基于奇异谱熵和支持向量机的变压器绕组松动识别及定位  

Recognition and location of transformer winding looseness based on singular value spectrum entropy and SVM

作　　者：张琳[1] 马宏忠[2] 姜宁[3] 高拓宇[3] 许宏华[4] 

机构地区：[1]国网泰州供电公司,江苏泰州225000 [2]河海大学能源与电气学院,江苏南京211100 [3]江苏省电力公司检修分公司,江苏南京211102 [4]江苏省电力公司南京供电公司,江苏南京210008

出　　处：《电力系统保护与控制》2017年第18期69-75,共7页Power System Protection and Control

基　　金：国家自然科学基金(51177039);江苏省电力公司科技项目(J2015054)~~

摘　　要：绕组压紧状态影响着变压器的机械性能和绝缘性能。为此,提出一种基于奇异谱熵和支持向量机的变压器绕组松动诊断及定位方法。首先进行110 k V变压器绕组松动实验并测取不同绕组状态下的振动信号,对信号进行时间序列重构,通过奇异值分解提取重构空间的最优特征序列,结合信息熵得出绕组松动的特征量——奇异谱熵,并作为诊断模型的输入,利用粒子群算法对多分类支持向量机进行参数优化。并将其测试结果与BP和PNN神经网络的诊断效果进行对比。实验结果证明,该方法能有效地判断绕组是否发生松动并正确识别绕组松动相,验证了上述方法的可行性和准确性。Transformers＇ mechanical properties and insulation performance are affected by pre-compression of winding. A new method for the diagnosis and localization of transformer winding looseness based on singular value spectrum entropy and SVM is proposed. First, 110 kV transformer winding looseness experiment is performed to measure vibration signals under different conditions. Then the time series of signals are reconstructed. Optimal feature sequence of reconstructing space is extracted by singular value decomposition and the features-singular value spectrum entropy is got by combining information to judge winding looseness, which are used as inputs of diagnostic model. The multi classification support vector machine is optimized by particle swarm optimization algorithm. The test result of the model is compared with BP and PNN neural network. The experimental result shows that this method can effectively determine whether the winding is loose and correctly identify the loose phase, which verifies this method＇s feasibility and accuracy.

关 键 词：变压器 绕组松动 奇异谱熵 支持向量机 松动定位 

分 类 号：TM41[电气工程—电器]