Power Quality Analysis in Microgrid System with Bayesian Regularized Neural Network

In this paper, power quality problems that occur in an AC part of the microgrid system are detected and classified with artificial neural network. The model for generating power quality problems is developed in MATLAB simulink The simulink model consists of a solar energy source, full bridge dc-ac converter, LC filters and load. The full bridge converter is used to convert the DC solar output to single phase AC. The LC filter is used to eliminate the harmonics present in the AC output of the converter. A series RLC load is used in the proposed system. The power quality detection system is based on the pattern recognition neural network. The neural network is trained with samples of sag, swell and interruption. These power quality problems are created with the help of a circuit breaker. The duration of these problems/distortions are changed for producing the datasets for training the neural network. The dataset for training and testing is generated with the help of simulation model converter circuit.