基于PCA-IHHO-LSSVM的变压器绕组热点温度预测  

Prediction of Transformer Winding Hot Spot Temperature Based on PCA-IHHO-LSSVM

作　　者：刘婧珂 肖添 魏阳 刘云飞 刘闯 陈海旭 LIU Jingke;XIAO Tian;WEI Yang;LIU Yunfei;LIU Chuang;CHEN Haixu(Jingmen Power Supply Company,State Grid Hubei Electric Power Co.,Ltd.,Jingmen 448000,China;College of Electrical and New Energy,Three Gorges University,Yichang 443002,China)

机构地区：[1]国网湖北省电力有限公司荆门供电公司,湖北荆门448000 [2]三峡大学电气与新能源学院,湖北宜昌443002

出　　处：《山东电力技术》2024年第8期59-66,共8页Shandong Electric Power

基　　金：国家自然科学基金项目“基于多稳定性理论的神经网络预定性能控制研究”(61876097)。

摘　　要：为保障变压器安全稳定运行,提高变压器绕组热点温度预测精度,提出一种基于主成分分析-改进哈里斯鹰-最小二乘支持向量机(principal component analysis-improved Harris hawks optimization-least squares support vector machine,PCA-IHHO-LSSVM)的变压器绕组热点温度预测方法。对变压器绕组热点温度预测的主要特征量进行主成分分析,有功功率、负载电流和顶层油温三个特征量的累计贡献率超过85%,确定有功功率、负载电流和顶层油温三个主元,对输入量进行指标重构。利用Tent混沌映射和学生分布扰动策略对哈里斯鹰算法进行改进,以提高IHHO算法的收敛精度和优化性能,采用IHHO算法对LSSVM进行参数优化,建立了基于PCA-IHHO-LSSVM的变压器绕组热点温度预测模型。采用变压器监测数据进行仿真分析,并与其他绕组热点温度预测方法进行对比,结果表明,所提PCA-IHHO-LSSVM模型的平均相对误差和均方根误差分别为2.92%和1.77℃,预测精度高于其他方法,验证了所提变压器绕组热点温度预测方法的实用性和优越性。In order to ensure the safe and stable operation of transformers and improve the accuracy of transformer winding hot spot temperature prediction,a transformer winding hot spot temperature prediction method based on principal component analysis-improved Harris hawks optimization-least squares support vector machine(PCA-IHHO-LSSVM)was proposed.Principal component analysis was conducted on the main characteristic variables for predicting the hot spot temperature of transformer windings.The cumulative contribution rate of the three characteristic variables,active power,load current,and top oil temperature,exceeded 85%.The three principal components of active power,load current,and top oil temperature were determined,and the input variables were reconstructed as indicators.Using Tent chaotic mapping and student distribution perturbation strategy,the Harris Eagle algorithm was improved to improve the convergence accuracy and optimization performance of the IHHO algorithm.The IHHO algorithm was used to optimize the parameters of LSSVM,and a transformer winding hot spot temperature prediction model based on PCA-IHHO-LSSVM was established.The simulation analysis was carried out with the transformer monitoring data,and compared with other winding hot spot temperature prediction methods.The results show that the average relative error and Root-mean-square deviation of the PCA-IHHO-LSSVM model proposed are 2.92%and 1.77℃respectively.The prediction accuracy is higher than other methods,which verifies the practicability and superiority of the transformer winding hot spot temperature prediction proposed in this paper.

关 键 词：变压器 绕组热点温度 最小二乘支持向量机 改进哈里斯鹰算法 主成分分析 

分 类 号：TM411[电气工程—电器] TP18[自动化与计算机技术—控制理论与控制工程]