Optimal locations and tuning of robust power system stabilizer using genetic algorithms

Sebaa, Karim; Boudour, Mohamed

doi:10.1016/j.epsr.2008.08.005

Cited by 74 publications

(38 citation statements)

References 9 publications

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“…Base on the participation factor in case study 1, for interarea with damping value less than 5% the contributed generators are coming from G2, G4, G5, G9, G10, G11, G12, G13, G14, and G15. Based on the [32,33] the minimum PSS that can be installed is half of the generator number. Hence, for this study, the PSS is installed to the G2, G4, G5, G9, G10, G11, G12, G13, G14, and G15.…”

Section: Results and Simulationsmentioning

confidence: 99%

Modal Analysis and Stability Enhancement of 150 kV Sulselrabar Electrical System u sing PSS and RFB based on Cuckoo Search Algorithm

Djalal¹,

Setiadi²,

Lastomo³

et al. 2017

ijeei

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Small signal stability is one of the factor to assesst the reliability of a power system. Small signal stability is related to the ability of a power system to maintain synchronization after being exposed by small disturbance. In Indonesia, the reliability of power system is one of the important aspect that has to be achieved. However, very scant attention has been paid on studied small signal stability performance on Indonesia electricity. Hence, this paper studied the small signal stability analysis and its enhancement. 150 kV Sulselrabar interconnected power system is used as test system. To enhance the performance of the system designing coordinated controller of power system stabilizer (PSS) and redox flow batteries (RFB) using cuckoo search algorithm (CSA) is conducted. Eigenvalue, damping ratio, participation factor analysis, and time domain simulation is performed to assess the small signal stability performance and the successfulness of the CSA to optimize the PSS and RFB parameter. From the cases studies, it is found that 150 kV Sulselrabar has 12 local mode and 4 inter-area mode. Furthermore, the coordinated controller between PSS and RFB based on CSA could enhance the small signal stability performance of 150 kV Sulselrabar interconnected system indicated by higher damping, smallest overshoot and fastest settling time.

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Section: Results and Simulationsmentioning

confidence: 99%

Modal Analysis and Stability Enhancement of 150 kV Sulselrabar Electrical System u sing PSS and RFB based on Cuckoo Search Algorithm

Djalal¹,

Setiadi²,

Lastomo³

et al. 2017

ijeei

View full text Add to dashboard Cite

show abstract

“…Previously reported approaches based on modified versions of genetic algorithm (Sebaa et al, 2009), particle swarm optimization (Eslami et al, 2009), and differential evolution (Wang et al, 2009) highlight the potential of metaheuristic optimization algorithms for solving the OPCDC. Due to the stochastic nature of the underlying evolutionary mechanism, further research is needed to ascertain the robustness of these algorithms, which also motivates the application and extension of emerging metaheuristic optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

MVMO-based approach for optimal placement and tuning of supplementary damping controller

Rueda¹,

González-Longatt²

2016

Int. J. Eng. Sci. Tech

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This paper introduces an approach based on the Swarm Variant of the Mean-Variance Mapping Optimization (MVMO-S) to solve the multi-scenario formulation of the optimal placement and coordinated tuning of power system supplementary damping controllers (POCDCs). The effectiveness of the approach is evaluated based on the classical IEEE 39-bus (New England) test system. Numerical results include performance comparisons with other metaheuristic optimization techniques, namely, comprehensive learning particle swarm optimization (CLPSO), genetic algorithm with multi-parent crossover (GA-MPC), differential evolution DE algorithm with adaptive crossover operator, linearized biogeography-based optimization with reinitialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES).

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“…Earlier reported approaches based on modified versions of genetic algorithm (GA) [5], particle swarm optimization (PSO) [6], and differential evolution (DE) [7] highlight the potential of metaheuristic optimization algorithms for solving the OPCDC. Due to the stochastic nature of the underlying evolutionary mechanism, further research is needed to ascertain the robustness of these algorithms, which also motivates the application and extension of emerging metaheuristic optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers

Rueda

González-Longatt

2015

2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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Additional Information:• . The proposed solution is tested using the classical IEEE 39-bus test system, New England test system. This papers includes performance comparisons with other emerging metaheuristic optimization: comprehensive learning particle swarm optimization (CLPSO), genetic algorithm with multi-parent crossover (GA-MPC), differential evolution DE algorithm with adaptive crossover operator, linearized biogeography-based optimization with re-initialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES). Numerical results illustrates the feasibility and effectiveness of the proposed approach.

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Optimal locations and tuning of robust power system stabilizer using genetic algorithms

Cited by 74 publications

References 9 publications

Modal Analysis and Stability Enhancement of 150 kV Sulselrabar Electrical System u sing PSS and RFB based on Cuckoo Search Algorithm

Modal Analysis and Stability Enhancement of 150 kV Sulselrabar Electrical System u sing PSS and RFB based on Cuckoo Search Algorithm

MVMO-based approach for optimal placement and tuning of supplementary damping controller

Application of Swarm Mean-Variance Mapping Optimization on location and tuning damping controllers

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