Variable leaky least mean-square algorithm-based power system frequency estimation

Abstract

Dynamic variation in power system frequency is required to be estimated in order to implement correcting measures. This study presents power system frequency estimation by using a leaky least mean-square algorithm. The proposed approach uses a variable leak adjustment technique to avoid drifting of the weights involved in the estimation mechanism. A variable adaptation step size is also incorporated in the algorithm to attain faster convergence. The performance of the proposed algorithm is studied through simulations for several critical cases that often arise in a power system. These studies show that the present algorithm is superior over the existing ones in estimating power system frequency. Studies made on experimental data also support the superiority.

FPGA implementation of modified leaky least mean square channel estimation algorithm

“…Because of its simplicity and reasonable performance LMS algorithm is wide used. The instantaneous performance function is given by (1) The three basic equations to describe this algorithm:…”

“…Least square (LS) [12,17], Least mean square (LMS) [10], normalized NLMS [15], variable step size LMS(VSS-LMS) [3,4] Recursive least square (RLS), Kalman filter, leaky least mean square [16]variable leaky LMS [1] orthogonal Frequency response filtering are some of the well known techniques. At the same time soft computing approaches such as fuzzy logic, neural network, genetic algorithm and simulated annealing [6] have also been used for channel estimation.…”

Improved Recursive Newton Type Algorithm based power system frequency estimation

“…In this paper, an IRNTA [19] based frequency estimation [20] technique is developed. The performance of such a formulation is studied for several critical cases that often arise in a power system, e.g., sudden change in frequency, amplitude and phase of signal, in presence of harmonics in the signal.…”

Harmonic Elimination and Reactive Power Compensation with a Novel Control Algorithm based Active Power Filter

“…The difference between the primary nonlinear load current i n and the estimated components i n induces an error signal e n . The weights of ADALINE are updated based on the VSSLLMS algorithm [19]- [21].…”

“…These modes make the system unstable and should therefore be pushed to zero. In this event, a leakage factor is introduced in the autocorrelation matrix [19]- [21]. Implementing a leaky adjustment technique makes the convergence rate faster than the conventional LMS algorithm in case of a high input eigenvalue spread.…”