Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00574864" target="_blank" >RIV/67985556:_____/23:00574864 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/1424-8220/23/16/7173" target="_blank" >https://www.mdpi.com/1424-8220/23/16/7173</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s23167173" target="_blank" >10.3390/s23167173</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/GC23-04676J" target="_blank" >GC23-04676J: Řiditelná úchopová mechanika: Modelování, řízení a experimenty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Svazek periodika

23

Číslo periodika v rámci svazku

16

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

19

Strana od-do

7173

Kód UT WoS článku

001055866700001

EID výsledku v databázi Scopus

2-s2.0-85168780552