混合遗传算法在电力参数测量中的应用

现有电力参数测量方法往往只针对一个误差因素,当系统采样数据同时受多个误差影响时,难以得到准确结果.针对这一问题,建立了电力参数极值优化模型,同时对衰减直流分量、非同步采样及谐波等多个误差参数加以精确表示,利用混合遗传算法(HGA)对该模型进行求解,可得到准确的系统幅值、相位、频率及谐波等电力参数.针对普通遗传算法(GA)收敛慢和经典迭代法初始点敏感问题,HGA将GA算子与混合拟牛顿算子结合起来,由GA算子进行解空间全局搜索,混合算子进行强局部搜索,可实现无需指定初值的电力参数快速求解.仿真实验表明,该方法能有效提高参数测量的运行效率和计算精度.

Application of Hybrid Genetic Algorithm in Power System Parameter Measurement

Existing power system parameter measuring methods often aim at only one error factor,which makes it difficult to obtain accurate results when data sampled from the power system is influenced by several errors.In view of this problem,a parameter extremum optimization model is developed,in which such error factors as the decaying DC components,nonsynchronous sampling and harmonics are precisely depicted.A new measuring method based on the hybrid genetic algorithm(HGA)is developed to use this model for finding accurate power system parameters such as the amplitude,phase,frequency and harmonics.As the ordinary genetic algorithm(GA)converges slowly and the classic method is sensitive to initial guess,the HGA combines the GA operator and hybrid quasi-Newton operator together and,with the GA operator performing global searching in the solution space,the hybrid operator carrying out the strongly local searching at the same time to achieve fast calculation of the power parameters.Simulations show that the HGA method can effectively improve the efficiency and accuracy of power parameter measurement,laying a solid foundation for further monitoring and control of power systems.