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

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Chamorro, Harold R
Orjuela Cañón, Alvaro D.
Ganger, David
Persson, Mattias
Gonzalez Longatt, Francisco
Alvarado Barrios, Lazaro
Sood, Vijay K.
Martinez, Wilmar
Abstract (Spanish)
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).
Abstract (English)
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.
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21 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/3260
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