一种基于智能改进算法的定制电力设备优化配置策略

为了更好地治理配电网谐波污染、低功率因数以及电力系统电压波动所带来的敏感负荷不能正常工作问题，研究了配电网多种定制电力设备的优化配置。综合考虑电能质量治理目标以及投资费用建立了优化配置数学模型，并提出一种将遗传算法与内点法有机结合的混合优化策略用于多种定制电力设备的优化配置。该混合策略利用遗传算法锁定各装置的最优安装位置并求得近似最优安装容量，将近似容量设为内点法初值寻找更加精确更优的安装容量。此外，分别基于约束越限和预判自适应对遗传算法和内点法做出改进，提高了寻优速度。理论分析和仿真结果表明，该混合算法比单独的遗传算法稳定性更高、寻优结果更加精确。

Optimal configuration strategy of custom power devices based on intelligent improved algorithm

In order to solve power quality problems more effectively such as harmonic pollution, low power factor as well as the sensitive loads not to work properly because of voltage sags, flickers and so on. The optimal configuration of custom power devices is studied. By considering power quality improving goals and total investment cost, a mathematical model is proposed for optimization. Besides, a mixed optimization algorithm that combines genetic algorithm and interior-point method is proposed for the optimal configuration of multi-type custom power devices. This mixed strategy uses genetic algorithm to find the optimal location and approximate optimal capacity of each device. Then the approximate capacity will be set as the initial value of the interior-point method, and more accurate capacity can be calculated through interior-point method. Besides, some improvements are made in the process of genetic algorithm and interior-point method. These improvements can speed up the convergence. Theoretical analysis and simulation results indicate that this mixed algorithm has better stability, and more accurate results can be obtained than through traditional genetic algorithm. This work is supported by National Natural Science Foundation of China (No. 51477184 and No. 61271001) and Fundamental Research Funds for the Central Universities (No. 14CX02085A).