Fault-Tolerant Three-Vector Model-Predictive-Control-Based Grid-Connected Control Strategy for Offshore Wind Farms

Author:

Wu Jiahui1,Li Jiangyong1,Wang Haiyun1,Li Guodong2,Ru Yalun1

Affiliation:

1. State Centre for Engineering Research, Ministry of Education for Renewable Energy Generation and Grid-Connected Control (Xinjiang University), Urumqi 830047, China

2. State Grid Xinjiang Integrated Energy Service Company Limited, Urumqi 830011, China

Abstract

In the conventional dual-loop vector control strategy of Voltage Source Converter-based High Voltage Direct Current (VSC-HVDC) systems employed in offshore wind farms, challenges such as complex PI parameter-tuning and slow response speed exist. Furthermore, a single-phase bridge-arm fault in the converter station can lead to a change in system parameters, resulting in the failure of the original control strategy. Hence, this paper proposes a fault-tolerant control strategy for grid-connected offshore wind farms, based on model predictive control (MPC). Firstly, the predictive models for both normal and fault-tolerant states of the grid-side converter station are established based on the system structure of the grid-side converter station and a super-local model. Subsequently, a cost function is constructed using the power error, with the optimization objective set as the value function. This approach allows for accurate prediction of the future switching states of the grid-tied inverter to track the reference power. Finally, a simulation model of the offshore wind power grid system is established in the MATLAB/Simulink (2022a) environment. The results demonstrate that the grid-side converter station can effectively operate in a fault-tolerant manner under the proposed control strategy, thereby enhancing the disturbance resistance and fault-recovery capabilities of the offshore wind VSC-HVDC system.

Funder

Key Laboratory in Xinjiang Uygur Autonomous Region of China

National Natural Science Foundation of China

Key Research and Development Project of Xinjiang Uygur Autonomous Region

Publisher

MDPI AG

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