Solar photovoltaic fault classifications under normal and shading conditions using machine learning models
TL;DR
This study suggested PV monitoring systems with lower maintenance costs and energy losses, and an accurate and efficient classifier for PV defects using normal and shading condition based on advanced ML techniques like Random Forest, K-Nearest Neighbors, Decision Tree, and SVM are proposed.
Abstract
Solar photovoltaic (PV) technology has become a major sustainable energy option in response to the increasing need for renewable energy. Nations are aiming for less fossil fuels, and promoting global solar PV capacity that was significantly expand to 710 GW in 2020. Some environmental factors which might render solar energy unusable include mud, trees and buildings. Alongside, hotspots, electrical imbalance and the possibility of damage to the PV modules by thermal reasons which reduces power output, especially partial and total shadowing will yield a power loss of 40–50%. These issues can be resolved through the advanced machine learning (ML) techniques which help to detect the defect on PV panel automatically and reduce downtime, and improve energy production reliability. In this work, an accurate and efficient classifier for PV defects using normal and shading condition based on advanced ML techniques like Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LG), eXtreme Gradient Boosting (XGBoost) and Support Vector Machines (SVM) is proposed. A dataset including characteristics of voltage, current, power and irradiance is used to test the classification accuracy and the computational efficiency of these algorithms. The results suggested that, RF is the top algorithm with a classification accuracy of 99.7%. KNN, DT, XGBoost, and SVM are next in line with 99% classification accuracy but LR had the lowest performance of 95%. This study suggested PV monitoring systems with lower maintenance costs and energy losses.