<?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>1390-0129</journal-id>
<journal-title><![CDATA[Revista Politécnica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev Politéc. (Quito)]]></abbrev-journal-title>
<issn>1390-0129</issn>
<publisher>
<publisher-name><![CDATA[Escuela Politécnica Nacional]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1390-01292023000100057</article-id>
<article-id pub-id-type="doi">10.33333/rp.vol51n1.05</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Sintonización Adaptativa del Estabilizador de Sistema de Potencia mediante Árboles de Decisión y Optimización Heurística]]></article-title>
<article-title xml:lang="en"><![CDATA[Power System Stabilizer Adaptive Tuning Based on Decision Trees and a Heuristic Optimization Process]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[José]]></surname>
<given-names><![CDATA[Oscullo,]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Jaime]]></surname>
<given-names><![CDATA[Cepeda,]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Escuela Politécnica Nacional  ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Autor de correspondencia  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<volume>51</volume>
<numero>1</numero>
<fpage>57</fpage>
<lpage>66</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S1390-01292023000100057&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S1390-01292023000100057&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S1390-01292023000100057&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Este artículo presenta un enfoque novedoso para amortiguar de forma adaptativa las oscilaciones de baja frecuencia mediante un clasificador de árboles de decisión que utilizan como entradas las señales de frecuencia y potencia de las barras de generación sensadas por el sistema de monitoreo de área amplia (WAMS). El ajuste adaptativo de los estabilizadores de sistemas de potencia (PSS) convencionales (p.ej., PSS1A, PSS2A, PSS2B), que se encuentran disponibles en el sistema, se realiza al modificar los parámetros óptimos de sintonización del PSS, mediante el análisis de los modos críticos de los escenarios operativos utilizando el algoritmo de optimización heurística de mapeo media- varianza (MVMO). Estos parámetros por escenario pueden ser seleccionados mediante un clasificador inteligente basado en árboles de decisión con el objetivo de adaptar la sintonización del PSS a las condiciones de operación. Dicho proceso se implementa en PowerFactory de DIgSILENT mediante la herramienta de análisis modal, se determinan los modos críticos, para el caso de simulación. Mientras en tiempo real, los modos críticos son obtenidos por medio del algoritmo de identificación modal establecido con la matriz Pencil de las señales WAMS. La metodología planteada se aplica al sistema New York-New England de 66-Barras, obteniéndose la mejor respuesta dinámica del sistema con los PSS adaptativos a las diferentes condiciones operativas del sistema. Además, se compara la respuesta con el PSS4B que se caracteriza por monitorear varias frecuencias de los modos de oscilación presentes en la operación del sistema y así presentar los beneficios a la contribución propuesta.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: This paper presents a novel approach for adaptively damping low-frequency electromechanical oscillations via the application of decision trees that uses as inputs frequency and power signals of generation buses monitored by the wide-area measurement system (WAMS). This methodology can be applied for adaptive tuning of conventional Power System Stabilizers (e.g., PSS1A, PSS2A, PSS2B), generally available in actual power systems. It is done by adjusting the PSS tuning parameters by analyzing critical oscillation modes for different operational scenarios using the Mean-Variance Mapping Optimization (MVMO) heuristic algorithm. An intelligent classifier selects the PSS&#8217;s optimal parameters for each scenario based on Decision Trees with the objective of adapting the PSS tuning to the operating conditions. This process uses PowerFactory of DIgSILENT and critical modes are determined using modal analysis for simulation. In real-time, binding modes are derived by modal identification algorithm established with matrix Pencil of WAMS-signals. Such a methodology is applied to the 66-Bus New York-New England test power system, showing an excellent dynamic response in adapting PSS tuning to the different power system conditions. In addition, the response is compared with the PSS4B, which is characterized by monitoring various frequencies of the oscillation modes present in the system operation, thus presenting the benefits of proposed contribution.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Estabilidad oscilatoria]]></kwd>
<kwd lng="es"><![CDATA[Árboles de decisión]]></kwd>
<kwd lng="es"><![CDATA[Optimización heurística]]></kwd>
<kwd lng="es"><![CDATA[Máquinas de aprendizaje]]></kwd>
<kwd lng="en"><![CDATA[Oscilatory Stability]]></kwd>
<kwd lng="en"><![CDATA[Decision Trees]]></kwd>
<kwd lng="en"><![CDATA[Heuristic Optimization]]></kwd>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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