Adaptive multi-objective reactive power optimization control strategy for offshore wind farms

Abstract

Aiming at the problem that traditional fixed-weight multi-objective reactive power optimization is unable to make the most suitable control decisions for real-time working conditions when dealing with the complex and changing working conditions of new power systems, an adaptive multi-objective reactive power optimization control strategy is proposed, which takes the weighted minimum of the deviation of the system active network loss and the voltage of the grid connection points as the objective function, and the weighting coefficients of the objective function are adaptively adjusted according to the deviation of the voltage of the grid connection points. The strategy takes the minimization of active network loss and the deviation of grid voltage as the objective function. Firstly, the relationship between voltage fluctuation at the grid-connected points of offshore wind farms and the active and reactive power outputs is analyzed to establish the corresponding reactive power allocation model, and the corresponding reactive power control model is established with respect to the input and output characteristics of the wind turbine and the static var generator (SVG). In addition, considering the power constraints and safe operation constraints of offshore operation, the variable inertia weight particle swarm optimization algorithm is used to solve the reactive power control strategy. Finally, the offshore wind farm model is built in MATLAB for simulation verification, and the simulation example shows that, compared with the traditional fixed-weight multi-objective reactive power optimization, the adaptive multi-objective reactive power optimization control strategy can quickly adjust the priority of each optimization objective according to the real-time working conditions of the grid, which can achieve the coordinated optimization of the active network loss and grid-connected point voltage.

Keywords

• offshore wind farm
• active voltage support
• multi-objective adaptive
• active network losses
• voltage deviation
• reactive power optimization
• particle swarm algorithm