Optimal Allocation of Wind Based Distributed Generators in Distribution System Using Cuckoo Search Algorithm

Sudabattula, Suresh Kumar; Kowsalya, M.

doi:10.1016/j.procs.2016.07.359

Cited by 26 publications

(15 citation statements)

References 18 publications

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“…The detailed modeling of D-STATCOM incorporated in the load flow study has been presented in reference [26]. The detailed description of VSI is presented in references [27][28], and the description of LSF is presented in references [29][30].…”

Section: A Distribution Load Flow (Dlf)mentioning

confidence: 99%

Optimal Allocation of DG and D-STATCOM in a Distribution System using Evolutionary based Bat Algorithm

Salkuti¹

2021

IJACSA

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In this work, a methodology to find the optimal allocation (i.e., sizing and location) of Distributed Generators (DGs) and Distribution-static compensators (D-STATCOM) in a radial distribution system (RDS) is proposed. Here, the voltage stability index (VSI) is utilized to find the optimal location for the D-STATCOM, and loss sensitivity factor (LSF) method is utilized to find the optimal location for distributed generation. In this work, the proposed work is formulated as a non-linear optimization problem and it is solved using the metaheuristic/evolutionary-based algorithm. The evolutionary-based Bat algorithm is used to find optimal sizes of D-STATCOM and DGs in RDSs. To check the validity and feasibility and validity of the proposed optimal allocation approach, two standard IEEE 34 and 85 bus RDSs are considered in this paper. The simulation results show reduction in power losses and enhancement in bus voltages in the RDSs.

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Section: A Distribution Load Flow (Dlf)mentioning

confidence: 99%

Optimal Allocation of DG and D-STATCOM in a Distribution System using Evolutionary based Bat Algorithm

Salkuti¹

2021

IJACSA

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“…Such as, artificial bee colony (Mohandas et al, 2015;Kefayat et al, 2015;C. K. Choton K Das et al, 2018), Cuckoo search algorithm S. Sudabattula and Kowsalya 2016;Nguyen & Truong, 2015;Devabalaji et al, 2018), Symbiotic organism search (B. Bikash Das et al, 2016), harmony search (Kayalvizhi & Vinod, 2018;Maleki & Pourfayaz, 2015;Rastgou et al, 2018), bacteria foraging (Kaveh et al, 2018;S.…”

Section: F Meta-heuristic Approachmentioning

confidence: 99%

“…Sudabattula and Kowsalya 2016;Nguyen & Truong, 2015;Devabalaji et al, 2018), Symbiotic organism search (B. Bikash Das et al, 2016), harmony search (Kayalvizhi & Vinod, 2018;Maleki & Pourfayaz, 2015;Rastgou et al, 2018), bacteria foraging (Kaveh et al, 2018;S. Devi & Geethanjali, 2014;Devabalaji & Ravi, 2016), Bat algorithm (Sureshkumar Sudabattula & Kowsalya, 2016;Yammani et al, 2016), Whale optimization (D. B. D. B. Prakash & Lakshminarayana, 2018), Stud Krill herd (ChithraDevi et al, 2017), Grey wolf optimizer (Lakum & Mahajan, 2019;Yahiaoui et al, 2017), Firefly algorithm (Fister et al, 2013;Othman et al, 2016).…”

Section: F Meta-heuristic Approachmentioning

confidence: 99%

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Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends

2020

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Numerous potential advantages to the requirements and effectiveness of the supplied electricity can be accomplished by the installation of distributed generation units. In order to take full advantage of these benefits, it is essential to position the Distributed Generation (DG) units in appropriate locations. Otherwise, their installation may have an adverse effect on the quality of energy and system operation. Several optimization techniques have been created over the years to optimize distributed generation integration. Optimization techniques are therefore constantly changing and have been the main attention of many fresh types of research lately. This article evaluates cutting-edge techniques of optimizing the issue of positioning and sizing distributed generation units from renewable energy sources based on recent papers that have already been applied to distribution

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“…Some examples of solved problems by the cuckoo search algorithm are the problems in satellite image segmentation [14], the resource allocation problems [3,15], the optimal power system stabilizers design problems [16], and the optimal allocation of wind based distributed generator problems [17] among others.…”

Section: Introductionmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

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Optimal Allocation of Wind Based Distributed Generators in Distribution System Using Cuckoo Search Algorithm

Cited by 26 publications

References 18 publications

Optimal Allocation of DG and D-STATCOM in a Distribution System using Evolutionary based Bat Algorithm

Optimal Allocation of DG and D-STATCOM in a Distribution System using Evolutionary based Bat Algorithm

Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

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