Recursive minimum kernel risk sensitive loss algorithm with adaptive gain factor for robust power system s estimation

Abstract

The development of effective harmonics estimation method plays a crucial role for safe operation of power system. Recently several existing parametric estimation approaches, however, are mainly developed and discussed under the assumption of Gaussian noise which is always unsuitable in real power systems. This work is mainly concerned with the robust parametric estimation methods. The kernel risk sensitive loss (KRSL) shows an advantage in that its performance surface is flat around the optimal solution while sharp far away from the optimal solution and it may exhibit outstanding performance in terms of faster convergence rate and higher estimation accuracy under non-Gaussian noise cases. Therefore, a minimum KRSL (MKRSL) algorithm with sinusoidal signal is designed, and we further develop a novel recursive MKRSL (RMKRSL) algorithm for harmonics estimation in non-Gaussian noise cases. In addition, an adaptive gain factor is introduced into the gain matrix of RMKRSL algorithm to enhance its tracking capability. Some simulations by using the signal generated from the given models and IEEE-1159-PQE databases are conducted to illustrate the performance of the proposed methods under different conditions.