Data Processing In Detection IED Of The Power Quality

IEC61850, the new international standard for SAS (Substation Automation System), defines the communication between IEDs (Intelligent Electionic Devices) and substation system, which represents the future direction of digital substation. The purpose of IEC61850 is to enhance the interoperability among IEDs for building a seamless communication system. At present, the applied research is carried out around the IEC61850 standard all over the world. Power quality detection and analysis is the basis of power quality control. Therefore, it is very necessary to build up IED of power quality detection in order that the power quality disturbances can be correctly examined, evaluated and classified.This paper deeply analyses the datum that are from the power quality detection IED based on IEC61850 in main station layer. It can help us to know safety running of power system. Of course, it is necessary to exactly understand the harmonic component in power system and correctly analyses the power quality. The main researching contents are introduced as following:1. Based on IEC61850, a framework of power quality detection device is constructed in this paper. Then, the significance of using IEC61850 in power quality detection system is discussed. Also, the modeling and LN extension of power quality detection IED are in-depth researched.2. An improved algorithm is proposed based on fast Fourier transform and wavelet transform for the harmonics analysis in the power quality detection device. The simulation results reveal that the proposed methods can be used to locate disturbing signals and analyze harmonic spectrum components. The results also show that the improved algorithm has perfect practicability and flexibility, especially under the condition of singularities and perturbations.3. After studying the detection and identification of power quality disturbance signal, an improved method based on complex wavelet transform and multi-class SVM classifier for recognizing and classifying algorithm of dynamic power quality disturbances is presented, because wavelet transform have the excellent characteristic of time-frequency localization, and support vector machine (SVM) has the excellent ability of statistic study. The wavelet transform is used to extract the feature vector of dynamic power quality disturbances, and the multi-class SVM classifier is used to recognize and classify dynamic power quality disturbances according to the feature vector extracted. The experiment results verify the validity and accuracy of this method.