基于AQPSO-LSSVM的电网线损率预测模型
投稿时间:2020-09-30  修订日期:2020-10-22  点此下载全文
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作者单位E-mail
汪司珂 国网湖北省电力有限公司营销服务中心 55855234@qq.com 
明东岳 国网湖北省电力有限公司营销服务中心  
郭雨 国网湖北省电力有限公司电力科学研究院  
易本顺 武汉大学电子信息学院 yibs@whu.edu.cn 
项勇 国网黄石供电公司  
潘志 国网黄石供电公司  
基金项目:国网湖北省电力公司科技项目资助(52153218003N)
中文摘要:为了对地区电网220kV线损率进行有效的评估,提出了一种基于自适应量子粒子群算法(AQPSO)优化的最小二乘支持向量机(LSSVM)的线损率预测模型。AQPSO在QPSO的基础上引入了遗传算法中的交叉与变异机制以扩大种群多样性,避免算法陷入局部最优。利用AQPSO搜索最佳的LSSVM参数并获取线损预测结果,通过训练集对预测模型进行训练,然后使用测试集进行实验。最后选择地区电网23条220kV线路的真实数据进行分析和预测,实验结果表明,文章所提出的AQPSO-LSSVM模型能够更有效地对线损率进行准确预测。
中文关键词:线损率  自适应量子粒子群  最小二乘支持向量机  220kV电网  预测
 
Line loss rate prediction model based on AQPSO-LSSVM
Abstract:In order to evaluate the line loss rate of 220kV power grid effectively, a line loss rate prediction model based on adaptive quantum particle swarm optimization (AQPSO) and least square support vector machine (LSSVM) is proposed. AQPSO introduces the crossover and mutation mechanism in genetic algorithm on the basis of QPSO to expand the population diversity and avoid the algorithm from falling into the local optimum. The model uses AQPSO to search for the best LSSVM parameters and obtain line loss prediction results, and trains the prediction model through the training set, and then uses the test set for experiments. Finally, the article selects the real data of 23 lines of 220kV regional power grid, for analysis and prediction, the experimental results show that the proposed AQPSO-LSSVM model can more effectively predict the line loss rate accurately.
keywords:line loss rate, AQPSO, LSSVM, 220kV grid, prediction
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