一种新的电能质量扰动信号压缩感知识别方法

A novel compressed sensing-based recognition method for power quality disturbance signals

针对现有电能质量扰动信号识别方法存在数据量大、准确率不高的不足,提出了一种基于压缩感知稀疏向量特征提取的电能质量扰动信号分类识别方法。该方法首先针对原始信号,利用压缩感知理论获取降维的测量信号,并基于?1范数正交匹配追踪算法获取稀疏向量。然后针对稀疏向量提取最大值、次大值、均方根、标准差、峭度和裕度因子等特征,作为神经网络的输入,实现电能质量扰动信号的分类识别。最后,针对六类典型电能质量扰动信号,开展仿真实验验证。仿真结果表明,现有识别方法需要处理的原始信号长度为1024,而所提方法特征提取时所处理的数据长度仅有30,从而大大减少了所需处理的数据量,并且由于实现了以非常少的数据量保存原有全部有用特征信息,因而更有利于提高识别准确率。通过与广泛采用的小波变换识别方法进行比较,所提方法的平均准确率高达98.71%,远远高于小波变换方法的92.86%。

In view of the large amount of data and low accuracy rate of the existing power quality disturbance signal recognition methods, a new recognition method of power quality disturbance signals is proposed by extracting the features from compressed sensing sparse vectors. In this method, the original signals are sampled to obtain the measurement signals based on the theory of compressive sensing, and the sparse vectors are obtained by the orthogonal matching pursuit algorithm of ？1-minimization. Then the features of the maximum, the second maximum, root mean square, standard deviation, kurtosis and margin factor are extracted as the inputs of the neural networks, and the power quality disturbance signal recognitions are realized. According to 6 kinds of typical power quality disturbance signals, the simulation experiments are conducted. The simulation results show that the data size of the proposed recognition method for feature extraction is greatly reduced with only 30 instead of 1024 for the existing methods. As a result of the realization of a very small amount of data to retain the original all useful feature information, the proposed method is more promising to improve the recognition accuracy. Compared with the widely-used wavelet transform recognition method, the average accuracy rate of this proposed method is as high as 98.71%, which is much higher than 92.86% of the wavelet transform method.