可变遗忘因子递推最小二乘法对时变参数测量

针对传统的递推最小二乘法对于非平稳环境下的突变和时变信号的跟踪能力不够,常常无法检测到信号特征参数的问题,提出了在指数加权递推最小二乘法中引入可变的加权遗忘因子λ,对电力系统时变信号的幅值、相位、频率进行测量的方法。加权λ对算法的收敛速度和跟踪能力有很大影响,如能很好的调节λ,既可确保对时变参数的快速跟踪能力,又能具备小的参数估计误差。仿真结果表明:与传统的递推最小二乘法相比,该方法测量精度和收敛速度更优越,即使在低信噪比环境下,也能较精确的测出时变参数值。

Time-varying Parameters Measurement by Least Square Method with Variable Forgetting Factors

Least Square Method(LSM)algorithm has weak tracking performance in the non-stationary environment,and it can not measure the time-varying parameters generally.With increasing in using the nonlinear loads' in the power system,the waves of voltage or current are time-varying,and the amplitude,phase,frequency changes with the time,these changes will cause the measurement error.In this paper,a method for measuring time-varying parameters(amplitude,phase,frequency)of power system is proposed.The forgetting factors impact the rate of convergence and the tracking performance,so the improved forgetting factors are introduced to the RLS algorithm,making sure the fast tracking and low parametric error variance properties,and improving the stabilization of algorithm,the self-adaptable sample interval,and the precision of measured frequency.Two instances are simulated:(1)the amplitude,phase change and the frequency changes;(2)the low signal-to-noise environment.The simulation results show that the proposed method is superior in the precision and the rate of convergence,even in the low signal-to-noise environment,the proposed method also can measure the time-varying parameters precisely.