基于最小二乘支持向量机的新型电力系统谐波分量预测
Novel Power System Harmonic Component Prediction Based on Least Squares Support Vector Machine
戴金1
作者信息
- 1. 国网湖北省电力有限公司配电部,湖北武汉 430000
- 折叠
摘要
电力电子设备在发电、输电、配电及用电各个领域均有广泛应用,在改善居民生活质量与提升工业生产效率的同时,也引入了大量的谐波,造成电力系统谐波污染.电力系统在不同采样点处的谐波含量不同,而最小二乘支持向量机(LSSVM)具有预测精度高、预测效率高等优点,可应用于谐波含量预测.为了验证所提出算法的有效性,搭建了仿真模型,对光伏发电系统、风力发电系统以及储能装置充放电处的电流的谐波含量进行了预测.仿真结果表明:在不同工况下和不同类型的谐波含量下,该算法均具有较高的预测精度.
Abstract
Power electronic equipment is widely used in various fields of power generation,transmission,distribu-tion and use.While improving the quality of life of residents and enhancing the efficiency of industrial production,it also introduce a large amount of harmonic content,resulting in power system harmonic pollution.The power system has different harmonic contents at different sampling points,and the least squares support vector machine(LSSVM),which has the advantages of high prediction accuracy and high prediction efficiency,can be applied to the harmonic content prediction.In order to verify the algorithm proposed in this study,a simulation model is con-structed to predict the harmonic content of the currents at the charging and discharging points of photovoltaic power generation system,wind power generation system,and energy storage device.The simulation results show that the algorithm has high prediction accuracy under different operating conditions and different types of har-monic contents.
关键词
新型电力系统/谐波含量/预测算法/最小二乘支持向量机Key words
new power system/harmonic content/prediction algorithm/least squares support vector machine(LSSVM)引用本文复制引用
出版年
2024
NETL
NSTL
万方数据