林顺富;汤继开;汤波;李东东;符杨;

• 1:上海电力学院电气工程学院

摘要(Abstract)：

电网典型电能质量稳态指标的准确预测对优化电网运行方式和提高电网供电质量具有重要意义。根据电能质量稳态指标似周期、非周期的特点,提出一种基于混沌理论和最小二乘支持向量机(least squares support vector machine,LSSVM)的电能质量稳态指标预测模型。首先采用混沌理论对典型电能质量稳态指标历史数据进行相空间重构,构造包含吸引子的新数据空间;其次利用最小二乘支持向量机在高维空间下进行样本训练,并结合粒子群算法优化最小二乘支持向量机参数;最后得到最佳预测模型。基于某地配电网电能质量实际监测数据,采用所提模型进行典型电能质量稳态指标预测,平均相对误差均在8%以下,优于传统BP神经网络预测方法。

关键词(KeyWords)： 电能质量稳态指标;混沌理论;最小二乘支持向量机;相空间重构;粒子群算法

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(51207088);; 上海市教委曙光计划(15SG50);; 国网公司科技项目(SGRI-DL-71-14-004)~~

作者(Author): 林顺富;汤继开;汤波;李东东;符杨;

Email:

DOI: 10.13335/j.1000-3673.pst.2017.0955

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