Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm

Abstract

In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to test the effectiveness of the proposed technique which are fuel cost minimization, carbon emission minimization and multiobjective function with fixed weighted sum. The effectiveness of the MOCSA’s performances are illustrated through comparative study with other techniques such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Particle Swarm Optimization (MOPSO) in terms of fitness functions. The proposed study was conducted on three generating unit system at various loading condition. The result proved that MOCSA provide better solution in minimizing fuel cost and carbon emission usage as compared to other techniques.

Fault classification in smart distribution network using support vector machine

“…d) Kernel function K (x, y) -SVM used non-linear mapping to map the training data points into a higher dimensional feature space. There are four types of kernel functions that can be used in SVM to construct mapping [21,[28][29][30]. In reference [19][20][21][27][28][29], the RBF kernel is chosen as the best choice for the SVM kernel function because the RBF kernel has fewer numerical problems.…”

Optimal volt/var control of distribution system using MOPSO

“…In this work, we present a modified Multi-Objective Particle Swarm Optimization (MOPSO), which allows the PSO algorithm to be able to solve multi-objective optimization problems. Our current work is an improvisation of the algorithm, in which we have added a constraint-handling mechanism and a mutation operator [13][14] that considerably improves the exploratory capabilities of the original algorithm [13,[15][16].…”

“…The performance of MOPSO is improvised by the use of an external archive of non-dominated solutions found in previous iterations. The Cauchy Mutation (CM) operator improves the exploratory capabilities of the algorithm, and prevents premature convergence [15]. However, it should be noted that the use of CD of each solution, as a diversity operator by NSGA-II was able to produce a better distribution of the generated Non-dominated solutions, compared to the results generated by MOPSO that uses an adaptive grid [15] in maintaining the diversity of the generated solutions.…”

“…The above stated heuristic algorithms have overcome the drawbacks in the traditional methods, but also have certain 1089 limitations, that is they easily get trapped in the local optima and premature convergence would occur. In order to focus on a better convergence, the problem formulation was formulated as multi-objective optimization problem for different power system problems [14][15].…”

The performance of COR optimisation using different constraint handling strategies to solve ELD

“…The conventional techniques have a higher probability of becoming trapped in local minima solution due to the complex fuel cost function problem related to the highly nonlinear characteristic of present power generating units such as ramp rate limit and prohibited operating zones. Nowadays, in order to handle the nonlinear fuel cost function, many advanced optimization techniques based on nature-inspired meta-heuristic has been implemented on ELD such as Genetic Algorithm [6], Particle Swarm Optimization [7], Artificial Bee Colony [8,9], and Ant Colony Optimization [10], Cuckoo Search Algorithm [11,12], to name a few. In 2014, Mohseni, Gholami, Zarei and Zadeh [13] introduced a new meta-heuristic algorithm called Competition Over Resources (COR), inspired by a group of animal communities which compete for resources.…”