Signal Parameter Estimation and Classification Using Mixed Supervised and Unsupervised Machine Learning Approaches

Abstract

The increasing use of modern power electronics raises the issue of harmonics in power systems which ultimately deteriorate its optimal performance in terms of: increased power loss, breaker failure and mal-operation of equipment. It has been found that the most severe harmonics in the system are odd ones due to their unsymmetrical nature. This work presents the new framework for estimation and classification of harmonics using machine learning approaches. Initially, a shallow neural network and fuzzy logic systems are used to estimate the harmonics contents in the voltage and currents signals. Based on the sequence components and IHD level of source signals, the estimation of harmonic content is achieved. The obtained results are compared with the analytically computed data for validating the performance of designed networks. The results from neural and fuzzy systems are then used to train the explainable convolutional neural network (xCNN) for harmonics classification. The xCNN consists of pertained ALEXNET network which trains the standard binary support vector machine (SVM) for classification of harmonics. The dictionary-based approach is used to add the explanations to the SVM classifier output as a prototype. The performance of proposed framework is measured in-terms of accuracy and loss function and evaluated on the basis of its scalability and computability. The proposed approach is called a Human with Machine-In-Loop (HMIL).