A Novel Opposition-Based Border Collie Optimization Approach for Fault Detection in Solar Photovoltaic Array

Authors

  • Sowthily Chandrasekharan Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli 620 015, India
  • Senthilkumar Subramaniam Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli 620 015, India
  • Malakonda Reddy Bhoreddy Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli 620 015, India
  • Veeramani Veerakgoundar Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli 620 015, India

DOI:

https://doi.org/10.13052/dgaej2156-3306.38313

Keywords:

Fault detection, solar photovoltaic system, line-line fault, multiclass SVM, one vs all classifier, machine learning, opposition-based learning, border collie optimization

Abstract

Solar photovoltaic systems installed in outdoor environments are susceptible to faults and partial shading, which leads to reduction in the production of maximum power. The conventional protection units are unable to detect the types of faults due to non-linear characteristics and they result in fire hazards and reduced system efficiency. In this paper, a fault detection method based on Multiclass Support Vector Machine (MSVM) is proposed to detect different faults like line-ground (L-G), line-line (L-L), and partial shading. The array voltage, array current and irradiance are used to detect the line-line and partial shading under different irradiation conditions. The novel Opposition-based Border Collie Optimization (OBCO) algorithm is used to improve the accuracy of fault classification by optimizing the hyper-parameters of MSVM. A 1.6 kW, 4 × 4 solar photovoltaic array is developed, and the fault conditions are experimentally tested to validate the proposed algorithm. The experimental results show that the proposed MSVM-OBCO fault detection algorithm has higher accuracy compared to that of the existing classification algorithms such as k-nearest neighbor, Naïve Bayes, Decision Tree and Random Forest.

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Author Biographies

Sowthily Chandrasekharan, Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli 620 015, India

Sowthily Chandrasekharan received her B.E. Degree in Instrumentation and Control Engineering from Saranathan College of Engineering, Trichy, affiliated to Anna University, Tiruchirappalli, Tamil Nadu, India in the year 2011. She received her M.E. Degree in Control and Instrumentation from J. J. College of Engineering, Trichy, affiliated to Anna University, Chennai, Tamil Nadu, India during the year 2016. She is currently pursuing Ph.D. degree in Electrical and Electronics Engineering at National Institute of Technology, Tiruchirappalli. She worked as an Assistant Professor in the Department of Electronics and Instrumentation Engineering, Mookambagai College of Engineering, Pudukottai, India during 2017–2018. She has also served as a Project Assistant in the DST Funded project during 2018–2020. Her research interests include Renewable Energy System and Optimisation techniques.

Senthilkumar Subramaniam, Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli 620 015, India

Senthilkumar Subramaniam received the B.E. degree in Electrical and Electronics Engineering from Madurai Kamaraj University, Madurai, India, in 1999, the M.Tech. degree in Electrical Drives and Control from Pondicherry University, Puducherry, India, in 2005, and the Ph.D. degree in Electrical Engineering from the National Institute of Technology, Tiruchirappalli, India, in 2013. He has 20 years of teaching experience at various engineering institutions. He is currently working as an Associate Professor with the National Institute of Technology. He has extensively researched on self-excited induction generators for standalone and grid-connected applications. His current research interests include the development of new power converter topologies for renewable energy systems and intelligent transportation systems.

Malakonda Reddy Bhoreddy, Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli 620 015, India

Malakonda Reddy Bhoreddy received the B.Tech degree in Electrical and Electronics Engineering from Narayana Engineering college (Affiliated to JNTU Anantapur), Gudur, S.P.S.R Nellore, Andhra Pradesh, India, in 2011, the M.Tech degree in Power Systems from N.B.K.R Institute of Science & Technology (Affiliated to JNTU Anantapur), Vidyanagar Kota, S.P.S.R Nellore, Andhra Pradesh, India, in 2015 and Ph.D degree in Electrical and Electronics engineering from National Institute of Technology, Tiruchirappalli, Tamil Nadu, India, in 2021. He is currently working as a senior engineer-Electronics with AMIDC (Ati motors), Benglore, Karnataka. His research interest includes power electronics, renewable energy systems and power quality.

Veeramani Veerakgoundar, Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli 620 015, India

Veeramani Veerakgoundar received the B.E degree in Electrical and Electronics Engineering from the National Engineering College, Kovilpatti, India, in 2008, and the M.E. degree in Power Electronics and Drives from Government College of Engineering, Tirunelveli, India, in 2012. He has 5 years of teaching experience at various engineering institutions. He is currently pursuing the Ph.D. degree at the National Institute of Technology, Tiruchirappalli, India. He has been working in the area of power converters for Electric Vehicles applications since 2019.

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Published

2023-03-03

How to Cite

Chandrasekharan, S. ., Subramaniam, S. ., Bhoreddy, M. R. ., & Veerakgoundar, V. . (2023). A Novel Opposition-Based Border Collie Optimization Approach for Fault Detection in Solar Photovoltaic Array. Distributed Generation &Amp; Alternative Energy Journal, 38(03), 1007–1032. https://doi.org/10.13052/dgaej2156-3306.38313

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