Search

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution. [ABSTRACT FROM AUTHOR]

It is difficult to detect the residual current of specific fault types in low-voltage distribution networks, which results in few labeled residual current samples. Thus, it is difficult to recognize the fault types of residual current. To solve this problem, a cooperative training classification model based on an improved squirrel search algorithm (ISSA) for a semi-supervised support vector machine (S3VM) and the k-nearest neighbor (KNN) is proposed (ISSA-S3VM–KNN). First, the residual current is decomposed into k intrinsic mode functions (IMFs) by variational mode decomposition (VMD), and the characteristic parameters of the IMFs are extracted to obtain a characteristic dataset for establishing a classification model. Second, to solve the problem where it is difficult to the select parameters (such as the penalty factors, slack variables and kernel function) of a S3VM, an ISSA parameter optimization method is proposed to self-adaptively select the optimal combination of parameters for the S3VM. Finally, the KNN is used to verify the classification results of an ISSA-S3VM through cooperative training, which further improves the classification accuracy of the S3VM for unlabeled residual current samples. Classification results of measured and simulation data show that the classification accuracy of the ISSA-S3VM–KNN is higher than that of the SVM–BPNN, WE–AE–BPNN, and PSO–SVM. The ISSA-S3VM–KNN provides a certain theoretical basis for achieving fast and accurate residual current fault type recognition. [ABSTRACT FROM AUTHOR]

Given the low accuracy of power quality disturbance (PQD) detection, a PQD detection method based on the wavelet packet transform (WPT) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed in this paper. First, the wavelet packet transform is used to preprocess the signal to suppress noise interference. Then, ICEEMDAN technology is adopted to calculate the local mean value by adding adaptive noise. In addition, different intrinsic mode functions (IMFs) are obtained through residual subtraction. Furthermore, the effective IMFs are calculated by the permutation entropy method to reduce false modal components and to suppress residual noise. Finally, a Hilbert transform (HT) is performed to extract the detection signal parameters. The obtained results demonstrate that this method can improve the detection accuracy and PQD speed, which results in a strong anti-noise capability. [ABSTRACT FROM AUTHOR]