基于卡尔曼滤波和深度置信网络的复合电能质量扰动分类

Recognition of multiple power quality disturbances based on a Kalman filter and deep belief network

针对实际电能质量扰动数据大、识别多重扰动精度不高的问题,提出了一种基于自适应最大似然卡尔曼滤波和深度置信网络相结合的电能质量扰动识别方法。首先,该方法使用自适应最大似然卡尔曼滤波对含有噪声的原始扰动信号进行去噪。然后,通过深度置信网络对去除噪声的扰动信号进行训练、分类,以此实现电能质量扰动类型的识别。最后,在20类不同噪声水平下的电能质量扰动信号上进行测试。由仿真结果可知,在不同的噪声水平下,该方法都具有较高的分类正确率,表明了该方法的有效性及对噪声的强鲁棒性。

In order to solve the problems of large actual power quality disturbance data and low accuracy in identifying multiple power quality disturbances, a method of power quality disturbance recognition based on an adaptive Kalman filter based on maximum likelihood(KF-ML) and a deep belief network is proposed. First, the adaptive Kalman filter based on maximum likelihood is used to denoise the original disturbance signal with noise. Then the deep belief network is used to train and recognize the clean signal so that the type of power quality disturbance can be recognized. Finally, it is tested on power quality disturbance signals under 20 types of different noise levels. From the simulation results, it can be seen that the method in this paper has a higher classification accuracy rate under different noise levels. This shows the effectiveness of the method and is very robust to noise.