Power Quality Disturbance Detection Method Based on Improved Adaptive S-Transform

Aiming at the problems of poor real-time and low time-frequency resolution of the composite power quality disturbance detection algorithm, a power quality disturbance detection method based on improved adaptive S-transform (IAST) is proposed. A global adaptive Gaussian window is constructed as the kernel function of IAST, which can adaptively adjust the effective window length and spectrum of the window with the change of the detection frequency, avoiding the frequent switching of window parameters to improve time-frequency resolution and reducing algorithm complexity. Window parameters are selected with the objective of enhancing signal energy concentration, ensuring accurate time-frequency positioning of various disturbances. An automatic threshold method is used to determine the main frequency point of the actual disturbance signal and performing time-frequency transformation on these points, further improving the execution efficiency of algorithm. Simulation and measurement results show that compared with existing composite power quality disturbance detection algorithms has good real-time performance and time-frequency resolution capabilities, low computational complexity, and is suitable for real-time and accurate detection of complex power quality disturbances.