An enhanced particle swarm optimization algorithm

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: Standard Objective Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sule

Mokhtar

Jamian

et al. 2020

“…The first part of (1), provides exploration ability for PSO. The second and third parts, 1× × ( − ) and, 1× × ( − ) represent private thinking and collaboration of particles respectively [23,24]. The PSO is initialized with randomly placing the particles in a problem space.…”

Section: The Standard Particle Swarm Optimization 21 Particle Swarmmentioning

confidence: 99%

Population based optimization algorithms improvement using the predictive particles

Elroby¹,

Mekhamer²,

Talaat³

et al. 2020

A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate.

“…This algorithm can be effectively useful in solving many non-linear hard optimization problems [10]. Unlike the mathematical methods for solving optimization problems, this algorithm does not need any gradient information about objective or error function and it can obtain the best solution independently [23]. According to the PSO algorithm, a swarm of particles that have predefined restrictions starts to fly on the search space.…”

Section: Overview Of Psomentioning

confidence: 99%

Optimal unit commitment of a power plant using particle swarm optimization approach

Anyaka¹,

Manirakiza²,

Chike³

et al. 2020

Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also three different combinations in the form of 6, 5 and 4 units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.