2026-04-13T13:24:01Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3558362026-01-23T04:51:22Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc González Abreu, Artvin Darién author Delgado Prieto, Miquel author Osornio Rios, Roque A. author Saucedo Dorantes, Juan Jose author Romero Troncoso, René de Jesús author 2021-05-02 Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization. This research work has been partially supported by FOFIUAQ-2018 FIN 201812 and CONACyT doctoral scholarship number 735042. This work has been co-financed by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014–2020, grant number 001-P-001643. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Energies::Energia elèctrica Electric power Deep learning Autoencoder Deep learning Power quality disturbances Power quality monitoring Energia elèctrica Aprenentatge profund A novel deep learning-based diagnosis method applied to power quality disturbances