|dc.description.abstract||Worldwide, many countries direct billions of dollars into the development of renewable energy sources; especially wind generation, in an effort to relieve global warming effects and other environmental concerns. As a result of increasing numbers of remotely-located large power offshore wind farms, the AC grid faces many technical challenges in integrating such plants; such as large submarine power transmission for extended distances, power sharing and transfer, as well as remotely located induction generation reactive power support. Offshore multi-terminal VSC based HVDC (MT VSC-HVDC) transmission systems represent a possible means of dealing with those challenges. This is due to their higher capacity, flexibility and controllability than offshore AC transmission. In addition, these offshore grids provide grid integration to remote offshore wind farms leading providing additional interconnection capacity to improve the trade of electricity between different AC grids.
This work presents a new centralized supervisory control strategy for controlling the power sharing and voltage regulation of MT VSC-HVDC integrating offshore wind farms. The main purpose of the proposed strategy is selecting the optimal parameters of the HVDC system VSCs' local controller. These optimal parameters are selected in order to achieve optimal system transient response and desired steady state operation.
In this work, an adaptive droop-based power-sharing control strategy is proposed. The primary objective is to control the sharing of the active power transmitted by a MT VSC-HVDC network among a number of onshore AC grids or offshore loads based on the desired percentage shares. The shared power is generated by remote generation plants (e.g., offshore wind farms) or is provided as surplus of AC grids. The desired percentage shares of active power are optimized by the system operator to fulfill the active power requirements of the connected grids with respect to meeting goals such as supporting energy adequacy, increasing renewable energy penetration, and minimizing losses. The control strategy is based on two hierarchal levels: voltage-droop control as the primary controller and an optimization based secondary (supervisory) controller for selecting the optimal droop reference voltages. Based on the DC voltage transient and steady state dynamics, a methodology for choosing the droop gains for droop controlled converters has been developed.
In addition, a new tuning methodology is proposed for selecting the optimum VSCs local controller gains to enhance the transient performance and the small-signal stability of the system to mitigate the change of the operating conditions, taking into consideration the overall dynamics of the MT HVDC system. The VSCs' local control loops gains are selected to maximize the system bandwidth and improve the system damping. As a part of the proposed methodology, the derivation of the aggregated linearized state-space model of a MT VSC-HVDC based offshore transmission system is provided. Based on the derived model, a small signal stability analysis was performed to show the interaction of the modes and define the dominant eigenvalues of the system.
Furthermore, a communication-free DC voltage control strategy is presented for mitigating the effects of the power imbalance caused by permanent or temporary power-receiving converter outages. The proposed control strategy is targeted at fast power reduction of the wind power generation from the wind farms (WFs) in order to eliminate power imbalances in the HVDC network. This process is performed by decentralized control rules in the local controllers of the WF voltage source converter (VSC) and its wind turbines. The proposed strategy was designed to work with WFs based on both doubly fed induction generators (DFIGs) and permanent magnet synchronous generators (PMSGs).
The proposed control strategies were validated on the B4 CIGRE MT VSC-HVDC test system and different case scenarios were applied to show its feasibility and robustness. The validation process was performed using Matlab software programming and Matlab/Simulink based time domain detailed model.||en