Neural Network Power Flow Approach to Detect Overload and Voltage Anomalies in Low-Voltage Unbalanced Networks, Agnostic of Network Topology

Amaia González-Garrido, Jon Ander Rivera, Juan Florez Zaballa, Jose Emilio Rodríguez-Seco, Eugenio Perea

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The application of Power Flow (PF) algorithms at Low Voltage (LV) becomes essential, to ensure safe and cost-effective operation. Deterministic approaches do not appear suitable and scalable for LV networks, with a higher risk of non-convergence. The proposed Neural Network Power Flow model (NN-PF) provides accurate power loading, voltage magnitudes and angles in LV unbalanced network, based on nodal consumption and generation power, while being agnostic of the LV network model. Broader dataset is generated for training and testing purposes, including solar generation and undesired voltage events. Despite challenges posed by limited dataset size and the absence of the network topology and features, the NN-PF demonstrates robust performance and high accuracy to identify voltage anomalies and overloads in LV networks. The highest Mean Absolute Error (MAE) is 2e-4 p.u. (0.48 V), 4.6 kW active and 1.51 kVAr reactive power flow at extreme steady-state conditions (V < 0.95 p.u.).

Idioma originalInglés
Título de la publicación alojada20th International Conference on the European Energy Market, EEM 2024 - Proceedings
EditorialIEEE Computer Society
ISBN (versión digital)9798350381740
DOI
EstadoPublicada - 2024
Evento20th International Conference on the European Energy Market, EEM 2024 - Istanbul, Turquía
Duración: 10 jun 202412 jun 2024

Serie de la publicación

NombreInternational Conference on the European Energy Market, EEM
ISSN (versión impresa)2165-4077
ISSN (versión digital)2165-4093

Conferencia

Conferencia20th International Conference on the European Energy Market, EEM 2024
País/TerritorioTurquía
CiudadIstanbul
Período10/06/2412/06/24

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