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Article

Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization

1
Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan
2
Marine Engineering Technology Department in a joint appointment with the Electrical and Computer Engineering Department, Texas A&M University, Galveston, TX 77553, USA
3
Electrical and Automotive parts Manufacturing unit, AA Industries, Chennai 600 123, Tamilnadu, India
4
School of Engineering and Technology, Central Queensland University, Melbourne, VIC 3000, Australia
5
Centre for Intelligent Systems, School of Engineering and Technology, Central Queensland University, Brisbane, QLD 4000, Australia
*
Authors to whom correspondence should be addressed.
Energies 2020, 13(15), 4037; https://doi.org/10.3390/en13154037
Received: 5 June 2020 / Revised: 17 July 2020 / Accepted: 29 July 2020 / Published: 4 August 2020
(This article belongs to the Special Issue Nano-Structured Solar Cells 2020-2022)
Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient. View Full-Text
Keywords: parameter estimation; particle swarm optimization; premature convergence; solar cell parameter estimation; particle swarm optimization; premature convergence; solar cell
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MDPI and ACS Style

Kiani, A.T.; Nadeem, M.F.; Ahmed, A.; Khan, I.; Elavarasan, R.M.; Das, N. Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization. Energies 2020, 13, 4037. https://doi.org/10.3390/en13154037

AMA Style

Kiani AT, Nadeem MF, Ahmed A, Khan I, Elavarasan RM, Das N. Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization. Energies. 2020; 13(15):4037. https://doi.org/10.3390/en13154037

Chicago/Turabian Style

Kiani, Arooj Tariq, Muhammad Faisal Nadeem, Ali Ahmed, Irfan Khan, Rajvikram Madurai Elavarasan, and Narottam Das. 2020. "Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization" Energies 13, no. 15: 4037. https://doi.org/10.3390/en13154037

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