基于时频特征分析的变压器有载分接开关运行状态识别

为了对变压器有载分接开关的运行状态进行识别,该研究首先对其运行状态和故障特征进行总结分析,针对分接开关运行过程中产生的振动信号,利用集合经验模态(EEMD)分解为多个固有模态函数分量(IMF),再经过希尔伯特变换法,结合能量熵提取得到基于时频分析的特征向量。将特征向量输入自适应遗传算法(AGA)优化的BP神经网络模型中进行故障识别,并进行数据仿真,与相空间重构后提取的特征向量(PPDC)进行对比,验证不同网络模型下,所提方法的识别准确率和收敛速度。结果表明,以PPDC故障样本作为模型输入时,AGA算法优化前后的BP神经网络模型的识别准确率分别为81.68%和88.32%,收敛次数为981和363,当以基于时频特征提取的故障样本作为模型输入时,AGA算法优化前后的BP神经网络模型的识别准确率分别为91.66%和96.68%,收敛次数为349和159,AGA算法可显著提高BP神经网络模型的性能。由此可见,可将时频特征提取方法与AGA-BP神经网络结合,实现有载分接开关运行状态的有效识别。

Identification of the Operation State of Transformer On-load Tap-changer Based on Timefrequency Characteristic Analysis

In order to identify the operation state of the transformer on-load tap-changer,this study first summarizes its operation state and fault characteristics,and uses the empirical mode(EEMD)to decompose the vibration signals generated during the operation of the tap-changer into Multiple intrinsic mode function components(IMF)are combined with Hilbert transform and energy entropy extraction to obtain feature vectors based on time-frequency analysis.The feature vectors are input into the BP neural network model optimized by the adaptive genetic algorithm(AGA)for fault identification,and the data simulation is performed.The feature vectors are compared with the feature vectors(PPDC)extracted after phase space reconstruction,and the results are verified under different network models.The recognition accuracy and convergence speed of the method are improved.The results show that when PPDC fault samples are used as the model input,the recognition accuracy of the BP neural network model before and after AGA algorithm optimization is 81.68%and 88.32%,and the number of convergence is 981 and 363.As the model input,the recognition accuracy of the BP neural network model before and after the AGA algorithm optimization is 91.66%and 96.68%,respectively,and the number of convergence is 349 and 159.The AGA algorithm can significantly improve the performance of the BP neural network model.It can be seen that the time-frequency feature extraction method can be combined with the AGA-BP neural network to realize the effective identification of the operating status of the on-load tap-changer.