With an increasing penetration of wind power in the modern electrical grid, the increasing replacement of large conventional synchronous generators by wind power plants will potentially result in deteriorated frequency regulation performance due to the reduced system inertia and primary frequency response. A series of challenging issues arise from the aspects of power system planning, operation, control and protection. Therefore, it is valuable to develop variable speed wind turbines (VSWTs) equipped with frequency regulation capabilities that allow them to effectively participate in addressing severe frequency contingencies. This paper provides a comprehensive survey on frequency regulation methods for VSWTs. It fully describes the concepts, principles and control strategies of prevailing frequency controls of VSWTs, including future development trends. It concludes with a performance comparison of frequency regulation by the four main types of wind power plants.

This paper presents a novel simulation approach to evaluate new ancillary service controls in the context of large-scale wind power integration. We compare different types of turbine inertial control methods with the proposed modifications to cope with realistic wind conditions in the field. The simulation procedure is started with the software-based simulation, in which we employ the high-fidelity wind turbine simulator FAST that models a real 3-bladed Controls Advanced Research Turbine (CART3). The advantages of using FAST is that it can provide convincing simulation results and address the interactions between turbine electrical and mechanical systems. The developed controller model is then rapidly prototyped for the real-time simulation with the hardware-in-the-loop (HIL) scheme. The CART3 will respond to a virtual frequency event triggered in the emulated electric grid modelled in a digital real-time simulator (DRTS). The introduced simulation platform streamlines the procedure of designing turbine auxiliary controls, and these simulations results give insights on the turbine controls and their impacts on the interconnected power system, as well as the effects on turbine mechanical components. The results indicate that inertial controls tend to reduce the out-of-plane mechanical loadings in region 2, while such loadings are dominated by pitch actions in region 3.

Capacitors and semiconductor power devices are the most fragile components in power electronic systems. Hence, designing a comprehensive method for online switch fault diagnosis (FD) and capacitor condition monitoring (CM) is conducive to guaranteeing the reliability of the three-level T-type (3L-T) inverter in an all-around way. First, based on modeling the 3L-T inverter on the dc link and ac side, the inherent harmonic components of the dc link under normal and open-circuit fault conditions are analyzed. Furthermore, the capacitor current reconstruction method is designed. Subsequently, a comprehensive FD and CM framework is proposed, which consists of signal injection, extra hardware, 3L-T model, FD, and CM submodules. In FD, the detection variable, which is the function of output current and its changing rate, is designed, and the fault location is carried out based on the phase current residual and the sum of the neutral-point voltage residual. The capacitance estimation method is introduced based on the input current reconstruction technology and recursive least squares algorithm using the inherent or injected ac component on the dc link. Finally, experimental results demonstrate that the proposed comprehensive method merits high estimation accuracy, fast dynamic response, and better FD performance under various conditions.

Time-series prediction technology plays a significant role in evaluating the health status of power transformers and forecasting inchoate operation failure. This study presents a variational mode decomposition (VMD) model with the application of autoregressive integrated moving average (ARIMA) to develop a prediction technology for the DGA time series. Additionally, the two-step stationary test method regulates the nonstationary of the time series. The VMD decomposes the dissolved gas analysis (DGA) time series into sub-components to reduce the nonstationary, where the boost marine predators algorithm (BMPA) optimizes the parameters K and α of the VMD to retain the useful information of the original signal. The ARIMA predicts the future result for each sub-components, and the ARIMA parameter is optimized by the BMPA for optimal solutions of the Bayesian information criterion (BIC). The performance of the proposed approach is measured by predicting the health status of the diurnal new values of the DGA of a power transformer. The experiment result shows that the proposed model exhibits high efficacy in predicting the DGA time series and obtains the health status of the power transformer.