Addressing various fault detection and classification in Grid Tied Photovoltaic System using Artificial Neural Network (ANN)

Authors

  • R.JaiGanesh, Dr.S.Muralidharan

Abstract

The purpose of this paper to improve optimum performance, diagnosis of PV plants by implementing an artificial neural network for Addressing various fault in Grid tied Photovoltaic systems. And also to improve the system accuracy by continuous monitoring of the Grid tied PV system; thereby the system should detect and classify the fault automatically. This system uses an Artificial Neural Network for detecting the fault and its classification in Grid Tied PV system. It consists of PV Array with an algorithm being applied to address the fault when tied to the utility grid. By using this method, detection and classification of faults can be obtained automatically. The literature study stated only on PV faults likes partial shading, open circuit and short circuit faults and also what kind of fault occurred in the Grid tied PV system was not specified. In this paper, a proper selection of fault has been found and it was carried out with good intelligent technique to address and classify the fault to improve the system performance and efficiency. This proposed work has planned to use MATLAB/SIMULINK tool for simulation. And this ANN based fault detection and classification system pro- vides better results when it was trained with such a wide collection of training data set. The practical experiment was done using the microcontroller and sensors for detecting the faults and its classification. It is evident that the proposed method performance was excellent and it was practically implemented and tested with a 100W solar PV panel.

Keywords- Renewable energy resources; Fault in Grid tied PV; ANN; Matlab/Simulink; Microcontroller;, Sensors.

Published

2020-12-07

Issue

Section

Articles