Abstract
Power quality signal feature selection is an effective method to improve the accuracy and efficiency of power quality (PQ) disturbance classification. In this paper, an entropy-importance (EnI)-based random forest (RF) model for PQ feature selection and disturbance classification is proposed. Firstly, 35 kinds of signal features extracted from S-transform (ST) with random noise are used as the original input feature vector of RF classifier to recognize 15 kinds of PQ signals with six kinds of complex disturbance. During the RF training process, the classification ability of different features is quantified by EnI. Secondly, without considering the features with zero EnI, the optimal perturbation feature subset is obtained by applying the sequential forward search (SFS) method which considers the classification accuracy and feature dimension. Then, the reconstructed RF classifier is applied to identify disturbances. According to the simulation results, the classification accuracy is higher than that of other classifiers, and the feature selection effect of the new approach is better than SFS and sequential backward search (SBS) without EnI. With the same feature subset, the new method can maintain a classification accuracy above 99.7% under the condition of 30 dB or above, and the accuracy under 20 dB is 96.8%.
Highlights
Power quality (PQ) is the main control target of the smart grid, and PQ signal recognition is the foundation of PQ problem management [1]
Features extracted from the time-frequency analysis (TFA) results are always used as the input of the classifier for PQ disturbances identification
random forest (RF) combines DT with ensemble learning to form a new kind of tree classifier: t f px, δk q, k “ 1, ̈ ̈ ̈u where f px, δk q is a meta classifier, and it is a tree construct classifier that can be formed by several algorithms; x is the input vector; δk is a random vector, independent with each other but sharing the same distribution, and it determines the growth of a single decision tree
Summary
Power quality (PQ) is the main control target of the smart grid, and PQ signal recognition is the foundation of PQ problem management [1]. The feature selection process of RF takes both the statistical conclusion of the characteristics and the classification results of the classifier into consideration. It combines the virtues of the filter method and wrapper method. For the sake of finding the optimizing feature subset and increasing the classification accuracy of PQ disturbances, a new method for PQ disturbances feature selection and classification using a entropy-importance (EnI)-based RF is proposed in this paper. According to EnI score of features obtained from the RF training process, classification ability of each feature can be sorted to construct the optimal subset.
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