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DTSTAMP:20260130T163101Z
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DTSTART;TZID=Europe/Copenhagen:20260127T130000
DTEND;TZID=Europe/Copenhagen:20260127T140000
DESCRIPTION:The reliability of power electronic converters is increasingly 
 critical as their deployment grows with renewable energy and electricity d
 emand. This thesis focuses on data-driven condition monitoring\, which ext
 racts health indicators directly from measured data. Four key challenges a
 re addressed: estimation accuracy\, noninvasive online implementation\, sy
 stematic tool development\, and sampling schemes. Contributions include: (
 i) improved capacitor health estimation using time-domain measurements and
  particle swarm optimization\; (ii) a noninvasive monitoring method for po
 wer semiconductors using control variables\; (iii) loss-landscape analysis
  to guide multi-parameter estimation\; and (iv) switching signal reconstru
 ction to enhance sampling efficiency. Overall\, the work advances low-cost
 \, accurate\, and practical monitoring methods to improve converter reliab
 ility.\n\nMore details can be found here: https://www.energy.aau.dk/01-27-
 phd-defence-by-shuyu-ou-e148410\n\nRoom: 1.177\, Pontoppidanstræde 111\, 
 Aalborg\, Frederiksberg Kommune\, Denmark\, 9220\, Virtual: https://events
 .vtools.ieee.org/m/532655
LOCATION:Room: 1.177\, Pontoppidanstræde 111\, Aalborg\, Frederiksberg Kom
 mune\, Denmark\, 9220\, Virtual: https://events.vtools.ieee.org/m/532655
ORGANIZER:sssa@energy.aau.dk
SEQUENCE:10
SUMMARY:PhD Defense on Data-Driven Condition Monitoring of Power Electronic
 s at Aalborg University
URL;VALUE=URI:https://events.vtools.ieee.org/m/532655
X-ALT-DESC:Description: <br /><p>The reliability of power electronic conver
 ters is increasingly critical as their deployment grows with renewable ene
 rgy and electricity demand. This thesis focuses on data-driven condition m
 onitoring\, which extracts health indicators directly from measured data. 
 Four key challenges are addressed: estimation accuracy\, noninvasive onlin
 e implementation\, systematic tool development\, and sampling schemes. Con
 tributions include: (i) improved capacitor health estimation using time-do
 main measurements and particle swarm optimization\; (ii) a noninvasive mon
 itoring method for power semiconductors using control variables\; (iii) lo
 ss-landscape analysis to guide multi-parameter estimation\; and (iv) switc
 hing signal reconstruction to enhance sampling efficiency. Overall\, the w
 ork advances low-cost\, accurate\, and practical monitoring methods to imp
 rove converter reliability.</p>\n<p>More details can be found here: https:
 //www.energy.aau.dk/01-27-phd-defence-by-shuyu-ou-e148410</p>
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