多核支持向量机预测电网系统可靠性
Reliability Prediction of Power Grid Systems Based on Multi-kernel Support Vector Machine
何井龙 1张福泉 2阳晟 1周智成1
作者信息
- 1. 广西电网有限责任公司 电力调度控制中心,广西 南宁 530023
- 2. 闽江学院 计算机与大数据学院,福建 福州 350108;福州大学 计算机与大数据学院,福建 福州 350108
- 折叠
摘要
为了改善电网系统可靠性预测性能,构建多个目标函数并采用多核支持向量机算法对配电网进行可靠性预测;从电网样本特征中筛选供电可用率、户均停电时间、户均停电次数3个关键指标,建立可靠性评价目标函数,且采用多核支持向量机训练可靠性指标特征;将高斯核函数、多项式核函数和Sigmoid核函数进行多核组合,采用多核支持向量机求解不同目标函数,获得电网系统可靠性预测结果,进而确定更佳的可靠性预测核函数组合.结果表明,合理选择核函数组合和电网可靠性指标,多核支持向量机对供电可用率、户均停电时间和户均停电次数指标预测准确率较高,且稳定性好,高斯核函数-Sigmoid核函数组合的可靠性预测准确性最佳,高斯核函数-多项式核函数-Sigmoid核函数组合的预测稳定性最好.
Abstract
To improve the reliability prediction performance of power grid system,multiple objective functions were con-structed and multi-kernel support vector machine algorithm was used to predict reliability.The reliability evaluation objec-tive functions were established by screening three key indicators of power supply availability,average outage time and average outage times of from the grid sample features,and the reliability index features are trained by multi-kernel support vector machine.The multi-kernel combination types of Gaussian kernel function,polynomial kernel function and Sigmoid kernel function were used to obtain the reliability prediction results of power grid system through multi-kernel support vec-tor machine solving of different objective functions,and then the better kernel function combination type for reliability prediction was determined.The results show that with reasonable selection of kernel function combination and grid relia-bility index,multi-kernel support vector machine has high prediction accuracy and good stability in power supply availa-bility,average outage time and average outage times,and the reliability prediction accuracy of the combination type of Gaussian kernel function and Sigmoid kernel function is the best,the combination type of Gaussian kernel function,poly-nomial kernel function and Sigmoid kernel function has the best predictive stability.
关键词
电网系统可靠性/多核函数/支持向量机/目标函数Key words
reliability of power grid system/multi-kernel function/support vector machine/objective function引用本文复制引用
基金项目
国家自然科学基金项目(61871204)
南方电网公司科技项目(GXKJXM20222188)
出版年
2024
NETL
NSTL
万方数据