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Optimal PV parameter estimation via double exponential function-based dynamic inertia weight particle swarm optimization

journal contribution
posted on 19.08.2020, 00:00 by AT Kiani, MF Nadeem, A Ahmed, I Khan, RM Elavarasan, Narottam Das
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.

Funding

Other

History

Volume

13

Issue

15

Start Page

1

End Page

25

Number of Pages

25

eISSN

1996-1073

Publisher

MDPI AG

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

29/07/2020

External Author Affiliations

AA Industries, India; University of Engineering and Technology Taxila, Pakistan; Texas A&M University, US

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

Energies