基于OS-ELM的变压器局部放电模式识别

Partial Discharge Pattern Recognition of Transformer Based on OS-ELM

传统的变压器局部放电模式识别算法由于需调整的参数多且难以确定最佳参数、学习速度慢等缺点,在实际工程应用中识别正确率低,识别速度慢。因此,提出了一种基于在线序列极限学习机（OS-ELM）算法的变压器局部放电模式识别方法,该算法是传统极限学习机（ELM）的在线学习改进算法,是一种新型的单隐含层前馈神经网络（SLFN）。本文基于特高频检测法在真型变压器上进行局部放电实验,并获得大量实验数据。将本文所提方法与ELM、支持向量机（SVM）以及BP神经网络（BPNN）的模式识别效果和性能进行了比较分析。结果表明OS-ELM算法识别正确率比SVM和BPNN分别高出5.2%和23.2%;逐渐减小训练样本集大小,OS-ELM识别结果的波动明显小于SVM和BPNN,表现出更好的泛化能力;OS-ELM的训练时间仅为0.031 2 s,远远小于SVM和BPNN。因此,OS-ELM更适用于大数据量样本的工程应用。

The traditional partial discharge（PD） pattern recognition algorithms have low recognition accuracies and slow recognition speed in practical engineering applications because of their limitations,including a large number of parameters to tune,the difficulty in optimizing parameters and low learning rates.Therefore,we proposed a PD pattern recognition method for transformer based on the Online Sequential-Extreme Learning Machine（OS-ELM） algorithm.OS-ELM is an online-learning and improved algorithm of Extreme Learning Machine（ELM）,and a new type of Single-hidden Layer Feed-forward neural network（SLFN）.Meanwhile,a lot of experimental data were obtained from real transformer in the high voltage laboratory on the PD experiments based on Ultra High Frequency（UHF） detection method.In addition,OS-ELM is analyzed and compared with ELM,Support Vector Machine（SVM） and BP neural network（BPNN） in both pattern recognition effect and performance aspects.The results show that the accuracy of OS-ELM is 5.2% and 23.2% higher than that of SVM and BPNN,respectively.When reducing the size of the training samples,the fluctuation of OS-ELM recognition results is significantly smaller than that of SVM and BPNN,showing better generalization ability.Besides,the training time of OS-ELM is only 0.031 2 s,far less than that of SVM and BPNN.Therefore,OS-ELM is more suitable for engineering applications of large data volume samples.