Optimal allocation of FACTS devices in power system using genetic algorithms

Metwally, M.M. El; Emary, A.A. El; Bendary, Fahmy; Mosaad, Mohamed I.

doi:10.1109/mepcon.2008.4562386

Cited by 31 publications

(15 citation statements)

References 11 publications

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“…The modern heuristics optimization techniques such as genetic algorithm (GA) [19], evolutionary programming (EP) [20,21], tabu search (TS) [22,23], simulated annealing (SA) [24,25], and particle swarm optimization (PSO) [26,27] are successfully implemented to solve complex problems efficiently and effectively [28,29]. In [30], OPF using GA is used to consider the optimal allocations of SVC. Test results showed that the purposed method can minimize the overall cost function, including generation costs of power plants and investment costs.…”

Section: Introductionmentioning

confidence: 99%

Power transfer capability enhancement with multitype FACTS controllers using hybrid particle swarm optimization

Chansareewittaya

Jirapong

2014

Electr Eng

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In this paper, a new hybrid particle swarm optimization (HPSO) based on particle swarm optimization (PSO), evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) is proposed. The aim of merging is to determine the optimal allocation of multi-type flexible AC transmission system (FACTS) controllers for simultaneously maximizing the power transfer capability of power transactions between generators and loads in power systems without violating system constraints. The particular optimal allocation includes optimal types, locations, and parameter settings. Four types of FACTS controllers are included: thyristor-controlled series capacitor, thyristorcontrolled phase shifter, static var compensator, and unified power flow controller. Power transfer capability determinations are calculated based on optimal power flow (OPF) technique. Test results on IEEE 118-bus system and Thai Power 160-Bus system indicate that optimally placed OPF with FACTS controllers by the HPSO could enhance the higher power transfer capability more than those from EP, TS, and hybrid TS/SA. Therefore, the installation of FACTS controllers with optimal allocations is beneficial for the further expansion plans.

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Section: Introductionmentioning

confidence: 99%

Power transfer capability enhancement with multitype FACTS controllers using hybrid particle swarm optimization

Chansareewittaya

Jirapong

2014

Electr Eng

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“…Many improvements have been made in distribution systems to increase the efficiency and reliability of the network, such as the implementation of reconfiguration techniques [1][2][3], the installation of FACTS devices [4,5] or capacitors [6,7], and the use of small-scale power generation technologies in the distribution network, also known as distributed generation (DG) [8][9][10][11]. Therefore, from the centralized power system in the last decade, the existence of DG units has transformed the topology of the network into a decentralized system.…”

Section: Introductionmentioning

confidence: 99%

Comparative learning global particle swarm optimization for optimal distributed generations' output

Jamian¹,

Mokhlis²,

Mustafa³

et al. 2014

Turk J Elec Eng & Comp Sci

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Abstract:The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network. Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value. In this paper, the comparative learning in global particle swarm optimization (CLGPSO) method is introduced. The implementation of individual cognitive and social acceleration coefficient values for each particle and a new fourth term in the velocity formula make the process of convergence faster. This new algorithm is tested on 6 standard mathematical test functions and a 33-bus distribution system. The performance of the CLGPSO is compared with the inertia weight particle swarm optimization (PSO) and evolutionary PSO methods. Since the CLGPSO requires fewer iterations, less computing time, and a lower standard deviation value, it can be concluded that the CLGPSO is the superior algorithm in solving small-dimension mathematical and simple power system problems.

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“…Multi-type FACTS devices can be placed in optimal locations to improve security margins and reduce losses in the network as in (Baghaee et al, 2008). GA can be applied to find the optimal location of SVC to increase the power transfer capability and to reduce the generation costs as in (Metwally et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Voltage Stability by Optimal Location of Static Var Compensator Using Genetic Algorithm and Particle Swarm Optimization

Nooritawat¹

2012

American Journal of Engineering and Applied Sciences

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Problem statement:Voltage instability and voltage collapse have been considered as a major threat to present power system networks due to their stressed operation. It is very important to do the power system analysis with respect to voltage stability. Approach: Flexible AC Transmission System (FACTS) is an alternating current transmission system incorporating power electronic-based and other static controllers to enhance controllability and increase power transfer capability. A FACTS device in a power system improves the voltage stability, reduces the power loss and also improves the load ability of the system. Results: This study investigates the application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to find optimal location and rated value of Static Var Compensator (SVC) device to minimize the voltage stability index, total power loss, load voltage deviation, cost of generation and cost of FACTS devices to improve voltage stability in the power system. Optimal location and rated value of SVC device have been found in different loading scenario (115%, 125% and 150% of normal loading) using PSO and GA. Conclusion/Recommendations: It is observed from the results that the voltage stability margin is improved, the voltage profile of the power system is increased, load voltage deviation is reduced and real power losses also reduced by optimally locating SVC device in the power system. The proposed algorithm is verified with the IEEE 14 bus, IEEE 30 bus and IEEE 57 bus.

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Optimal allocation of FACTS devices in power system using genetic algorithms

Cited by 31 publications

References 11 publications

Power transfer capability enhancement with multitype FACTS controllers using hybrid particle swarm optimization

Power transfer capability enhancement with multitype FACTS controllers using hybrid particle swarm optimization

Comparative learning global particle swarm optimization for optimal distributed generations' output

Enhancement of Voltage Stability by Optimal Location of Static Var Compensator Using Genetic Algorithm and Particle Swarm Optimization

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