Power Loss Prediction of Large Blackouts in Power Grid Based on ARMA-GABP Combined Model

doi:10.11930/j.issn.1004-9649.201801046

Abstract

Abstract: Power loss is an important index to measure the risk of blackouts, and it is very important to accurately predict it for the safe operation of power grid. In this paper, the power loss of large blackouts in Northeast Power Grid and Northwest Power Grid from 1981 to 2016 are selected as experimental data. In order to eliminate the influence of power grid development on data analysis, the relative value method is used to process the power loss of grid blackouts. According to the characteristics of the experimental data, the data structure of the relative value of power loss is decomposed into linear and non-linear residuals. A combined ARMA and GABP neural network model is established to comprehensively analyze and predict the large-scale blackout accidents in Northeast China Power Grid. The prediction results of the proposed model are compared with those of the single model and the ARMA-BP model. The results show that the proposed model has higher prediction accuracy and better prediction effect. In order to further verify the validity of the forecasting model, the data of large blackouts in Northwest Power Grid are substituted into the forecasting model. The experimental results indicate that the forecasting model has a good forecasting effect in terms of power loss caused by large blackouts.

Key words: power system, large blackouts, power loss, ARMA model, GA-BP neural network, combined model, prediction

CLC Number:

TM732

YU Qun, ZHANG Zheng, QU Yuqing, HE Qing. Power Loss Prediction of Large Blackouts in Power Grid Based on ARMA-GABP Combined Model[J]. Electric Power, 2018, 51(11): 38-44.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201801046

https://www.electricpower.com.cn/EN/Y2018/V51/I11/38

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