Multi-Objective Differential Evolution Based PerformanceOptimisation for Switched Reluctance Motor Drives

Yahia, Hedi; Liouane, Noureddine; Dhifaoui, Rachid

doi:10.3906/elk-1110-53

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…Only the one with the best fitness value will be chosen and passed for population of the next generation. The optimisation procedure of DE / best /1/ bin [32] is given by the following steps and the pseudo‐code of the selected DE scheme is provided in the Appendix.…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Differential evolution method‐based output power optimisation of switched reluctance generator for wind turbine applications

Yahia¹,

Liouane²,

Dhifaoui³

2014

IET Renewable Power Generation

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The use of wind energy for electric power generation provides a clean and renewable source. Therefore there is an increasing interest in developing and exploiting natural energy generation system. Switched reluctance generators (SRGs) have the potential to be a robust and highly efficient electrical conversion system for variable-speed wind applications. This study presents a new approach for optimising performance of a SRG intended for variable-speed direct drive wind turbine applications. DC bus voltage level and phase voltage switching angles have been identified as control variables affecting power generation. Owing to highly non-linear characteristics of SRG, iterative simulation of the generator model on the range of control variables can be used for finding output power profile. Since it is a multidimensional search space, the number of iterations is very big. Differential evolution (DE) strategy has been introduced to find optimal firing angles and DC bus voltage level under multiple operating conditions. Optimisation of the control variables is performed using a machine model based on the measured characteristics. Selected operating points are experimentally tested using a 4 kW 1500 rpm SRG prototype. DE algorithm is a viable alternative for generating optimal control in multidimensional optimisation of SRG wind energy generation.

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Section: Differential Evolution Algorithmmentioning

confidence: 99%

Differential evolution method‐based output power optimisation of switched reluctance generator for wind turbine applications

Yahia¹,

Liouane²,

Dhifaoui³

2014

IET Renewable Power Generation

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“…[25], to achieve balance between efficiency and torque ripple criteria. The turn on and off angles are optimized to achieve the best motoring operation of SRM for electric vehicles in Refs [26,27], where a multiobjective function is developed to compromise maximum torque smoothness factor (TSF), maximum average torque and maximum average torque per RMS current.…”

Section: Introductionmentioning

confidence: 99%

Optimal dynamic and steady‐state performance of switched reluctance motor using water cycle algorithm

El-Hay

Elkholy

2018

IEEJ Transactions Elec Engng

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Saliency and nonlinear magnetization characteristics of switched reluctance motor (SRM) are causes for its poor performance regarding torque ripples. This paper presents an innovative methodology based on a water cycle algorithm (WCA) for SRM speed control with minimum torque ripples and maximum torque per ampere. WCA enhances the dynamic and steady-state performance of this motor by optimizing a multiobjective function consisting of three parts. The first part is the integral time absolute error (ITAE) of motor speed with respect to a desired reference speed, the second part is an index for a motor torque smoothness factor (TSF), and the third part is the torque per ampere ratio (TAR). The desired output of WCA is the appropriate gains of the PI speed controller and optimum turn on and off angles of the H-bridge driving circuit. The results obtained from WCA are used to construct an adaptive controller-based artificial neural network (ANN) to automatically tune the gains of the PI speed controller and the turn on and off angles. The ANN controller ensures superior steady-state and dynamic motoring operation over a wide range of speed commands and different loads. The results of WCA are verified by other well-known meta-heuristic optimized techniques, such as the Genetic algorithm (GA). The suggested method exhibits an excellent speed transient response in addition to minimum torque ripples and maximum TAR.

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“…In the last few decades, EC has been studied for dealing with nondeterministic multiobjective optimization problems. The multiobjective evolutionary optimization algorithms (MOEAs) have become a very popular and fast-growing field [3][4][5]. Since Schaffer's pioneering work in 1985 [6], various MOEAs have been proposed in this field to solve several domains of science and engineering problems.…”

Section: Introductionmentioning

confidence: 99%

A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization)

Sağ¹,

Çunkaş²

2016

Turk J Elec Eng & Comp Sci

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This paper presents a new metaheuristic algorithm based on the artificial bee colony (ABC) algorithm for multiobjective optimization problems. The proposed hybrid algorithm, an improved bee colony algorithm for multiobjective optimization called IBMO, combines the main ideas of the simple ABC with nondominated sorting strategy corresponding to the principal framework of multiobjective optimization such as Pareto-dominance and crowding distance. A fixed-sized external archive to store the nondominated solutions and an improvement procedure to promote the convergence to true Pareto front are used. The presented approach, IBMO, is compared with four representatives of the state-of-the-art algorithms: NSGA2, SPEA2, OMOPSO, and AbYSS. IBMO and the selected algorithms from specialized literature are applied to several multiobjective benchmark functions by considering the number of function evaluations. Then four quality indicators are employed for performance evaluations: general distance, spread, maximum spread, and hypervolume. The results show that the IBMO is superior to the other methods.

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Multi-Objective Differential Evolution Based PerformanceOptimisation for Switched Reluctance Motor Drives

Cited by 6 publications

References 34 publications

Differential evolution method‐based output power optimisation of switched reluctance generator for wind turbine applications

Differential evolution method‐based output power optimisation of switched reluctance generator for wind turbine applications

Optimal dynamic and steady‐state performance of switched reluctance motor using water cycle algorithm

A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization)

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