An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems

Khaled Dhibi,Mohamed Nounou,Majdi Mansouri,Hazem Nounou,Kais Bouzrara

doi:10.1109/access.2021.3128749

Abstract

The main objective of this article is to develop an enhanced ensemble learning (EL) based intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high-performance operation of Grid-Connected Photovoltaic (PV) systems. The developed EL based techniques consist in combining multiple learning models instead of using a single learning model. To do that, three EL-based FDD techniques are proposed. First, an EL technique that merges the benefits of Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT) is presented. The developed method contributes to the reduction of the overall diagnosis error and has the ability to combine various models. However, classical EL models ignore the time-dependence of PV measurements. In addition, the PV system data are frequently time-correlated. Therefore, kernel PCA (KPCA)-based EL and reduced KPCA (RKPCA)-based EL techniques are developed to take into consideration the dynamic and multivariate natures of the PV measurements. The two proposed KPCA -based EL and RKPCA-based EL techniques are addressed so that the features extraction and selection phases are performed using the KPCA and RKPCA models and the sensitive and significant characteristics are transmitted to the EL model for classification purposes. The presented results prove that the proposed EL based methods offer enhanced diagnosis performances when applied to PV systems.

Highlights

In order to overcome the limitations of the proposed ensemble learning technique due to the direct use of the raw data, an intelligent framework based on features extraction and selection step using the kernel principal component analysis (PCA) (KPCA) technique will be developed
PV IMPLEMENTATION AND DATA COLLECTION Figure 2 shows the synoptic of the PV system under study, where PV and grid emulators are used to emulate the operation of PV panels and a 3-phase grid respectively
The main contributions are threefold: first, using the Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and tree models, we constructed an ensemble learners in order to obtain accurate performance than single learner to distinguish between the different PV system operating modes using the extracted raw data

Summary

Introduction

Despite the proven performances of numerous works that use ensemble learning techniques, most of these methods use only a specific type of classifier Another main drawback of the existing FDD techniques based on ensemble learning methods is the direct use of the raw information from the process data. To overcome this challenge, several FDD techniques based on features extraction and selection step using a single classifier are proposed in the literature [35], [36]. In order to overcome the limitations of the proposed ensemble learning technique due to the direct use of the raw data, an intelligent framework based on features extraction and selection step using the KPCA technique will be developed.

Objectives

Methods

Results

Conclusion