Complex-space recursive least squares power system identification

2007 ◽  
Author(s):  
S. Cobreces ◽  
P. Rodriguez ◽  
D. Pizarro ◽  
F. J. Rodriguez ◽  
E. J. Bueno
Keyword(s):  
System Identification ◽  
Power System ◽  
Least Squares ◽  
Complex Space ◽  
Recursive Least Squares

scholarly journals Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms

2021 ◽  
Vol 11 (18) ◽  
pp. 8656
Author(s):  
Ionuț-Dorinel Fîciu ◽  
Cristian-Lucian Stanciu ◽  
Cristian Anghel ◽  
Camelia Elisei-Iliescu
Keyword(s):  
System Identification ◽  
Least Squares ◽  
Adaptive Systems ◽  
Stable Solution ◽  
Tensor Decomposition ◽  
Low Complexity ◽  
Recursive Least Squares ◽  
Rls Algorithm ◽  
Multilinear Forms ◽  
Single Output

Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods.

scholarly journals Performance Comparison of Adaptive Estimation Techniques for Power System Small-Signal Stability Assessment

2010 ◽  
Vol 7 (2) ◽  
pp. 10
Author(s):  
E. A. Feilat
Keyword(s):  
Power System ◽  
Least Squares ◽  
Operating Conditions ◽  
Recursive Least Squares ◽  
Small Signal ◽  
Loading Conditions ◽  
Small Signal Stability ◽  
Signal Stability ◽  
Wide Range ◽  
Damping Torque

This paper demonstrates the assessment of the small-signal stability of a single-machine infinite- bus power system under widely varying loading conditions using the concept of synchronizing and damping torques coefficients. The coefficients are calculated from the time responses of the rotor angle, speed, and torque of the synchronous generator. Three adaptive computation algorithms including Kalman filtering, Adaline, and recursive least squares have been compared to estimate the synchronizing and damping torque coefficients. The steady-state performance of the three adaptive techniques is compared with the conventional static least squares technique by conducting computer simulations at different loading conditions. The algorithms are compared to each other in terms of speed of convergence and accuracy. The recursive least squares estimation offers several advantages including significant reduction in computing time and computational complexity. The tendency of an unsupplemented static exciter to degrade the system damping for medium and heavy loading is verified. Consequently, a power system stabilizer whose parameters are adjusted to compensate for variations in the system loading is designed using phase compensation method. The effectiveness of the stabilizer in enhancing the dynamic stability over wide range of operating conditions is verified through the calculation of the synchronizing and damping torque coefficients using recursive least square technique.

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