Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection

With the growth of nonlinear electrical equipment, power quality disturbances (PQDs) often appear in electrical systems. To solve this, a practical heuristic methodology for PQD detection and classification based on empirical wavelet transform has been proposed. By using a multiresolution analysis tool, empirical wavelet transform, the voltage waveform signal is decomposed into several sub-signals, and some potential features are extracted in the statistical method. To reduce the feature vector dimensions, the ReliefF algorithm is used for feature selection and optimized for dimensionality reduction, which reduces the complexity of system calculation while ensuring accuracy. Finally, a classifier based on support vector machines (SVM) was built, and with the ranked feature vectors’ input, the PQD can be recognized. The experimental results verify that the classification results achieved high accuracy, which confirms the properties and robustness of the proposed approach in noisy environments.

Classification and Identification of Power Quality Disturbances Based on Stochastic Forest Model

With the emergence and use of a large number of new power electronic equipment, the power supply department has begun to pay extensive attention to the problem of power quality, and the majority of users have put forward higher requirements for the quality of power supply. This paper studies the classification and identification method of power quality disturbances based on random forest model. In this paper, according to IEEE power quality standard, the normal waveform and 16 common power quality disturbance waveforms are mathematically modeled, and the power quality disturbance signal is analyzed by S-transform. In this paper, the power quality disturbance identification algorithm based on random forest is optimized. Experimental data show that the optimized method has higher disturbance recognition accuracy and better anti noise ability. Therefore, using the power quality disturbance identification method proposed in this paper to monitor the power quality of power grid is of great significance to ensure the safe and stable operation of power grid and improve economic benefits.

Power Quality Disturbance Identification and Optimization Based on Machine Learning

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.