Denoising Auto-associative Measurement Screening and Repairing

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Krstulovic, J (Autor) Outra; Vladimiro Miranda (Autor) FEUP

Ata de Conferência Internacional

Título: 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00K-QBA

DOI: 10.1109/isap.2015.7325548

Abstract (EN): This paper offers an efficient and robust concept for a decentralized bad data processing, able to supply in real-time a power system state estimator with a repaired measurement set. Corrupted measurement vectors are funneled through a denoising auto-associative neural network in order to project the biased vector back to the data manifold learned during an offline training process. In order to improve accuracy, a maximum similarity with the solution manifold, measured with Correntropy, is searched for by a meta-heuristic. The extreme robustness and scalability of the process is demonstrated in multiple characteristic case studies.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6