Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for...

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Autores:
Chamorro, Harold R
Orjuela Cañón, Alvaro D.
Ganger, David
Persson, Mattias
Gonzalez Longatt, Francisco
Alvarado Barrios, Lazaro
Sood, Vijay K.
Martinez, Wilmar
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
eng
OAI Identifier:
oai:repositorio.escuelaing.edu.co:001/3260
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/3260
https://repositorio.escuelaing.edu.co/
Palabra clave:
Frecuencia (Sistemas eléctricos)
Frequency (Electrical Systems)
Análisis de sistemas
System analysis
Redes neurales artificiales
Artificial neural networks
Inteligencia artificial - Procesamiento de datos
Artificial intelligence - Data processing
Generación no síncrona
Respuesta de frecuencia
Sistemas de energía de baja inercia
Control primario de frecuencia
Energía eólica
Estimación nadir
Aprendizaje automático
Aprendizaje profundo
non-synchronous generation
Frequency response
low-inertia power systems
Primary frequency control
Wind power
Nadir estimation
Machine learning
Deep learning
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closedAccess
License
http://purl.org/coar/access_right/c_14cb
id ESCUELAIG2_28f26af51f02cffb46b415c34353818b
oai_identifier_str oai:repositorio.escuelaing.edu.co:001/3260
network_acronym_str ESCUELAIG2
network_name_str Repositorio Institucional ECI
repository_id_str
dc.title.eng.fl_str_mv Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models
title Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models
spellingShingle Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models
Frecuencia (Sistemas eléctricos)
Frequency (Electrical Systems)
Análisis de sistemas
System analysis
Redes neurales artificiales
Artificial neural networks
Inteligencia artificial - Procesamiento de datos
Artificial intelligence - Data processing
Generación no síncrona
Respuesta de frecuencia
Sistemas de energía de baja inercia
Control primario de frecuencia
Energía eólica
Estimación nadir
Aprendizaje automático
Aprendizaje profundo
non-synchronous generation
Frequency response
low-inertia power systems
Primary frequency control
Wind power
Nadir estimation
Machine learning
Deep learning
title_short Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models
title_full Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models
title_fullStr Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models
title_full_unstemmed Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models
title_sort Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models
dc.creator.fl_str_mv Chamorro, Harold R
Orjuela Cañón, Alvaro D.
Ganger, David
Persson, Mattias
Gonzalez Longatt, Francisco
Alvarado Barrios, Lazaro
Sood, Vijay K.
Martinez, Wilmar
dc.contributor.author.none.fl_str_mv Chamorro, Harold R
Orjuela Cañón, Alvaro D.
Ganger, David
Persson, Mattias
Gonzalez Longatt, Francisco
Alvarado Barrios, Lazaro
Sood, Vijay K.
Martinez, Wilmar
dc.contributor.researchgroup.spa.fl_str_mv GiBiome
dc.subject.armarc.none.fl_str_mv Frecuencia (Sistemas eléctricos)
Frequency (Electrical Systems)
Análisis de sistemas
System analysis
Redes neurales artificiales
Artificial neural networks
Inteligencia artificial - Procesamiento de datos
Artificial intelligence - Data processing
topic Frecuencia (Sistemas eléctricos)
Frequency (Electrical Systems)
Análisis de sistemas
System analysis
Redes neurales artificiales
Artificial neural networks
Inteligencia artificial - Procesamiento de datos
Artificial intelligence - Data processing
Generación no síncrona
Respuesta de frecuencia
Sistemas de energía de baja inercia
Control primario de frecuencia
Energía eólica
Estimación nadir
Aprendizaje automático
Aprendizaje profundo
non-synchronous generation
Frequency response
low-inertia power systems
Primary frequency control
Wind power
Nadir estimation
Machine learning
Deep learning
dc.subject.proposal.spa.fl_str_mv Generación no síncrona
Respuesta de frecuencia
Sistemas de energía de baja inercia
Control primario de frecuencia
Energía eólica
Estimación nadir
Aprendizaje automático
Aprendizaje profundo
dc.subject.proposal.eng.fl_str_mv non-synchronous generation
Frequency response
low-inertia power systems
Primary frequency control
Wind power
Nadir estimation
Machine learning
Deep learning
description Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Autoregressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021
dc.date.accessioned.none.fl_str_mv 2024-09-14T13:38:33Z
dc.date.available.none.fl_str_mv 2024-09-14T13:38:33Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.escuelaing.edu.co/handle/001/3260
dc.identifier.eissn.spa.fl_str_mv 2079-9292
dc.identifier.instname.spa.fl_str_mv Universidad Escuela Colombiana de Ingeniería
dc.identifier.reponame.spa.fl_str_mv Repositorio Digital
dc.identifier.repourl.spa.fl_str_mv https://repositorio.escuelaing.edu.co/
url https://repositorio.escuelaing.edu.co/handle/001/3260
https://repositorio.escuelaing.edu.co/
identifier_str_mv 2079-9292
Universidad Escuela Colombiana de Ingeniería
Repositorio Digital
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationedition.spa.fl_str_mv Vol. 10 No. 151, 2021
dc.relation.citationendpage.spa.fl_str_mv 21
dc.relation.citationissue.spa.fl_str_mv 151
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 10
