Open AccessAdaptive fuzzy based optimized proportional‐integral controller to mitigate the frequency oscillation of multi‐area photovoltaic thermal system
Author(s) -
Gulzar Muhammad M.,
Sibtain Daud,
Murtaza Ali F.,
Murawwat Sadia,
Saadi Muhammad,
Jameel Ahlam
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12643
Subject(s) - photovoltaic system , control theory (sociology) , oscillation (cell signaling) , controller (irrigation) , thermal , materials science , computer science , engineering , physics , electrical engineering , chemistry , thermodynamics , control (management) , biology , agronomy , biochemistry , artificial intelligence
Background The unprecedented growth in human population and industrialization, the present‐day energy demands have soared dramatically. Therefore, it is essential to integrate renewable energy resources with the conventional ones to keep the energy demand and supply at equilibrium. Aims While integrating these two energy resources, it is important that the power generated through renewable energy resources should be able to sustain inherent variability and uncertainty of conventional resources. The interaction of renewable into thermal power system degrades the frequency and it is important to mitigate such a problem by introducing an optimal controller. Materials & Methods In this paper, an optimized Adaptive Fuzzy Logic Controller based Proportional‐Integral (AFLC‐PI) is proposed for Load Frequency Control (LFC) for a multi‐area system. The proposed controller has the capability to tune the Proportional‐Integral (PI) controller on the basis of frequency error, frequency oscillations and minimize the LFC problem for Photovoltaic (PV) connected thermal system. Moreover, the Adaptive Fuzzy Logic Controller (AFLC) response under different real‐time load changing conditions is simulated and analyzed along with the uncertainty in governor and turbine time‐constant. Finally, the comparison analysis with Fuzzy Logic Controller tuned PI (FLC‐PI), Genetic Algorithm tuned PI (GA‐PI) and Firefly tuned PI (FA‐PI) based optimized controllers is carried out. Results We have analyzed our proposed controller response at various load variations, under critical load variation and observed how fast our proposed technique mitigates the frequency oscillations. Discussion The results clearly depict that the proposed controller is meeting the critical targets (undershoot, overshoot and settling time) under varying load conditions, sudden variation in a turbine, and governor where AFLC‐PI has the ability to deal any ambiguity in a system effectively. Conclusion The proposed controller mitigate the frequency oscillation in a robust way as compared to other state of the art controllers.