Prediction Model of Transmission Line Fault ProbabilityApplying Attention Mechanism

doi:10.12204/j.issn.1000-7229.2022.03.005

Abstract

Abstract:

The fault probability of transmission lines is directly related to meteorological conditions. Due to the extreme weather conditions showed activity in the world scope, for the research of the fault of the transmission line under different meteorological conditions and probability of stable level has important significance to improve the operation. Starting from the analysis of typical weather related transmission line fault mechanism and statistical characteristics, this paper proposed a deep neural network based on attention mechanism to predict the fault probability of transmission lines. We use the power outage data to test the model and compares it with the multi-layer perceptron model using back propagation algorithm, which is referred as the BP network in the paper. The result validates the effectiveness of the model on transmission line fault probability prediction, which provides the possibility for the electric utilities to better carry out the construction of protective measures and the repair plan and shows that the method is conducive to the safe, stable and reliable operation of the power system.

Key words: transmission lines, meteorological disasters, fault probability, neural network, attention

CLC Number:

TM755

YANG Yue, SUN Bo, MA Xiaochen, LUO Yadi, SUN Yingyun. Prediction Model of Transmission Line Fault ProbabilityApplying Attention Mechanism[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(3): 42-49.

Figures/Tables 9

Fig.1 Inter-meteorological attention featuremining network

Fig.2 Attentional feature mining networkbetween meteorological and faults

Fig.3 Schematic diagram of total structure offault probability prediction network

Table 1 Case data statistics

Table 2 Effect comparison between theproposed network and BP network

Fig.4 Comparison of training loss between theproposed network and a BP neural network

Fig.5 Comparison of the accuracy of the trainingset between the proposed network and BP network

Fig.6 Comparison of the accuracy of the test setbetween the proposed network and BP network

Table 3 Comparison of typical data

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