dc.relation.ispartofjournal.eng.fl_str_mv Electronics
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spelling Chamorro, Harold R063bd147866ae1b7d932787a281f7fb3Orjuela Cañón, Alvaro D.a1cbcc1bf84dc076ff7ac679a8e4a998Ganger, Davida5f786f95adc9564f5b7f68469bdeb61Persson, Mattias6d533ed15bda701b34f9c869e56beddbGonzalez Longatt, Francisco009bacd027df643dfd38da8f6eef5da1Alvarado Barrios, Lazaro512f11b15493d90cd38ccbfa2bedc2daSood, Vijay K.be7ffe4ade6c8866a466709543e7b526Martinez, Wilmarfea9697d592081dc097b47e8c8555a66GiBiome2024-09-14T13:38:33Z2024-09-14T13:38:33Z2021https://repositorio.escuelaing.edu.co/handle/001/32602079-9292Universidad Escuela Colombiana de IngenieríaRepositorio Digitalhttps://repositorio.escuelaing.edu.co/Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Autoregressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.La frecuencia en los sistemas eléctricos es una información en tiempo real que muestra el equilibrio entre generación y demanda. Una buena observación de la frecuencia del sistema es vital para la seguridad y protección del sistema. Este artículo analiza la respuesta de frecuencia del sistema después de perturbaciones y propone una enfoque basado en datos para predecirlo mediante el uso de técnicas de aprendizaje automático como redes neuronales (NN) autorregresivas no lineales (NAR) y redes de memoria a corto plazo (LSTM) de datos simulados y medidos de la Unidad de Medición de Fasores (PMU). El método propuesto utiliza un Ventana de horizonte que reconstruye los datos de series de tiempo de entrada de frecuencia para predecir características de frecuencia como el Nadir. Los escenarios simulados se basan en la reducción gradual de la inercia por incluida la generación no síncrona en el sistema de prueba Nordic 32, mientras que la PMU recopiló los datos se toman de diferentes ubicaciones en el Nordic Power System (NPS). Varias ventanas de horizonte se experimentan para observar un margen de predicción adecuado. Escenarios considerando ruidosos Las señales también se evalúan para proporcionar un índice de robustez y previsibilidad. Los resultados muestran la desempeño adecuado del método y el nivel adecuado de predicción basado en la Media Raíz Índice de error cuadrático (RMSE).21 páginasapplication/pdfengMDPI (Multidisciplinary Digital Publishing Institute)Basel (Suiza)https://www.mdpi.com/2079-9292/10/2/151Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural ModelsArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Vol. 10 No. 151, 202121151110ElectronicsFrankl, P. Energy System Debate: What Lies Ahead for the Future [In My View]. IEEE Power Energy Mag. 2018, 17, 100–198. [CrossRef]Miettinen, J.; Holttinen, H.; Ämmälä, J.; Piironen, M. Wind power forecasting at Transmission System Operator’s control room. 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[CrossRef]info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbFrecuencia (Sistemas eléctricos)Frequency (Electrical Systems)Análisis de sistemasSystem analysisRedes neurales artificialesArtificial neural networksInteligencia artificial - Procesamiento de datosArtificial intelligence - Data processingGeneración no síncronaRespuesta de frecuenciaSistemas de energía de baja inerciaControl primario de frecuenciaEnergía eólicaEstimación nadirAprendizaje automáticoAprendizaje profundonon-synchronous generationFrequency responselow-inertia power systemsPrimary frequency controlWind powerNadir estimationMachine learningDeep learningTEXTData-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models.pdf.txtData-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models.pdf.txtExtracted texttext/plain60127https://repositorio.escuelaing.edu.co/bitstream/001/3260/4/Data-Driven%20Trajectory%20Prediction%20of%20Grid%20Power%20Frequency%20Based%20on%20Neural%20Models.pdf.txtf34cccc6e4c90f1a2639f0b28acac8c4MD54metadata only accessTHUMBNAILPortada Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models.JPGPortada Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models.JPGimage/jpeg102081https://repositorio.escuelaing.edu.co/bitstream/001/3260/3/Portada%20Data-Driven%20Trajectory%20Prediction%20of%20Grid%20Power%20Frequency%20Based%20on%20Neural%20Models.JPG2d3169f38e07722c5dbb139b4cec282eMD53open accessData-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models.pdf.jpgData-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models.pdf.jpgGenerated Thumbnailimage/jpeg15901https://repositorio.escuelaing.edu.co/bitstream/001/3260/5/Data-Driven%20Trajectory%20Prediction%20of%20Grid%20Power%20Frequency%20Based%20on%20Neural%20Models.pdf.jpg12640921089fba07aaf82fef7d938cd2MD55metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/3260/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessORIGINALData-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models.pdfData-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models.pdfapplication/pdf2152945https://repositorio.escuelaing.edu.co/bitstream/001/3260/1/Data-Driven%20Trajectory%20Prediction%20of%20Grid%20Power%20Frequency%20Based%20on%20Neural%20Models.pdf7ece9845aabbaf0a82763722131e6cd5MD51metadata only access001/3260oai:repositorio.escuelaing.edu.co:001/32602024-09-15 03:02:38.502metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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