Voting-Based Ensemble Learning Algorithm for Fault Detection in Photovoltaic Systems under Different Weather Conditions

Nien-Che Yang,Harun Ismail

doi:10.3390/math10020285

Abstract

A photovoltaic (PV) system is one of the renewable energy resources that can help in meeting the ever-increasing energy demand. However, installation of PV systems is prone to faults that can occur unpredictably and remain challenging to detect. Major PV faults that can occur are line-line and open circuits faults, and if they are not addressed appropriately and timely, they may lead to serious problems in the PV system. To solve this problem, this study proposes a voting-based ensemble learning algorithm with linear regression, decision tree, and support vector machine (EL-VLR-DT-SVM) for PV fault detection and diagnosis. The data acquisition is performed for different weather conditions to trigger the nonlinear nature of the PV system characteristics. The voltage-current characteristics are used as input data. The dataset is studied for a deeper understanding, and pre-processing before feeding it to the EL-VLR-DT-SVM. In the pre-processing step, data are normalized to obtain more feature space, making it easy for the proposed algorithm to discriminate between healthy and faulty conditions. To verify the proposed method, it is compared with other algorithms in terms of accuracy, precision, recall, and F-1 score. The results show that the proposed EL-VLR-DT-SVM algorithm outperforms the other algorithms.

Highlights

linear regression (LR)-decision tree Bayes (DT)-support vector machine (SVM)) for PV fault detection is developed in this work
The proposed algorithm can be rated as a well-performed model for detecting PV faults when the F1 score is close to 1
Results and Discussion loadThis is important because it shows long ala perso section presentsto theconsider results and discussion of the proposed how ensemble learning (EL)-VLR-DT-SVM

Summary

Introduction

Solar energy harvesting is a promising option because of its massive potential for power systems. This is evident from increasing photovoltaic (PV) installation, which is estimated to grow annually by approximately 18% with 123 GW power generated in. Its contribution to generating energy and supplying power to end-users is believed to meet the ever-increasing demand for energy. Installation of PV systems is prone to faults that can occur unpredictably and remain challenging to detect. This has motivated engineers and researchers to pay more attention to overcome the interference of the PV system, such as fault detection and diagnosis (FDD) [2]

Methods

Results

Conclusion