Open Access

ARTICLE

An Efficient Fuzzy Logic Fault Detection and Identification Method of Photovoltaic Inverters

by Mokhtar Aly^1,2, Hegazy Rezk^3,4,*

1 Department of Electrical Engineering, Aswan University, Aswan, 81542, Egypt
2 Electronics Engineering Department, Universidad Tecnica Federico Santa Maria, Valparaiso, 2390123, Chile
3 College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser, 11991, Saudi Arabia
4 Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt

* Corresponding Author: Hegazy Rezk. Email:

Computers, Materials & Continua 2021, 67(2), 2283-2299. https://doi.org/10.32604/cmc.2021.014786

Received 16 October 2020; Accepted 17 November 2020; Issue published 05 February 2021

Abstract

Fuzzy logic control (FLC) systems have found wide utilization in several industrial applications. This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic (PV) inverters. Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere. Power converters represent the main parts for the grid integration of PV systems. However, PV power converters contain several power switches that construct their circuits. The power switches in PV systems are highly subjected to high stresses due to the continuously varying operating conditions. Moreover, the grid-tied systems represent nonlinear systems and the system model parameters are changing continuously. Consequently, the grid-tied PV systems have a nonlinear factor and the fault detection and identification (FDI) methods based on using mathematical models become more complex. The proposed fuzzy logic-based FDI (FL-FDI) method is based on employing the fuzzy logic concept for detecting and identifying the location of various switch faults. The proposed FL-FDI method is designed and extracted from the analysis and comparison of the various measured voltage/current components for the control purposes. Therefore, the proposed FL-FDI method does not require additional components or measurement circuits. Additionally, the proposed method can detect the faulty condition and also identify the location of the faulty switch for replacement and maintenance purposes. The proposed method can detect the faulty condition within only a single fundamental line period without the need for additional sensors and/or performing complex calculations or precise models. The proposed FL-FDI method is tested on the widely used T-type PV inverter system, wherein there are twelve different switches and the FDI process represents a challenging task. The results shows the superior and accurate performance of the proposed FL-FDI method.

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