2019
DOI: 10.11591/ijai.v8.i4.pp342-351
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Intelligent swarm-based optimization technique for oscillatory stability assessment in power system

Abstract: <p>This paper discussed the prediction of oscillatory stability condition of the power system using a particle swarm optimization(PSO) technique. Indicators namely synchronizing(<em>K<sub>s</sub></em>)and damping(<em>K<sub>d</sub></em>) torque coefficients is appointed to justify the angle stability condition in a multi-machine system. PSO is proposed and implemented to accelerate the determination of angle stability. The proposed algorithm has been confirm… Show more

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Cited by 9 publications
(6 citation statements)
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“…Interoperability and Standardization: Encourage international norms for wireless communication, AI ethics, and sustainable technologies. Interoperability is guaranteed through standardization, which also makes cross-border technology integration possible [31].…”
Section: Recommendations For Maximizing Synergymentioning
confidence: 99%
“…Interoperability and Standardization: Encourage international norms for wireless communication, AI ethics, and sustainable technologies. Interoperability is guaranteed through standardization, which also makes cross-border technology integration possible [31].…”
Section: Recommendations For Maximizing Synergymentioning
confidence: 99%
“…Xiao et al [113] used a multivariate random forest regression (MRFR) algorithm for OSA on an 18 bus test system, and the results presented high accuracy and robustness. Kamari et al [114] deployed a PSO scheme to accelerate the determination of OSA.…”
Section: Small-signal Stability Assessmentmentioning
confidence: 99%
“…To improve the accuracy of line trip fault prediction, Wang et al [131] proposed a stacked sparse autoencoder-based network with SVM and PCA to demonstrate its application to real-world data. [106] 2017 TSA ELM, TF Tan et al [108] 2017 TSA CNN, SAEs Liu et al [109] 2017 TSA Ensemble, NN, ELM Ashraf et al [115] 2017 VSA ANN Amroune et al [118] 2017 VSA SVR, FL Baltas et al [99] 2018 TSA Decision tree, SVM, ANN Mosavi et al [105] 2018 TSA ANN Yu et al [107] 2018 TSA RNN, LSTM Amroune et al [119] 2018 VSA SVR Mohammadi et al [116] 2018 VSA SVM Hu et al [104] 2019 TSA SVM Wang et al [14] 2019 FSA ELM Kamari et al [114] 2019 OSA PSO Amroune et al [122] 2019 VSA Survey Wang et al [110] 2020 TSA DBN Shi et al [111] 2020 TSA CNN Shi et al [111] 2020 OSA CNN Xiao et al [113] 2020 OSA MRFR Yang et al [120] 2020 VSA Spectrum estimation method Meng et al [117] 2020 VSA Decision tree Liu et al [121] 2021 VSA Random Forest…”
Section: Faults Detectionmentioning
confidence: 99%
“…It was stimulated by the social performance of the bird and fish schooling and has been attained to be robust in resolving nonlinear complex optimization difficulties [21]. The PSO algorithm is launched with initialization, after that the revise of velocity and position, fitness computation, the best position and convergence examination are updated [24]. The PSO algorithms are applied to search its globally optimal gains (kp, ki) for PI controller.…”
Section: Pi Controller Design Based On Particle Swarm Optimizationmentioning
confidence: 99%