Prediction of Transmission Line Icing Thickness Applying AMPSO-BP Neural Network Model

doi:10.12204/j.issn.1000-7229.2021.09.015

Abstract

Abstract:

Icing of transmission line seriously threatens the safe operation of power system. Therefore, it is necessary to investigate icing prediction of transmission lines. With the development of its performance, artificial intelligence technology gradually shows advantages in power grid icing monitoring. To a certain extent, the existing statistical regression model of ice thickness prediction for transmission line can partly predict the ice accretion growth. However, these traditional models are only suitable for short icing periods and difficult to realize in actual engineering because of their requirement of high data acquisition frequency. This research collected the transmission line's ice observation data from 2015 to 2019 got by Chongqing Transmission and Transformation Engineering Co, Ltd.. By analyzing the data, the characteristics and rules of transmission line icing under high humidity environment in southwestern China are obtained. Then, according to the ice growth's physical process on transmission line, the research selects measurable parameters in practical work as the impact factor of ice accretion growth. On this basis, an artificial intelligence ice-thickness prediction model based on adaptive mutation particle swarm optimization (AMPSO) is proposed. According to the training results, the AMPSO-BP neural network is more accurate and reliable on ice thickness prediction, compared to the traditional BP neural network.

Key words: artificial intelligence, ice thickness prediction of transmission line, adaptive mutation, BP neural network

CLC Number:

TM75

LI Xianchu, ZHANG Xi, LIU Jie, HU Jianlin. Prediction of Transmission Line Icing Thickness Applying AMPSO-BP Neural Network Model[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(9): 140-146.

Figures/Tables 12

Table 1 Factors of transmission line icing growth

Fig.1 Correlation analysis between daily minimum temperature and daily ice accretion thickness

Fig.2 Correlation analysis between wind speed and daily ice accretion thickness

Fig.3 Correlation analysis between air humidity and daily ice accretion thickness

Table 2 Data conversion table of weather condition categories

Fig.4 Correlation analysis between weather condition category and daily ice accretion thickness

Fig.5 Correlation analysis between basic icing thickness and daily ice accretion thickness

Table 3 Analysis of basic icing thickness and daily ice accretion thickness

Fig.6 Structure diagram of AMPSO-BP neural network

Table 4 Prediction results of the modelmm

Fig.7 Icing prediction results of the model

Table 5 Error statistics of three prediction methods

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