Optimal siting of capacitors in radial distribution network using Whale Optimization Algorithm

Prakash, D.B.; Lakshminarayana, C.

doi:10.1016/j.aej.2016.10.002

Cited by 216 publications

(126 citation statements)

References 23 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…Zeinalzadeh et al (2015), Khodabakhshian and Andishgar (2016) and Rahmani-andebili (2016) proposed simultaneous DGs and capacitors placement in distribution networks. Prakash and Lakshminaraya (2016) proposed whale optimization algorithm for sizing of capacitors.…”

Section: Open Accessmentioning

confidence: 99%

Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems

2017

View full text Add to dashboard Cite

Distributed generator (DG) resources are small scale electric power generating plants that can provide power to homes, businesses or industrial facilities in distribution systems. Power loss reductions, voltage profile improvement and increasing reliability are some advantages of DG units. The above benefits can be achieved by optimal placement of DGs. Whale optimization algorithm (WOA), a novel metaheuristic algorithm, is used to determine the optimal DG size. WOA is modeled based on the unique hunting behavior of humpback whales. The WOA is evaluated on IEEE 15, 33, 69 and 85-bus test systems. WOA was compared with different types of DGs and other evolutionary algorithms. When compared with voltage sensitivity index method, WOA and index vector methods gives better results. From the analysis best results have been achieved from type III DG operating at 0.9 pf.

show abstract

Section: Open Accessmentioning

confidence: 99%

Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems

2017

View full text Add to dashboard Cite

show abstract

“…Whale optimization algorithm is a novel, metaheuristic, nature-inspired algorithm which is an imitation of the hunting mechanism of humpback whales. 25 This algorithm is very effective and has also been applied to other power system problems such as optimal sizing of capacitors, 26 optimal reactive power dispatch, 27 and in hybrid forms such as in optimal power flow problem. 28 With continuous growth in energy demand, the planning of available resources as well as the addition of clean and pollution-free sources is very imperative and needs of the time.…”

Section: Introductionmentioning

confidence: 99%

Least‐cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources

Nawaz

Malik

Ashraf

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Ever‐increasing energy demand and technology development are continuously pursuing the attention of power system planners to design an optimum strategy for supplying future electrical demand. Generation expansion planning turns out to be a crucial step for an efficient energy management system in a modern power grid. In this paper, a novel whale optimization algorithm with penalty factor approach (WOA‐PFA) framework has been developed to solve a nonlinear, discrete, dynamic, highly complex, heavily constrained GEP problem. Reliability evaluation is accomplished using equivalent energy function approach. A virtual mapping procedure is utilized to add computational strength to the WOA‐PFA framework. Emission reduction is performed by utilizing total emission reduction constraint. The results are organized in four case studies starting with base power plants and adding wind, solar, and solar with storage facility in succession for the rest of case studies. Simulation results prove that the proposed WOA‐PFA shows promising results in terms of the least cost and computational time as compared with the techniques presented in the literature.

show abstract

“…It is shown from Mirjalili and Lewis that the WOA is more superior to DE, PSO, fast evolutionary programming (FEP), and GSA. It is clear from Prakash and Lakshminarayana that the WOA is more efficient as compared with plant growth simulation, bacteria foraging optimization algorithm, PSO, and mixed integer nonlinear programming methods for voltage profile improvement. Further, the WOA technique is compared with PSO and PSO with time varying acceleration coefficients method to dispatch of reactive power, which is shown in Ben oualidMedani et al From the literature study, it is found that the WOA has not been utilized for the coordinated design of FACTS controller with PSS.…”

Section: Introductionmentioning

confidence: 99%

Modified whale optimization algorithm for coordinated design of fuzzy lead‐lag structure‐based SSSC controller and power system stabilizer

Sahu

Hota

Panda

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary In this paper, a new fuzzy lead‐lag controller for a coordinated structure combining static synchronous series compensator and power system stabilizer is designed to improve the power system stability. The controller parameters are optimized by using a modified whale optimization algorithm (MWOA). The newly proposed MWOA attains a suitable balance among exploration and exploitation phases of WOA. This potential of this MWOA is certified by means of utilizing the benchmark functions and by further comparing with that of a few heuristic algorithms such as whale optimization algorithm, fast evolutionary programming algorithm, gravitational search algorithm, differential evolution, and particle swarm optimization. Here, the MWOA technique is utilized to tune the parameters of the proposed controller. The controller is tested under various loading conditions as well as system configurations such as single‐machine infinite bus system and multimachine power system. Finally, the simulation results so obtained using the designed controller under different disturbance conditions are contrasted with that of the conventional lead‐lag structure controller.

show abstract

Optimal siting of capacitors in radial distribution network using Whale Optimization Algorithm

Cited by 216 publications

References 23 publications

Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems

Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems

Least‐cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources

Modified whale optimization algorithm for coordinated design of fuzzy lead‐lag structure‐based SSSC controller and power system stabilizer

Contact Info

Product

Resources

About