基于加权果蝇优化算法的多区域频率协同控制

随着大规模可再生能源的开发和应用,电网变得越来越庞大且复杂,如何保证大量不同控制器之间的协调是最值得关注的问题之一。利用微分博弈理论可以解决协同控制的问题。然而,传统算法难以求解带约束的微分博弈问题。此外,现有研究建立的仿真模型几乎是线性的,不利于实际工程应用。针对上述问题,提出了一种基于加权果蝇优化算法(Weighting Fruit Fly Optimization Algorithm,WFOA)的协同进化算法来求解具有非线性约束的多区域频率协同控制模型。仿真结果表明,与协同进化遗传算法和协同多目标粒子群优化算法相比,该方法具有更好的控制效率,同时对系统出现的外部扰动变化及内部机组参数变动具有很好的鲁棒性。

Load frequency control of a multi-area power system based on weighting fruit fly optimization algorithm

With the development and application of large-scale renewable energy sources, the electric power grid is becoming ever larger and more complicated. One of the most concerning problems is how to ensure coordination between a large number of varied controllers. Differential games theory is used to solve the problem of collaborative control. However, it is difficult to solve the differential game problem with constraints using the traditional algorithm. Furthermore, simulation models established by existing research are almost linear, which is not conducive to practical engineering application. To solve the above problem, this paper proposes a co-evolutionary algorithm based on the Weighted Fruit Fly Optimization Algorithm (WFOA) to solve a multi-area frequency collaborative control model with nonlinear constraints. Simulation results show that compared with a co-evolutionary genetic algorithm and a collaborative multi-objective particle swarm optimization algorithm, the method exhibits better control efficiency and better robustness to the changes in external disturbance and the internal unit parameters of systems. This work is supported by National Natural Science Foundation of China (No. 51867005).