Knowledge-based Neural Network for Classification of Power Quality Disturbances

This paper develops a knowledge-based neural network (KBNN) for the classification of power quality (PQ) disturbances. Initially, the tunable-q wavelet transform (TQWT) is employed for the extraction of the 50 Hz component from a voltage signal with any sort of disturbance. This is achieved by varying the quality factor of wavelet according to the signal information. The KBNN is a combined model of neural network and rule-based approach. This paper explores the potential of the KBNN for classification of the most common power quality disturbances. The efficacy of the KBNN approach is evaluated on a wide range of time-varying signals with noise, fundamental frequency deviation, and variation in signal parameters. The performance analysis elucidates the efficiency and robustness of the proposed approach using KBNN classifier for classification of the normal and eight PQ disturbances.