<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2602-8492</journal-id>
<journal-title><![CDATA[Revista Técnica energía]]></journal-title>
<abbrev-journal-title><![CDATA[Revista Técnica energía]]></abbrev-journal-title>
<issn>2602-8492</issn>
<publisher>
<publisher-name><![CDATA[Operador Nacional de Electricidad CENACE]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2602-84922019000200017</article-id>
<article-id pub-id-type="doi">10.37116/revistaenergia.v16.n1.2019.331</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Minería de Datos para Reconocimiento de Patrones en el Análisis de Seguridad Estática de Sistemas de Potencia ante Eventos de Contingencia]]></article-title>
<article-title xml:lang="en"><![CDATA[Data Mining for Patterns Recognition of Power Systems Static Security Assessment with Contingency Events]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Soria]]></surname>
<given-names><![CDATA[Mauricio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tovar]]></surname>
<given-names><![CDATA[Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Maldonado]]></surname>
<given-names><![CDATA[Diego]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fabara]]></surname>
<given-names><![CDATA[Cristian]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Escuela Politécnica Nacional Facultad de Ingeniería Eléctrica y Electrónica ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Escuela Politécnica Nacional Facultad de Ingeniería Eléctrica y Electrónica ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Escuela Politécnica Nacional Facultad de Ingeniería Eléctrica y Electrónica ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Escuela Politécnica Nacional Facultad de Ingeniería Eléctrica y Electrónica ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>16</volume>
<numero>1</numero>
<fpage>17</fpage>
<lpage>22</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S2602-84922019000200017&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S2602-84922019000200017&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S2602-84922019000200017&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen:  El presente artículo busca analizar la seguridad estática del sistema, aplicando técnicas avanzadas de minería de datos que permitan evaluar los patrones de seguridad de un sistema eléctrico de potencia en un análisis de estado estacionario ante eventos de contingencia N-1. Los datos son obtenidos a través de flujos de potencia, para efectuar simulaciones de Monte Carlo con scripts desarrollados en Python. Usando el software de simulación DIgSILENT PowerFactory se analizan 10000 escenarios, lo que permite considerar la incertidumbre del sistema según la naturaleza probabilística del mismo. Se calculan los índices de seguridad estática del sistema para clasificar los tipos de contingencias como segura, críticamente segura, insegura y altamente insegura. La minería de datos es desarrollada mediante un algoritmo programado en lenguaje Python con el cual se realiza el diseño del clasificador tipo máquina de soporte vectorial multiclase (SVM Multiclass) el cual es entrenado para determinar si una contingencia es segura o insegura. Los parámetros del SVM fueron obtenidos mediante una optimización con un algoritmo de evolución diferencial (Differential Evolution). Los resultados de la validación del clasificador demostraron que la técnica es muy efectiva para clasificar nuevas contingencias. La metodología se aplica a un sistema de prueba IEEE de 39 barras.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: 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.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Eventos de contingencia]]></kwd>
<kwd lng="es"><![CDATA[minería de datos]]></kwd>
<kwd lng="es"><![CDATA[reconocimiento de patrones]]></kwd>
<kwd lng="es"><![CDATA[simulación de Monte Carlo]]></kwd>
<kwd lng="es"><![CDATA[evaluación de la seguridad estática]]></kwd>
<kwd lng="en"><![CDATA[Contingency events]]></kwd>
<kwd lng="en"><![CDATA[data mining]]></kwd>
<kwd lng="en"><![CDATA[pattern recognition]]></kwd>
<kwd lng="en"><![CDATA[Monte Ca rlo simulation]]></kwd>
<kwd lng="en"><![CDATA[static security assessment]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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