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

作     者：Siti Nor Azlina M.Ghazali Muhamad Zahim Sujod Mohd Shawal Jadin 

作者机构：Department of Electrical EngineeringUniversiti Malaysia PahangPekan 26600Malaysia Faculty of Electrical and Electronics Engineering TechnologyUniversiti Malaysia PahangPekan 26600Malaysia 

出 版 物：《Chinese Journal of Electrical Engineering》 (中国电气工程学报（英文）)

年 卷 期：2023年第9卷第3期

页      面：99-110页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：Supported by the Universiti Malaysia Pahang (UMP)for the Financial Support Received under Project Number RDU223001 and PGRS2003189 

主　　题：Autoregressive PV fault diagnosis supervised machine learning simulation waveform length 

摘      要：Nonlinear photovoltaic(PV)output is greatly affected by the nonuniform distribution of daily irradiance,preventing conventional protection devices from reliably detecting *** fault diagnosis and good maintenance systems are essential for optimizing the overall productivity of a PV system and improving its life ***,a multiscale smart fault diagnosis model for improved PV system maintenance strategies is *** 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/*** 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 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 *** RF-WLAR model achieved an average fault-type classification accuracy of 98%,with 100%accuracy in classifying partial shading and line-line faults.