Evolutionary Particle Swarm Optimization (EPSO) based technique for multiple SVCs optimization

Jumaat, Siti Amely; Musirin, Ismail; Othman, Muhammad Murtadha; Mokhlis, Hazlie

doi:10.1109/pecon.2012.6450202

Cited by 11 publications

(4 citation statements)

References 35 publications

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“…Based on the data from [10,11], the cost function of SVC is modeled as follows the Eq. (2) and ( 3) :…”

Section: B Cost Of Installationmentioning

confidence: 99%

“…This section explains Evolutionary Particle Swarm Optimization (EPSO) that is one of the optimization techniques based on swarm intelligence [11][12][13]. The optimal location and sizing of FACTS devices with objective function to minimize the transmission loss along with monitoring the voltage profile of buses in the system and calculating the cost of installation.…”

Section: Evolutionary Particle Swarm Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid meta-heuristics optimization technique for loss minimization and cluster identification in power system network

Jumaat

Musirin

Othman

et al. 2014

2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014)

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This paper introduces a new approach of hybrid metaheuristics based technique for minimizing the transmission loss and the clustering of FACTS device installation in power system network. The proposed Evolutionary Particle Swarm Optimization technique has been implemented to minimize the transmission loss, to improve the voltage and monitor the cost of installation in the system. Subsequently, cluster identification through FACTS device installation in power system is proposed which aims to demonstrate the area for FACTs devices to be installed. Comparison with respect to conventional Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) has been done to realize the effectiveness of proposed techniques.

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“…Based on the data from [10,11], the cost function of SVC is modeled as follows the Eq. (2) and ( 3) :…”

Section: B Cost Of Installationmentioning

confidence: 99%

Section: Evolutionary Particle Swarm Optimizationmentioning

confidence: 99%

A hybrid meta-heuristics optimization technique for loss minimization and cluster identification in power system network

Jumaat

Musirin

Othman

et al. 2014

2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014)

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“…The PSO algorithm was originally developed in 1995 by Kennedy and Eberhant based on the analogy of swarm of bird and school of fish [15]. The update position and velocity of each particle can be referring in [3], [10], [16][17].…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Cluster Identification for Optimal Placement of Static Var Compensator

Jumaat

Musirin

Othman

et al. 2013

2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO)

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This paper introduces a new concept of artificial intelligence based algorithm for clustering the placement of SVCs in power system. The algorithm is based on particle swarm optimization (PSO) technique with objective function to minimize the transmission loss in the system. Experiments were performed on the IEEE 30-and IEEE 118-bus RTS to realize the effectiveness of the proposed technique, while verification was conducted through comparative studies with evolutionary programming (EP).

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“…A multi-agent based PSO (MAPSO) is presented in [9] to solve the ORPD problems. An ORPD considering static voltage stability and voltage deviations is proposed using seeker optimization algorithm (SOA) in [10]. The SOA is based on the concept of simulating the act of human searching, where search direction is based on the empirical gradient.…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Management in Loss and Voltage Control via Integrated Clonal Accelerated Evolutionary Programming

Rahmatul Hidayah

2024

jes

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Optimal Reactive Power Dispatch (ORPD) plays a crucial role in maintaining voltage stability, enhance power transfer capability, improve system efficiency, regulate voltages, manage congestion, facilitate renewable energy integration, and achieve cost savings in power systems. This paper presents a new optimization technique for optimal reactive power management in power transmission system, called Integrated Clonal Accelerated Evolutionary Programming (ICAEP) algorithm to optimize the reactive power to be dispatched by the generator buses under 3 scenarios with cases. The ICAEP was developed based on hybridization of the traditional Artificial Immune System (AIS) and Evolutionary Programming (EP). The two cases demonstrate the effect of reactive load increment at the chosen load buses; while the three scenarios represent the ORPD schemes which indicate the involvement of generator buses either 3 generators, 4 generators or 5 generators. Implementation of ICAEP outperformed AIS and EP in both objective functions either in minimum voltage maximization or power loss minimization, involving all the cases under the three scenarios. The performance of the ICAEP is evaluated on the IEEE 30-bus test system. The results would be beneficial to power system operators and planning expansion and knowing the status of their systems in their utilities. The developed optimization engine is also robust and feasible for further optimization problem solving initiatives.

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Evolutionary Particle Swarm Optimization (EPSO) based technique for multiple SVCs optimization

Cited by 11 publications

References 35 publications

A hybrid meta-heuristics optimization technique for loss minimization and cluster identification in power system network

A hybrid meta-heuristics optimization technique for loss minimization and cluster identification in power system network

Cluster Identification for Optimal Placement of Static Var Compensator

Reactive Power Management in Loss and Voltage Control via Integrated Clonal Accelerated Evolutionary Programming

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