Multiobjective load dispatch by fuzzy logic based searching weightage pattern

Brar, Yadwinder Singh; Dhillon, J.S.; Kothari, D.P.

doi:10.1016/s0378-7796(02)00092-5

Cited by 51 publications

(29 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The membership function increases from 0.5165955 to 0.5241862 with the decrease in step size from 0.05 to 0.02 and further 0.5241862 to 0.5430062 with the decrease in step size from 0.02 to 0.01. The results obtained in the proposed method are also compared with the results of Brar et al (2002). It has been observed that, the solutions achieved in proposed method have more membership satisfaction as compared to the results presented in Brar et al (2002).…”

Section: Test System and Resultsmentioning

confidence: 89%

“…Fuzzy based mechanism is employed to extract the best compromise solution over the trade-off curve. Brar et al (2002) have used fuzzy logic based weightage pattern searching to obtain the solution of multiobjective load dispatch problem. The evolutionary optimization technique has been employed in which the 'preferred' weightage pattern is searched to get the 'best' optimal solution in non-inferior domain.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cardinal priority ranking based decision making for economic-emission dispatch problem

Singh¹,

Dhillon²

2010

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

This paper deals with the economic emission dispatch (EED) problem relating to real and reactive power scheduling of thermal power generating units. The formulated EED problem is solved using weighting method to generate non-inferior solutions which allows explicit trade-offs between objective levels for each non-inferior solutions. Fuzzy decision making methodology is exploited to decide the generation schedule. To access the indifference band, interaction with the decision maker is obtained via cardinal priority ranking (CPR) of the objectives. The cardinal priority ranking is constructed in the functional space and then transformed into the decision space, so the cardinal priority ranking of objectives relate the decision maker's preferences to non-inferior solutions through normalized weights. Regression analysis is performed between the cardinal priority ranking and simulated weights to decide the 'best' compromised solution. Decoupled load flow analysis is performed to find the loss coefficients and transmission losses. The validity of the proposed method is demonstrated on IEEE 11-bus system which comprises 3-generators.

show abstract

Section: Test System and Resultsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Cardinal priority ranking based decision making for economic-emission dispatch problem

Singh¹,

Dhillon²

2010

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

show abstract

“…In recent years certain artificial intelligence (AI) techniques such as Fuzzy Logic (FL) [4], Artificial Neural Network (ANN) [5][6], Genetic Algorithm (GA) [7][8], Particle Swarm Optimization (PSO) [11][12][13], Bacteria Foraging Optimization (BFO) [14], Differential Evolution (DE) [15][16] etc. have been successfully applied to the ED problems for units having non-linear cost functions.…”

Section: Introductionmentioning

confidence: 99%

Application of Biogeography-based Optimization for Economic Dispatch Problems

Khokhar¹,

Parmar²,

Dahiya³

2012

IJCA

View full text Add to dashboard Cite

In this paper, Biogeography-based optimization (BBO) algorithm has been presented for solving the economic dispatch (ED) problems. An optimal short-term thermal generation schedule for 24 time intervals has been presented for the same purpose. The BBO algorithm has been applied to two different test systems, one consisting of three generators and the other of six generators.The results obtained are compared with the conventional Lagrange multiplier method and the particle swarm optimization (PSO) method. The results show that the presented BBOalgorithm provides comparatively better solutions in terms of total fuel cost as compared to other methods.Also, the global search capability is enhanced and premature convergence is avoided.

show abstract

“…-may be overcome by applying the artificial intelligence techniques. The most common optimization techniques based upon artificial intelligence used for solving economic power dispatch problems are: the genetic algorithm [3][4][5][6], the Hopfield neural networks [2,7], the differential algorithm [8], the evolutionary programming [9,10], fuzzy-optimization [12,13], tabu search [14], particle swarm optimization [15,16,27,28]. Also, the EPD can be formulated as a multi-objective optimization problem [11,13,17].…”

Section: Introductionmentioning

confidence: 99%

“…The most common optimization techniques based upon artificial intelligence used for solving economic power dispatch problems are: the genetic algorithm [3][4][5][6], the Hopfield neural networks [2,7], the differential algorithm [8], the evolutionary programming [9,10], fuzzy-optimization [12,13], tabu search [14], particle swarm optimization [15,16,27,28]. Also, the EPD can be formulated as a multi-objective optimization problem [11,13,17]. TWe mention that the particle swarm optimization method was successfully applied to other optimization problems, such as optimal power flow [18][19][20], reactive power optimization and voltage control [23], power loss reduction in distribution systems [24], network reconfiguration [25], unit commitment problem [26], due to its good convergence, low computational time and good quality solutions.…”

Section: Introductionmentioning

confidence: 99%

A Swarm Intelligence Approach to the Power Dispatch Problem

Secui

Felea

Dziţac

et al. 2010

INT J COMPUT COMMUN

View full text Add to dashboard Cite

This paper examines how two techniques of the Particle Swarm Optimization method (PSO) can be used to solve the Economic Power Dispatch (EPD) problem. The mathematical model of the EPD is a nonlinear one, PSO algorithms being considered efficient in solving this kind of models. Also, PSO has been successfully applied in many complex optimization problems in power systems. The PSO techniques presented here are applied to three case studies, which analyze power systems having four, six, respectively twenty generating units.

show abstract

Multiobjective load dispatch by fuzzy logic based searching weightage pattern

Cited by 51 publications

References 12 publications

Cardinal priority ranking based decision making for economic-emission dispatch problem

Cardinal priority ranking based decision making for economic-emission dispatch problem

Application of Biogeography-based Optimization for Economic Dispatch Problems

A Swarm Intelligence Approach to the Power Dispatch Problem

Contact Info

Product

Resources

About