Dynamic Equivalents of Power System Based on Extended Two Particle Swarm Optimization

Yang, Jian; Zhang, Jing; Pan, Wulue

doi:10.1109/icnc.2007.339

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…The choice of DE was motivated by the comparison reported in [29], where it outperformed other algorithms on various benchmark problems. A systematic comparison with other methods -such as those used in [30], [31], [32], [33] -was outside the scope of this research. While other algorithms could offer a most welcome speed-up, DE has been found to be a reliable solver for the optimization problem (1)-( 4) in a large number of cases.…”

Section: Solving the Optimization Problem (1)-(4)mentioning

confidence: 99%

An Active Distribution Network Equivalent Derived From Large-Disturbance Simulations With Uncertainty

Chaspierre

Denis²,

Panciatici³

et al. 2020

IEEE Trans. Smart Grid

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A reduced-order, "grey-box" model of an active distribution network, intended for dynamic simulations of the transmission system, is derived. The network hosts inverter-based generators as well as static and motor loads, whose dynamic parameters are affected by uncertainty. This issue is addressed using Monte-Carlo simulations. The parameters of the equivalent are adjusted to match as closely as possible the average of the randomized responses, while their dispersion is accounted for through the weights of the weighted least-square minimization. A procedure is used to remove from the identification the parameters with negligible impact. To avoid over-fitting, the equivalent is tuned for multiple large-disturbance simulations. A recursive procedure is used to select the smallest possible subset of disturbances involved in the least-square minimization. Simulation results with a 75-bus MV test-system are reported. They show that the equivalent is able to reproduce with good accuracy the discontinuous controls of inverter-based generators, such as reactive current injection and tripping. Index Terms-active distribution network, inverter-based generator, dynamic equivalent, grey-box model, uncertain dynamic model, Monte-Carlo simulations, LASSO method.

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Section: Solving the Optimization Problem (1)-(4)mentioning

confidence: 99%

An Active Distribution Network Equivalent Derived From Large-Disturbance Simulations With Uncertainty

Chaspierre

Denis²,

Panciatici³

et al. 2020

IEEE Trans. Smart Grid

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“…However, the computation burden is still significant comparing to conventional derivative-based optimization methods. The application cases of PSO in parameter identification of power system, as we know, are focused upon induction motor [12] and dynamic equivalents [13]. This paper proposes a hybrid method combining particle swarm optimization (PSO) and sensitivity analysis (SA) for dynamic parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Dynamic parameter identification of generators for smart grid development

Cheng¹,

Lee

Huang³

et al. 2011

2011 IEEE Power and Energy Society General Meeting

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“…Theses algorithms along with their variants proved their efficacy to solve real-world complex problems [23]. Some of the eminent population-based optimization algorithms such as genetic algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) were successfully applied to the parameter identification problem [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A novel measurement-based procedure for dynamic equivalents of electric power systems for stability studies using improved sine cosine algorithm

Benmiloud

Arif

2019

Journal of Electrical Engineering

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Dynamic equivalent (DE) is an important process of multi-area interconnected power systems. It allows to perform stability assessment of a specific area (area of interest) at minimum cost. This study is intended to investigate the dynamic equivalent of two relatively large power systems. The fourth-order model of synchronous generators with a simplified excitation system is used as equivalent to the group of generators in the external system. To improve the accuracy of the estimated model, the identification is carried in two stages. First, using the global search Sine Cosine Algorithm (SCA) to find a starting set values, then this set is used as starting point for the fine-tuning made through the Pattern Search (PS) algorithm. To increase the reliability of the model’s parameters, two disturbances are used to avoid the identification based on a specific event. The developed program is applied on two standard power systems, namely, the New England (NE) system and the Northeast Power Coordinating Council (NPCC) system. Simulation results confirm the ability of the optimized model to preserve the main dynamic properties of the original system with accuracy.

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Dynamic Equivalents of Power System Based on Extended Two Particle Swarm Optimization

Cited by 10 publications

References 16 publications

An Active Distribution Network Equivalent Derived From Large-Disturbance Simulations With Uncertainty

An Active Distribution Network Equivalent Derived From Large-Disturbance Simulations With Uncertainty

Dynamic parameter identification of generators for smart grid development

A novel measurement-based procedure for dynamic equivalents of electric power systems for stability studies using improved sine cosine algorithm

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