基于ARMA-GABP组合模型的电网大停电事故损失负荷预测

doi:10.11930/j.issn.1004-9649.201801046

中国电力 ›› 2018, Vol. 51 ›› Issue (11): 38-44.DOI: 10.11930/j.issn.1004-9649.201801046

基于ARMA-GABP组合模型的电网大停电事故损失负荷预测

于群¹, 张铮¹, 屈玉清¹, 贺庆²

1. 山东科技大学电气与自动化工程学院, 山东青岛 266590;
2. 全球能源互联网发展合作组织, 北京 100031

收稿日期:2018-01-08 修回日期:2018-04-20 出版日期:2018-11-05 发布日期:2018-11-16
作者简介:于群(1970-),男,博士,教授,从事电力系统运行与控制、继电保护研究,E-mail:yuqun_70@163.com;张铮(1994-),男,硕士研究生,从事电力系统运行与控制研究,E-mail:zhangzheng0806@126.com;屈玉清(1988-),男,硕士研究生,从事电力系统运行与控制研究,E-mail:01quyuqing@163.com;贺庆(1980-),男,博士,高级工程师,从事电力系统运行与控制研究,E-mail:heqing@epri.sgcc.com.cn
基金资助:
国家电网公司科技项目（基于多沙堆理论的互联电网停电事故预警技术及系统研发）。

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

YU Qun¹, ZHANG Zheng¹, QU Yuqing¹, HE Qing²

1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;
2. Global Energy Interconnection Development and Cooperation Organization, Beijing 100031, China

Received:2018-01-08 Revised:2018-04-20 Online:2018-11-05 Published:2018-11-16
Supported by:
This work is supported by the Science and Technology Project of SGCC (Interconnected Power Grid Blackouts Forewarning and System Research and Development Based on Multi-sandpile Theory).

摘要/Abstract

摘要： 损失负荷作为衡量大停电事故风险的重要指标，如何对其进行准确预测对于电网的安全运行具有十分重要的参考意义。选取1981—2016年东北电网和西北电网的大停电事故损失负荷作为实验数据进行分析，为消除电网发展对数据分析产生的影响，采用相对值法对电网大停电事故损失负荷进行处理。根据实验数据的特点，将损失负荷相对值的数据结构分解为线性和非线性残差部分，建立自回归滑动平均(ARMA)模型和遗传算法(GA)优化的误差反向传播(BP)神经网络组合模型，对东北电网大停电事故进行综合分析与预测。将所提模型的预测结果与单一模型和ARMA-BP模型的预测结果相对比，结果表明，所提模型的预测精度更高，预测效果较为理想。为进一步验证该预测模型的有效性，将西北电网大停电事故数据代入预测模型，实验结果表明该预测模型在电网大停电事故损失负荷方面具有良好的预测效果。

关键词: 电网, 大停电事故, 损失负荷, ARMA模型, GA-BP神经网络, 组合模型, 预测

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

中图分类号:

TM732

于群, 张铮, 屈玉清, 贺庆. 基于ARMA-GABP组合模型的电网大停电事故损失负荷预测[J]. 中国电力, 2018, 51(11): 38-44.

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

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基于ARMA-GABP组合模型的电网大停电事故损失负荷预测

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

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基于ARMA-GABP组合模型的电网大停电事故损失负荷预测

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

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