Flower Pollination Algorithm based solar PV parameter estimation

Alam, D.F.; Yousri, Dalia; Eteiba, Magdy B.

doi:10.1016/j.enconman.2015.05.074

Cited by 406 publications

(152 citation statements)

References 19 publications

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“…Different procedures were developed in time for extracting the seven parameters (I L , I 01 , I 02 , m 1 , m 2 , R S and R P ) of the two-diode model. For example, flower pollination algorithm proposed recently in [5] as an optimization procedure to extract the two-diode model parameters, leads to very accurate results: comparing the measured and estimated I-V characteristics showed unnoticed difference between them. However, in practice, the one-diode model (Fig.…”

Section: Introductionmentioning

confidence: 95%

A simple but accurate procedure for solving the five-parameter model

Mares

Paulescu

Badescu

2015

Energy Conversion and Management

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Section: Introductionmentioning

confidence: 95%

A simple but accurate procedure for solving the five-parameter model

Mares

Paulescu

Badescu

2015

Energy Conversion and Management

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“…PA has been efficiency adapted to deal with a number of real-world design GO problems in engineering and science. Alam et al [66] used FPA for solving problems in the area of solar PV parameter estimation. Dubey et al [63] modified FPA to deal with the fuzzy selection of dynamic GO problems.…”

Section: Flower Pollination Algorithm Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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“…The drawbacks of a GA were overcome using the particle swarm optimization (PSO) algorithm in [31][32][33]; however, the results of PSO were still influenced by the initial values of the parameters that were generated randomly. Alam et al [34] proposed the flower pollination algorithm as a method to extract the parameters of both single-and double-diode PV models. This extraction method has exhibited good performance, particularly at low solar irradiance levels.…”

Section: Introductionmentioning

confidence: 99%