BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20221017T132837Z UID:4DAE934D-6008-44D3-B2DE-9257991C032F DTSTART;TZID=America/New_York:20221014T120000 DTEND;TZID=America/New_York:20221014T130000 DESCRIPTION:When it comes to electric power distribution\, the fundamental goals of today’s electric utilities have not changed much in fifty years . They comprise a safer distribution system that poses minimal risks to th e public and to utility workers\, the reliable and stable delivery of powe r to critical\, commercial\, and residential customers\, and economically sound business operations that meet both shareholder and ratepayer expecta tions.\n\nThe challenges to these goals\, however\, have grown significant ly over the past decade. Increasingly intense and frequent weather events raise the threat of outages and environmental risks\; the process of shift ing from a centralized to decentralized grid to meet decarbonization manda tes creates complexities\; and the integration of renewable energy sources and the proliferation of electric vehicles are escalating the demands on an aging distribution grid. This presentation gives an overview of how big these underground power grid challenges are and how machine learning and deep learning are tools to tackle some of these challenges.\n\nCo-sponsore d by: Eversource Energy Center\n\nSpeaker(s): Steffen Ziegler \, Tim Morel lo\n\nVirtual: https://events.vtools.ieee.org/m/324708 LOCATION:Virtual: https://events.vtools.ieee.org/m/324708 ORGANIZER:junbo@uconn.edu SEQUENCE:2 SUMMARY:Underground Power Cable Grid Monitoring and Partial Discharge Chara cterization URL;VALUE=URI:https://events.vtools.ieee.org/m/324708 X-ALT-DESC:Description: <br /><p>When it comes to electric power distributi on\, the fundamental goals of today&rsquo\;s electric utilities have not c hanged much in fifty years. They comprise a safer distribution system that poses minimal risks to the public and to utility workers\, the reliable a nd stable delivery of power to critical\, commercial\, and residential cus tomers\, and economically sound business operations that meet both shareho lder and ratepayer expectations.</p>\n<p>The challenges to these goals\, h owever\, have grown significantly over the past decade. Increasingly inten se and frequent weather events raise the threat of outages and environment al risks\; the process of shifting from a centralized to decentralized gri d to meet decarbonization mandates creates complexities\; and the integrat ion of renewable energy sources and the proliferation of electric vehicles are escalating the demands on an aging distribution grid. This presentati on gives an overview of how big these underground power grid challenges ar e and how machine learning and deep learning are tools to tackle some of t hese challenges.&nbsp\;</p> END:VEVENT END:VCALENDAR