State-of-the-art review on frequency response of wind power plants in power systems

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 surveyon 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.

Data-driven Optimal Control Strategy for Virtual Synchronous Generator via Deep Reinforcement Learning Approach

This paper aims at developing a data-driven optimal control strategy for virtual synchronous generator (VSG) in the scenario where no expert knowledge or requirement for system model is available. Firstly, the optimal and adaptive control problem for VSG is transformed into a reinforcement learning task. Specifically, the control variables, i.e., virtual inertia and damping factor, are defined as the actions. Meanwhile, the active power output, angular frequency and its derivative are considered as the observations. Moreover, the reward mechanism is designed based on three preset characteristic functions to quantify the control targets: ① maintaining the deviation of angular frequency within special limits;② preserving well-damped oscillations for both the angular frequency and active power output;③ obtaining slow frequency drop in the transient process. Next, to maximize the cumulative rewards, a decentralized deep policy gradient algorithm, which features model-free and faster convergence, is developed and employed to find the optimal control policy. With this effort, a data-driven adaptive VSG controller can be obtained. By using the proposed controller, the inverter-based distributed generator can adaptively adjust its control variables based on current observations to fulfill the expected targets in model-free fashion. Finally, simulation results validate the feasibility and effectiveness of the proposed approach.

Electro-mechanical Modeling of Wind Turbine and Energy Storage Systems with Enhanced Inertial Response

In this paper,a coordinated control scheme for wind turbine generator(WTG)and supercapacitor energy storage system(ESS)is proposed for temporary frequency supports.Inertial control is designed by using generator torque limit considering the security of WTG system,while ESS releases its energy to compensate the sudden active power deficit during the recovery process of turbine rotor.WTG is modeled using the fatigue,aerodynamic,structure,turbulence(FAST)code,which identifies the mechanical loadings of the turbine and addresses electro-mechanical interactions in the wind energy system.A damping controller is augmented to the inertial control to suppress severe mechanical oscillations in the shaft and tower of the turbine during frequency supports.Furthermore,the result of small-signal stability analysis shows that the WTGESS tends to improve the stability of the whole multi-energy power grid.The major contributions of this paper will be validated by utilizing the proposed control method that combines the grid support capability and maintaining the integrity of structural design of the turbine for normal operations.