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Development of a multi-output feed-forward neural network for fault detection in photovoltaic Systems

ENERGY REPORTS 2022年 8卷 33-42页

作者： Voutsinas, Stylianos Karolidis, Dimitrios Voyiatzis, Ioannis Samarakou, Maria Univ West Attica Dept Informat & Comp Engn Athens 12243 Greece

The main role of a fault detection and identification technique is to interpret what is causing a PVS's real-time energy production to drop. To keep photovoltaic (PV) systems performing at their best, fault detection is crucial. Various fault detection systems have been proposed in the literature over the last decade. Some of the fault detection methods presented solely used data from I-V curves, while others relied on ANN techniques to find faults. This paper presents the development of a multioutput Artificial Neural Network (ANN), for fault detection and identification on the DC side of a photovoltaic System (PVS). Measurements and benchmarking were carried out in order to validate the algorithm's capacity to detect both normal operation at maximum power and the ability to determine and identify faults introduced during the experiment's procedure. It is observed that the classification output, compared with the regression output, provides a better assessment of the operational status of the photovoltaic cell. The developed method has a 93.4 percent accuracy in detecting open-circuit, short-circuit, and mismatch faults, using the classification output, while the regression output provides an operational status estimation with mean absolute error (MAE) equal to 0.305. When compared to other implementations, the findings are encouraging, indicating that the approach could be used in PVS. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： photovoltaic Systems photovoltaic fault detection algorithms I-V curves Artificial Neural Networks

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Development of a fault detection algorithm for photovoltaic Systems 25

Development of a fault detection algorithm for Photovoltaic ...

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25th Pan-Hellenic Conference on Informatics with International Participation (PCI)

作者： Stylianos Voutsinas Dimitrios Karolidis Ioannis Voyiatzis Maria Samarakou Univ West Attica Dept Informat & Comp Engn Egaleo Greece

ISBN: (纸本)9781450395557

The use of an algorithm based on data from I-V curves, a simple and cost-effective method for fault detection and identification in photovoltaic Systems (PVS), is presented. When determining whether or not to invest in a PVS, life expectancy and reliability are critical considerations. In this paper, the development of an I-V curve-based algorithm for fault detection and identification in PVS is presented. The method calculates the single diode model that describes the photovoltaic cell in use, for the irradiance and temperature of a certain location. After that, a threshold monitoring approach identifies the presence and the nature of a fault. Measurements were performed to certify the ability of the algorithm to detect both the normal operation at maximum power point and the ability to detect and identify errors introduced during the operation of the experiment. The algorithm can identify open-circuit, short-circuit and mismatch faults. The results are promising, implying that the method could be applied in PVS.

关键词： photovoltaic systems photovoltaic fault detection algorithms I-V curves

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Development of an IoT power management system for photovoltaic power plants 11

Development of an IoT power management system for photovolta...

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11th International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Voutsinas, Stylianos Karolidis, Dimitrios Voyiatzis, Ioannis Samarakou, Maria Univ West Attica Dept Informat Dept Informat & Comp & Comp Engn Athens Greece

ISBN: (数字)9781665467179

ISBN: (纸本)9781665467179

This study describes the development of a power management system for photovoltaic systems (PVS). A web application was developed, receiving data from a hardware interface, installed in each photovoltaic array allowing online power monitoring, fault detection, and configuration of remote power plant installations. The designed system is based on opensource / hardware technologies. Reports for variable periods of time of power generation at PV array, PV string, and at PV cell level are available to the system operator. The benefits, and drawbacks of the developed system are compared along with other systems presented in the literature using criteria in order to evaluate the performance of the designed system. The developed system was tested and compared to the other implementations provides many advantages, such as fault detection per PV cell, power monitoring on all components of the PV array (PV array, string and cell), and the use of opensource / hardware that significantly reduces costs, making the system ideal for small and medium enterprises.

关键词： photovoltaic systems Power management photovoltaic fault detection algorithms IoT

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photovoltaic faults: A comparative overview of detection and identification methods 10

Photovoltaic Faults: A comparative overview of detection and...

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10th International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Voutsinas, Stylianos Karolidis, Dimitrios Voyiatzis, Ioannis Samarakou, Maria Univ West Attica Dept Informat & Comp Engn Athens Greece

ISBN: (纸本)9781665418478

During the last decade, exponential growth in energy production by photovoltaic systems (PVS) has been observed. Although very promising concerning energy production, PVS are often prone to faults that arise either due to environmental conditions or to the quality of materials used for their manufacturing and handling during installation. If these faults are left untreated, a risk arises both to the operation of the system itself (risk of destruction) and to its very ability to produce energy reliably. This paper discusses methods for fault detection and identification on the DC side of the photovoltaic systems. The methods are studied for their ability to identify various fault types as well as their complexity and limitations.

关键词： photovoltaic systems photovoltaic fault detection algorithms faults

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A predictive machine learning approach for early fault identification in solar photovoltaic systems

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Electric Power Systems Research 2025年 247卷

作者： Nora EL-Rashidy Zainab H. Ali Yara A. Sultan Department of Machine Learning and Information Retrieval Faculty of Artificial Intelligence Kafrelsheikh University El-Geish st Kafrelsheikh 33516 Egypt Department of Embedded Network Systems and Technology Faculty of Artificial Intelligence Kafrelsheikh University El-Geish st Kafrelsheikh 33516 Egypt Department of Electronics and Computer Engineering School of Engineering and Applied Sciences at Nile University Giza Egypt Mechatronics Department Faculty of Engineering Horus University-Egypt New Damietta Egypt

The reliability of photovoltaic (PV) panels plays a pivotal role in optimizing energy output, particularly as solar energy becomes an indispensable pillar in global initiatives aimed at achieving sustainable and decarbonized energy infrastructures. Since faults in PV panels can significantly impair system performance and reduce energy yield, early detection is essential to maintaining operational efficiency. This study proposes an advanced fault detection framework leveraging contemporary machine learning techniques to identify anomalies early, thereby preventing performance degradation and minimizing downtime. The proposed model incorporates single- and multi-objective feature selection strategies to enhance fault identification accuracy. Specifically, single-objective techniques utilize Information Gain (IG), Gini Ratio (GR), and Gini Importance (GI). At the same time, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed as a multi-objective method to select an optimal subset of fault-relevant features. The model was trained on a dataset comprising 600 instances. Results indicate that gradient boosting achieved 97.6 % accuracy using single-objective selection, whereas the NSGA-II-based approach reduced the feature set to 15 optimal variables and improved accuracy to 99.3 %. Implementing this model enhances the operational reliability of solar PV systems and supports the broader goal of advancing the sustainability of solar energy production.

关键词： Machine learning Optimization techniques photovoltaic fault detection algorithms photovoltaic systems

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