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DTSTART:20251102T010000
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DTSTAMP:20251205T203326Z
UID:4CF2467E-71B4-45D2-8B67-E308C94C147E
DTSTART;TZID=America/Los_Angeles:20251201T140000
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DESCRIPTION:The growing complexity of modern power systems and the prolifer
 ation of inverter-based resources (IBRs) demand intelligent\, adaptive\, a
 nd autonomous control strategies. Traditional tuning approaches for invert
 er controllers often rely on linearized models\, fixed assumptions\, or ma
 nual parameter optimization methods that are increasingly inadequate in dy
 namic\, data-rich grid environments.\n\nIn this talk\, a reinforcement lea
 rning (RL)-based framework for inverter controller tuning\, presenting a p
 aradigm shift toward data-driven\, self-optimizing control\, will be intro
 duced. At the core of this approach is Control RL\, an open-source reinfor
 cement learning library purpose-built for control and power system applica
 tions.\n\nThis webinar will demonstrate how Control RL enables rapid proto
 typing\, training\, and deployment of RL agents capable of achieving robus
 t and optimal inverter control under diverse grid conditions. Beyond the t
 heoretical foundation\, attendees will see how this framework was used to 
 develop and validate the results of our recent research on autonomous inve
 rter tuning. By bridging the gap between RL theory and practical control e
 ngineering\, this talk aims to empower researchers and practitioners to ha
 rness reinforcement learning for next-generation power system control.\n\n
 Co-sponsored by: University of California Riverside\n\nSpeaker(s): Dr. Shu
 vangkar Das\, \n\nVirtual: https://events.vtools.ieee.org/m/506443
LOCATION:Virtual: https://events.vtools.ieee.org/m/506443
ORGANIZER:mail@maxcherubin.com
SEQUENCE:36
SUMMARY:Tune Inverter Control Gains using Reinforcement Learning
URL;VALUE=URI:https://events.vtools.ieee.org/m/506443
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;The growin
 g complexity of modern power systems and the proliferation of inverter-bas
 ed &lt;/span&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;resources (IBRs) demand intellig
 ent\, adaptive\, and autonomous control strategies. Traditional &lt;/span&gt;&lt;sp
 an style=&quot;font-size: 14pt\;&quot;&gt;tuning approaches for inverter controllers of
 ten rely on linearized models\, fixed assumptions\, or &lt;/span&gt;&lt;span style=
 &quot;font-size: 14pt\;&quot;&gt;manual parameter optimization methods that are increas
 ingly inadequate in dynamic\, data-rich &lt;/span&gt;&lt;span style=&quot;font-size: 14p
 t\;&quot;&gt;grid environments.&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;In 
 this talk\, a reinforcement learning (RL)-based framework for &lt;/span&gt;&lt;span
  style=&quot;font-size: 14pt\;&quot;&gt;inverter controller tuning\, presenting a parad
 igm shift toward data-driven\, self-optimizing control\, will be introduce
 d. &lt;/span&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;At the core of this approach is 
 Control RL\, an open-source reinforcement learning library purpose-built f
 or control and power system applications. &lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span style=&quot;fon
 t-size: 14pt\;&quot;&gt;This webinar will demonstrate how Control RL enables rapid
  &lt;/span&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;prototyping\, training\, and deplo
 yment of RL agents capable of achieving robust and optimal &lt;/span&gt;&lt;span st
 yle=&quot;font-size: 14pt\;&quot;&gt;inverter control under diverse grid conditions. Be
 yond the theoretical foundation\, attendees will &lt;/span&gt;&lt;span style=&quot;font-
 size: 14pt\;&quot;&gt;see how this framework was used to develop and validate the 
 results of our recent research on &lt;/span&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;a
 utonomous inverter tuning. By bridging the gap between RL theory and pract
 ical control &lt;/span&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;engineering\, this tal
 k aims to empower researchers and practitioners to harness reinforcement &lt;
 /span&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;learning for next-generation power s
 ystem control.&lt;/span&gt;&lt;/p&gt;
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