CAJ | 학술논문

针对暂态电能质量电压多扰动信号的检测与分类问题，提出一种基于广义S变换及模糊SOM神经网络的暂态电能质量检测和识别方法。针对常见的电压多扰动信号，特别是两种扰动叠加的情况，采用广义 S 变换对扰动信号的时频特征进行提取，并取变换后的时间幅值平方和均值和特征频点作为神经网络的输入样本，采用模糊SOM 神经网络进行训练，再用新的多扰动数据进行网络检验。仿真与实验结果表明，广义S 变换能有效提高电能质量多扰动特征检测，模糊SOM 神经网络能精确对其进行分类，该方法能够较好的解决电压多扰动叠加情况的定性和定量分类问题。
침대잠태전능질량전압다우동신호적검측여분류문제，제출일충기우엄의S변환급모호SOM신경망락적잠태전능질량검측화식별방법。침대상견적전압다우동신호，특별시량충우동첩가적정황，채용엄의 S 변환대우동신호적시빈특정진행제취，병취변환후적시간폭치평방화균치화특정빈점작위신경망락적수입양본，채용모호SOM 신경망락진행훈련，재용신적다우동수거진행망락검험。방진여실험결과표명，엄의S 변환능유효제고전능질량다우동특정검측，모호SOM 신경망락능정학대기진행분류，해방법능구교호적해결전압다우동첩가정황적정성화정량분류문제。
To solve the problem of detecting and classifying power quality multi-disturbances, this paper proposed a new method based on the generalized S-transform and the fuzzy self-organizing maps (SOM) neural network to extract features and to recognize the disturbance patterns. As to all kinds of the disturbance voltage signals, especially the superposition of two kinds of voltage disturbances, the generalized S-transform is used to extract multi-disturbance time-frequency features. Then, the average square-sum of S-transform amplitudes are used to train the fuzzy SOM neural network, and the new collected data are tested using the trained fuzzy SOM neural network. Simulation and experiment results show that the generalized S-transform can detect power quality multi-disturbance effectively, and the fuzzy SOM neural network can classify it accurately. The problem of voltage super imposed disturbance classification can be resolved successfully from both qualitative and quantitative ways.