Modeling and Fault Categorization in Thin-Film and Crystalline PV Arrays Through Multilayer Neural Network Algorithm

Azhar Ul-Haq,Hatem F Sindi,Marium Jalal,Saba Gul

doi:10.1109/access.2020.2996969

Abstract

Categorization of PV faults is an essential task for improving the efficiency and reliability of a photovoltaic (PV) system. Output characteristics of a solar (PV) system can be severely affected under various fault conditions including short circuit, module mismatch, open circuit, and multiple faults under shading conditions. Such PV faults can potentially be analyzed through the PV characteristic curve analysis using a multilayer neural network with a scaled conjugate gradient algorithm (SCG). This paper presents an extensive investigation for categorization, i.e., classification of the above-mentioned PV faults using the SCG algorithm. The major contribution of the presented research work is the categorization of PV faults in sixteen different classes considering polycrystalline and thin-film PV technologies with two different configurations, including SP and TCT. The fault classification is achieved with high accuracy of 99.6% and a fast-computational time of 0.08 sec. The results are validated through the plot of the Confusion Matrix and Region of Convergence (ROC) with their performance evaluation in MATLAB. The achieved accuracy and fast computational time prove the effectiveness of the multilayer neural network-based approach for classification of the PV faults to increase power output, efficiency, and lifespan of PV systems.

Highlights

A massive body of research has been focused on the advancement of solar photovoltaic (PV) technology with an aim to improve the latter’s efficiency and high variability due to its non-linear nature and high reliance on external atmospheric conditions [1]
PV faults are categorized into two different PV configurations, including SP and Total Cross Tied (TCT), through neural networks with high accuracy of 99.6%, which is not achieved in the previous research works
Classifiers like support vector machine (SVM) and K-nearest neighbor (KNN) achieved an accuracy of 80.3% and 56.8% respectively, while neural network (NN) classifier performed better as a fault classifier and achieved an overall accuracy of 92.8% in classification of faults in the PV module

Summary

INTRODUCTION

A massive body of research has been focused on the advancement of solar photovoltaic (PV) technology with an aim to improve the latter’s efficiency and high variability due to its non-linear nature and high reliance on external atmospheric conditions [1]. This research has classified all simulated faults in PV array with differentiation of different PV materials, including thin-film and crystalline technology, which is not reported in the literature. 3. PV faults are categorized into two different PV configurations, including SP and TCT, through neural networks with high accuracy of 99.6%, which is not achieved in the previous research works. None of the previous research works has classified PV module mismatch, open and short circuit, multiple faults under shading conditions in different PV materials and configurations with 99.6% accuracy and fast computational time [26]–[32].

SYSTEM MODELING

DEVELOPED FAULTS IN PV ARRAY

RESULTS AND DISCUSSION

CONCLUSION