Detection and Classification of Voltage Swells Using Wavelet Transforms

Abstract

Through this paper a novel technique for detecting and characterizing disturbances in power systems based on wavelet transforms is proposed. The voltage signal under investigation is often corrupted by noises, therefore the signal is first de-noised and then wavelet transform is applied. Using the first detail wavelet coefficients, voltage disturbance is detected and its duration is determined. The combination of an adaptive prediction filter based sub-band decomposition structure with a rule based histogram analysis block produce successful detection and classification results on our real life power system transient data. In this paper, voltage swell is considered for comparing both approaches. Proposed scheme is implemented using MATLAB, Simulink, DSP and Wavelet toolboxes. Transmission-Line relaying involves three major tasks: detection, classification, and location of the fault. It must be done as fast and accurate as possible to de-energize the faulted line and protecting the system from the harmful effects of the fault. With the wide application of high-power electronics switchgears, problems of Power Quality (PQ) are becoming more serious day by day. At the same time, the demand on power quality gets more critical. Thus it is essential to establish a power quality monitoring system to detect power quality disturbance (1)-(2). Practically, a power quality monitoring system should have the following functions: detect power quality disturbance, identify the type and duration of disturbance signals, calculate disturbance amplitude and other relevant parameters, etc. Thus the power quality monitoring system should be as precise and of real time as possible. In recent years, a lot of methods for detection and identification of power quality disturbance