Abstract
In the existing research of power quality disturbance (PQD) identification, the efficiency of signal processing is low and cannot meet the needs of practical application analysis. Furthermore, due to the lack of effective analysis of features, the complexity of classifiers is increased, and the efficiency of classification reduced by the redundant features. In this paper, in order to overcome these shortcomings, a PQD recognition method based on image enhancement techniques and feature importance analysis is proposed. First, PQD signals are converted into gray images, and three image enhancement techniques include gamma correction, edge detection, and peaks and valley detection are used to enhance the disturbance features. Then, the disturbance features are extracted from the binary images, and the original feature set is constructed, the classification ability of each feature is measured by Gini importance. Based on the descending order of the Gini importance, the sequence forward search (SFS) method is used for feature selection to determine the optimal feature subset. Finally, random forest (RF) classifier is constructed by the optimal feature subset to identify the PQD signals. The results of the simulation and contrast experiments show that the new method can determine the optimal classification subset, which recognizes the PQD signals effectively in different noise environments. Furthermore, the new method has higher signal processing efficiency compared with the EMD and ST methods.
Highlights
The matter of the power quality disturbance (PQD) is more serious because of the application of power electronic devices, nonlinear loads and solid-state switching devices in the power system
In order to extract the effective features that can reflect the characteristics of PQD signals, the disturbance signals are transformed into gray images
In this paper, in order to improve the efficiency of signal processing in PQD recognition and remove redundant features from the original feature set, a new PQD recognition method based on image enhancement techniques and feature selection is proposed
Summary
The matter of the PQD is more serious because of the application of power electronic devices, nonlinear loads and solid-state switching devices in the power system. In literature [12], the power quality signal in the form of original time series are converted into gray image, and the disturbance features are enhanced through different image enhancement techniques. In literature [12] and [13], the methods of gamma correction, edge detection and peaks and valley detection are used for different classes of disturbances to enhance the gray image features On this basis, morphological features are extracted from the binary images for disturbance analysis. It is of great practical significance to design a feature selection method for analyzing feature combination classification ability and optimization efficiency. The result of experiment shows that at the time that the number of features is large, the feature selection method based on Gini index analysis can achieve the best classification effect. The optimal feature subset obtained by feature selection is used to reconstruct the RF classifier, and the optimal RF classifier is used to classify PQ signals
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