Brazilian System Operator Online Security Assessment System

Jardim, Jorge L.; Neto, Célia; Santos, Maider

doi:10.1109/psce.2006.296219

Cited by 19 publications

(27 citation statements)

References 9 publications

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“…Therefore, in their simplest form, SVMs learn linear decision rules as (2) so that are determined to classify correctly the training examples and to maximize .…”

Section: Support Vector Machine Classifiersmentioning

confidence: 99%

“…The integration of automatic learning/pattern recognition techniques with analytical TSA methods can provide more accurate monitoring, improved use of power systems resources (e.g., reduced spinning reserves), flexibility in maintenance scheduling, etc. [2]. Besides avoiding the repetitive burden of analyzing similar operating points, the pattern recognition approach for online TSA [or even assessment (i.e., including control)] can deal with modeling uncertainties (e.g., dynamic load modeling [6]) and measurement errors.…”

Section: Introductionmentioning

confidence: 99%

“…Neural networks (NNs) technology has been reported as an important contributor for reaching the goals of online TSA [1], [2], [7]- [16]. It presents desirable characteristics, such as fast response in simple format (stable/unstable or stability margin), heavy computational burden is paid offline, failure tolerant with respect to data requirement, and it can allow better real-time control.…”

Section: Introductionmentioning

confidence: 99%

“…Recent proposals of NNs' application to online TSA show how these properties can be turned into practical use. In general, these proposals present one of the following ideas: a) to rank or screen the contingencies, and after that perform detailed time-domain simulations [2], [12], [13]; b) to provide a stability evaluation during time-domain simulations, halting the cases clearly evaluated as stable [14]; c) to provide fast stability evaluations and allow border identification [11], [18]. In most of the NN proposals for online TSA, multilayer perceptrons (MLPs) are used, which present, as a major drawback, the extensive training process.…”

Section: Introductionmentioning

confidence: 99%

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Support Vector Machines for Transient Stability Analysis of Large-Scale Power Systems

Moulin

Silva

El-Sharkawi

et al. 2004

IEEE Trans. Power Syst.

304

136

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Abstract-The pattern recognition approach to transient stability analysis (TSA) has been presented as a promising tool for online application. This paper applies a recently introduced learning-based nonlinear classifier, the support vector machine (SVM), showing its suitability for TSA. It can be seen as a different approach to cope with the problem of high dimensionality. The high dimensionality of power systems has led to the development and implementation of feature selection techniques to make the application feasible in practice. SVMs' theoretical motivation is conceptually explained and they are tested with a 2684-bus Brazilian system. Aspects of model adequacy, training time, classification accuracy, and dimensionality reduction are discussed and compared to stability classifications provided by multilayer perceptrons.Index Terms-Feature selection, neural networks, support vector machine, transient stability analysis.

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“…Therefore, in their simplest form, SVMs learn linear decision rules as (2) so that are determined to classify correctly the training examples and to maximize .…”

Section: Support Vector Machine Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Support Vector Machines for Transient Stability Analysis of Large-Scale Power Systems

Moulin

Silva

El-Sharkawi

et al. 2004

IEEE Trans. Power Syst.

304

136

View full text Add to dashboard Cite

show abstract

“…Multi-basin process is one of the widely used dynamical systems that have plenty of application areas, including machine learning, electrical engineering, industrial engineering, applied mathematics, and so on (Chiang et al, 1995;Jardim et al, 2000;Jung et al, 2010;Jung et al, 2011;Kim and Lee, 2007;Lee, 2003;Lee and Chiang, 2004;Lee and Lee, 2005;Lee, 2005;Lee et al, 2009;Lee and Lee, 2010). In particular, let us consider a nonlinear dynamic system described by ( )/ ( ( )) dz t dt F z t =…”

Section: Introductionmentioning

confidence: 99%

Estimating Basin of Attraction for Multi-Basin Processes Using Support Vector Machine

Lee¹,

Lee²

2012

Management Science and Financial Engineering

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Enhancing the situational awareness of voltage security region via probabilistic reliability evaluation

Netto¹,

Borges

2019

Int Trans Electr Energ Syst

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Summary In power systems operation and control rooms, the operator must not only supervise the system but also issue commands in order to ensure that the system meets certain security criteria. However, they are generally blind about the influence of several control variables on the system response. This paper presents an efficient probabilistic security region construction tool for this situational awareness problem, meeting the time constraints of an online application by using parallel computing. Besides, a tabu list algorithm has been introduced into the Monte Carlo simulation so that only nonvisited operating points are actually evaluated. Results for the Brazilian Interconnected Power Systems on different load conditions will be presented and compared with the ones obtained with the conventional voltage security assessment tool to illustrate the feasibility of this tool in the online environment. By visualizing the probabilistic voltage security assessment nomogram, the operator can take adequate corrective actions according to the level of risk the system is running into or even take no action at all if the operating procedures allow it.

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Brazilian System Operator Online Security Assessment System

Cited by 19 publications

References 9 publications

Support Vector Machines for Transient Stability Analysis of Large-Scale Power Systems

Support Vector Machines for Transient Stability Analysis of Large-Scale Power Systems

Estimating Basin of Attraction for Multi-Basin Processes Using Support Vector Machine

Enhancing the situational awareness of voltage security region via probabilistic reliability evaluation

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