A MEMETIC ALGORITHM APPLIED TO INDUCTION MACHINE PARAMETERS IDENTIFICATION BASED ON AN OUTPUT ERROR
MEMETIC ALGORITHM FOR INDUCTION MACHINE IDENTIFICATION
DOI:
https://doi.org/10.59277/RRST-EE.2023.3.3Keywords:
Identification, Genetic Algorithm, Memetic algorithm, Hooke-Jeeves method, Induction machineAbstract
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.
References
(1) C.M. Gheorghe, S.Piperca, The induction machine in Eastern Europe: A research agenda”, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 63, 4, pp. 371–378 (2018)
(2) F. Bettache, M. Bouhedda, A. Abbadi, Implementation of an adaptive fuzzy pi regulator on an industrial programmable plc s7-300 for the speed control of an asynchronous machine, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 67, 3, pp 253–258 (2022).
(3) J.E. Brown, K.P. Kovacs, P. Vas, A Method of including the effects of main flux path saturation in the generalized equations of ac machines, IEEE Trans. On Power Apparatus and Systems, 102, 1, (1983).
(4) M. Abel-Salam, S. Abou-Shadi, Y. Sayed, Analysis of induction motors fed from constant current source taking core-loss and saturation into consideration, Electric Machines and Power Systems, 27, pp. 581–599 (1999).
(5) J. Pedra, I. Candela, A. Barrera, Saturation model for squirrel-cage induction motors, Electric Power Systems Research, 79, pp. 1054–1061 (2009).
(6) A. Accetta, F. Alonge, M. Cirrincione, M. Pucci, A. Sferlazza, Parameter identification of induction motor model by means of state space-vector model output error minimization, XXI International Conference on Electrical Machines (ICEM), pp. 843–849, (2014).
(7) J.A. De Kock, F.S. van der Merweand, H.J. Vermenlen, Induction motor parameter estimation through an output error technique, IEEE Transactions on Energy Conversion, 9, 1, pp. 69–76 (1994).
(8) M. Mihalache, Equivalent circuit parameters and operating performances of the three-phase asynchronous motor, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 55, 1, pp. 32–41 (2010).
(9) T. Tudorache, I.D. Ilina, L. Melcescu, Parameters estimation of an induction motor using optimization algorithms, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 61, 2, pp. 121–125 (2016).
(10) F. Chich-Hsing, L. Shir-Kuanand, W. Shyh-Jier, On-line parameter estimator of an induction motor at a standstill, Control Engineering Practice, 13, pp.535-540 (2005).
(11). A.M. Onofre, E.C. Castañeda, R. Ruiz-Cruz, A.F. Valenzuela, A.M. Murillo, E.A. Quezada, N. Padilla., The squirrel-cage induction motor model and its parameter identification via steady and dynamic tests, Electric Power Components and Systems, pp 1–14 (2018).
(12). D.G Luenberger, Linear and Nonlinear Programming, Addison Wesley (1989).
(13) M.S. Bazaraa, H.D. Sherall, C.M. Shetty, Nonlinear programming theory and algorithms, John Wiley & Sons Inc. (1993).
(14) E. Boudissa, F. Habbi, N. Gabour, M. Bounekhla, A new dynamic genetic selection algorithm: application to induction machine identification, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 66, 3, pp. 145–151, (2021).
(15) D. Naas, E. Boudissa M. Bounekhla, I. Dif., Firefly algorithm improvement with application to induction machine, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 65, 1, pp. 35–40 (2020).
(16) M. Abdelwanis, R. El-Sehiemy, Efficient parameter estimation procedure using sunflower optimization algorithm for six-phase induction moto, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 67, 3, pp. 259–264 (2022).
(17) A.A. Mekki, A. Kansab, M. Matallah, M. Feliachi, Optimization of the inductor of an induction cooking system using particle swarm optimization method and fuzzy logic controller, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 65, 3–4, pp. 185–190 (2020).
(18) M. Bounekhla, F. Habbi, E. Boudissa, M. Maamoun, A. Guessoum, Combination of quadratic ranking selection real-coded genetic algorithm with the Hooke-Jeeves optimisation method for solar photovoltaic parameter estimation, Int. J. of Ambient Energy (2022).
(19) O.A. Mohammed, G.G. Uler, A hybrid technique for the optimal design of electromagnetic devices using direct search and genetic algorithms, IEEE Trans. Magn. 33, pp. 1931–1934 (1997).
(20) M.A. Hannan, J.A. Ali, A. Mohamed, A. Hussain, Optimization techniques to enhance the performance of induction motor drives: A review, Renewable and Sustainable Energy Reviews, 81, 2, pp. 1611–1626 (2018).
(21) K.D. Hwa, GA–PSO based vector control of indirect three phase induction motor, Applied Soft Computing, 7, 2, pp. 601–611, (2007)
(22. M.I. Abdelwanis, R.A. Sehiemy, M. A. Hamida, Hybrid optimization algorithm for parameter estimation of poly-phase induction motors with experimental verification, Energy and AI, 5 (2021).
(23) J. Bosworth, Foo, N., Zeigler, B. P., Comparison of Genetic Algorithms with Conjugate Gradient Methods, Washington, DC: NASA Report, CR-2093 (1972).
(24) S.W. Mahfoud, D.E. Goldberg, A genetic algorithm for parallel simulated annealing, Parallel problem solving from nature II, par Manner et Manderick B (Ed.) Elsevier, pp. 301–310 (1992).
(25) H. Bersini, G. Seront, In search of a good evolution optimization crossover, Parallel problem solving from nature II, par Manner et Manderick B (Ed.) Elsevier, pp.479–488 (1992).
(26) M. de la Maza, B. Tidor, An analysis of selection procedures with particular attention paid to proportional & Boltzmann selection, Proceedings of the Fifth International Conference on GAs, Morgan Kaufamann, pp. 124–131 (1993).
(27) R. Cheng, M. Gen, Y. Tsujimura, A tutorial survey of job-shop scheduling problems using genetic algorithms: Part II. Hybrid genetic search strategies, Int. J. Computers Industrial Engineering 37, 1, pp.51–55 (1999).
(28) M. Bounekhla, M. E. Zaim, A. Rezzoug, Comparative Study of Three Minimization Methods Applied to the Induction Machine Parameters Identification Using Transient Stator Current, Electric Power Components and Systems 33, 8, pp. 913–930 (2005).
(29) C. Li, A. Rahman, Three-phase induction motor design optimization using the modified Hooke-Jeeves method, Electric Machines and Power Systems, 18, 1, pp. 1-12, Jan. (1990).
(30) L. Benasla, A. Belmadani, M. Rahli, Hooke-Jeeves method applied to a new economic dispatch problem formulation, Journal of Information Science and Engineering, 24, pp. 907–917 (2008).
(31) E. Boudissa, M. Bounekhla, Genetic Algorithm with Dynamic Selection Based on Quadratic Ranking Applied to Induction Machine Parameters Estimation, Electric Power Components and Systems, 40, 10, pp.1089–1104 (2012).
(32) J. Chatelain, Machines electriques, in Traité d’Electricité, X, Lausanne, France: Presses Polytechniques Romandes (1983).
Downloads
Published
Issue
Section
License
Copyright (c) 2023 REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
