Classification of Faults in Grid-Connected Photovoltaic System based on Wavelet Packet Transform and an Equilibrium Optimization Algorithm-Extreme Learning Machine

Abstract

A novel intelligent scheme using the wavelet packet transform (WPT) and extreme learning machine (ELM) is proposed for fault event classification in the grid-connected photovoltaic (PV) system. The WPT is applied for preprocessing the cycle of the post-fault voltage samples at the point of common coupling (PCC) measurement to get the normalized logarithmic energy entropy (NLEE). The ELM is applied to classify the different fault cases. To enhance the performance of ELM for faults classification, a hybrid optimization mechanism based on an equilibrium optimization algorithm (EOA) is proposed to optimize the selection of input feature subset and the number of ELM hidden nodes. Furthermore, to evaluate the proposed scheme's performance, a comprehensive evaluation was conducted on a 250 kW grid-connected photovoltaic system. From simulation, the classification accuracy is recorded to be 100% under the no-noise condition, while at the signal-to-noise ratios (SNR) of 30, 35, and 40 dB, the accuracies are 98.96, 99.04, and 99.36%, respectively. Moreover, the practical performance of the EOA-ELM classifier is validated using IEEE 34 bus system. The obtained results validate the effectiveness of the proposed scheme in terms of robustness against measurement noise, computation time, and detection accuracy.

Department(s)

Electrical and Computer Engineering

Comments

Ministry of Higher Education, Malaysia, Grant LRGS/1/2018/UNITEN/01/1/3

Keywords and Phrases

Equilibrium optimizer algorithm (EOA); Extreme Learning Machine (ELM); Fault classification; Feature selection; Grid-connected photovoltaic systems; Wavelet packet transform (WPT)

International Standard Serial Number (ISSN)

0263-2241

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Elsevier, All rights reserved.

Publication Date

30 Jun 2022

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