Implementation of Levenberg-Marquardt Based Multilayer Perceptron (MLP) for Detection and Classification of Power Quality Disturbances

Abstract

Power Quality Disturbances (PQD) has result in numerous failures and damage to electrical equipment. This paper utilized MATLAB Application to propose ways in detecting and classifying Voltage Sag, Swell and Transient. The proposal was divided into three parts which are detection, classification, and performance evaluation. The detection stage was done using Discrete Wavelet Transform in Wavelet Analyzer to obtain signal decomposition in different energy levels to be used in Energy Distribution Deviation (EDD) method. The classification stage was done in Classification Learner to check how good Multilayer Perceptron Neural Network able to trains, validates, and predicts as a classification model. The performance evaluation stage was done in Neural Net Fitting using Levenberg-Marquardt (LM) as training algorithm to see how well the model perform in term of Mean Square Error (MSE) and regression. This paper also discusses the effect of input ratio, activation function (Sigmoid, Tangent Hyperbolic, Rectified Linear Unit) and training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scale Conjugate Gradient) towards accuracy in a Neural Network model. This study found that EDD was able to detect the difference in energy distribution of PQD properly. The Multilayer Perceptron model was observed to performed better and had higher accuracy when fed with more sample data, bigger layer size and activated using Tangent Hyperbolic (Tanh) activation function. Increasing layer size also resulted in slower prediction speed and longer training time. The model performance was evaluated with the lowest MSE and highest regression when Levenberg-Marquardt (LM) was implemented compared to Bayesian Regularization (BR) and Scale Conjugate Gradient (SCG).