论文信息 - Fast tracking of transient power system signals using fuzzy LMS algorithm

Fast tracking of transient power system signals using fuzzy LMS algorithm

The paper presents an adaptive least mean squares (LMS) algorithm for the fast estimation of voltage and current signals in power networks. The new estimator is based on the use of linear combiners. The learning parameter of the proposed algorithm is constrained by two variable parameters which causes an automatic suitable adjustment of the step size using a fuzzy gain scheduling method to provide fast convergence and noise rejection for the tracking of fundamental and harmonic components from distorted signals. Several numerical tests have been conducted for the adaptive estimation of fundamental and harmonic components from simulated waveforms from power networks supplying converter loads and switched capacitors.

Pradipta Kishore Dash | A. C. Liew | D. P. Swain | B. R. Mishra

[1] G. Hostetter,et al. Recursive discrete Fourier transformation , 1980 .

[2] Bernard Widrow,et al. 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[3] Andrzej Cichocki,et al. Artificial neural networks for real-time estimation of basic waveforms of voltages and currents , 1993 .

[4] Innocent Kamwa,et al. Fast adaptive schemes for tracking voltage phasor and local frequency in power transmission and distribution systems , 1991 .

[5] Hiroyuki Mori,et al. An artificial neural-net based method for predicting power system voltage harmonics , 1992 .

[6] Saifur Rahman,et al. An adaptive linear combiner for on-line tracking of power system harmonics , 1996 .

[7] Vladimir Terzija,et al. Voltage phasor and local system frequency estimation using Newton type algorithm , 1994 .

[8] Adly Girgis,et al. Optimal Estimation Of Voltage Phasors And Frequency Deviation Using Linear And Non-Linear Kalman Filtering: Theory And Limitations , 1984, IEEE Transactions on Power Apparatus and Systems.