Minería de Datos para Reconocimiento de Patrones en el Análisis de Seguridad Estática de Sistemas de Potencia ante Eventos de Contingencia

Mauricio Santiago  Soria Colina; Antonio  Tovar; Diego  Maldonado; Cristian Fabara

doi:10.37116/revistaenergia.v16.n1.2019.331

Data Mining for Patterns Recognition of Power Systems Static Security Assessment with Contingency Events

Minería de Datos para Reconocimiento de Patrones en el Análisis de Seguridad Estática de Sistemas de Potencia ante Eventos de Contingencia

Published 2019-07-31

https://doi.org/10.37116/revistaenergia.v16.n1.2019.331

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Vol. 16 No. 1 (2019): Revista Técnica "energía", Edición No. 16

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SISTEMAS ELÉCTRICOS DE POTENCIA

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Data Mining for Patterns Recognition of Power Systems Static Security Assessment with Contingency Events. (2019). Revista Técnica "energía", 16(1), PP. 17-22. https://doi.org/10.37116/revistaenergia.v16.n1.2019.331

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https://doi.org/10.37116/revistaenergia.v16.n1.2019.331

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How to Cite

Data Mining for Patterns Recognition of Power Systems Static Security Assessment with Contingency Events. (2019). Revista Técnica "energía", 16(1), PP. 17-22. https://doi.org/10.37116/revistaenergia.v16.n1.2019.331

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Mauricio Santiago Soria Colina

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https://orcid.org/0000-0002-5617-5999

Antonio Tovar

Diego Maldonado

Cristian Fabara

Perfil Google Scholar

https://orcid.org/0000-0001-7870-7872

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Contingency events

Data mining

Pattern recognition

Monte Carlo simulation

Static security assessment

This paper analyzes the static security of the power system, applying advanced data mining techniques that allow the evaluation of safety patterns of a power electrical system in a steady state analysis with contingency events N-1. Data are obtained through power flows, to perform Monte Carlo simulations with scripts developed in Python. Using the DIgSILENT PowerFactory simulation software, 10,000 scenarios are analyzed, which allows us to consider the uncertainty of the system according to the probabilistic nature of the system. The static security indexes of the system are calculated to

classify the types of contingencies as safe, critically safe, insecure and highly unsafe. Data mining is developed by means of an algorithm programmed in Python language with which the design of the multiclass support vector machine classifier (SVM Multiclass) is carried out. It is trained to determine if a contingency is safe or unsafe. The parameters of the

SVM were obtained through an optimization with a differential evolution algorithm (Differential Evolution). The results of the validation of the classifier showed that the technique is very effective in classifying new contingencies. The methodology is applied to an IEEE test system of 39 buses.

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