Optimal placement of UPFC for maximizing system loadability and minimize active power losses by NSGA-II

Wartana, I Made Hedy; Singh, Jai Govind; Ongsakul, Weerakorn; Buayai, Kittavit; Sreedharan, Sasidharan

doi:10.1109/icuepes.2011.6497710

Cited by 15 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…1 1 Subject to (4) where VL is the thermal and bus violation limit factor, OLL i and BVV j represent the overloaded line factor and branch the bus voltage violation factor, respectively; N l and N b are the total numbers of transmission lines and buses, respectively. In addition Ȝ 1 is a load parameter of the system, which intends to locate the maximum sum of power that the network is able to supply within the system security margin [13], [14].…”

Section: Problem Formulationmentioning

confidence: 99%

“…The optimization problem for the best promising placement of the SVC controller is changed into an unconstrained optimization problem using a penalty factor (PF) as given in (14). This becomes the fitness function in (FF) the PSO technique.…”

Section: B Calculation Of Fitness Functionmentioning

confidence: 99%

“…The last term relates to a constraints violation that is multiplied by a PF to calculate the fitness function given by (14) for each particle. Constant μ i is the weighting coefficient which is used to adjust the slope of the PSO.…”

Section: Function Fitnessmentioning

confidence: 99%

“…The NRPF method is performed to gauge the voltage at each bus and line flow. Using these results, the value of VL for each particle is attained by using (4) and the FF of each particle is calculated by using (14). The particle that gives the maximum value for the fitness function in the population is considered as g best particle.…”

Section: Function Fitnessmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Placement of a Shunt FACTS Controller in Java-Bali 24-Bus Indonesian System by Evolutionary Optimization Technique

Wartana

Agustini

2013

2013 1st International Conference on Artificial Intelligence, Modelling and Simulation

View full text Add to dashboard Cite

One type of Evolutionary Optimization Technique namely Particle Swarm Optimization (PSO) have been proposed in this paper to obtain the optimal placement a Shunt Flexible AC Transmission System (FACTS) Controller i.e. Static VAr Compensator (SVC) in the network. An Optimal Power Flow (OPF) problem with mixed integer programming has been formulated for simultaneously optimizing multiobjectives optimization problem viz., enhancing the power system loadability, minimizing the active power loss of transmission line, and by considering installation cost of the controller whereas maintaining the system security and stability margins, e.g., small signal stability, fast voltage stability index, and line stability factor in their acceptable margins. The effectiveness of the proposed methodology has been investigated on a practical Java-Bali 24-bus Indonesia system. Results demonstrate that the static and dynamic performances of the power system can be effectively enhanced by the optimal allocation of the SVC in the network.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Section: B Calculation Of Fitness Functionmentioning

confidence: 99%

Section: Function Fitnessmentioning

confidence: 99%

Section: Function Fitnessmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Placement of a Shunt FACTS Controller in Java-Bali 24-Bus Indonesian System by Evolutionary Optimization Technique

Wartana

Agustini

2013

2013 1st International Conference on Artificial Intelligence, Modelling and Simulation

View full text Add to dashboard Cite

show abstract

“…Bhandakar has also used GA for optimal placement of an UPFC and HPFC in a transmission network [9]. Made Wartana has used the Nondominated Sorting Genetic Algorithm-II (NSGA-II) to place an UPFC that has improved transmission capacity and reduced losses in a transmission network [10]. Mahdi M. et al have also used NSGA-II to do the optimal positioning of FACTS devices such as: UPFC, SVC and TCSC to reduce losses and risks of voltage instability [11].…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of an Unified Power Flow Controller in a Transmission Network by Unified Non Dominated Sorting Genetic Algorithm-III and Differential Evolution Algorithm

Oloulade¹,

Adolphe²,

François-Xavier³

et al. 2019

IJECEC

View full text Add to dashboard Cite

The interconnected transmission network of the Benin Electric Community (CEB) presents excessive losses that will increase further by 2025 and severely degrade the power quality supplying. This paper presents the quarto-criterion optimization of the positioning of a UPFC in the CEB's HTB network by U-NSGA III and DE. The positioning of an UPFC on line 3 and node 6 of this network has qualitatively improved the technical performance of this transmission network with an optimal installation cost of US $ 13126657.03. The hypervolumes of the two calculated methods showed that the U-NSGA-III is more accurate in terms of trend toward global solutions than DE. These convincing results confirm the performance of the U-NSGA-III method compared to the DE which also resulted in results close to those obtained by the U-NSGA-III with more or less narrow difference.

show abstract

Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability

Pathan

Shahriar

Rahman

et al. 2022

Artificial Intelligence‐based Smart Power Systems

View full text Add to dashboard Cite

Optimal placement of UPFC for maximizing system loadability and minimize active power losses by NSGA-II

Cited by 15 publications

References 22 publications

Optimal Placement of a Shunt FACTS Controller in Java-Bali 24-Bus Indonesian System by Evolutionary Optimization Technique

Optimal Placement of a Shunt FACTS Controller in Java-Bali 24-Bus Indonesian System by Evolutionary Optimization Technique

Optimal Placement of an Unified Power Flow Controller in a Transmission Network by Unified Non Dominated Sorting Genetic Algorithm-III and Differential Evolution Algorithm

Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability

Contact Info

Product

Resources

About