Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation

0574864 - ÚTIA 2024 RIV CH eng J - Článek v odborném periodiku
Schimmack, M. - Belda, Květoslav - Mercorelli, P.
Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation.
Sensors. Roč. 23, č. 16 (2023), č. článku 7173. E-ISSN 1424-8220
Grant CEP: GA ČR(CZ) GC23-04676J
Institucionální podpora: RVO:67985556
Klíčová slova: soft sensing * fault detection * state estimation of electrical systems * transformers
Obor OECD: Electrical and electronic engineering
Impakt faktor: 3.9, rok: 2022
Způsob publikování: Open access
http://library.utia.cas.cz/separaty/2023/AS/belda-0574864.pdf https://www.mdpi.com/1424-8220/23/16/7173

This paper deals with a specific approach to fault detection in transformer systems using the extended Kalman filter (EKF). Specific faults are investigated in power lines where a transformer is connected and only the primary electrical quantities, input voltage, and current are measured. Faults can occur in either the primary or secondary winding of the transformer. Two EKFs are proposed for fault detection. The first EKF estimates the voltage, current, and electrical load resistance of the secondary winding using measurements of the primary winding. The model of the transformer used is known as mutual inductance. For a short circuit in the secondary winding, the observer generates a signal indicating a fault. The second EKF is designed for harmonic detection and estimates the amplitude and frequency of the primary winding voltage. This contribution focuses on mathematical methods useful for galvanic decoupled soft sensing and fault detection. Moreover, the contribution emphasises how EKF observers play a key role in the context of sensor fusion, which is characterised by merging multiple lines of information in an accurate conceptualisation of data and their reconciliation with the measurements. Simulations demonstrate the efficiency of the fault detection using EKF observers.
Trvalý link: https://hdl.handle.net/11104/0344798