A Bayesian Learning Based Scheme for Online Dynamic Security Assessment and Preventive Control

Liu, Tingjian; Liu, Youbo; Wang, Lingfeng; Xu, Lixiong; Qiu, Gao; Gao, Hongjun

doi:10.1109/tpwrs.2020.2983477

Cited by 48 publications

(23 citation statements)

References 32 publications

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“…Similarly, the particle swarm optimization was also conducted in [80] to solve an optimization whose constraints were modeled by a radial basis function neural network. Reference [81] formulated a preventive control model with a Bayesian neural network that could predict the steady-states. This model was later solved by Bayesian optimization.…”

Section: Category 4 Hybrid Modelmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

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Section: Category 4 Hybrid Modelmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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“…Therefore, control center aims to assess dynamic security behavior after each disturbance occurred in the power grid via DSA tool [2]. The target is to keep power system in the security state following disturbances and prevent system collapse and blackouts [3]. Recently, power system has witnessed dramatic changes related to deregulation and the installation of new devices and technologies such as renewable generation sources [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…However, these renewable sources bring new challenges to dynamic security behavior because of nature of these sources are and their inconsistency in terms of voltage and frequency that leads to alter the power system's dynamic operation  ISSN: 2089-3272 IJEEI, Vol.9, No. 3, September 2021: 563 -574 564 behavior frequently [11][12][13][14]. Therefore, it is necessary to solve these dynamic security challenges to protect power system form the insecure state and use their renewable sources to benefit the generation sector with fewer problems in the operating environment.…”

Section: Introductionmentioning

confidence: 99%

“…DSA is used for the evaluation of power system is ability to withstand sudden disturbances and to survive the transition to an acceptable steady state [3]. Traditionally, DSA has multiple differential-algebraic equations to solve step by step by a time-domain simulation in offline environments [2,15].…”

Section: Introductionmentioning

confidence: 99%

“…ei Gi Gi P Re(V .I ) = (3) where t is time, Mi denotes the i th machine inertia constant, ωi and (  ) represent its angular speed and angle, respectively, Pmi and Pei are its mechanical input and electrical output powers, respectively, IGi and VGi are its generator current and voltage, which are related to algebraic power flow equations. In general, based on the above equations, time-domain simulations need to study rotor angle waveform at the contingency based on the change in angular speed with respect to time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic security assessment for the power system in the presence of wind turbines

Khalel

Al-Rawe

Alabbawi

2021

IJEEI

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Dynamic security is an essential requirement for operating a modern power system. Due to the global increase in load demand, modern power systems witness several dramatic changes in terms of size and implementation of new renewable sources. At the same time, the deregulation process in power operation policy is being pushed to operate closer to its security boundary limits. Based on the combined Decision Tree (DT) algorithm, namely Random Forest (RF) and advance attribute selection technique, this paper presents an approach to address these challenges related to dynamic security assessment (DSA) in the modern power system. The performance of study approach is demonstrated on a modified version of IEEE 9 and 14-bus test system models with presence of two wind turbines (WTs) type WTG 3. Results show the superiority of RF compared to other DT algorithms that are used in this study. In addition, the attribute selection technique could significantly affect the number of attributes required for DSA. This makes DT classifier more effectiveness in the online application. Thus, this approach can provide control center with vital information with high accuracy results and less attributes about security state direction that will help operator to take the right and fast steps to remedy problems and prevent a blackout from occurring.

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