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G. Gokulakrishnan

Bio: G. Gokulakrishnan is an academic researcher from VIT University. The author has contributed to research in topics: Smart grid & Control reconfiguration. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

Papers
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Proceedings ArticleDOI
01 Apr 2019
TL;DR: The work gives a novel evolutionary technique for the re-configuration test IEEE systems to find optimal reconfiguration and to improve the active loss in the consumer side.
Abstract: The work gives a novel evolutionary technique for the re-configuration test IEEE systems. The framework applied for optimization is Symbiotic Organism Search Algorithm (SOSA). The aim is to find optimal reconfiguration and to improve the active loss in the consumer side. This approach is examined on 16 and 33 IEEE test circuits. The results shows a valid improvement of active losses. The time required for execution is less when compared to other approaches.

1 citations

Book ChapterDOI
25 Mar 2017
TL;DR: In this paper, the concept of load shifting to low voltage consumers using several types of appliances and in large numbers is discussed. Load shifting with respect to day ahead forecast is formulated as a minimization problem and are solved using learning based evolutionary algorithm.
Abstract: To withstand the quick development with the demand for energy and the overall demand cost, enhanced effectiveness, dependability and adaptability, smart strategies should be carried forth in energy sector for our earth and vitality protection. In the electrical domain DSM can be a part of smart grid where the consumers can participate themselves to decrease the peak load and eventually the load profile can be reshaped. A portion of the DSM method is peak clipping, load shifting, valley filling and energy conservation. The paper involves the concept of load shifting to low voltage consumers using several types of appliances and in large numbers. Load shifting with respect to day ahead forecast is formulated as a minimization problem and are solved using learning based evolutionary algorithm. Simulations were carried out with a specific test case using Mat Lab and the results show a substantial peak reduction and cost savings for the future smart grid.

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Proceedings ArticleDOI
10 Jul 2020
TL;DR: A Symbiotic Organisms Search (SOS) algorithm based solution to optimize the control variables simultaneously to optimized the power system real power loss and voltage stability limit is proposed.
Abstract: This paper proposes a Symbiotic Organisms Search (SOS) algorithm based solution to optimize the control variables simultaneously to optimize the power system real power loss and voltage stability limit. This issue can be mathematically formulated as a problem of nonlinear equality and inequality limited optimization, with an objective function integrating real power loss and voltage stability limit. The problem formulation included transformers taps, unified power flow controller and its parameters as control variables. The efficacy of the proposed algorithm has been tested on Indian 24-bus system. The simulation results obtained with the proposed algorithm are compared with the real coded genetic algorithm (RCGA) for a single objective of minimizing real power loss and multi-objective minimization of real power loss and maximization of voltage stability. Results from simulation demonstrate the ability of the proposed algorithm in solving the optimization problem.