An adaptive technique to control the load frequency of hybrid distributed generation systems View Full Text


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Article Info

DATE

2019-03-11

AUTHORS

Ch. Bhavanisankar, K. R. Sudha

ABSTRACT

This paper deals with an adaptive technique-based design of load frequency controller for hybrid distributed generation systems. The hybrid system model is consisting of multiple power generating units and energy storage units. The proposed adaptive technique is the modified cuckoo search algorithm and support vector machine (SVM), which is utilized for obtaining the optimal solutions. In this paper, the proposed adaptive technique is utilized for optimized load frequency control (LFC) of a hybrid distributed generation systems model. The studied hybrid distributed generation systems consist of renewable/nonrenewable energy-based generating units such as wind turbine generator, solar photovoltaic, solar thermal power generator, diesel engine generator, fuel cell with aqua-electrolyzer, while energy storage units consist of battery energy storage system, flywheel energy storage system and ultra-capacitor. Here, fractional-order proportional–integral derivative (FOPID) controller is utilized to mitigate any frequency deviation owing to sudden generation/load change. The gain of the FOPID controller is optimized using the proposed adaptive technique. The proposed adaptive method-based LFC is implemented in the MATLAB/Simulink platform and analyzed their results. In order to analyze the effectiveness of the proposed adaptive technique, this is compared with that of other well-established techniques such as FOPID controller, GA with ANN-FOPID and SVM-FOPID controller. Moreover, the proposed tuned frequency controllers improve the overall dynamic response in terms of settling time, overshoot and undershoot in the profile of frequency deviation and power deviation of the interconnected hybrid power system. More... »

PAGES

1-16

Journal

TITLE

Soft Computing

ISSUE

N/A

VOLUME

N/A

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00500-019-03779-w

DOI

http://dx.doi.org/10.1007/s00500-019-03779-w

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https://app.dimensions.ai/details/publication/pub.1112685099


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