Method for predicating contamination severity of insulator based on BP neural network and fuzzy logic

Abstract

The invention discloses a method for predicating contamination severity of insulator based on BP neural network and fuzzy logic, and belongs to the field of safety analysis and evaluation of power grids. The predication of the contamination severity of the insulator is essentially a state evaluation process of an uncertain and nonlinear system. The method is characterized in that the temperature, humidity, rainfall, wind speed and other basic factors are used as the basic input quantity to construct a BP neutral network predication model for the contamination severity of the insulator; the influence of attitude related to complex geographic position, air pressure ratio, freezing water conductivity and other factors on the contamination severity of the insulator is fully considered; the fuzzy logic compensation method is carried out to correct the evaluation result; the fuzzy mathematical method is carried out to comprehensively predicate the contamination severity of the insulator, and thus the problem of typical uncertainty of unclear factors influencing the contamination severity of the insulator during the evolution process can be solved; the method provides important scientific basis for determining the operation state of the power grid.

Description

technical field [0001] The invention relates to the field of safety analysis and evaluation of power grids, and is particularly suitable for predicting the pollution degree of insulators operating for a long time in complex natural environments. Background technique [0002] The essence of insulator pollution degree prediction is an evolution process of an uncertain state space. The evolution (transition) process of the state is random, and the characteristic information representing the pollution degree of insulators is inaccurate, and the validity of the influencing factors is not clear. , the definition and extension of the operating state are ambiguous, and the state evaluation knowledge is incomplete. It is a complex uncertainty problem for the prediction and calculation of the pollution degree of insulators. [0003] The BP neural network model includes its input and output model, action function model, error calculation model and self-learning model. A multi-layer ne...

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Problems solved by technology

It is impossible to consider too many factors just by using the neural network for fitting. The main reasons include: 1) Considering too many factors will bring certain difficulties to the establishment of the model, and at the same time there will be problems such as complex calculations and numerical instability. ; 2) The geographic location information (altitude, air pressure ratio) and ice-coated water conductivity related to insulator pollution have no specific reference data in the input layer of the neural network

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