An Approach to Solve Transient Stability-Constrained Optimal Power Flow Problem Using Support Vector Machines

Nguyen-Duc, Huy; Tran-Hoai, Linh; Cao-Duc, Huy

doi:10.1080/15325008.2017.1292328

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…In these studies the objective function typically has been to minimise generation cost. Some of the recent developments on the application of CI for solving TSC‐OPF include (a) population based methods: micro‐PSO method [16], NSGA‐II [17], self‐adaptive differential evolution [18], krill herd algorithm [19], (b) direct search methods: improved group search optimisation [20], pattern search [21], and (c) support vector machine: support vector machines (SVMs) are used to classify whether an operating condition satisfies predefined transient contingencies in [22].…”

Section: Approachesmentioning

confidence: 99%

Solution techniques for transient stability‐constrained optimal power flow – Part II

Geng

Abhyankar

Wang³

et al. 2017

IET gener. transm. distrib.

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Section: Approachesmentioning

confidence: 99%

Solution techniques for transient stability‐constrained optimal power flow – Part II

Geng

Abhyankar

Wang³

et al. 2017

IET gener. transm. distrib.

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Optimal mitigation and control over power system dynamics for stochastic grid resilience

Stewart,

Arguello,

Hoffman

et al. 2023

Optim Eng

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Optimal mitigation planning for highly disruptive contingencies to a transmission-level power system requires optimization with dynamic power system constraints, due to the key role of dynamics in system stability to major perturbations. We formulate a generalized disjunctive program to determine optimal grid component hardening choices for protecting against major failures, with differential algebraic constraints representing system dynamics (specifically, differential equations representing generator and load behavior and algebraic equations representing instantaneous power balance over the transmission system). We optionally allow stochastic optimal pre-positioning across all considered failure scenarios, and optimal emergency control within each scenario. This novel formulation allows, for the first time, analyzing the resilience interdependencies of mitigation planning, preventive control, and emergency control. Using all three strategies in concert is particularly effective at maintaining robust power system operation under severe contingencies, as we demonstrate on the western system coordinating council 9-bus test system using synthetic multi-device outage scenarios.

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Support Vector Machine and Neural Network Applications in Transient Stability

Baltas

Mazidi

Fernández

et al. 2018

2018 7th International Conference on Renewable Energy Research and Applications (ICRERA)

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An Approach to Solve Transient Stability-Constrained Optimal Power Flow Problem Using Support Vector Machines

Cited by 5 publications

References 24 publications

Solution techniques for transient stability‐constrained optimal power flow – Part II

Solution techniques for transient stability‐constrained optimal power flow – Part II

Optimal mitigation and control over power system dynamics for stochastic grid resilience

Support Vector Machine and Neural Network Applications in Transient Stability

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