With the large-scale wind power integration into the power system, the inertia and damping characteristics of the system have been weakened, which is not conducive to fulfill the grid-friendly operation of wind power. Aiming at this, an adaptive control scheme combining virtual inertia control (VIC) and supplementary damping controller (SDC) is proposed in this paper. Firstly, the mathematical model of wind grid-connected power system with VIC is established. Secondly, the polytopic linear parameter varying (LPV) system is built to describe the uncertainty of system operating points, and the gap metric-based nonlinear evaluation method is used for polytope vertex configuration. Next, Youla-Kucera parametrization are extended to polytopic LPV system to design an adaptive damping control, in which VIC and SDC are coordinated to provide damping and virtual inertia simultaneously. Finally, the proposed adaptive control scheme is compared with several existing control techniques to validate its effectiveness and advantages in suppressing system oscillation in the modified IEEE 39-bus system integrated with 2 wind turbines.

In contrast to the traditional high-voltage direct current (HVDC) based offshore wind farms, series-connected offshore wind farms do not require transformers, voltage source converters (VSCs), and offshore platforms, thereby significantly decreasing costs. In this study, the onshore converter, which is based on the half bridge (HB) modular multilevel converter (MMC) and full bridge (FB) MMC, is proposed to reduce the costs and power loss of the series-connected offshore windfarm. In this type of onshore converter, the DC voltage of the HB-MMC is controlled to be the rated DC voltage, while the DC voltage of FB-MMC can vary from negative rated DC voltage to positive rated DC voltage. With the variable DC voltage of the FB-MMC, the DC link current of the series-connected offshore windfarm can be controlled at will. Moreover, the DC link current control strategies is proposed to reduce the power loss of the offshore windfarm. The capacitor voltage balance control, which is suitable for FB-MMC with variable DC voltage, is proposed to ensure the normal operation of the onshore converter. Finally, simulation and experimental results verify the feasibility of the proposed onshore converter and the corresponding control algorithm.

Commutation failure (CF) in the line-commutated converter based high-voltage direct-current system will result in a large amount of reactive power surplus on the rectifier ac system. It will cause the transient overvoltage and lead to the massive disconnection of the renewable sources. In this article, the mechanism of the transient overvoltage caused by CF is analyzed and the suppression strategy based on improved voltage-dependent current order limiter (VDCOL) control is proposed. First, during the process of CF, the dynamic behavior of reactive power consumption of the rectifier is analyzed. It is proposed that when the inverter returns to normal commutation, the sudden increase of the inverter dc voltage will cause the decreasing of the dc current, which will result in the large decreasing of reactive power consumption of the rectifier. Then, the dynamic change process of the rectifier ac voltage is deduced. Second, the peak value of transient overvoltage as affected by the occurrence and removal time of the short-circuit fault is deduced and analyzed. Then, by improving the operating characteristic of the VDCOL control, the control strategy of overvoltage suppression is proposed. Based on the PSCAD platform's CIGRE-HVDC benchmark model, the effectiveness of the proposed strategy is verified by the simulation results.

In order to reduce the dimension of wind farm model and improve the simulation efficiency under the premise of ensuring the accuracy, this article proposes a dynamic equivalent method for permanent magnet synchronous generator (PMSG) based wind farm cluster utilizing the adaptive extended Kalman filters. Combining the dynamic model of PMSG, the equivalent wind turbine parameters that need to be identified are incorporated into the extended state variables, and an online parameters identification method based on extended Kalman filter (EKF) theory is put forward. To eliminate the influence of random noise on the accuracy of identification, the adaptive technology is introduced into EKF to estimate the process and measurement noise covariance in real time through the Sage–Husa estimator. Thus, a wind farm cluster equivalent model is obtained. Finally, the effectiveness and robustness of the proposed method are verified by time-domain simulation and frequency-domain simulation in IEEE 39-bus system.