Fault Detection at PCC Using Wavelet Theory in Grid-Tied Solar PV Battery-Based AC Microgrid

In power systems, detecting faults and maintaining power quality are very important factors. Today’s unpredictable load scenario necessitates the maintenance of voltage profile under fluctuating load conditions. This paper aims to improve and analyze voltage fluctuations power quality during faulty conditions with the aid of the wavelet transform method which accurately detects fault and the type of fault in the proposed AC–DC microgrid. For MPPT, this paper concentrates on advanced artificial intelligence algorithm called particle swarm optimization (PSO). MATLAB software is used to simulate the proposed model, the results have been displayed on the command window and through waveforms. In this system, fault identification is made quick and accurate using ideas from wavelet theory and signal processing. The performance analysis and dynamic modelling of a grid-tied 6.75 kW solar PV system has been done along with a solution to the issue of rapid fault detection at PCC. To increase the stability and dependability of the system, a battery energy storage system has been created and implemented. Using the MATLAB Simulink tool, different curves are obtained for use in result analysis. In this proposed system, different irradiance levels are used in accordance with our daily schedule so that results which are extremely comparable with or very close to reality have been observed.