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Application of spatio-temporal data-driven and machine learning algorithms for security assessment (TR104)
21 Nov 2022
Rafael Segundo, Yanli Liu, Emilio Barocio, Petr Korba, Aharon de la Torre, Al-Amin Bugaje, Alejandro Zamora-Mendez, Alexandra Karpilow, Carlos Toledo, Claudia Caro-Ruiz, Daniel Dotta, Daniel Müller, David Panchi, Diego Echeverría, Federica Bellizio, Francisco Zelaya, Gabriel V. de S. Lopes, Gao Qiu, Garibaldi Pineda-Garcia, Goran Strbac, Hector Chavez, Hjörtur Jóhannsson, Jaime Cepeda, Jochen L. Cremer, Jose Ortiz-Bejar, José Zarate, José Antonio de la O Serna, Juan Quiroz, Juan M. Ramirez, Junbo Zhao, Lucas Lugnani, Luis Gonzalez, Luis Mendieta, Marcos Netto, Mario Paolone, Mario R. Arrieta Paternina, Miguel Ramirez-Gonzalez, Panagiotis Papadopoulos, Rodrigo D. Reyes de Luna, Salvador Lara, Tong Su, Yoshihiko Susuki, Youbo Liu, Yutaka Ota
Analytical Methods in Power Systems (AMPS)
Subcommittee on Big Data & Analytics for Power Systems, Task Force on Application of Big Data Analytics on Transmission Systems for Dynamic Security Assessment
Chair: Rafael SegundoVice Chair: Yanli Liu
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This technical document summarizes recent advancements on spatio-temporal data-driven and machine learning methods for static and dynamic security assessment, and their particular use cases. It is a collective effort of different research groups with the aim of providing transmission system operators (TSOs) with innovative tools and ideas for their potential implementation. The algorithms presented here are classified as non-training and training approaches, namely spatio-temporal and machine learning based, considering as input time series from time domain simulations, and or synchrophasor data from wide-area monitoring systems. The efficacy of these algorithms is then evaluated in different IEEE benchmark models and real-power systems such as the Mexican, USA, Chilean, Brazilian, Ecuadorian, Japanese and Swedish systems, respectively.