Title Data-driven trajectory prediction of grid power frequency based on neural models
Authors Chamorro H.R., Orjuela-Cañón A.D., Ganger D., Persson M., Gonzalez-Longatt F., ALVARADO BARRIOS, LÁZARO, Sood V.K., Martinez W., ALVARADO BARRIOS, LÁZARO
External publication No
Means Electronics (Switzerland)
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 1
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099418991&doi=10.3390%2felectronics10020151&partnerID=40&md5=33476ad5752059be201b133ba115b1f6
Publication date 01/01/2021
Scopus Id 2-s2.0-85099418991
DOI 10.3390/electronics10020151
Abstract 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 pro-tection. 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. © 2021 by the authors.
Keywords Deep learning; Frequency response; Low-inertia power systems; Machine learning; Nadir estimation; Non-synchronous generation; Primary frequency control; Wind power
Universidad Loyola members