RLS and Kalman Filter Identifiers Based Adaptive SVC Controller

This paper presents a prospective application of a static VAr compensator (SVC) in power systems, with particular emphasis on the use of an SVC with a supplementary adaptive controller to enhance system damping. The SVC adaptive controller consists of an on-line identified system model and a pole-sh...

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Bibliographic Details
Main Authors: Barnawi, A., Albakkar, A., Malik, O.P.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive control
Adaptive PS Controller
Control systems
Damping
Kalman Filter Estimator
Least squares approximation
Parameter estimation
Power system modeling
Programmable control
Recursive estimation
Resonance light scattering
RLS Algorithm
Static VAr compensators
SVC
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Summary:This paper presents a prospective application of a static VAr compensator (SVC) in power systems, with particular emphasis on the use of an SVC with a supplementary adaptive controller to enhance system damping. The SVC adaptive controller consists of an on-line identified system model and a pole-shift (PS) feedback controller. Recursive least squares (RLS) identification algorithm and Kalman Filter as a parameters estimator are used for on-line model identification to obtain a dynamic equivalent model of the system. The two methods are compared to determine the most appropriate identification algorithm for this application. The PS controller is then adapted using the identified model. The proposed technique is tested on a single machine infinite bus system and a fifth-order multi-machine system. The results obtained demonstrate improvement in the overall system damping characteristics by applying the proposed adaptive controller as well as an enhancement of the power system stability in comparison to the conventional controller.
DOI:10.1109/NAPS.2007.4402374