Xiao Weichu’s research while affiliated with Hunan University and other places

In order to increase the accuracy ofpower quality disturbance detection in noising environment, this study puts forward a denoising algorithm ofpower quality distu?bance based on self-adapting to neutral fuzzy control. It establishes a structure with double-input and single-output neural network fuzzy system. Meanwhile, it presents the principle of denoising and makes a simulation analysis on disturbance circumstances with colored noise such as harmonic wave, voltage sag, voltage swell, voltage interruption etc. The results indicate that the proposed algorithm can well remove the signal-disturbing noise in grid. The waveform is visual clarity; the process ofanalyzing and calculating is simple and fast; the data got from calculation is few, which make the disturbance denoising ofpower quality more practical.

This study proposes a new algorithm, which makes use of S-transform modulus time-frequency matrix to detect the power quality transient disturbance. And respective mathematical models are established according to transient disturbances such as voltage sags, voltage rises, voltage interruption and transient oscillation. It calculates the modulus time-frequency matrix of the four disturbance signals by using S-transform and extracts their features. Simulation and analysis are made in Matlab. The simulation results show that the proposed algorithm can extract the features of different disturbances effectively. It provides a new option for power quality transient disturbance detection.