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BFO Optimized Automatic Load Frequency Control of a Multi-Area Power System

BFO Optimized Automatic Load Frequency Control of a Multi-Area Power System

Pravat Kumar Ray, Sushmita Ekka
Copyright: © 2016 |Pages: 44
ISBN13: 9781522504276|ISBN10: 1522504273|EISBN13: 9781522504283
DOI: 10.4018/978-1-5225-0427-6.ch016
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MLA

Ray, Pravat Kumar, and Sushmita Ekka. "BFO Optimized Automatic Load Frequency Control of a Multi-Area Power System." Handbook of Research on Computational Intelligence Applications in Bioinformatics, edited by Sujata Dash and Bidyadhar Subudhi, IGI Global, 2016, pp. 369-412. https://doi.org/10.4018/978-1-5225-0427-6.ch016

APA

Ray, P. K. & Ekka, S. (2016). BFO Optimized Automatic Load Frequency Control of a Multi-Area Power System. In S. Dash & B. Subudhi (Eds.), Handbook of Research on Computational Intelligence Applications in Bioinformatics (pp. 369-412). IGI Global. https://doi.org/10.4018/978-1-5225-0427-6.ch016

Chicago

Ray, Pravat Kumar, and Sushmita Ekka. "BFO Optimized Automatic Load Frequency Control of a Multi-Area Power System." In Handbook of Research on Computational Intelligence Applications in Bioinformatics, edited by Sujata Dash and Bidyadhar Subudhi, 369-412. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0427-6.ch016

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Abstract

This chapter presents an analysis on operation of Automatic Load Frequency Control (ALFC) by developing models in SIMULINK which helps us to understand the principle behind ALFC including the challenges. The three area system is being taken into account considering several important parameters of ALFC like integral controller gains (KIi), governor speed regulation parameters (Ri), and frequency bias parameters (Bi), which are being optimized by using Bacteria Foraging Optimization Algorithm (BFOA). Simultaneous optimization of certain parameters like KIi, Ri and Bi has been done which provides not only the best dynamic response for the system but also allows us to use much higher values of Ri than used in practice. This will help the power industries for easier and cheaper realization of the governor. The performance of BFOA is also investigated through the convergence characteristics which reveal that that the Bacteria Foraging Algorithm is quite faster in optimization such that there is reduction in the computational burden and also minimal use of computer resource utilization.

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