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Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System
This work provides an enhanced novel cascaded controller-based frequency stabilization of a two-region interconnected power system incorporating electric vehicles. The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative...
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Published in: | Fractal and fractional 2023-04, Vol.7 (4), p.315 |
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description | This work provides an enhanced novel cascaded controller-based frequency stabilization of a two-region interconnected power system incorporating electric vehicles. The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. The results indicate the new controller’s steady operation and frequency regulation capability with an optimal controller coefficient and without the prerequisite for a complex layout procedure. |
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The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. The results indicate the new controller’s steady operation and frequency regulation capability with an optimal controller coefficient and without the prerequisite for a complex layout procedure.</description><identifier>ISSN: 2504-3110</identifier><identifier>EISSN: 2504-3110</identifier><identifier>DOI: 10.3390/fractalfract7040315</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Alternative energy sources ; Automobiles, Electric ; Comparative analysis ; Control methods ; Control systems design ; Controllers ; Design optimization ; Driver education ; Electric power systems ; Electric rates ; Electric vehicles ; Electricity ; Electricity distribution ; Energy industry ; Energy storage ; Evolutionary algorithms ; fractional order controller ; Frequency stabilization ; Heuristic methods ; heuristic techniques ; Hybrid systems ; Imperialism ; load frequency control ; Mathematical optimization ; Optimization techniques ; Parameter uncertainty ; power system ; Proportional derivative ; renewable energy resources ; Renewable energy sources ; Renewable resources ; Training ; Wind power</subject><ispartof>Fractal and fractional, 2023-04, Vol.7 (4), p.315</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. 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The proposed controller combines a cascade structure comprising a fractional-order proportional integrator and a proportional derivative with a filter term to handle the frequency regulation challenges of a hybrid power system integrated with renewable energy sources. Driver training-based optimization, an advanced stochastic meta-heuristic method based on human learning, is employed to optimize the gains of the proposed cascaded controller. The performance of the proposed novel controller was compared to that of other control methods. In addition, the results of driver training-based optimization are compared to those of other recent meta-heuristic algorithms, such as the imperialist competitive algorithm and jellyfish swarm optimization. The suggested controller and design technique have been evaluated and validated under a variety of loading circumstances and scenarios, as well as their resistance to power system parameter uncertainties. The results indicate the new controller’s steady operation and frequency regulation capability with an optimal controller coefficient and without the prerequisite for a complex layout procedure.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/fractalfract7040315</doi><orcidid>https://orcid.org/0000-0002-1318-4203</orcidid><orcidid>https://orcid.org/0000-0002-5532-9175</orcidid><orcidid>https://orcid.org/0000-0002-1872-2197</orcidid><orcidid>https://orcid.org/0000-0003-1517-0611</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Alternative energy sources Automobiles, Electric Comparative analysis Control methods Control systems design Controllers Design optimization Driver education Electric power systems Electric rates Electric vehicles Electricity Electricity distribution Energy industry Energy storage Evolutionary algorithms fractional order controller Frequency stabilization Heuristic methods heuristic techniques Hybrid systems Imperialism load frequency control Mathematical optimization Optimization techniques Parameter uncertainty power system Proportional derivative renewable energy resources Renewable energy sources Renewable resources Training Wind power |
title | Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System |
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