Harmonic Impact Assessment and Prediction in a Power System with Nonlinear Loads

Abstract

This thesis presents a simulation-based and machine learning framework for predicting power quality (PQ) indices in residential power systems with nonlinear loads. Four MATLAB/Simulink models representing nonlinear loads, induction motor loads, thyristor converters, and photovoltaic grid-connected systems were developed to generate voltage and current waveforms under different operating conditions. Time-domain and frequency-domain features were extracted from the simulated signals and used to train artificial neural network (ANN) models for predicting six key PQ indices: Total Harmonic Distortion (THD), Crest Factor, Form Factor, Power Factor, RMS Voltage, and Peak Voltage. Both shallow and deep neural network architectures were implemented and evaluated using statistical performance metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Pearson correlation coefficient. The results demonstrate that the proposed approach accurately predicts future power quality conditions while reducing the need for repeated analytical calculations. This framework provides an efficient tool for power quality monitoring and supports the development of intelligent and proactive power system management strategies.