基于增强复对数双曲余弦算法的分布式电力系统频率估计

Distributed Enhanced Complex Logarithmic Hyperbolic Cosine Algorithm for the Frequency Estimation of Power Systems

近年来,自适应算法在电力系统频率估计中已经得到了广泛的应用。不少学者尝试将经典的自适应滤波理论应用于频率估计中,都取得了不错的效果。然而在各种复杂的电力系统运行条件下,传统的自适应算法的频率估计性能存在恶化的问题。因此为了提高算法的频率估计性能,本文结合扩散型拓扑网络,基于lncosh代价函数,提出了分布式复对数双曲余弦(DClncosh)算法和分布式增强复对数双曲余弦(DAClncosh)算法。然后在MATLAB仿真中,模拟了电力系统不平衡条件、高斯噪声环境、非高斯噪声环境和使用实测数据下算法的频率估计性能,并与分布式复数最小均方(DCLMS)算法和分布式增强复数最小均方(DACLMS)算法进行了对比,仿真结果验证了分布式增强复对数双曲余弦算法的频率估计性能的优越性。

In recent years,adaptive algorithms have been widely used in power system frequency estimation.Many scholars have tried to apply the classical adaptive filter theory to frequency estimation,and have achieved good results.However,under various complex power system operating conditions,the frequency estimation performance of traditional adaptive algorithms has the problem of deterioration.Therefore,in order to improve the frequency estimation performance of the algorithm,this paper proposes a distributed complex logarithmic hyperbolic cosine(DClncosh)algorithm and a distributed enhanced complex logarithmic hyperbolic cosine(DAClncosh)algorithm based on the lncosh cost function based on the diffusion topological network.Then,in MATLAB simulation,the power system imbalance conditions,Gaussian noise environment,non-Gaussian noise environment and the frequency estimation performance of the algorithm using measured data are simulated,and compared with the distributed complex minimum mean square(DCLMS)algorithm and distributed enhanced complex minimum mean square(DACLMS)algorithm,the simulation results verify the superiority of the frequency estimation performance of the distributed enhanced complex logarithmic hyperbolic residual algorithm.