Diagnostics of Interturn Short Circuits in PMSMs With Online Fault Indicators Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F24%3APU151025" target="_blank" >RIV/00216305:26620/24:PU151025 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10449893" target="_blank" >https://ieeexplore.ieee.org/document/10449893</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIE.2024.3363775" target="_blank" >10.1109/TIE.2024.3363775</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Diagnostics of Interturn Short Circuits in PMSMs With Online Fault Indicators Estimation

Popis výsledku v původním jazyce

This article presents novel model-based diagnostics of interturn short circuits in permanent magnet synchronous machines that enable estimating fault location and its severity, even during transients. The proposed method utilizes recursive parametric estimation and model comparison approaches cast in a decision-making framework to track motor parameters and fault indicators from a machine's discrete-time model. The discrete-time prototype is derived from an advanced motor model that reflects the stator winding arrangement in a motor's case. The fault detection is then performed by tracking the changes in the estimated probability density function of the electrical parameters, using the Kullback-Leibler divergence. The fault location is subsequently evaluated by performing a recursive comparison of the predefined fault models in the different phases, utilizing a growing-window approach. Ultimately, a parametric estimation algorithm applied to the fault current model allows identifying the fault severity. The diagnostic algorithm has been validated via laboratory experiments, and its capabilities are compared with other approaches enabling severity estimation.

Název v anglickém jazyce

Diagnostics of Interturn Short Circuits in PMSMs With Online Fault Indicators Estimation

Popis výsledku anglicky

This article presents novel model-based diagnostics of interturn short circuits in permanent magnet synchronous machines that enable estimating fault location and its severity, even during transients. The proposed method utilizes recursive parametric estimation and model comparison approaches cast in a decision-making framework to track motor parameters and fault indicators from a machine's discrete-time model. The discrete-time prototype is derived from an advanced motor model that reflects the stator winding arrangement in a motor's case. The fault detection is then performed by tracking the changes in the estimated probability density function of the electrical parameters, using the Kullback-Leibler divergence. The fault location is subsequently evaluated by performing a recursive comparison of the predefined fault models in the different phases, utilizing a growing-window approach. Ultimately, a parametric estimation algorithm applied to the fault current model allows identifying the fault severity. The diagnostic algorithm has been validated via laboratory experiments, and its capabilities are compared with other approaches enabling severity estimation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Industrial Electronics

ISSN

0278-0046

e-ISSN

1557-9948

Svazek periodika

71

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

15001-15011

Kód UT WoS článku

001181523500001

EID výsledku v databázi Scopus

2-s2.0-85186978267