An Improved Differential Evolution Algorithm for Congestion Management considering Voltage Stability

Suganthi, S.; Devaraj, D.; Thilagar, S. Hosimin

doi:10.17485/ijst/2015/v8i24/80243

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…The details of the IEEE 30-bus test system are given in [4]. The bids submitted by generators and the loads to alter their schedule are given in table 1.…”

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

“…The real power losses of the system also got reduced with the DSB model. [11], Differential Evolution (DE) [12] algorithm, Improved Differential Evolution (IDE) [4], and conventional TLBO Algorithm. All the algorithms were implemented on the MATLAB platform.…”

Section: 1a Calculation Of Congestion Management Cost and Reschedulmentioning

confidence: 99%

“…Rescheduling of generators [3,4], load curtailment [5][6][7], usage of regulating transformers/tap setting transformers [8], FACTS devices, etc., [9,10] are the proposed techniques to relieve congestion. SO generally prefers to use the rescheduling of generators instead of any other techniques so that the system topology remains unaltered.…”

Section: Introductionmentioning

confidence: 99%

“…The application of Differential Evolution (DE) for CM is discussed in [17,18]. An attempt is made in [4], to improve the convergence of DE with DBMO (Double Best Mutation Operator) for CM.…”

Section: Introductionmentioning

confidence: 99%

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Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

Suganthi¹,

Devaraj

Thilagar

et al. 2018

Sādhanā

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This article proposes a new strategy for Congestion Management (CM) through Generation Rescheduling (GR) with Distributed Slack Bus (DSB) model using Improved Teaching Learning Based Optimization (ITLBO) algorithm. Most of the previous research mainly focuses on rescheduling the existing generators for CM and least concerned about the consequence of this rescheduling on the slack bus. As a result, the slack bus is supposed to carry the entire residual effect of all other generators which leads to increased Congestion Management Cost (CMC). This work proposes a distributed slack bus model for reducing the excess burden on the slack bus and a twofold contribution is made to develop the proposed technique. First, is the selection of the participating generator for CM by means of Incremental Generator Sensitivity Factor (IGSF), which adopts the generator sensitivity as well as the bidding cost. Next, CM is formulated as an optimization problem with the objective function involving congestion management cost and solved using ITLBO algorithm, which incorporates the self-motivated learning concept with the basic TLBO operators. For evaluation, the standard IEEE 30-bus and IEEE 118-bus systems are used and the obtained results are compared with the other CM systems reported through literature.

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“…The details of the IEEE 30-bus test system are given in [4]. The bids submitted by generators and the loads to alter their schedule are given in table 1.…”

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

Section: 1a Calculation Of Congestion Management Cost and Reschedulmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

Suganthi¹,

Devaraj

Thilagar

et al. 2018

Sādhanā

View full text Add to dashboard Cite

show abstract

“…There are many methods available in automated tuning approaches such as genetic algorithm (GA), differential evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm and Artificial Bee Colony (ABC) algorithm. Based on comparative studies, used DE is more suitable compared to others because DE is more robust [16] and only involved a few parameters control [17], [18]. In this work, the DE is used to represent the variables in nonlinear equations system in chromosome form.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method

Ismail¹,

Mezhuyev²,

Deris³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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In this paper, an improved method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contains many components. In addition, the multi-objective problem also needs to be considered. Due to that, this study proposed and improved a method that comprises with Newton method, differential evolution algorithm (DE) and competitive co-evolutionary algorithm(ComCA). The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems, and the experimental results show that the proposed method performs well compared to other works.

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Optimized Renewable Energy Resource Planning for a Rural Microgrid with Linear and Nonlinear Algorithms

Soni

Fernandez

Kamal

et al. 2022

Sustainable Technology and Advanced Computing in Electrical Engineering

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An Improved Differential Evolution Algorithm for Congestion Management considering Voltage Stability

Cited by 13 publications

References 21 publications

Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method

Optimized Renewable Energy Resource Planning for a Rural Microgrid with Linear and Nonlinear Algorithms

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