Fault analysis of photovoltaic based DC microgrid using deep learning randomized neural network

Abstract

The implementation of solar photovoltaic (PV) based Direct Current (DC) microgrid is limited due to lack of well-defined protection standards. These low voltage DC distribution networks are facing implementation challenges especially under multi-distributed generations subjected to various events such as PV arc faults, load switching faults, PV partial shading, change in PV irradiance and DC cable faults, etc. Thus a new deep learning neural network model is proposed for the accurate fault classification and distance calculation for an effective monitoring and protection coordination of photovoltaic based DC microgrid. The proposed model is the combination of an Adaptive variational mode decomposition (AVMD) and deep minimum variance random vector functional link network (DRVFLN). The optimal parameters of AVMD are selected using chaotic sine–cosine firefly algorithm (CSCFA) and efficient weighted kurtosis index (EWKU). Further, a novel non-iterative DRVFLN network is applied for accurate fault classification and location. In the DRVFLN direct connections are present from preceding layers to the forward layers of the network like random vector functional link network. These connections help to reduce the model complexity and also regularize the randomization. Further, the denoising criterion is also introduced in this network where the uncorrupted input can be recovered from the corrupted versions by using the autoencoder to obtained better results than the traditional networks. The performances of various models are evaluated using some performance index such as overall accuracy and sensitivity. Results conclude that the proposed AVMD-DRVFLN model classified the faults with an accuracy and sensitivity of 100% for all the events. The model performance is also tested and validated against the unwanted noise by considering different signal-to-noise ratio to ensure the robustness of the proposed model. Results conclude that the proposed model correctly classified the faults against such incorporation of noise. Similarly, the performances of various models for the estimation of fault distance are validated by computing the relative error. Results conclude that the proposed AVMD-DRVFLN model produces promising result in terms of relative error(which is less than 5%) for all the events. Comparisons with some existing models like extreme learning machine, support vector machine, random vector functional link network and deep extreme learning machine are included to validate the efficacy of the proposed AVMD-DRVFLN model. Results conclude that proposed method outperforms all the other methods. The effectiveness of the proposed AVMD-DRVFLN model for fault classification and distance estimation is established through rigorous case studies in MATLAB augmented by some real time test results.