Denoising Auto-associative Measurement Screening and Repairing

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Krstulovic, J (Author) Other; Vladimiro Miranda (Author) FEUP

Conference proceedings International

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

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6