Monitoring Tool for Stand-Alone Photovoltaic System Using Artificial Neural Network

Sabri, Nassim; Tlemçani, Abdelhalim; Chouder, Aissa

doi:10.1007/978-3-030-04789-4_12

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…The MLP gained popularity in the 1980s with the increase in computer processing power and the introduction of the backpropagation algorithm (RUMELHART, HINTON, WILLIAMS, 1985). The training technique for MLP, known as backpropagation, was popularized through the book "Parallel Distributed Processing", published in 1986 by Rumelhart and McClelland. In The MLP has been applied in various fields using machine learning techniques, including in the domain of electrical systems for the analysis and diagnosis of faults in photovoltaic generation systems (BASNET; CHUN; BANG, 2020; BHARATH; HAQUE; KHAN, 2018;CHINE et al, 2016;CHOUAY;OUASSAID, 2017;DA COSTA et al, 2019;DJALAB et al, 2020;LAZZARETTI et al, 2020;RAO;SPANIAS;TEPEDELENLIOGLU, 2019;SABRI;TLEMÇANI;CHOUDER, 2019;VIEIRA, 2021).…”

Section: Introductionmentioning

confidence: 99%

Comparison of multilayer perceptron neural network architecture in photovoltaic plants fault classification

Bezerra,

Ferreira,

Brito

et al. 2023

CLIUM

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The goal of this study is to assess the application of Multilayer Perceptron Artificial Neural Networks in fault classification within photovoltaic panels, focusing on key characteristics such as the number of neurons and layers, activation functions, training techniques, and the resulting accuracy. The study employs a comparative analysis approach, examining various characteristics and hyperparameters applied to Multilayer Perceptron Artificial Neural Networks for fault classification in photovoltaic panels. The research methodology involves reviewing publications from the past decade to gather data on these characteristics and their impact on fault analysis in photovoltaic generation systems. This study contributes to the originality of the field by providing a comprehensive comparison of various parameters and techniques used in Multilayer Perceptron Artificial Neural Networks for fault classification in photovoltaic panels. The findings offer valuable insights for researchers and practitioners in the renewable energy sector, aiding in the development of more efficient and reliable fault diagnosis systems for photovoltaic generation.

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Section: Introductionmentioning

confidence: 99%

Comparison of multilayer perceptron neural network architecture in photovoltaic plants fault classification

Bezerra,

Ferreira,

Brito

et al. 2023

CLIUM

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“…This latter provides an analysis of SAPV system, where the battery takes a part in this system. Fault diagnosis in SAPV system including the specific fault battery external short-circuit is proposed in [9,10] using Artificial Neural Network, the study is experimentally validated an additionally integrated with the system using Matlab Graphical User Interface (GUI) [11]. Battery performance evaluation in PV-diesel hybrid system is proposed in [12] where the analysis requires many days and provides inaccurate results concerning the battery state.…”

Section: Introductionmentioning

confidence: 99%

Battery Internal Fault Monitoring Based on Anomaly Detection Algorithm

Sabri¹,

Tlemçani²,

Chouder³

2020

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

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Battery internal faults are one of the major factors causing safety concern, performance degradation, and cost increases. To extend the lifetime of the battery and bring more security in the system, internal fault detection of solar battery is proposed in this paper using an unsupervised machine learning algorithm based on anomaly detection method. The advantages of adopting such a method consist of using unlabeled data that meet the battery case in the difficulty of obtaining the fault data. In contrast, healthy data can easily be obtained from the battery and therefore allows building the anomaly detection algorithm. The effectiveness of the proposed method is validated using a simulation platform of a stand-alone photovoltaic system developed in Matlab/Simulink that takes as system input a real profile of irradiance and temperature captured from the Centre de Development des Energies renewable (CDER), Algeria. The test results in real-time data show the ability of the proposed approach to detect the fault occurrence in the battery.

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An Improved Fault Diagnosis in Stand-Alone Photovoltaic System Using Artificial Neural Network

Sabri,

Tlemçani,

Chouder

et al. 2023

Iran J Sci Technol Trans Electr Eng

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Monitoring Tool for Stand-Alone Photovoltaic System Using Artificial Neural Network

Cited by 3 publications

References 12 publications

Comparison of multilayer perceptron neural network architecture in photovoltaic plants fault classification

Comparison of multilayer perceptron neural network architecture in photovoltaic plants fault classification

Battery Internal Fault Monitoring Based on Anomaly Detection Algorithm

An Improved Fault Diagnosis in Stand-Alone Photovoltaic System Using Artificial Neural Network

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