BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20230312T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20231105T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230704T165601Z UID:30CA0108-9E33-4C5E-924E-6CA8DF14DFDA DTSTART;TZID=America/Los_Angeles:20230630T140000 DTEND;TZID=America/Los_Angeles:20230630T150000 DESCRIPTION:Recently\, the integration of inverter-based resources (IBRs) i n renewable energy systems has introduced new dynamic challenges to the po wer grid. These challenges include converter-driven oscillations and the e xpansion of power outage areas\, which pose a significant risk to the reli ability of future grids. Grid sensor technology has been developed\, enabl ing the collection of a vast amount of measurement data. This data present s an opportunity for machine-learning experts to leverage big data analyti cs and tackle various grid reliability analysis challenges specific to the future power system. However\, additional technical hurdles are related t o data integrity and consistency\, particularly regarding labeling and oth er factors. In power system analysis\, time-domain simulation models have traditionally been used for contingency analysis\, post-mortem analysis\, grid controller design\, and grid-wide protection. Synthetic data\, which refers to simulated responses\, has emerged as a promising approach for im puting missing events and measurements in the data. However\, the validati on of simulation models\, especially for grid-wide events\, has been insuf ficient. This means that synthetic data from numerical simulation models a re accurate for limited power system dynamic studies but may not be adequa tely validated for broader applications. In addition\, there is a lack of collaboration between modeling experts specializing in electromagnetic tra nsient modeling\, electromechanical modeling\, IBR modeling\, and grid mod eling. Furthermore\, no generally accepted model validation procedure or r equirements is in place. As a result\, well-validated model parameters for simulation studies are scarce\, even though the models themselves are rea dily available. Given these challenges\, machine-learning experts must per ceive the potential missing dynamics or model errors when utilizing synthe tic data for data-driven research studies. This presentation aims to addre ss the current quality of synthetic data and provide insights on navigatin g the complexities associated with big data in grid reliability analysis. Join us for an engaging discussion on this topic.\n\nThis is a Virtual Pre sentation. You must register through the link provided under "REGISTRATION ". After registering\, you will receive a confirmation email containing in formation about joining\n\nCo-sponsored by: University of California\, Riv erside\n\nVirtual: https://events.vtools.ieee.org/m/364577 LOCATION:Virtual: https://events.vtools.ieee.org/m/364577 ORGANIZER:mail@maxcherubin.com SEQUENCE:13 SUMMARY:Grid modeling challenge for data-driven research studies URL;VALUE=URI:https://events.vtools.ieee.org/m/364577 X-ALT-DESC:Description: <br /><p>Recently\, the integration of inverter-bas ed resources (IBRs) in renewable energy systems has introduced new dynamic challenges to the power grid. These challenges include converter-driven o scillations and the expansion of power outage areas\, which pose a signifi cant risk to the reliability of future grids. Grid sensor technology has b een developed\, enabling the collection of a vast amount of measurement da ta. This data presents an opportunity for machine-learning experts to leve rage big data analytics and tackle various grid reliability analysis chall enges specific to the future power system. However\, additional technical hurdles are related to data integrity and consistency\, particularly regar ding labeling and other factors. In power system analysis\, time-domain si mulation models have traditionally been used for contingency analysis\, po st-mortem analysis\, grid controller design\, and grid-wide protection. Sy nthetic data\, which refers to simulated responses\, has emerged as a prom ising approach for imputing missing events and measurements in the data. H owever\, the validation of simulation models\, especially for grid-wide ev ents\, has been insufficient. This means that synthetic data from numerica l simulation models are accurate for limited power system dynamic studies but may not be adequately validated for broader applications. In addition\ , there is a lack of collaboration between modeling experts specializing i n electromagnetic transient modeling\, electromechanical modeling\, IBR mo deling\, and grid modeling. Furthermore\, no generally accepted model vali dation procedure or requirements is in place. As a result\, well-validated model parameters for simulation studies are scarce\, even though the mode ls themselves are readily available. Given these challenges\, machine-lear ning experts must perceive the potential missing dynamics or model errors when utilizing synthetic data for data-driven research studies. This prese ntation aims to address the current quality of synthetic data and provide insights on navigating the complexities associated with big data in grid r eliability analysis. Join us for an engaging discussion on this topic.</p> \n<p>This is a Virtual Presentation. You must register through the link pr ovided under "REGISTRATION". After registering\, you will receive a confir mation email containing information about joining</p> END:VEVENT END:VCALENDAR