data obtained previously; and secondly used in online mode to track the maximum output power of the fuel cell power system. Comparative simulation results prove the superiority of the proposed neural net

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ALFarra, Mohammed, and Hatem Elaydi. "Improving Solar Power System's Efficiency Using Artificial Neural Network." Israa University Journal for Applied Science 3 (October 1, 2019): 64–88. http://dx.doi.org/10.52865/rffg5440.

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Renewable energy sources are the best solution to reduce dependence on conventional and nonrenewable sources that also cause environmental pollution. With the increase in the prices of conventional fuels globally, the increase of gas emissions resulting from its use, and the impact on the environment and the global climate; various renewable energy sources have emerged as an alternative to traditional sources of energy. Ssolar energy is one of the most important renewable energy sources used globally; The technology used is relatively simple and uncomplicated, compared to the technology used i

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Raut, Avinash Banduji. "Modelling and Simulation of Hybrid Electric Vehicle Based on MATLAB/ Simulink." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 4062–67. http://dx.doi.org/10.22214/ijraset.2023.54393.

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Abstract: Due to the more vigorous regulations on carbon gas emissions and fuel economy, Fuel Cell Electric Vehicles (FCEV) are becoming more popular in the automobile industry. This paper presents a neural network based Maximum Power Point Tracking (MPPT) controller for 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC), supplying electric vehicle powertrain through a high voltage-gain DC-DC boost converter. The proposed neural network MPPT controller uses Radial Basis Function Network (RBFN) algorithm for tracking the Maximum Power Point (MPP) of the PEMFC. High switching frequency and high

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Ahmed, G. Abdullah, Sh. Aziz Mothanna, and A. Hamad Bashar. "Comparison between neural network and P&O method in optimizing MPPT control for photovoltaic cell." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5083–92. https://doi.org/10.11591/ijece.v10i5.pp5083-5092.

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The demand for renewable energy has increased because it is considered a clean energy and does not result in any pollution or emission of toxic gases that negatively affect the environment and human health also requiring little maintenance, and emitting no noise, so it is necessary to develop this type of energy and increase its production capacity. In this research a design of maximum power point tracking (MPPT) control method using Neural Network (NN) for photovoltaic system is presented. First we design a standalone PV system linked to dc boost chopper with MPPT by perturbation and observat

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Huang, Cong Hui, Chih Ming Hong, Yih Feng Su, et al. "Elman Neural Network for Dynamic Control of Wind Power Systems." Applied Mechanics and Materials 479-480 (December 2013): 570–74. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.570.

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This paper presents Elman neural network for the dynamic control strategies of a hybrid power system that include wind/photovoltaic/diesel system. Wind and PV power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. A simulation model for the hybrid energy system has been developed using MATLAB/Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN) and an modified Elman Neural Network (ENN) for maximum power p

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Sahu, Jayanta Kumar, Babita Panda, and Jyoti Prasad Patra. "Artificial neural network for maximum power point tracking used in solar photovoltaic system." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (2023): 1694. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1694-1701.

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<span lang="EN-US">Nowadays, non-conventional energy sources like solar, wind, geothermal, and small hydro play a vital role in generating electricity. Among these, solar energy is utilized in urban and rural areas. When the sunlight falls on the solar plate, the PV cell produces charge carriers that produce an electric current. A photo voltaic cell is used when it works at the maximum power point. Traditional maximum power point tracking (MPPT) techniques are easier to structure and apply but perform worse than AI-based systems. The main objective of this paper is to develop an intellig

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Jayanta, Kumar Sahu, Panda Babita, and Prasad Patra Jyoti. "Artificial neural network for maximum power point tracking used in solar photovoltaic system." International Journal of Power Electronics and Drive Systems 14, no. 3 (2023): 1694~1701. https://doi.org/10.11591/ijpeds.v14.i3.pp1694-1701.

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Nowadays, non-conventional energy sources like solar, wind, geothermal, and small hydro play a vital role in generating electricity. Among these, solar energy is utilized in urban and rural areas. When the sunlight falls on the solar plate, the PV cell produces charge carriers that produce an electric current. A photo voltaic cell is used when it works at the maximum power point. Traditional maximum power point tracking (MPPT) techniques are easier to structure and apply but perform worse than AI-based systems. The main objective of this paper is to develop an intelligent system to determine t

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Drir, Nadia, Linda Barazane, and Malik Loudini. "Neural network and fuzzy logic to track maximum power point in photovoltaic system." Journal of Renewable Energies 17, no. 2 (2023): 253–61. http://dx.doi.org/10.54966/jreen.v17i2.440.

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The output characteristics of photovoltaic systems are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminal of photovoltaic generator. This study explores two intelligent controller based on a neural networks and fuzzy logic to track this point. These both controllers have prove, by their results, a good tracking of the MPPT compare with the other methods which are proposed up to now.

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Ostrenko, Dmytro, and O. Kollarov. "Development of a neural network with the characteristics of MPPT controller." Journal of Electrical and power engineering, no. 1 (2018): 23–27. http://dx.doi.org/10.31474/2074-2630-2018-1-23-27.

