Juan Wei

To address the challenges of rapid voltage fluctuations and operational economy in new power systems with high wind power integration under sandstorm weather, this paper proposes a multi-timescale cooperative voltage control strategy for new power systems. On the second-level timescale, a discrete state-space model of wind turbines and reactive power devices is established considering sudden wind speed changes. Model predictive control (MPC) is then used to rapidly calculate the optimal reactive power references for wind turbines, static var generator (SVG), and on-load tap changer (OLTC), thereby effectively ensuring rapid stabilization of the grid-connection point voltage. On the minute-level timescale, a two-stage topology reconfiguration method is adopted. A feasible radial network is first generated through a sequential switch opening strategy, followed by iterative optimization via a switch exchange strategy. This approach rapidly identifies the optimal switch configuration to minimize network losses and improve operational economy. Simulation results demonstrate the effectiveness of the proposed strategy in voltage regulation and loss reduction, highlighting its capability to enhance the robustness of new power systems under sandstorm weather.

In response to the severe challenges posed by extreme sandstorm weather to the operational safety of WTs and grid stability, this paper proposes an MPC-based power optimization control strategy for WFs. Simulation results indicate that, compared with the traditional PD strategy, the proposed MPC strategy significantly reduces the active power fluctuations of individual WTs, smoothly tracks grid dispatch orders with an overall power tracking accuracy improvement, and effectively lowers the operational risk index of turbines across the farm (ranging from 6.90% to 57.14% for the ten evaluated turbines). Furthermore, the proposed strategy substantially mitigates the angular acceleration fluctuation amplitude of the drive train components (e.g., reducing peak angular accelerations of drive-train masses by up to 35%) and reduces the fore-aft and lateral displacement oscillations of the tower top (reducing peak displacement variations by approximately 25% and 40%, respectively), providing comprehensive structural load mitigation while ensuring WF power output stability and grid safety. This study provides a theoretical basis and technical approach for the intelligent operation and risk prevention and control of WFs under extreme meteorological conditions.

As wind power integration expands, stochastic output fluctuations increasingly affect grid dispatch tracking and voltage stability. To address these challenges, this paper quantifies the active-reactive power capability boundary of doubly fed induction generator (DFIG)-based wind turbine (WT) and develops a hierarchical coordinated framework for dispatch tracking and operational security. First, a DFIG-based WT output boundary model is established by incorporating WT losses, converter capacity, and voltage constraints to characterize the asymmetric feasible region. Based on this model, the upper layer employs model predictive control algorithm to optimize reactive power allocation, aiming to reduce voltage deviations and operating losses, and to determine the energy storage system (ESS) participation mode according to the available active power capacity. The optimized reactive power references, active power limits, and ESS mode signal obtained from the upper layer are passed to the lower layer as boundary constraints and mode-dependent inputs for WT-ESS active-power coordination. In the lower layer, the alternating direction method of multipliers is adopted to coordinate the WT and ESS active power outputs, thereby improving the automatic generation control (AGC) power tracking, smoothing high-frequency power fluctuations, and mitigating fatigue loads. MATLAB/Simulink case studies verify the effectiveness of the proposed method in power secure dispatch and voltage fluctuation suppression.

As wind power integration expands, stochastic output fluctuations increasingly affect grid dispatch tracking and voltage stability. To address these challenges, this paper quantifies the active-reactive power capability boundary of doubly fed induction generator (DFIG)-based wind turbine (WT) and develops a hierarchical coordinated framework for dispatch tracking and operational security. First, a DFIG-based WT output boundary model is established by incorporating WT losses, converter capacity, and voltage constraints to characterize the asymmetric feasible region. Based on this model, the upper layer employs model predictive control algorithm to optimize reactive power allocation, aiming to reduce voltage deviations and operating losses, and to determine the energy storage system (ESS) participation mode according to the available active power capacity. The optimized reactive power references, active power limits, and ESS mode signal obtained from the upper layer are passed to the lower layer as boundary constraints and mode-dependent inputs for WT-ESS active-power coordination. In the lower layer, the alternating direction method of multipliers is adopted to coordinate the WT and ESS active power outputs, thereby improving the automatic generation control (AGC) power tracking, smoothing high-frequency power fluctuations, and mitigating fatigue loads. MATLAB/Simulink case studies verify the effectiveness of the proposed method in power secure dispatch and voltage fluctuation suppression.

Floating offshore wind farms are characterized by long offshore distances and poor transportation accessibility, which result in higher failure loss cost under fault conditions in existing radial networks. Therefore, it is urgent to propose a more reliable ring topology planning scheme for the collection system. To solve the problem, a two-stage joint planning method (TJPM) is proposed for floating offshore wind farm collection system to enhance the operational reliability and reduce the full life cycle cost. In the first stage, the offshore substation location is optimized by the covariance matrix adaptation evolution strategy (CMA-ES), which performs an adaptive search by continuously and dynamically adjusting its probability distribution parameters. The optimal offshore substation location obtained in the first stage can be jointed as the constraints of the relationship optimization between wind turbines (WTs) and offshore substation areas in the second stage. In the second stage, the relationship is optimized based on a tabu search algorithm, which is customized by integrating neighborhood operations such as reallocation and swapping on key WTs. The continuous Hopfield neural network is used for cable topology planning by linearizing the full life cycle cost of floating offshore wind farms, thereby improving the computational speed in the whole topology process of the collection system. Case studies demonstrate the superiority of the proposed joint planning method for floating offshore wind farms in operational reliability and economic performance.

Floating offshore wind farms are characterized by long offshore distances and poor transportation accessibility, which result in higher failure loss cost under fault conditions in existing radial networks. To solve the problem, a two-stage joint planning method (TJPM) is proposed for floating offshore wind farm collection system to enhance the operational reliability and reduce the full life cycle cost. In the first stage, the offshore substation location is optimized by the covariance matrix adaptation evolution strategy (CMA-ES), which performs an adaptive search by continuously and dynamically adjusting its probability distribution parameters. The optimal offshore substation location obtained in the first stage can be jointed as the constraints of the relationship optimization between WTs and offshore substation areas in the second stage. In the second stage, the relationship is optimized based on a tabu search algorithm, which is customized by integrating neighborhood operations such as reallocation and swapping on key WTs. The continuous Hopfield neural network is used for cable topology planning by linearizing the full life cycle cost of floating offshore wind farms, thereby improving the computational speed in the whole topology process of the collection system. Case studies demonstrate its superiority in operational reliability and economic performance.

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