Recognition of Multiple PQ Disturbances Using Dynamic Structure Neural Networks - Part 1: Theoretical Introduction

This work develops a new approach to recognize multiple disturbances for a power quality (PQ) event in power systems. It is usual that several different types of disturbances simultaneously exist in a PQ event. However, most of the existing methods treat a PQ event as a single type of PQ disturbance. The performance of these methods might be limited and impracticable for application in the real power systems. This work proposes a novel approach integrated the wavelet transform and dynamic structural neural network (DSNN) to identify disturbance waveforms. The proposed neural network has the capability of adapting to multiple disturbances for a PQ event. In the proposed approach, the disturbance waveforms are extracted by the wavelet transform and then fed to the DSNN for identifying the types of disturbances. The distinctive features of the proposed method are that it can estimate the amplitude of the considering event, recognize transient and steady state disturbances which are simultaneous existed in a PQ event