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Backtracking Search Algorithm for PV Module Electrical Parameter Estimation
- Source :
- 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA).
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- The equivalent electric circuit models reflect the electrical characteristics of the photovoltaic (PV) modules. Estimation of PV module parameters is considered as one of the challenging tasks while evaluating the performance. This article presents a new and useful approach to estimate the five-parameter PV module electrical circuit model. It translates the PV module parameter estimation process into an optimization problem using the information provided by the manufacturer on the rear side of the PV modules. It then employs an efficient metaheuristic technique, namely the backtracking search algorithm, to solve the developed optimization problem. The efficacy of the proposed approach is investigated by predicting the parameters of three PV module technologies: monocrystalline, poly-crystalline, and thin film. Finally, to check the feasibility of the proposed technique, this paper compares the approximate parameters of modeled I-V curves with experimental curves. The findings confirm the reliability of the estimated model parameters in simulating the near realistic characteristics of the PV modules.
- Subjects :
- 0209 industrial biotechnology
Optimization problem
Backtracking
Estimation theory
Computer science
Photovoltaic system
Approximation algorithm
02 engineering and technology
law.invention
020901 industrial engineering & automation
law
Electrical network
0202 electrical engineering, electronic engineering, information engineering
Electronic engineering
020201 artificial intelligence & image processing
Metaheuristic
Electronic circuit
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)
- Accession number :
- edsair.doi...........234ba132a985d5a46846814c5d10d420
- Full Text :
- https://doi.org/10.1109/caida51941.2021.9425196