NDLI: Artificial intelligence based tuning of SVC controller for co-generated power system

Content Provider	IEEE Xplore Digital Library
Author	Najibi, N.
Copyright Year	2009
Description	Author affiliation: University of Tehran, Faculty of Engineering, Department of Surveying and Geomatics Engineering, P.O.Box : 11155-4563, Iran (Najibi, N.)
Abstract	The gain of SVC depends upon the type of reactive power load for optimum performance. As the load and input wind power conditions are variable, the gain setting of SVC needs to be adjusted or tuned. In this paper, an ANN based approach has been used to tune the gain parameters of the SVC controller over a wide range of load characteristics. The multi-layer feed-forward ANN tool with the error back-propagation training method is employed. Loads have been taken as the function of voltage. Analytical techniques have mostly been based on impedance load reduced network models, which suffer from several disadvantages, including inadequate load representation and lack of structural integrity. The ability of ANNs to spontaneously learn from examples, reason over inexact and fuzzy data and provide adequate and quick responses to new information not previously stored in memory has generated high performance dynamical system with unprecedented robustness. ANNs models have been developed for different hybrid power system configurations for tuning the proportional-integral controller for SVC. Transient responses of different autonomous configurations show that SVC controller with its gained tuned by the ANNs provide optimum system performance for a variety of loads.
Starting Page	1
Ending Page	5
File Size	651262
Page Count	5
File Format	PDF
ISBN	9781424434800
DOI	10.1109/ISMA.2009.5164830
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-23
Publisher Place	United Arab Emirates
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Static Var Compensator (SVC) Power system control Performance gain Control systems Artificial Neural Network (ANN) Reactive power Wind energy Static VAr compensators Voltage Autonomous Hybrid Power System (AHPS) Power systems Artificial intelligence Feedforward systems
Content Type	Text
Resource Type	Article

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