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Volume 11
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10.3390/systems11050237
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Article

Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies

by
Qais Ibrahim Ahmed
1,
Hani Attar
2,*,
Ayman Amer
2,
Mohanad A. Deif
3 and
Ahmed A. A. Solyman
4
1
Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul 34310, Turkey
2
Department of Energy Engineer, Zarqa University, Zarqa 13133, Jordan
3
Department of Bioelectronics, Modern University of Technology and Information (MTI) University, Cairo 11728, Egypt
4
Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nişantaşı University, Istanbul 34398, Turkey
*
Author to whom correspondence should be addressed.
Systems 2023, 11(5), 237; https://doi.org/10.3390/systems11050237
Submission received: 15 February 2023 / Revised: 29 April 2023 / Accepted: 30 April 2023 / Published: 8 May 2023
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Abstract

Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault).
Keywords: solar energy; intelligent grid system; power plant anomalies; PV solar energy; intelligent grid system; power plant anomalies; PV

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MDPI and ACS Style

Ahmed, Q.I.; Attar, H.; Amer, A.; Deif, M.A.; Solyman, A.A.A. Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies. Systems 2023, 11, 237. https://doi.org/10.3390/systems11050237

AMA Style

Ahmed QI, Attar H, Amer A, Deif MA, Solyman AAA. Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies. Systems. 2023; 11(5):237. https://doi.org/10.3390/systems11050237

Chicago/Turabian Style

Ahmed, Qais Ibrahim, Hani Attar, Ayman Amer, Mohanad A. Deif, and Ahmed A. A. Solyman. 2023. "Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies" Systems 11, no. 5: 237. https://doi.org/10.3390/systems11050237

APA Style

Ahmed, Q. I., Attar, H., Amer, A., Deif, M. A., & Solyman, A. A. A. (2023). Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies. Systems, 11(5), 237. https://doi.org/10.3390/systems11050237

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