Feature extraction and classification of load dynamic characteristics based on lifting wavelet packet transform in power system load modeling

Abstract

Load dynamic characteristics classification and synthesis is the main approach to solve the problem of load time-variation. The basis and prerequisite of load dynamic characteristics classification is load dynamic characteristics feature extraction. Load model parameter space or the model response space gained by a standard voltage excitation is usually selected as the feature vector space in current practice of load dynamic characteristics feature extraction. However, both methods need to determine the load model structure and identify the model parameters. It would increase not only calculation error but also calculation time in the process of load model structure determination and parameter identification. Then the accuracy of the final classification results would be affected. It is reasonable and scientific to extract feature vector space of load dynamic characteristics directly from the measured response space. In this paper, a feature extraction method based on lifting wavelet packet transform is proposed for load dynamic characteristics classification. The load measured current response data is decomposed and reconstructed, then the wavelet packet coefficients can be extracted to construct energy moment based feature vector. On this basis, the load dynamic characteristics classification can be realized using fuzzy c-means (FCM) method. Finally, the validity and practicality of the proposed method have been proved by feature extraction and classification of dynamic simulation data acquired using Matlab/Simulink and field measurement data. Compared with traditional wavelet packet transform, the lifting wavelet packet transform has shown advantages both in computational speed and reconstruction error and can improve the accuracy of load dynamic characteristics classification.