变压器作为电力系统的关键设备,其绕组松动状态的识别对电网的稳定运行具有重要意义.针对传统监测方法环境干扰较大、应用复杂等问题,提出了使用两类不同的布拉格光纤光栅(Fiber bragg grating,FBG)传感器采集变压器绕组关键测点温度与应变信号,经快速解耦与自适应噪声完备集合经验模态分解后(Fast decoupling and complete ensemble empirical mode decomposition with adaptive noise,DECE),提取关键参数并进行主元分析(Principal component analysis,PCA).对降维后的特征采用基于黑洞优化的支持向量机(Support vector machine based on black hole optimization,BHOSVM)进行分类,实现对变压器绕组径向松动状态的监测与定位.诊断结果表明,所提诊断方法对变压器绕组径向松动状态的识别准确率达到96.8％.
Assessment of Radial Loosening State in Transformer Windings Based on FBG Signals and DECE-PCA-BHOSVM
As a critical component in the power system,transformers play a vital role in ensuring the stable operation of the electrical grid.The identification of loose winding conditions in transformers holds significant importance.Addressing issues related to traditional monitoring methods,such as susceptibility to environmental interference and complexity of application,a novel approach is proposed.This approach involves the utilization of two different types of fiber bragg grating(FBG) sensors to collect temperature and strain signals at key points within the transformer windings.After the data is processed through fast decoupling and complete ensemble empirical mode decomposition with adaptive noise(DECE),essential parameters are extracted and subjected to principal component analysis(PCA).The reduced-dimensional features are then classified using support vector machine based on black hole optimization(BHOSVM),enabling the monitoring and localization of radial loosening in transformer windings.The diagnostic method proposed achieves an accuracy rate of 96.8％ in identifying the radial loosening condition of transformer windings.
万方数据
维普
