A novel hybrid optimizer for solving Economic Load Dispatch problem

Parouha, Raghav Prasad; Das, Kedar Nath

doi:10.1016/j.ijepes.2015.11.058

Cited by 46 publications

(23 citation statements)

References 47 publications

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“…The wind turbine model consist of 8.5KW wind farm of base power 9.5 KVA connected to the distribution system which exports power to a grid through feeder [8]. The wind turbine existing in this demonstration contains synchronous generator connected to a diode rectifier, a DC-DC MOSFET based PWM boost converter and a DC/AC MOSFET based PWM converter modelled by voltage sources [9][10].…”

Section: A Wind Turbine Model Descriptionmentioning

confidence: 99%

Fuzzy Based ALO Implementation for Fast Patrol Craft Tests in Marine Industry Using Wind Energy Conversion System for Multi Objective Power Dispatch

Yuvaraja¹

2017

IJRASET

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Shipbuilding and Repair actions are characterized as marine conveyance equipment sector, though they also cover supply of fixed and floating structures to the offshore industry. The Marine industrial master plan between the year 2006 -2020 identify the following challenges i) global competition ii) rising costs of raw materials iii) excess capacity iv) shortage of qualified personnel v) inadequate technology vi) lack of infrastructure and support facilities. This manuscript deals with the new technology that is required for Fast Patrol Craft Tests. The Fast Patrol Craft Tests is designed using fuzzy based ALO implementation in WECS used in Microgrid uses multi objective genetic algorithm which works on different swept speed using a set of wind turbines that are applied to different dimensions. The Power generation using WECS depends on the wind speed and swept area of the wind turbine. The objective of the simulation is to investigate the entire system operation. The objective dictates the extent of idealization of every model in the system. The MFS problem is formulated as a multi-objective search, aiming at searching for a set of control variable settings that are comparatively 'equally good' for multiple objectives. In order to select a suitable representative solution for the multiple objectives, fuzzy decision making mechanism concept is used. In this manuscript the WECS is modeled and FGALO algorithm is implemented on the controller to attain stability under different swept rotor speeds.

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Section: A Wind Turbine Model Descriptionmentioning

confidence: 99%

Fuzzy Based ALO Implementation for Fast Patrol Craft Tests in Marine Industry Using Wind Energy Conversion System for Multi Objective Power Dispatch

Yuvaraja¹

2017

IJRASET

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“…The statistical performance over twenty trials is tabulated in Table 9. The best operating cost achieved by the KHAM CD method is 136446.4053$/hr .The co mparison of result has been made with hybrid genetic algorithm with ant colony optimization (GAAPI) [42], shuffled differential evolution (SDE) [43], teaching learning based optimization (TLBO) [44], oppositional real coded chemical reaction optimizat ion (ORCCRO) [45] , krill herd algorith m (KHA) [46], oppositional invasive weed optimization (OIWO) [47] and most recently reported Opposition-based krill herd algorith m (OKHA) [53], hybrid PSO DE approach [54]. Here it can be observed that proposed method KHAMCD is able to achieve cheapest generation cost as compared to other reported methods.…”

Section: ) Forty Unit Nonconvex Systemmentioning

confidence: 99%

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Agrawal¹,

Pandit²,

Dubey³

2016

IJISA

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Abstract-Krill herd algorith m (KHA) is a novel nature inspired (NI) optimization technique that mimics the herding behavior of krill, which is a kind of fish found in nature. The mathematical model of KHA is based on three phenomena observed in the behavior of a herd of krills, which are, mo ment induced by other krill, fo raging motion and random physical d iffusion. These three key features of the algorith m provide a good balance between global and local search capability, which makes the algorith m very powerfu l. The objective is to minimize the distance of each krill fro m the food source and also from the point of highest density of the herd, which helps in convergence of population around the food source. Improvisation has been made by introducing neighborhood distance concept along with genetic reproduction mechanism in basic KH A lgorith m. KHA with mutation and crossover is called as (KHAM C) and KHA with neighborhood distance concept is referred here as (KHAMCD). This paper compares the performance of the KHA with its two imp roved variants KHAMC and KHAM CD. The performance of the three algorithms is compared on eight benchmark functions and also on two real world economic load dispatch (ELD) problems of power system. Results are also compared with recently reported methods to establish robustness, validity and superiority of the KHA and its variant algorithms. Index Terms-Krill Herd A lgorith m (KHA), mutation and crossover, neighborhood distance concept, unimodal function, multimodal function, economic load dispatch.

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“…Recently, metaheuristic techniques, such as evolutionary programming, genetic algorithm, simulated annealing, harmony search, differential evolution (DE), bacteria foraging optimization, ant colony optimization, artificial bee colony, and particle swarm optimization (PSO), have been successfully applied to solve ELD problems due to their ability to handle complex optimization problems. In addition, some hybridization of these methods has been used to solve ELD problems effectively . Despite this, much progress has been made on metaheuristic methods for ELD problems.…”

Section: Introductionmentioning

confidence: 99%

Nonconvex/nonsmooth economic load dispatch using modified time‐varying particle swarm optimization

Parouha

2019

Computational Intelligence

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In this paper, a modified time‐varying particle swarm optimization (MTVPSO) is proposed for solving nonconvex economic load dispatch problems. It is a variant of the traditional particle swarm optimization (PSO) algorithm. In an MTVPSO, novel acceleration coefficients for cognitive and social components are presented as linear time‐varying parameters in the velocity update equation of the PSO algorithm. In the early stages of the optimization process, it improves the global search capability of particles and directs the global optima at the end stage. Additionally, a linearly decreased inertia weight is introduced in an MTVPSO, instead of a fixed constant value, which helps improve the diversity of the population. Through this modification mechanism in PSO, the proposed algorithm has a higher probability of avoiding local optima, and it is likely to find global optima more quickly. Six complex benchmark functions have been used to validate the effectiveness of the proposed algorithm. Furthermore, to demonstrate its efficiency, feasibility, and fastness, six different cases (3‐, 6‐, 13‐, 15‐, and 40‐unit systems and one large‐scale Korean power 140‐unit system) of the economic load dispatch problem are solved by an MTVPSO. The results of the proposed algorithm have been compared with state‐of‐the‐art algorithms. It was found that the proposed MTVPSO can deliver better results in terms of solution quality, convergence characteristics, and robustness.

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A novel hybrid optimizer for solving Economic Load Dispatch problem

Cited by 46 publications

References 47 publications

Fuzzy Based ALO Implementation for Fast Patrol Craft Tests in Marine Industry Using Wind Energy Conversion System for Multi Objective Power Dispatch

Fuzzy Based ALO Implementation for Fast Patrol Craft Tests in Marine Industry Using Wind Energy Conversion System for Multi Objective Power Dispatch

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Nonconvex/nonsmooth economic load dispatch using modified time‐varying particle swarm optimization

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