Power system static security assessment using the Kohonen neural network classifier

Niebur, D.; Germond, A.

doi:10.1109/59.141797

Cited by 98 publications

(28 citation statements)

References 16 publications

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“…The assessment addresses whether, after a disturbance, the system reaches a steady state operating point without violating system operating constraints called security constraints. These constraints ensure the power in the network is properly balanced as given by (11), bus voltage magnitudes and thermal limit of transmission lines are within the acceptable limits given by (12). Violations, if any, are identified as an insecure state that could result in a black-out.…”

Section: A Static Security Assessment (Ssa)mentioning

confidence: 99%

“…Artificial neural network systems obtained popularity in respect of the conventional methods as they are efficient in discovering similarities among large bodies of data and synthesize complex mappings accurately and rapidly. Research work related to static security analysis [12], [13] and dynamic security analysis [14] have been reported in literature. Most of the published work in this area utilizes multilayer perceptron (MLP) model based on back propagation (BP) algorithm, which usually encounters to local minima and over-fitting problems.…”

Section: Introductionmentioning

confidence: 99%

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Static security assessment using radial basis function neural networks based on growing and pruning method

Javan

Mashhadi

Rouhani

2010

2010 IEEE Electrical Power &Amp; Energy Conference

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Power system security is one of the major concerns in recent years due to the deregulation of power systems which are forced to operate under stressed operating conditions. This paper presents a novel method based on growing and pruning training algorithm using radial basis function neural network (GPRBFNN) and winner-take-all neural network (WTA) to examine whether the power system is secure under steady-state operating conditions. Hidden layer neurons have been selected with the proposed algorithm which has the advantage of being able to automatically choose optimal centers and distances. A feature selection technique-based class separability index and correlation coefficient has been employed to identify the inputs for the GPRBF network. The advantages of this method are simplicity of algorithm and high accuracy in classification. The effectiveness of the proposed approach has been demonstrated on IEEE 14-bus and IEEE 30-bus systems.Index Terms -Static security assessment, Radial basis function neural network, Class separability index, Correlation coefficient, Winner-take-all neural network.

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Section: A Static Security Assessment (Ssa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Static security assessment using radial basis function neural networks based on growing and pruning method

Javan

Mashhadi

Rouhani

2010

2010 IEEE Electrical Power &Amp; Energy Conference

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“…Literatures have reported the use of AI techniques like Kohonen Neural Network [9], Multilayer feed forward with back propagation algorithm [10][11][12][13], fuzzy logic combined with neural network [14,15], genetic-based neural network [16] for static and transient security evaluation process. Application of Artificial Neural Network (ANN) based pattern recognition approach [17][18][19] has also been widely exploited in the literatures.…”

Section: Introductionmentioning

confidence: 99%

Pattern analysis and classification for security evaluation in power networks

Kalyani¹

2013

International Journal of Electrical Power & Energy Systems

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“…Over the past few years, several approaches using "Artificial Neural Networks" (ANN) have been proposed as alternative methods for "Static Security Assessment" in power system management, both using supervised and unsupervised architectures [7,10]. However, both classical and ANN-based methods must deal with the following problems:…”

Section: Introductionmentioning

confidence: 99%

Overload screening of transmission systems using neural networks

Santos

Gómez

Martínez

et al. 1998

Lecture Notes in Computer Science

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Abstract. The process of determining whether a power system is in a secure or insecure state is a crucial task which must be addressed on-line in any Energy Management System. In this paper, an Artificial Neural Network, capable of accurately identifying the set of harmful contingencies, is presented, along with several results obtained from a real-size power network. The proposed approach makes use of classical numerical techniques to compensate the ANN's inputs so that it can deal with topological changes in the power system.

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Power system static security assessment using the Kohonen neural network classifier

Cited by 98 publications

References 16 publications

Static security assessment using radial basis function neural networks based on growing and pruning method

Static security assessment using radial basis function neural networks based on growing and pruning method

Pattern analysis and classification for security evaluation in power networks

Overload screening of transmission systems using neural networks

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