Swarm algorithms in dynamic optimization problem of reactive power compensation units control

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.

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

“…where Uns2 and Ups1 are the negative and positive voltage sequence components, respectively. The current unbalance factor kI2 can be determined similarly to equation (4) for the evaluation of the influence of the load unbalance on the power quality level. The indicator of the degree of correction of voltage dips and deviations ΔU is determined according to the following:…”

Section: The Third Stagementioning

confidence: 99%

“…It is undeniable that ensuring power quality improvement and electromagnetic compatibility is critical in centralized power supply systems, distributed generation systems, and combined power supply systems [1][2][3][4]. The main negative influences of the poor power quality level on electrical equipment and power supply systems are well-known and have been studied in detail [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

The assesement of the shunt active filter efficiency under varied power supply source and load parameters

Sychev

Abramovich

Prokhorova

2020

The results of theoretical and experimental studies on the problems of effective application of shunt active filters for power quality improvement and electromagnetic compatibility ensuring were presented in this article. Based on the different theories and methods, the most effective and well-studied control algorithms of shunt active filters were determined and analyzed to ensure their effective application in distributed generation systems and combined power supply systems. Mathematical and computer simulation models of shunt active filters with different control algorithms in distributed generation systems and combined power supply systems were developed. According to the results of mathematical modeling and computer simulation, the dependences were detected, reflecting the influences of internal and external parameters on the factors, determining the efficiency of the correction of power quality indicators. The range of permissible changes in the parameters of shunt active filters by saving the required efficiency level of power quality correction was detected under varied power supply system characteristics, load parameters and also in hybrid structures. The adaptive algorithm of the shunt active filter functioning under varied application conditions for automated power quality improvement is developed on the basis

“…Swarm Intelligence (SI) algorithms are one of the most effective ways to solve complex optimization problems [18,19] including optimization of power systems [20][21][22]. We mean non-linear, non-differentiable, high-dimensional problems with complex topology of the solution search space, stochastic and dynamic properties.…”

Section: Swarm Intelligence Applicationmentioning

confidence: 99%

“…In this research, a separate study of the influence of the heuristic coefficients was not carried out. We used several sets of heuristic coefficient values that showed high efficiency in our previous studies about [20]. The FFO algorithm requires comparing each particle to each other, so the number of operations quadratically depends on the number of particles.…”

Section: Swarm Intelligence Applicationmentioning

confidence: 99%

Application of swarm intelligence algorithms to energy management of prosumers with wind power plants

Matrenin

Manusov

Khasanzoda

et al. 2020

Self Cite

The paper considers the problem of optimal control of a prosumer with a wind power plant in smart grid. It is shown that control can be performed in non-deterministic conditions due to the impossibility of accurate forecasting of the generation from renewable plants. A control model based on a priority queue of logical rules with structural-parametric optimization is applied. The optimization problem is considered from a separate prosumer, not from the entire distributed system. The solution of the optimization problem is performed by three swarm intelligence algorithms. Computational experiments were carried out for models of wind energy systems on Russky Island and Popov Island (Far East). The results obtained showed the high effectiveness of the swarm intelligence algorithms that demonstrated reliable and fast convergence to the global extreme of the optimization problem under different scenarios and parameters of prosumers. Also, we analyzed the influence of accumulator capacity on the variability of prosumers. The variability, in turn, affects the increase of the prosumer benefits from the interaction with the external global power system and neighboring prosumers.