Performance Evaluation of Machine Learning Techniques for Fault Detection and Classification in PV Array Systems

Pahwa, Khushbu; Sharma, Mayank; Saggu, Mankaran Singh; Mandpura, Anup Kumar

doi:10.1109/spin48934.2020.9071223

Cited by 16 publications

(14 citation statements)

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“…Furthermore, among these schemes, a comparison of the predicted features with the measured ones is the most common type. However, this kind of analysis could be fully realized inside one ANN by adding the measured features as inputs as it has been already done like in [37,46,52]. Therefore, the capabilities of the ANN should be fully exploited instead of complexifying the methodology.…”

Section: ) Limitations and Prospectsmentioning

confidence: 99%

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

Delpha

Diallo

et al. 2021

Renewable and Sustainable Energy Reviews

183

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Section: ) Limitations and Prospectsmentioning

confidence: 99%

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

Delpha

Diallo

et al. 2021

Renewable and Sustainable Energy Reviews

183

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“…In studies [ 91 , 92 , 94 , 95 , 96 , 97 , 98 ], researchers detect, classify, and localize [ 98 ] different failures of a solar plant system based on non-NN [ 91 , 92 , 95 , 97 ], ANN [ 97 ], ANFIS [ 98 ], and LSTM [ 94 ] that simply process signals from ordinary sensors ( Figure 4 (1)).…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

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“…On the other hand, (Pahwa et al, 2020) present an evaluation of the performance of different Machine Learning techniques for the automatic classification of failures in photovoltaic panels, they evaluated the performance of classifiers based on Decision Tree, XGBoost, Random Forest and Neural Networks. The results obtained by simulation reveal that the neural network classifier has the highest accuracy greater than 99.5% (and, therefore, the lowest mean square error).…”

Section: Literature Reviewmentioning

confidence: 99%

Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

et al. 2021

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This article presents a methodology for automatic fault detection in photovoltaic arrays. Due to the great importance in the construction of increasingly robust photovoltaic plants, automatic fault detection has become a necessary tool to extend the useful life of these plants, avoid system shutdowns and reduce serious safety problems. In the present study, nine possible faults are detected, caused by malfunction in the bypass and blocking diodes. The solution consists of training two models based on artificial neural networks, the first model is a binary classifier that detects whether or not a fault occurs, the second is a multiclass classifier that detects the fault type. The obtained models were trained from simulation data, in an architecture of 9 photovoltaic panels interconnected in three rows by three columns matrix (extendable to larger systems). The evaluation shows that the prediction system has a total accuracy of 92.95%. Finally, this methodology is intended to be implemented in Colombia, in zones with difficult access and not interconnected to the electricity grid, seeking to reduce corrective maintenance.

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Performance Evaluation of Machine Learning Techniques for Fault Detection and Classification in PV Array Systems

Cited by 16 publications

References 0 publications

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

A Review on Machine Learning Applications for Solar Plants

Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

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