A Multi-scale Smart Fault Diagnosis Model Based on Waveform Length and Autoregressive Analysis for PV System Maintenance Strategies*

doi:10.23919/CJEE.2023.000023

Chinese Journal of Electrical Engineering ›› 2023, Vol. 9 ›› Issue (3): 99-110.doi: 10.23919/CJEE.2023.000023

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A Multi-scale Smart Fault Diagnosis Model Based on Waveform Length and Autoregressive Analysis for PV System Maintenance Strategies^*

Siti Nor Azlina M. Ghazali^1,*, Muhamad Zahim Sujod¹, Mohd Shawal Jadin²

1. Department of Electrical Engineering, Universiti Malaysia Pahang, Pekan 26600, Malaysia;
2. Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Malaysia

Received:2022-12-23 Revised:2023-03-25 Accepted:2023-06-12 Online:2023-09-25 Published:2023-06-20
Contact: *E-mail: PES19001@ump.edu.my
About author:Siti Nor Azlina M. Ghazali was born in Kelantan, Malaysia in 1978. She received her B.Eng. degree in Electrical Engineering from the University of Mara Technology, Malaysia, in 2002. Then she received her MS.c. in Energy Studies from the University of Otago, New Zealand, in 2013. She is currently working towards her Ph.D. at the College of Engineering, Department of Electrical and Engineering, Universiti Malaysia Pahang, Malaysia. Her current research interests include PV forensic electrical, PV smart maintenance strategies and PV smart fault monitoring system.
Muhamad Zahim Sujod was born in Selangor, Malaysia in 1976. He received the B.Eng. degree and M.Eng. degree in Electrical & Electronics Engineering from the University of Ehime, Ehime, Japan, in 2000 and 2002, respectively, and the Ph.D. degree from the University Duisburg-Esssen, Germany, in Power System Engineering, in 2014. He is a member in the Board of Engineer Malaysia BEM) since January 2004 and has been appointed as Professional Engineer in January 2009. Currently, he is an Associate Professor within the College of Engineering, Universiti Malaysia Pahang, Malaysia. His primary research activities involve renewable energy system (wind turbine and photovoltaic), energy conversion, energy management and electrical machines.
Mohd Shawal Jadin received his B.Sc. (Hons) from the Universiti Sains Malaysia in Electrical and Electronic Engineering in 2002. From 2002, he has held a Research Officer in Electrical Power position at the USM. Awarded M.Sc. and Ph.D. degrees from Universiti Sains Malaysia in 2006 and 2018, respectively. He worked as a part-time Lecturer between 2005 and 2006 at UiTM, Malaysia. In 2006, he became a Lecturer at the Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang. His research interests include power electronics and drives, renewable energies, thermography, image processing, and condition monitoring.
Supported by:
*Universiti Malaysia Pahang (UMP) for the Financial Support Received under Project Number RDU223001 and PGRS2003189.

Abstract

Abstract: Nonlinear photovoltaic (PV) output is greatly affected by the nonuniform distribution of daily irradiance, preventing conventional protection devices from reliably detecting faults. Smart fault diagnosis and good maintenance systems are essential for optimizing the overall productivity of a PV system and improving its life cycle. Hence, a multiscale smart fault diagnosis model for improved PV system maintenance strategies is proposed. This study focuses on diagnosing permanent faults (open-circuit faults, ground faults, and line-line faults) and temporary faults (partial shading) in PV arrays, using the random forest algorithm to conduct time-series analysis of waveform length and autoregression (RF-WLAR) as the main features, with 10-fold cross-validation using Matlab/Simulink. The actual irradiance data at 5.86 °N and 102.03 °E were used as inputs to produce simulated data that closely matched the on-site PV output data. Fault data from the maintenance database of a 2 MW PV power plant in Pasir Mas Kelantan, Malaysia, were used for field testing to verify the developed model. The RF-WLAR model achieved an average fault-type classification accuracy of 98 %, with 100% accuracy in classifying partial shading and line-line faults.

Key words: Autoregressive, PV fault diagnosis, supervised machine learning, simulation, waveform length

Siti Nor Azlina M. Ghazali, Muhamad Zahim Sujod, Mohd Shawal Jadin. A Multi-scale Smart Fault Diagnosis Model Based on Waveform Length and Autoregressive Analysis for PV System Maintenance Strategies^*[J]. Chinese Journal of Electrical Engineering, 2023, 9(3): 99-110.

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A Multi-scale Smart Fault Diagnosis Model Based on Waveform Length and Autoregressive Analysis for PV System Maintenance Strategies^*

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