Information Measurement Based Fault Discrimination In Power System

The fault transient signal in the power system contains rich information, and making use of this information can help to realize the power fault identification, fault detection and relay protection. During the analysis and application of the fault transient, there would be a critical problem to be solved which is how to extract the fault feature information from the fault transient signals effectively and reliably. On one hand, because of its intermixture with the stable signals and due to its low energy and magnitude, the fault transient signals are tending to be affected by the stable signals and the systematic noise. On the other hand, the information data contained in the fault transient signals is usually enormous and irregular, so it is difficult to employ the information directly to reach the fault discrimination purpose. Thus, these two aspects of reasons make it a tough issue to extract the feature information effectively from the fault transients. Aiming at the extraction issue of fault feature information in the fault discrimination, especially the processing issue of the enormous and uncertain information, this thesis tries to introduce the information-measurement theory into the fault feature extraction. The information-measurement theory is good at processing the information and has been applied successfully in other fault research fields, and its application in this thesis has been proved to be valid as well through the testing. Therefore, the information-measurement based method for feature extraction provides a novel solution or assistant way to the fault discrimination in power system.Based on the study of information-measurement theory, this thesis combines it with the signal-procession methods of different domains, like time domain, frequency domain and time-frequency domain, and proposed the concept of extended information-measurement. This is because the information-measurement theory defines the measurements originally in the time domain only, but the feature information of the power fault should be usually considered in various domains. Therefore, the extension of the original information-measurement theory into different domains can be beneficial to the processing of the fault feature information. The thesis mainly discusses the application of the information-entropy measurement and the approximate-entropy measurement into the fault feature extraction. The research has used the time-domain information-entropy, frequency-domain information-entropy, wavelet entropy and time-domain approximate entropy, time-frequency-domain approximate entropy into the application of fault identification.First, this thesis studies the application of time-domain information entropy into fault discrimination. The time-domain information entropy is the combination of information entropy and the time-domain statistic analyzing method, and it is applied to discriminate the magnetizing inrush from the fault current. The simulations under various conditions, like dissymmetrical inrush current, symmetrical inrush current, CT saturation, inrush current switching onto fault, prove that this method can overcome the limitation of the traditional method and verifies the advantage of the time-domain information entropy in information processing.Second, the thesis studies the application of wavelet singular entropy into the fault-type discrimination. The wavelet singular entropy is one kind of the time-frequency domain information entropy which combines wavelet transformation with information entropy. The thesis employs it into the fault-type discrimination in high-voltage transmission line uner various conditions, like different fault type, fault inception time, fault resistance, fault position, and has obtained a satisfactory result.Third, the thesis studies the analysis of ideal power signals by the approximate entropy, then studies the application of time-domain approximate entropy into the fault-line selection and fault identification during power swing. The simulation results verify the feasibility and validity of the information-measurement based fault discrimination.Finally, according to the information fusion theory, this thesis fuses the feature information from various information measurements together to discriminate the faults. On one hand, we establish the fusing model of different wavelet entropies to fuse the information from 4 different kinds of wavelet entropy measurements; on the other hand, we establish the'frequency-domain information entropy and time-domain approximate entropy' model to fuse the information from different measurements. The first model has been used into the fault line and fault type discrimination, and the second model has been used into the fault type discrimination in EHV transmission line. Through the fusion of information measurements, the effect of systematic uncertainty in the fault study can be overcome and a better fault discrimination can be achieved.This thesis is supported by National Natural Science Foundation of China:'Wavelet entropy theory and its application in power fault detection and classification'(No.50407009, 2005-2007) and'Information theory based power network fault diagnosis of multi-sourced signal'(No.50877068,2009-2011).