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Original scientific paperIn this study, an attempt is made to present the application and comparative performance analysis of optimal control approach for automatic generation control (AGC) of electric power generating systems. Optimal controller is designed utilizing performance index minimization criterion. To conduct the study, various single and multi-area models with/without system nonlinearities from the literature are simulated under sudden load perturbation. In this comparative study, to corroborate the worth of optimal controller, the performance of optimal AGC controller is compared with that of I/PI controller optimized adopting recently published the best established techniques such as teacher learning based optimization (TLBO), differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), hybrid bacteria foraging optimization algorithm-PSO (hBFOA-PSO), craziness based PSO (CBPSO), firefly algorithm (FA), krill herd algorithm (KHA), moth-flame optimization (MFO), glow swarm optimization (GSO), simulated annealing (SA), bat algorithm (BA), stochastic fractal search (SFS) and hybrid SFSlocal unimodal sampling (hSFS-LUS) technique. The simulated results are compared in terms of settling time (ST), peak undershoot (PU)/overshoot (PO), various performance indices (PIs), minimum damping ratio (ξmin) and system eigenvalues. A sensitivity study is conducted to certify the robustness of optimal controller.Komparativna analiza primjene optimalnog upravljanja za automatsko upravljanje sustavima za proizvodnju električne energije. U ovom radu se razmatra primjena i komparativna analiza sustava za automatsko planiranje proizvodnje proizvoača električne energije. Sinteza optimalnog regulatora proporcionalno-integralne strukture je provedena korištenjem integralnih kriterija. Različiti modeli s jednim područjem i više područja te s i bez nelinearnosti korišteni su u simulaciji nagle promjena opterećenja. Kako bi se pokazala važnost optimalnog regulatora, u komparativnoj analizi su performanse dizajniranog optimalnog regulatora usporeene s peformansama postignutim korištenjem I i PI regulatora sintetiziranih primjenom postojećih uobičajeno korištenih metoda kao što su teacher learning optimization, diferencijska evolucija, genetski algoritam, optimizacija rojemčestica, hybrid bacteria foraging optimizacijski algoritam, craziness based optimizacija rojemčestica, firefly algoritam, krill herd algoritam, moth-flame optimizacija, glow swarm optimizacija, metoda simuliranog kaljenja, bat algoritam, stohastično fraktalno traženje (eng. stochastic fractal search,SFS) i metoda hibridnog SFS lokalnog unimodalnog uzorkovanja. Performanse primijenjenih algoritama upravljanja vrednovani su usporedbom ostvarenih vremena ustaljivanja, iznosa podbačaja i prebačaja te drugih pokazatelja performansi, minimalnih relativnih koeficijentima prigušenja i svojstvenih vrijednosti sustava upravljanja. Provedena analiza osjetljivosti potvruje robusnost parametara optimalnog regulatora za širok raspon radnih točaka i ...

Expert, intelligent and robust automatic generation control (AGC) scheme is requisite for stable operation and control of power system (PS) integrated with renewable energy sources (RES) under sudden/random small load disturbances. Large frequency deviations appear if AGC capacity is inept to compensate for the imbalances of generation and demand. In this paper, a cascade-fractional order ID with filter (C-I λ D μ N) controller is proposed as an expert supplementary controller to promote AGC recital adequately in electric power systems incorporated with RES like solar, wind and fuel cells. The imperialist competitive algorithm is fruitfully exploited for optimizing the controller parameters. First, a 2-area reheat thermal system is examined critically and then to authorize the worth of the proposed controller, the study is protracted to a 2-area multi-source hydro-thermal system. The prominent benefits of C-I λ D μ N controller with/without renewable energy sources consist of its great indolence to large load disturbances and superiority over various optimized classical/fuzzy controllers published recently. The sensitivity study validates the robustness of the recommended controller against ±20% deviations in the system parameters and random step load perturbations. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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