纵横交叉算法与模糊聚类相结合的变压器故障诊断

针对FCM(模糊C-均值聚类)在变压器故障诊断中的不足,提出采用纵横交叉算法优化FCM(CSOFCM)聚类来进行故障诊断。溶解气体分析与FCM相结合,能有效提高变压器故障诊断的准确率,但FCM存在聚类结果不稳定和容易陷入局部最优等问题。而纵横交叉算法是一种基于种群的随机搜索算法,在算法中首次提出了维局部最优概念和纵横交叉双搜索思想。实验证明,相比其它主流群智能优化算法,CSO算法在解决维数灾问题和收敛精度问题方面取得了较大突破,能有效克服局部最优的问题。新诊断模型有效弥补了单一诊断法的不足,拥有全局收敛性强和处理模糊信息的能力。实例分析表明,该方法与传统FCM相比,能获得更优的聚类中心,有效提高了变压器故障诊断的准确性和快捷性。

Fault Diagnosis Method of Transformer Based on Crisscross Optimization and Fuzzy Clustering

Optimized the FCM clustering by the proposed CSO ( CSO-FCM) is introduced to diagnose the fault of transformer in order to conquer the shortages of FCM clustering.The combination of dissolved gas analysis and FCM clustering is effective on improving the accuracy rate of power transformer fault diagnosis, but the result of FCM cluste-ring is unstable and easy getting stuck in a local optimum.The CSO algorithm includes horizon cross as well as verti-cal cross, whose combining can enhance the global convergent ability while the introduction of competitive mechanism drives the potential solutions approximate the global optima in an accelerating fashion without sacrificing the conver-gence speed.This novel method effectively compensates the demerits of single intelligent algorithm, which not only has the ability to dispose the unstable information of fuzzy theory, also has an advantage of global convergence of CSO. Simulation and case analysis indicate that, compared with the traditional FCM clustering, the CSO-FCM clustering can obtain high performance clustering center and effectively raise the accuracy and diagnosis speed of power transformer fault diagnosis.