Naslov
Denoising auto-associative measurement screening and repairing

Autori
Krstulović, Jakov ; Miranda, Vladimiro

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
IEEE / - , 2015, 1-6

ISBN
978-1-5090-0191-0

Skup
18th International Conference on Intelligent System Application to Power Systems (ISAP), 2015

Mjesto i datum
Porto, Portugal, 11.09.2015

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Information Theoretic Learning; Gross Errors; State Estimation; Autoencoders; Neural Networks; Denoising

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

POVEZANOST RADA