AI-enhanced load frequency control in multi-area power systems via a self-tuning PIDF with ANN-based NMPC and hybrid cat-pikas optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258682" target="_blank" >RIV/61989100:27240/25:10258682 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/25:10258682

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2590123025035170" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123025035170</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2025.107462" target="_blank" >10.1016/j.rineng.2025.107462</a>

Jazyk výsledku

angličtina

Název v původním jazyce

AI-enhanced load frequency control in multi-area power systems via a self-tuning PIDF with ANN-based NMPC and hybrid cat-pikas optimization

Popis výsledku v původním jazyce

Ensuring frequency stability in multi-area power systems under diverse disturbances remains a major challenge. This paper proposes an AI-enhanced self-tuning nonlinear-proportional-integrator-derivative denoising filter (NL-PIDF) controller designed within an artificial neural network (ANN)-based nonlinear model predictive control (NMPC) framework and optimized using a novel Hybrid Cat-Pikas Optimization (HCPO) algorithm. The ANN predictor identifies the nonlinear system dynamics, while an error compensator mitigates steady-state offsets caused by prediction errors. To further enhance dynamic stability, a superconducting magnetic energy storage (SMES) unit is integrated in Area 1, and high-voltage direct current (HVDC) tie-lines are employed between selected areas. The approach is evaluated on a nonlinear three-area power system including steam, gas, and combined-cycle turbines, considering key nonlinearities such as the reheater, generation rate constraint (GRC), governor deadband (GDB), and boiler dynamics (BD). Simulation results, supported by time-domain and eigenvalue analyses, demonstrate significant improvements in damping frequency oscillations and inter-area power exchanges compared with conventional controllers. The proposed strategy achieves faster settling, reduced overshoot/undershoot, and enhanced robustness under random step, sinusoidal load disturbances, and wide parameter variations. In such a way that the proposed strategy reduces frequency overshoot by approximate to 45%, improves settling time by approximate to 38%, and lowers ITSE by approximate to 52% compared with conventional tuned PID and recent metaheuristic-based controllers, confirming its robustness against load disturbances and system nonlinearities.

Název v anglickém jazyce

AI-enhanced load frequency control in multi-area power systems via a self-tuning PIDF with ANN-based NMPC and hybrid cat-pikas optimization

Popis výsledku anglicky

Ensuring frequency stability in multi-area power systems under diverse disturbances remains a major challenge. This paper proposes an AI-enhanced self-tuning nonlinear-proportional-integrator-derivative denoising filter (NL-PIDF) controller designed within an artificial neural network (ANN)-based nonlinear model predictive control (NMPC) framework and optimized using a novel Hybrid Cat-Pikas Optimization (HCPO) algorithm. The ANN predictor identifies the nonlinear system dynamics, while an error compensator mitigates steady-state offsets caused by prediction errors. To further enhance dynamic stability, a superconducting magnetic energy storage (SMES) unit is integrated in Area 1, and high-voltage direct current (HVDC) tie-lines are employed between selected areas. The approach is evaluated on a nonlinear three-area power system including steam, gas, and combined-cycle turbines, considering key nonlinearities such as the reheater, generation rate constraint (GRC), governor deadband (GDB), and boiler dynamics (BD). Simulation results, supported by time-domain and eigenvalue analyses, demonstrate significant improvements in damping frequency oscillations and inter-area power exchanges compared with conventional controllers. The proposed strategy achieves faster settling, reduced overshoot/undershoot, and enhanced robustness under random step, sinusoidal load disturbances, and wide parameter variations. In such a way that the proposed strategy reduces frequency overshoot by approximate to 45%, improves settling time by approximate to 38%, and lowers ITSE by approximate to 52% compared with conventional tuned PID and recent metaheuristic-based controllers, confirming its robustness against load disturbances and system nonlinearities.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/EH23_021%2F0008759" target="_blank" >EH23_021/0008759: Zvýšení odolnosti energetických sítí v kontextu dekarbonizace, decentralizace a udržitelného socioekonomického rozvoje</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

28

Číslo periodika v rámci svazku

December 2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

41

Strana od-do

1-41

Kód UT WoS článku

001598167400011

EID výsledku v databázi Scopus

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