Wavelet-Based Power Network Disturbance Identifying Part I: Theory and Method

Abstract

It is important that control center operators be alerted to system disturbances, including where, when and what disturbance occurs, so that proper anticipatory actions can be promptly taken if necessary, avoiding oscillation spreads in the power network. In this paper, the wavelet multi-resolution analysis based method is proposed to identify power system disturbances. Energy of wavelet coefficients are used as a criterion to choose optimal wavelet function and decomposition scale, which are then used for obtaining the maximum wavelet coefficients by identifying the frequency signals from wide area measurement system (WAMS). The maximum wavelet coefficients are then selected to be the indicators for disturbance identifying. The detailed procedure and effectiveness of the proposed method is demonstrated by simulations of a 10-machine 39-bus system. Introduction The synchronized phasor measurement units (PMUs) are able to measure and record the rotor angle, the frequency, the magnitude and phase of voltage and current of power systems. The measured data are sent to the phasor data concentrator (PDC) through high speed internet. Accordingly, a wide area measurement system (WAMS) is constructed based on the PMUs, PDC and internet. Thereby, the operational condition of the power grid can be monitored continuously and the dynamics of the power system can be recorded[1]. One of the most important application of WAMS is using the data recorded by PMUs to identify disturbances in power systems, including fault diagnosis[2], power quality disturbance identifying[3] as well as power network disturbance identifying[4]. The power network disturbance identifying mainly focus on disturbances leading to serious accidents in power system, such as cutting machine, load shedding, etc. Wavelet transform (WT) is a powerful and systematic way of analyzing the abrupt-changing feature of signals, belonging to a type of time-frequency-domain analysis[5]. Using WT, the frequency features of the analyzed signal could be located in time domain. Thus, as a promising tool, WT deserves a thorough study when it is applied to WAMS-based disturbance analysis. In[6], for the first time, the WT was introduced to disturbance identification, detecting the time when disturbance occurred. In this paper, the theory of a wavelet-based power network disturbance identifying method is described. First, the WT-based multi-resolution analysis is introduced in Section 1. As the key part of the method, the theory of power network disturbance identifying method, based on WT, is described in Section 2. The case study is given in Section 3. The conclusion is discussed in Section 4. WT-Based Multi-Resolution Analysis (MRA) WT is a partial time-frequency-domain analysis method, the frequency and time window of which can be changed, obtaining both the time and frequency domain information at the same time. Using WT, signals can be decomposed into several sub-spaces in different resolutions, so that signals with different frequencies are displayed in the different sub-spaces, revealing their characteristics clearly. © 2016. The authors Published by Atlantis Press 1313 4th National Conference on Electrical, Electronics and Computer Engineering (NCEECE 2015)