Artificial neural network integrated with bio-inspired approach for optimal VAr management and voltage profile enhancement in grid system

Kumar, C.; Selvaraj, Jaisiva; Joseph, X. Felix; Kandasamy, Prabaakaran

doi:10.1080/15567036.2021.1919790

Cited by 7 publications

(2 citation statements)

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“…Chandrasekaran et al have suggested the use of the artificial intelligence technique for voltage profile enhancement using optimization algorithm in the IEEE 30 bus system. 6 Shaaban et al 7 derived an exact loss formula integrated with sensitivity index and coefficient of voltage sensitivity in the distributed generation for reduction in power losses and improvement in voltage profiles with the use of Gray Wolf Optimization (GWO). Prem Prakash et al 8 has proposed an analytical method for locating the best and most suitable locations for connecting distributed generations in radial distribution networks to minimize system losses.…”

Section: Introductionmentioning

confidence: 99%

“…Singh 5 has suggested radial distribution systems with varied densities with a specific technique for the determination of best placements for distributed generations. Chandrasekaran et al have suggested the use of the artificial intelligence technique for voltage profile enhancement using optimization algorithm in the IEEE 30 bus system 6 . Shaaban et al 7 derived an exact loss formula integrated with sensitivity index and coefficient of voltage sensitivity in the distributed generation for reduction in power losses and improvement in voltage profiles with the use of Gray Wolf Optimization (GWO).…”

Section: Introductionmentioning

confidence: 99%

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Optimal allocation of distributed generation for power flow enhancement in distribution network

Jayaraman Panneerselvam,

Krishnamoorthi,

Ramamoorthy

et al. 2023

Optim Control Appl Methods

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This article articulates the enhancement of voltage profiles in transmission systems by reducing the voltage deviation and system power losses. Electric power flows uniformly throughout Regional Distribution Networks in the power grid station, resulting in low voltage consistency, significant voltage dips and power losses in the distribution network, highlighting high R/X ratios even with a heavy load or power failure issues, despite installing distributed generation in the radial distribution system for enhancing voltage profile in the grid network. The meta‐heuristic optimization algorithm plays a vital role in determining optimal location of DG for attaining the objective of research. In the practical network, a single objective optimization strategy cannot be employed to solve of power system optimization problems of all kinds. Hence multi‐objective function needs to be addressed. The prime task of this research is to investigate and allocate an optimal location to connect the Distributed Generation, with evaluation of the best DG configuration, with minimized power losses and improved voltage profile of the distribution network using the Hummingbird Optimization Algorithm. A standard IEEE 37‐bus Radial Distribution system is employed as the test bus system to test the performance and effectiveness of the optimization technique. To show the stiffness of the proposed algorithm the results are compared with various optimization approaches that employ similar objective functions.

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Section: Introductionmentioning

confidence: 99%