Fault Diagnosis Method for Distribution Networks Based on the Rough Sets and Decision Tree Theory
Guangyu Tu · Gao dianya jishu · 2008
To improve the indeterminacy and imperfection of distribution networks fault information that make it difficult to obtain accuracy fault diagnosis results,a new fault diagnosis algorithm based on the rough sets and decision tree theory is proposed,which can extract diagnosis rules directly from reduced decision table.The rough sets theory as a new mathematical tool is used to deal with inexact and uncertain knowledge for pattern recognition.The target is mainly to remove redundant information and to seek for reduced decision tables by use of discernible matrix.As a quickly learning theory and classification tool,according to the value of the information entropy,the decision tree is used to extract diagnosis rules directly from reduced decision table.The rules from decision tree can be with clear relationship and easy to explain.With the proposed method the blindness and redundancy of ruling can be avoided and the space for decision table is evidently diminished.According to the value of information entropy,the priority of different characteristic information can be conducive to acquiring quick and satisfactory results for distribution networks without looking up the whole rules.In addition when the fault information is imperfect,the results can be still acquired according to the remained information, so the proposed method possesses strong tolerant ability.This method is thus developed to ensure diagnosis precision and speed up the implementation of distribution fault diagnosis system.Finally,examples are given to verify its effectiveness,and the comparison with the former method shows its advantages.