Power Quality Disturbances Recognition Using Modified S Transform and Parallel Stack Sparse Auto-encoder

摘要

The effective automatic recognition and classification of power quality (PQ) disturbance is of significance to the control of power grid pollution before any reasonable solution is taken. In this paper, a novel method to PQ disturbances recognition is proposed based on the modified S transform (MST) and parallel stacked sparse auto encoder (PSSAE). A Kaiser window is used in MST for a better energy concentration in time-frequency matrix. Thereafter, not only the time-frequency matrix but also the Fourier transform spectrum is utilized to automatically extract features, as input of the two sub-model in PSSAE. Furthermore, the dimensionality reduction and visual analysis of features are achieved as an example. The recognition of PQ disturbances is then identified with the softmax classifier. The effectiveness and robustness of the proposed algorithm is validated by conducting a series of experiments with different types of single and combined signals.