Transformer winding hot spot temperature prediction based on IWOA-SVM
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.
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