Information Theoretic Generalized State Estimation in power systems

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Meneghetti, R (Author) Other; Costa, AS (Author) Other; Vladimiro Miranda (Author) FEUP; Ascari, LB (Author) Other

Journal

Title: Electric Power Systems Research

Vol. 182

ISSN: 0378-7796

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 1 Citation

Other information

Authenticus ID: P-00R-Q46

DOI: 10.1016/j.epsr.2020.106251

Abstract (EN): This paper introduces an Information Theoretic approach for Generalized State Estimation, aiming at achieving reliable topology and state variables co-estimation results, even in the presence of both topology errors and gross measurements. Attention is focused on the final bad data processing stage in which only relevant parts of the power network are represented at the bus-section level. The proposed generalized strategy applied at physical level relies on the superior outlier rejection properties of state estimators based on Maximum Correntropy, a concept borrowed from Information Theoretical Learning. A single objective function unifies the treatment of analog measurements and topology data, leading to an algorithm that does not require re-estimation runs for bad data suppression, and is simpler and more efficient than previously proposed co-estimation methods. Case studies conducted for distinct test-systems are presented, including various types of topology errors and simultaneous occurrence of topology and gross measurement errors. The results suggest that the proposed information-theoretic co-estimation algorithm is able to successfully provide bad data-free real-time network models even in the presence of multiple topology errors, simultaneous gross measurements and inaccurate topology information. Finally, additional tests confirm its superior computational performance as compared with other co-estimation algorithms.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13