A comparison between ANN based methods of critical clearing time estimation

Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies. In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the prediction accuracy, of neural networks for online transient stability assessment of power systems. We introduce a new multilabel learning method, which uses a contingency clustering step to learn similar contingencies together in the same multilabel multilayer perceptron. Experimental results on two different power systems demonstrate improved accuracy, as well as significant reduction in both training and testing time.

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“…In large power systems, dedicating one MLP for each credible contingency [9][10][11][12] imposes two difficulties:…”

Section: Introductionmentioning

confidence: 99%

Multilabel learning for the online transient stability assessment of electric power systems

Beyranvand¹,

Genç²,

Çataltepe³

2018

Turk J Elec Eng & Comp Sci

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“…Probabilistic methods that determine the probability distributions for the stability of the system are suitable for system planning due computational requirements [5]. On the other hand, pattern recognition methods such as artificial neural networks (ANNs) assess the security of an operating point (OP) as they develop a mapping between the features representing the states of the system and its security through the use of a knowledge base (KB) generated off-line [6][7][8][9][10][11][12][13][14]. As a specific implementation of ANNs, multi-layer perceptrons (MLPs) can be used for the prediction of security indices during the DSA of a large power system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Selection Method for the Dynamic Security Assessment of Power Systems Based on Multi-Layer Perceptrons

Beyranvand

Kucuktezcan

Çataltepe

et al. 2018

ijisae

Self Cite

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In this study, the effect of feature selection methods on the performance of multi-layer perceptrons used for the dynamic security assessment of electric power systems is investigated. The existence of many measurable parameters (features) characterizing the power system security status complicates the use of multi-layer perceptron both in terms of prediction accuracy and training time. In this paper, the dynamic security of a power system subject to a number of critical contingencies is assessed as the critical clearing time of any credible fault is predicted by a multi-layer perceptron. In addition to the study of two different feature selection methods, which are Minimum Redundancy Maximum Relevance (mRMR), and Regressional ReliefF (RReliefF), a novel multi-layer perceptron based feature selection method is proposed to be applied in the prediction of security indices. The performance of the feature selection methods on the dynamic security assessment is investigated on a 16-generator, 68-bus test system.

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An optimization method for preventive control using differential evolution with consecutive search space reduction

Kucuktezcan

Genç

Erol

2016

2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

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A comparison between ANN based methods of critical clearing time estimation

Cited by 4 publications

References 12 publications

Multilabel learning for the online transient stability assessment of electric power systems

Multilabel learning for the online transient stability assessment of electric power systems

A Novel Feature Selection Method for the Dynamic Security Assessment of Power Systems Based on Multi-Layer Perceptrons

An optimization method for preventive control using differential evolution with consecutive search space reduction

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