This article proposes a fast adaptive S-transform (FAST) to improve the time-frequency resolution and computational efficiency of power quality disturbances (PQDs) feature extraction. By directly controlling the standard deviation instead of other parameters, FAST can reduce the difficulty of optimizing time-frequency resolution. Based on the frequency spectrum of PQD signals, FAST only needs to calculate characteristic frequency points determined by maximum envelope curve, which can eliminate redundant calculation without losing effective feature information. In fact, the computational complexity of parameter optimization step is often higher than that of S-transform (ST) calculation step. To address this problem, a window matching spectrum (WMS) method is proposed to optimize the time-frequency resolution. Matching the effective window width with the main spectrum energy interval of signals, WMS determines the standard deviation without iterative calculation. Based on the time-frequency representation of FAST, four features are extracted as the feature vectors and applied to the support vector machine, probabilistic neural network, extreme learning machine (ELM), convolutional neural network, decision tree (DT-C4.5) and random forest classifiers. Classification results of the six classifiers show that FAST has better time-frequency resolution and lower computational complexity than that of generalized S-transform and ST. In addition, the FAST-ELM method has stronger noise immunity and better performance than other combination methods with the simulation signals and experimental signals.

The scarcity of inertial resources in the new AC–DC hybrid grids makes the grid frequency prone to fluctuation. In this paper, the relationship between the grid-side and load-side characteristics of the power router is constructed. By adjusting the port load parameters, the load power can respond quickly to the fluctuation of the grid frequency, thereby realizing rapid support of the grid frequency. Firstly, two kinds of mathematical models for sensitivity identification of load characteristics, variable voltage and variable frequency, are established to calculate the characteristic parameters of a multi-port load. The allocation rules of port power and allocation coefficients are designed according to the parameters. A frequency modulation control method that matches the load response capability of the multi-port router is proposed. Then, taking into consideration the uncertainty of load group characteristics and power, a variable coefficient frequency modulation control strategy for a multi-port power router that can adapt to the adjustable margin of loads is proposed. The proposed model is built based on a Simulink platform for validation. The simulation results show that the proposed frequency modulation strategy can be added, and the frequency modulation performance of the power grid is further improved compared to the situation without this method. The frequency is suppressed to 49.93 HZ. It is verified that this method can make the controllable load respond sensitively and effectively to grid disturbance.