Transient characteristics and adaptive fault ride through control strategy of DFIGs considering voltage phase angle jump

Wind power in China has experienced fast development in recent years. However, areas rich in wind power resources are often far away from loads centers,which leads to weak connection between wind turbines and power grid. When a grid fault occurs, new transient characteristics in weak grid integrated with doubly-fed induction generators(DFIGs) may present, such as voltage phase angle jump. Current control strategies for wind turbine with strong grid connection are hard to be adapted under weak gird connection. This paper explores the transient characteristics of DFIGs under voltage phase angle jump through analyzing the operation and control characteristics of DFIGs connected into weak grid when the voltage phase angle jumps. Fault ride through(FRT) control strategy of DFIGs based on adaptive phase-locked loop is proposed to adapt weak grid condition. The reference frame of the proposed strategy will be changed in real-time to track the operation condition of DFIGs according to the terminal voltage, and different phase tracking method is adopted during the grid fault. Field data analysis and time domain simulation are carried out. The results show that voltage phase angle jumps when a grid fault occurs, which weakens the FRT capability of DFIGs, and the proposed FRT control strategy can optimize transient characteristics of DFIGs, and improve the FRT capability of DFIGs.

MSSSA:a multi-strategy enhanced sparrow search algorithm for global optimization

The sparrow search algorithm(SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However, SSA still faces challenges of premature convergence and imbalance between exploration and exploitation, especially when tackling multimodal optimization problems. Aiming to deal with the above problems, we propose an enhanced variant of SSA called the multi-strategy enhanced sparrow search algorithm(MSSSA) in this paper. First, a chaotic map is introduced to obtain a high-quality initial population for SSA, and the opposition-based learning strategy is employed to increase the population diversity. Then, an adaptive parameter control strategy is designed to accommodate an adequate balance between exploration and exploitation. Finally, a hybrid disturbance mechanism is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA, a large number of experiments are implemented, including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering optimization problems. The results demonstrate the superiority of the MSSSA in addressing practical problems.