Comparison between the particle swarm optimisation and differential evolution approaches for the optimal proportional–integral controllers design during photovoltaic power plants modelling

Deželak, Klemen; Bracinik, Peter; Höger, Marek; Otcenasova, Alena

doi:10.1049/iet-rpg.2015.0108

Cited by 19 publications

(10 citation statements)

References 35 publications

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“…Fig. 6a shows the convergence of the objective function which is defined by (29). The corresponding attainments of the values of B S , B A , C S and C A are shown in Fig.…”

Section: Resultsmentioning

confidence: 95%

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Differential evolution‐based pulse‐width modulation technique for multiphase MC

Mohammad

Iqbal

Anees

et al. 2019

IET Power Electronics

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This paper addresses multiphase matrix converter (MPMCs) modulated with Venturini's classical scalar approach. The solution to obtain time-varying modulation functions was limited to 3 × 3 configuration, which was later extended to 3 × n configuration. The equations had four constants that were common to all modulation functions; out of which two were defined arbitrarily and other two were found analytically by reducing the equation to a two-variable optimisation problem. However, in this work, an attempt is made to provide flexibility in finding other solutions by taking all the constants as variables. Thus, Differential Evolution (DE) algorithm is adopted in the proposed modulation approach to solve the complex equations thus obtained. Another aim is to find the solution for the MPMC, where inputs are more than three. It is found that the optimal solutions exist only for 3 × n configuration and are in close approximation with the results of analytical approach. Other solutions were found for certain input and output frequencies and voltage transfer ratios whose results are discussed. It is also found that the solution does not exist for configurations with input phases are other than three. The discussion is supported by simulation and experimental results on a 3 × 7 matrix converter at various values of voltage transfer ratios and input/output frequencies.

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“…Fig. 6a shows the convergence of the objective function which is defined by (29). The corresponding attainments of the values of B S , B A , C S and C A are shown in Fig.…”

Section: Resultsmentioning

confidence: 95%

“…The main benefits of this scheme are: its efficiency in finding global extrema; a guaranteed convergence (provided that the objective function is correctly defined); and its simplicity [25,26]. It is now being extensively used in solving complex problems in the area of power systems and drives [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Differential evolution‐based pulse‐width modulation technique for multiphase MC

Mohammad

Iqbal

Anees

et al. 2019

IET Power Electronics

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“…The use of optimization algorithms as alternative methods for tuning PID controllers has been a recent topic of research in electric machines control. New optimization techniques are proposed, for instance, the Imperialist Competitive Algorithm (ICA) [18], evolutionary algorithm [19], Genetic Algorithm (GA) [20][21], BAT algorithm [22] ,Particle Swarm Optimization (PSO) [23][24][25][26], and Ant Colony Optimization (ACO) algorithm [27], Harmony Search (HS) [28], hybrid GA [29][30], adaptive Cuckoo Search algorithm (CS) [31].…”

Section: Introductionmentioning

confidence: 99%

Direct torque control of dual star induction motor using a fuzzy-PSO hybrid approach

Boukhalfa

Belkacem

Chikhi

et al. 2020

ACI

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This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.

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“…Many application of these algorithms for engineering systems are performed. Some recent studies based on these algorithms can be found elsewehere [5][6][7]. An aircooling systems optimization is performed by [5], where it is concluded that both PSO and DE algorithms shows higher performance than that of Lagrangian methodology (LM) for a simple optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Comparison Of Particle Swarm And Differential Evolution Optimization Algorithms Considering Various Benchmark Functions

Özcan¹

2017

Journal of Polytechnic

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Comparison of particle swarm and differential evolution optimization algorithms considering various benchmark functions Parçacık sürüsü ve diferansiyel evrim optimizasyonu algoritmalarının farklı ölçü fonksiyonları kullanılarak karşılaştırılması Yazar(lar) (Author(s)): Hasan OZCANBu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Ozcan H., "Comparison of particle swarm and differential evolution optimization algorithms considering various benchmark functions", Politeknik Dergisi, 20(4): 899-905, (2017). Politeknik Dergisi, 2017; 20 (4) : 899-905 Journal of Polytechnic, 2017; 20 (4) ABSTRACTThis study aims to compare Particle Swarm Optimization (PSO) and Differential Evolution (DE) methods for various input parameters. Both optimization methods show high performance in optimization of any physical system including simple and complex constraints and objectives. Average and standard values of both methods were evaluated by utilizing 8 benchmark functions and a graphical representation and comparison of corresponding methods was presented for 50x50 and 100x100 population sizes and dimensionalities. It is concluded that DE and PSO show the best fitness value for Sum of Different Powers benchmark function for both number of populations. Approach to the optimum is found to be faster through the PSO method. Both methods are flexible to be used for simple and complex engineering problems with high performances with ease of programming.Keywords : Particle swarm, differential evolution, optimization, benchmark function. Parçacık Sürüsü ve Diferansiyel Evrim Optimizasyonu Algoritmalarının Farklı Ölçü Fonksiyonları Kullanılarak Karşılaştırılması ÖZBu çalışma parçacık sürüsü optimizasyonu ve diferansiyel evrim algoritmalarını farklı giriş parametreleri kullanarak karşılaştırmayı amaçlamaktadır. Bu iki optimizasyon algoritması basit ve kompleks kısıtlar ve sınırlayıcıları da içine alan herhangi bir fiziksel sistemin optimizasyonunda yuksek performans gostermektedirler. Sekiz adet ölçü fonksiyonu kullanılarak iki algoritma için averaj ve standart değerler hesaplanmış ve 50x50 ve 100x100 boyutlarında ve uzaylarında ilgili algoritmaların karşılaştırılması yapılmıştır. İki algoritma da (Farklı kuvvetlerin toplamı) ölçü fonsiyonunda en iyi değeri göstermektedir. Optimum değere parçacık sürüsü optimizasyonu ile daha hızlı ulaşılsa da iki metot da basit ve komplex optmizasyon problemlerinin çözümünde etkin performans ve kolay programlanabilirlik avantajları göstermektedir.Anahtar kelimeler: Parçacık sürüsü, diferansiyel evrim, optimizasyon, ölçü fonskiyonu.

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Comparison between the particle swarm optimisation and differential evolution approaches for the optimal proportional–integral controllers design during photovoltaic power plants modelling

Cited by 19 publications

References 35 publications

Differential evolution‐based pulse‐width modulation technique for multiphase MC

Differential evolution‐based pulse‐width modulation technique for multiphase MC

Direct torque control of dual star induction motor using a fuzzy-PSO hybrid approach

Comparison Of Particle Swarm And Differential Evolution Optimization Algorithms Considering Various Benchmark Functions

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