Optimal location and reactive power injection of wind farms and SVC’s units using voltage indices and PSO

Cherkaoui, Nazha; Belfqih, Abdelaziz; Mariami, Faissal El; Boukherouaa, Jamal; Berdai, Abdelmajid

doi:10.11591/ijece.v9i5.pp3407-3414

Cited by 7 publications

(9 citation statements)

References 15 publications

(15 reference statements)

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“…Also, the random numbers 1 and 2 are selected from 0 to 1; this facilitates the algorithm to carry out the random search. Furthermore, the pbest is the individuals solutions, while the gbest is the global attained by the search agents [35].…”

Section: Particle Swarm Optimization Tuning Methodsmentioning

confidence: 99%

Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

Sule

Mokhtar

Jamian

et al. 2020

IJECE

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The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the Matlab 2016b to search the optimal gains of the Proportional and Integral controller through minimization of the objective function. A comparison was made between the results obtained from the GWO tuning method against PSO and GA tuning techniques. The GWO computed the smallest value of the objective function minimized. It exhibited faster convergence and better time response specification compared to other methods. These and more performance indicators show the superiority of the GWO tuning method.

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Section: Particle Swarm Optimization Tuning Methodsmentioning

confidence: 99%

Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

Sule

Mokhtar

Jamian

et al. 2020

IJECE

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“…In each iteration, the PSO algorithm searches for the optimal solution by updating the velocity (28) and the position (29) of each i-th particle taking into consideration its previous best position Pbesti and the best position of the group gbest. At each iteration k, the equations allowing to update the velocity and the position of each particle are given by [19].…”

Section: Particle Swarm Optimization Technique (Pso)mentioning

confidence: 99%

Active power ouptut optimization for wind farms and thermal units by minimizing the operating cost and emissions

Cherkaoui¹,

Belfqih²,

Mariami³

et al. 2020

IJECE

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In recent years, many works have been done in order to discuss economic dispatch in which wind farms are installed in electrical grids in addition to conventional power plants. Nevertheless, the emissions caused by fossil fuels have not been considered in most of the studies done before. In fact, thermal power plants produce important quantities of emissions for instance, carbon dioxide (CO2) and sulphur dioxide (SO2) that are harmful to the environment. This paper presents an optimization algorithm with the objective to minimize the emission levels and the production cost. A comparison of the results obtained with different optimization methods leads us to opt for the grey wolf optimizer technique (GWO) to use for solving the proposed objective function. First, the method used to estimate the wind power of a plant is presented. Second, the economic dispatch models for wind and thermal generators are presented followed by the emission dispatch model for the thermal units.Then, the proposed objective function is formulated. Finally, the simulation results obtained by applying the GWO and other known optimization techniques are analysed and compared.

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“…EP was first proposed as an approach to intelligence, it has been used recently successfully for many numerical and computational optimization problems [18]. EP is a mutation-based evolution algorithm used for discrete search space [19][20][21][22]. EP is a search technique.…”

Section: Evolutionary Programmingmentioning

confidence: 99%

Multi-verse optimization based evolutionary programming technique for power scheduling in loss minimization scheme

Lokman

Musirin

Suliman

et al. 2019

IJ-AI

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The growth of computational intelligence technology has witnessed its application in numerous fields. Power system study is not left behind as far as computational intelligence trend is concerned. In power system community, optimization process is one of the crucial efforts for most remedial action to maintain the power system security. Basically, power scheduling refers to prior to fact action(such as scheduling generators to generate certain powers for next week). Power scheduling process is one of the most important routines in power systems. Scheduling of generators in a power transmission system is an important scheme; especially its offline studies to identify the security status of the system. This determines the cost effectiveness in power system planning. This paper investigates the performance of multi-verse based evolutionary programming(lowest EP) technique in the application of power system scheduling to ensure loss is gained by the system. Losses in the system can be controlled through this implementation which can be realized through the validation on a chosen reliability test system as the main model. Validation on <span style="font-size: 10.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">IEEE 30-Bus Reliability Test System resulted that both techniques are reliable and robust in addressing this issue.

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Optimal location and reactive power injection of wind farms and SVC’s units using voltage indices and PSO

Cited by 7 publications

References 15 publications

Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

Active power ouptut optimization for wind farms and thermal units by minimizing the operating cost and emissions

Multi-verse optimization based evolutionary programming technique for power scheduling in loss minimization scheme

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