宁夏电力Issue(1)：62-68,7.DOI:10.3969/j.issn.1672-3643.2024.01.010
基于IWOA-SVM的变压器绕组热点温度预测
Transformer winding hot spot temperature prediction based on IWOA-SVM
马成 1罗亭然 1刘闯 2卢银均2
作者信息
- 1. 国网湖北省电力有限公司荆州供电公司,湖北 荆州 434000
- 2. 国网湖北省电力有限公司荆门供电公司,湖北 荆门 448000
- 折叠
摘要
Abstract
To reduce the risk associated with the high-temperature operation of transformers and to improve the accuracy of winding hot spot temperature prediction,this study proposes a method based on the improved whale optimization algorithm(IWOA)combined with an optimized support vector machine(SVM).As determined through grey correlation analysis,key influencing factors such as load current,active power,top oil temperature,and ambient temperature are identified as the main characteristic variables affecting winding hot spot temperature changes.These factors are utilized as support vectors for the winding hot spot temperature prediction model.The whale algorithm is refined by incorporating a cosine adjustment for control factors and introducing adaptive weight coefficients,which enhance the optimization performance of the IWOA.The SVM parameters are optimized using the IWOA algorithm,establishing an IWOA-SVM-based transformer winding temperature prediction model.Results from case studies show that the proposed method's root mean square error is 1.21℃,determination coefficient is 0.897,and average relative error is 2.14%.All three indica-tors surpass other methods in performance.This validation underscores the practicality and effectiveness of the proposed method.关键词
变压器/绕组热点温度/改进鲸鱼算法/支持向量机/灰色关联分析Key words
transformer/winding hot spot temperature/improved whale optimization algorithm/support vector machine/grey correlation analysis分类
信息技术与安全科学引用本文复制引用
马成,罗亭然,刘闯,卢银均..基于IWOA-SVM的变压器绕组热点温度预测[J].宁夏电力,2024,(1):62-68,7.
