Power distribution network fault analyzing method and device based on tidal current distribution characteristics

Abstract

The invention relates to a power distribution network fault analyzing method and device based on tidal current distribution characteristics, and belongs to the field of power distribution network fault analysis. According to the method, historical fault section data of a power distribution network is intercepted, tidal current distribution in case of a fault of the power distribution network is described through computing a sensitivity matrix norm by adopting a generalized sensitivity analytical method, faults are classified by utilizing an automatic dynamic self-adapting clustering method, and a fault pattern base is established; network topology of a current fault is dynamically analyzed, and a to-be-measured branch circuit in the current fault power distribution network is confirmed; and the fault is diagnosed in an online manner. The method disclosed by the invention mainly aims to extract the network topology and the physical characteristics at the moment of the fault and extract corresponding numerical characteristics so as to perform accurate diagnosis on the fault. As the fault pattern base is directly established, alteration and deletion of intermediate regulations are avoided, simulation of functional relation between fault information and fault elements is not needed, the problem brought by a nonlinear system is solved, and the power distribution network fault analyzing method and the power distribution network fault analyzing device are suitable for online / offline fault diagnosis of any linear / nonlinear system.

Description

technical field [0001] The invention belongs to the field of distribution network fault analysis, in particular to a distribution network fault analysis method and device based on power flow distribution characteristics. Background technique [0002] With the rapid development of the smart grid, a large number of uncertain access of distributed power sources, the fault information of the distribution network is becoming more and more complex, and the rapid and accurate analysis and diagnosis of faults is becoming more and more difficult. In recent years, scholars at home and abroad have proposed a series of fault diagnosis methods and ideas from different perspectives. These methods can be divided into two types: data-driven and model-driven, mainly including expert system method, artificial neural network method, fuzzy set method, rough set method, etc. [0003] The expert system method is the earliest and most mature branch in the field of artificial intelligence. It uses...

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Problems solved by technology

[0003] The expert system method is the earliest and most mature branch in the field of artificial intelligence. It uses computer technology to integrate theoretical knowledge in related professional fields with expert experience and knowledge. The main defects are: establishing a knowledge base and verifying its completeness. Difficult; poor fault tolerance; combinatorial explosion in complex fault diagnosis

This type of method is difficult to meet the needs of online diagnosis of large-scale systems, and can only be used in offline analysis occasions.

[0004] The artificial neural network method is a typical data-driven method. Its essence is to find a hypersurface in a high-dimensional space to simulate the functional relationship between fault information and fault components through sample training. The main disadvantages are: its performance depends on Training samples, but in a large system, it is very difficult to obtain samples; it is a "black box" between its input and output, lacking the ability to explain and output results; it is not good at processing heuristic knowledge, because the neural network is not good at learning after the sample is completed. , generally has better interpolation results, but may produce larger errors during extrapolation, especially when the system is nonlinear

This method also has some problems that need to be overcome: there is no quantitative index for the selection of membership function to describe the uncertainty problem; in addition, it is difficult to establish the fuzzy model of large-scale complex system, and when its structure changes, fuzzy knowledge Library or rule ambiguity needs to be modified accordingly

[0006] Therefore, although the research work in this area has made progress and achievements, there are still deficiencies: some methods require the collected grid data to be complete, correct and credible, require a large amount of information, have poor fault tolerance, and are difficult to be practical; The fault information based is single and partial, and the diagnosis results are difficult to reflect the operation status of the entire network, and the accuracy is not high; some diagnosis tools rely too much on artificial intelligence methods, and do not take enough consideration of the physical characteristics of the power grid

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