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Abood Abdul Kadhim, Afrah, Abdulhasan F. Abdulhasan, and Fatimah F. Jaber. "Improvement of Extracted Photovoltaic Power Using Artificial Neural Networks MPPT with Enhanced Flyback Controller." Iraqi Journal for Electrical and Electronic Engineering 21, no. 2 (2025): 237–50. https://doi.org/10.37917/ijeee.21.2.22.

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Due to the nonlinear electrical properties of PV generators, the width and performance of these frames could be enhanced by carrying them to operate at ultimate energy mark tracking. In this study, a versatile maximum power point tracking (MPPT) model using a modified Flyback controller with artificial neural network (ANN) technique as our proposed system. The hybrid Flyback/ANN controller is based on teaching and training a neural network, where the dataset is utilized to adjust the levitation converter which is taken care of by a stand-alone photovoltaic generator (PVG) with a Flyback contro

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Makhloufi, Mohamed Tahar, Yassine Abdessemed, and Mohamed Salah Khireddine. "A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1421. http://dx.doi.org/10.11591/ijece.v6i4.9704.

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<p class="References">This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of th

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Makhloufi, Mohamed Tahar, Yassine Abdessemed, and Mohamed Salah Khireddine. "A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1421. http://dx.doi.org/10.11591/ijece.v6i4.pp1421-1433.

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<p class="References">This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of th

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Zhang, Yan, Ya-jun Wang, Han Li, Jia-Bao Chang, and Jia-qi Yu. "A Firefly Algorithm and Elite Ant System-Trained Elman Neural Network for MPPT Algorithm of PV Array." International Journal of Photoenergy 2022 (November 8, 2022): 1–16. http://dx.doi.org/10.1155/2022/5700570.

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This article proposes a novel MPPT algorithm based on the firefly algorithm and elite ant system-trained Elman neural network (FA-EAS-ElmanNN). First, the position of fireflies is randomly initialized by the firefly algorithm (FA), meanwhile the firefly individuals with higher attractiveness degree value are selected as the optimal solution. Second, the extra pheromones are artificially released to boost the positive feedback effect and convergence rate of the elite ant system (EAS). Third, the weight and threshold of the Elman neural network (ElmanNN) are updated by the FA and EAS. Also, the

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Troudi, Fathi, Houda Jouini, Abdelkader Mami, et al. "Comparative Assessment between Five Control Techniques to Optimize the Maximum Power Point Tracking Procedure for PV Systems." Mathematics 10, no. 7 (2022): 1080. http://dx.doi.org/10.3390/math10071080.

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Solar photovoltaic (PV) energy production is important in reducing global energy crises since it is transportable, scalable, and highly customizable dependent on the needs of the industry or end-user. In addition, compared to other renewable resources, photovoltaic systems can produce electricity without moving parts and have a long lifespan. Nevertheless, solar photovoltaic (PV) systems provide intermittent output electricity with a nonlinear output voltage. Due to this intermittent availability, PV installations are facing significant challenges. As a result, in PV power systems, a Maximum P

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Abd Kadir, Mahamad, and Saon Sharifah. "Development of Artificial Neural Network Based MPPT for Photovoltaic System during Shading Condition." Applied Mechanics and Materials 448-453 (October 2013): 1573–78. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1573.

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This paper presents Feedforward Neural network (FFNN) and Elman network controllers to control the maximum power point tracking (MPPT) of photovoltaic (PV). MPPT is a method used to extract the maximum available power from photovoltaic module by designs them to operate efficiently. Thus, cell temperatures and solar irradiances are two critical variable factors to determine PV output powers. The performances of the controller is analyzed in four conditions which are i) constant irradiation and temperature, ii) constant irradiation and variable temperature, iii) constant temperature and variable

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Shamim, Kaiser M., Subrata Kumar Aditya, and Rezaul Karim Mazumder. "Performance Evaluation of a Maximum Power Point Tracker (MPPT) for Solar Electric Vehicle Using Artificial Neural Network." DIU Journal of Science & Technology 1, no. 1 (2024): 45–50. https://doi.org/10.5281/zenodo.13637626.

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In this paper, the performance of an ArtificialNeural Network (ANN) based maximum power pointtracker (MPPT) for solar electric vehicles has beenevaluated. The core component of a MPPT is boostconverter with insulated gate bipolar transistor (IGBT)power switch. The reference voltage for MPPT isobtained by ANN with gradient descent algorithm. Thetracking algorithm changes the duty-cycle of theconverter so that the PV-module voltage equals thevoltage corresponding to the MPPT at any givenirradiance, temperature, and load conditions. For fastresponse, the system is implemented using digital signal

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Sriharibabu, A., and G. Srinivasa Rao. "MPPT Design for Photo Voltaic Energy System Using Backstepping Control with a Neural Compensator." International Journal of Engineering & Technology 7, no. 4.24 (2018): 129. http://dx.doi.org/10.14419/ijet.v7i4.24.21872.

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It is very important to have maximum power point trackers for photo voltaic systems to improve their efficiency. This paper deals with the converter based maximum power point tracking by robust backstepping controller along with the neural network. The neural network provides the output reference PV voltage to the backstepping controller. Back propagation neural network is used for a standalone photovoltaic system under robust environmental conditions. Unlike Conventional solar-array mathematical model, neural network does not require any physical data for modeling since it has the superior po

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