Combination of quadratic ranking selection real-coded genetic algorithm with the Hooke-Jeeves optimisation method for solar photovoltaic parameter estimation

Bounekhla, M.; Habbi, Fatiha; Boudissa, El-Ghalia; Maamoun, Mountassar; Guessoum, Abderrezak

doi:10.1080/01430750.2022.2080267

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…On the other hand, MA can be evolved in another way, such as a GA exploring the search space to discover promising areas and providing a solution located within the attraction pool of the global minimum. Then, a local search uses the solution provided by the GA as an initial solution and continues the disruption process until the convergence criterion is satisfied [18].…”

Section: Introductionmentioning

confidence: 99%

A Memetic Algorithm Applied to Induction Machine Parameters Identification Based on an Output Error

BOUDISSA,

HABBI,

BOUNEKHLA

et al. 2023

RRST-EE

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Based on an output error, several evolutionary methods have been applied to identify the parameters of an Induction Machine (IM). The main drawback of these methods is their premature convergence in many situations. To overcome this issue and achieve a more accurate solution, this paper proposes a Memetic Algorithm (MA), which combines a Genetic Algorithm (GA) and a local search method. This approach uses the Hooke-Jeeves (HJ) method for the local search as a mutation operator.GA has proven good ability in global search. The HJ method has a good ability to refine the local search and achieve the optimal accuracy solution. The proposed MA, which maintains a tradeoff between exploration and exploitation strategies, is applied to minimize the related objective function to obtain the electrical and mechanical machine parameters. The validation of the method is confirmed by an experiment carried out on an (0.4 kW) IM with parameters estimated using the measured data. Using the estimated parameters, the computed transient and steady-state currents agree well with the measured data.

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Section: Introductionmentioning

confidence: 99%