群智能算法优化支持向量机在变压器故障诊断中的应用

在变压器故障诊断领域,传统诊断方法基于过往故障数据的不断总结,主要有特征气体识别法和三比值法,然而传统的故障诊断方法存在识别精度低、无法涵盖所有故障场景的局限性,从而影响故障诊断准确率。随着人工智能技术的不断发展,不少智能方法也开始在故障诊断领域中发挥作用。支持向量机在针对样本数据量小的情形下有着良好泛化能力,可通过核函数解决非线性问题。支持向量机结合群智能算法优化其参数可获得更好性能。最后实验证明智能算法能够有效改善支持向量机的分类性能,提升故障诊断准确率。

In the field of transformer fault diagnosis,traditional diagnosis methods are based on the continuous summary of past fault data,mainly including characteristic gas identification method and three-ratio method.However,traditional fault diagnosis methods have the limitation of low identification accuracy and cannot cover all fault scenarios.This affects the accuracy of fault diagnosis.With the continuous development of artificial intelligence technology,many intelligent methods have also begun to play a role in the field of fault diagnosis.Support vector machine has good generalization ability in the case of small amount of sample data,and can solve nonlinear problems through kernel function.Support vector machines combine with intelligent algorithms to optimize their parameters for better performance.Finally,the experiment proves that the intelligent algorithm can effectively improve the classification performance of SVM and improve the accuracy of fault diagnosis.