Morphology Singular Entropy-Based Phase Selector Using Short Data Window for Transmission Lines

Abstract

This paper presents a new technique called morphology singular entropy (MSE), based on which a phase selector for transmission lines is developed. MSE combines mathematical morphology, singular value decomposition (SVD), and entropy theory, making it insensitive to noise and easy to extract the features of the fault-induced transients. Voltage signals are used as inputs of the proposed MSE-based phase selector. Each signal is processed by a multiscale morphological filter first, and a matrix consisting of the outputs of the filter is formed. By decomposing the matrix using SVD, the singular values are obtained, and then the entropy in association with this signal can be calculated. Afterwards, in order to improve the sensitivity and reliability of the phase selection, four classification indices derived from the entropies are defined. The phase selection is performed by comparing these four indices to a preset threshold. Simulation data generated using PSCAD/EMTDC and real-life data have been employed to verify the performance of the proposed method.

Fault classification and faulted phase selection for transmission line using morphological edge detection filter

“…In [36], a high impedance fault detection method based on mathematical morphology (MM) is presented for the distribution system. The combination of MM, SVD and entropy theory is also proposed for phase selection [37]. However, the MM filter used in these methods could only detect the amplitude of travelling waves and could not detect the polarity.…”

Fast phase selection method based on transient current for UHV transmission lines

“…Incorporating the advantages of WT, singular value decomposition (SVD) and Shannon entropy, the technique called wavelet singular entropy is demonstrated in [9], which is immune to the noise in the fault transient. Replacing WT with mathematical morphology (MM), the FPS method can perform in a short data window by combining MM with SVD and Shannon entropy [10]. These FPS schemes all need complicated calculation, which weaken their practical value.…”

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line

“…The features of short-circuit fault signals can be extracted from the time-frequency matrix of short-circuit faults. In the present study, various entropies such as Shannon entropy [ 16 ], Shannon energy entropy (EE) [ 17 ], Shannon energy spectrum entropy [ 18 ], Shannon time entropy (TE) [ 5 ], and Shannon singular entropy (SE) [ 19 ] have been used to characterize the time-frequency characteristics of short-circuit faults. When short-circuit fault signals are described by entropy, it is difficult to satisfy the requirement of different sampling rates by a uniform standard of selecting sliding window parameters.…”