scholarly journals Solution of Economic Load Dispatch problem using Conventional methods and Particle Swarm Optimization

2020 ◽  
Vol 9 (10) ◽  
pp. 243-249
Keyword(s):  
Particle Swarm Optimization ◽  
Gradient Method ◽  
Power Output ◽  
Particle Swarm ◽  
Design Parameters ◽  
Economic Load Dispatch ◽  
Swarm Optimization ◽  
Network Cost ◽  
Conventional Methods ◽  
B Coefficients

Economic load dispatch is the method to find the optimum power output of the generators in a network cost-effectively with adherence to all the constraints. In this paper, the Economic Load Dispatch (ELD) problem has been tested on IEEE 14 Bus System by implementing conventional methods like Classical Coordination method, Gradient method, Modified Coordination method, and Particle Swarm Optimization (PSO). Conventional methodologies provide the solution in the simplest way but it does not handle the constraints effectively. Modified coordination method provides a better solution without the use of B-coefficients and the calculation of penalty factors is much easier because they can be obtained from the already available solution of FDLF involving some computations. PSO also provides a better solution but the initial design parameters are slightly difficult to determine. The performance of all the methods is compared and results reveal that the Modified coordination method proves to be the fastest among other solutions particularly if larger systems are involved.

scholarly journals Solving Economic Load Dispatch Problem UsingParticle Swarm Optimization Technique

2021 ◽  
Vol 12 (3) ◽  
pp. 3203-3209
Author(s):  
Aditya Tiwari Et.al
Keyword(s):  
Particle Swarm Optimization ◽  
Conventional Method ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Load Dispatch ◽  
System Operation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Conventional Methods

Economic load dispatch (ELD)is one of the important problems ofpower system operation. Conventional methods like Lambda iteration methodare not efficientfor complex ELD problems. Particle swarm optimization is preferred in ELD problem due to its high performance.The Inertia Weight PSO and Constriction Factor PSO algorithms are performed on threeunit and sixunit systems. The analysis of ELD problem is performed by Conventional method and PSO method. In this paper,losses are neglected in the ELD problem. PSO algorithm obtains the best solution forELD problem.

scholarly journals Stochastic Economic Load Dispatch with Multiple Fuels using Improved Particle Swarm Optimization

2015 ◽  
Vol 48 (30) ◽  
pp. 490-494 ◽  
Author(s):  
Mirtunjay K. Modi ◽  
A. Swarnkar ◽  
N. Gupta ◽  
K.R. Niazi ◽  
R.C. Bansal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

scholarly journals Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (20) ◽  
pp. 9772
Author(s):  
Xueli Shen ◽  
Daniel C. Ihenacho
Keyword(s):  
Particle Swarm Optimization ◽  
Research Effort ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Design Parameters ◽  
Initial Population ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value ◽  
Sensitivity Approach

The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/second.

Adaptive Shewhart Control Charts Under Fuzzy Parameters with Tuned Particle Swarm Optimization Algorithm

2021 ◽  
pp. 1-36
Author(s):  
Amirhossein Amiri ◽  
Ali Salmasnia ◽  
Meraj Zarifi ◽  
Mohammad Reza Maleki
Keyword(s):  
Particle Swarm Optimization ◽  
Control Chart ◽  
Adaptive Sampling ◽  
Control Charts ◽  
Particle Swarm ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Inference System ◽  
Scale Parameters ◽  
Adaptive Control Charts

In recent years, adaptive control charts in which the design parameters depend on the observed samples have been successfully used as efficient alternatives for traditional control charts with constant parameters. In crisp run control rules, the process state may change very sharply from in-control to out-of-control conditions which increase the rate of false alarms. To overcome this drawback, this paper presents an adaptive Shewhart-type control chart, where the design parameters (sample size ([Formula: see text]), sampling interval ([Formula: see text]), and control limit coefficients ([Formula: see text] and [Formula: see text])) are defined with linguistic variables. To accomplish that, the chart parameters are determined based on the location of eight previous chart statistics using a set of fuzzy rules in a continuous environment. In order to improve the sensitivity of the proposed control chart in detecting small shifts in both location and scale parameters, the adaptive procedure is designed by integration of fuzzy Western Electric rules and fuzzy adaptive sampling rules. After designing the control charts using a fuzzy inference system (FIS), in order to provide an economic design of the proposed control chart, a tuned Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal values corresponding to membership functions of the control chart parameters. Finally, using simulation studies, the capability of the proposed control chart is analyzed and compared with common charts in the literature. The results confirm that under different shifts in location and scale parameters, the proposed control chart outperforms other charts in terms of both economic and statistical criteria.

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