METHODS AND SYSTEMS FOR FAULT DETECTION, DIAGNOSIS AND LOCALIZATION IN SOLAR PANEL NETWORK

摘要

This disclosure relates generally to the methods and systems for fault detection, diagnosis and localization in solar panel network. Conventional fault detection and diagnosis (FDD) techniques for the solar panel network are limited and confined to identifying faults either at voltage level or current level, or to studying one specific fault type at a time. The present disclosure solve the problems of detecting various fault types present inside the solar panel network and identifying associated fault locations, by generating a fault detection, diagnosis and localization (FDDL) model. The convolutional neural network (CNN) model is trained with fault datasets and no-fault datasets covering various fault scenarios and no-fault scenarios respectively, to generate the FDDL model. The plurality of fault datasets and the plurality of no-fault datasets are determined based on the network simulation model of the solar panel network.