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 12 publications

(3 citation statements)

References 36 publications

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“…In [22], three different evolutionary techniques, the big bang-big crunch algorithm, gravitational search algorithm and bacterial foraging algorithm are used in conjunction to train and validate an ANN. This research's strategy manages reactive power and corrects the voltage profile.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Neural Network as a Strategy for Optimal Sizing and Location of Reactive Compensation Considering Power Consumption Uncertainties

Jaramillo¹,

Carrion²,

Muñoz³

2022

Energies

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This research proposes a methodology for the optimal location and sizing of reactive compensation in an electrical transmission system through a deep neural network (DNN) by considering the smallest cost for compensation. An electrical power system (EPS) is subjected to unexpected increases in loads which are physically translated as an increment of users in the EPS. This phenomenon decreases voltage profiles in the whole system which also decreases the EPS’s reliability. One strategy to face this problem is reactive compensation; however, finding the optimal location and sizing of this compensation is not an easy task. Different algorithms and techniques such as genetic algorithms and non-linear programming have been used to find an optimal solution for this problem; however, these techniques generally need big processing power and the processing time is usually considerable. That being stated, this paper’s methodology aims to improve the voltage profile in the whole transmission system under scenarios in which a PQ load is randomly connected to any busbar of the system. The optimal location of sizing of reactive compensation will be found through a DNN which is capable of a relatively small processing time. The methodology is tested in three case studies, IEEE 14, 30 and 118 busbar transmission systems. In each of these systems, a brute force algorithm (BFA) is implemented by connecting a PQ load composed of 80% active power and 20% reactive power (which varies from 1 MW to 100 MW) to every busbar, for each scenario, reactive compensation (which varies from 10 Mvar to 300 Mvar) is connected to every busbar. Then power flows are generated for each case and by selecting the scenario which is closest to 90% of the original voltage profiles, the optimal scenario is selected and overcompensation (which would increase cost) is avoided. Through the BFA, the DNN is trained by selecting 70% of the generated data as training data and the other 30% is used as test data. Finally, the DNN is capable of achieving a 100% accuracy for location (in all three case studies when compared with BFA) and objective deviation has a difference of 3.18%, 7.43% and 0% for the IEEE 14, 30 and 118 busbar systems, respectively (when compared with the BFA). With this methodology, it is possible to find the optimal location and sizing of reactive compensation for any transmission system under any PQ load increment, with almost no processing time (with the DNN trained, the algorithm takes seconds to find the optimal solution).

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Neural Network as a Strategy for Optimal Sizing and Location of Reactive Compensation Considering Power Consumption Uncertainties

Jaramillo¹,

Carrion²,

Muñoz³

2022

Energies

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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%

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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Computational Intelligence Theory

Jaisiva,

Kumar,

Ram

et al. 2024

Computational Intelligence

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

Cited by 12 publications

References 36 publications

A Deep Neural Network as a Strategy for Optimal Sizing and Location of Reactive Compensation Considering Power Consumption Uncertainties

A Deep Neural Network as a Strategy for Optimal Sizing and Location of Reactive Compensation Considering Power Consumption Uncertainties

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

Computational Intelligence Theory

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