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DTSTAMP:20241202T123701Z
UID:EACF6DAB-1180-48F3-A6E0-8B2D2C8BB06D
DTSTART;TZID=Europe/London:20241204T103000
DTEND;TZID=Europe/London:20241204T113000
DESCRIPTION:[]\n\nDear All PES members\,\n\nThis year\, the IEEE PES UK&amp;I C
 hapter will host a technical talk &quot;Can machine learning help secure power 
 systems?&quot; given by Dr Panagiotis Papadopoulos from 10:30 am -11:30 am (GMT
 ) on 4th Dec 2024 at the University of Manchester &amp; Online.\n\nThis event 
 is co-hosted by the PES Student Branch Chapter at The University of Manche
 ster and all other PES Student Branch Chapters in UK and Ireland\, and is 
 sponsored by The University of Manchester. All PES members based in UK and
  Ireland are welcome to attend!\n\nTime &amp; Date: 10:30 am - 11:30 am\, Wedn
 esday\, 4th of December 2024\n\nVenue: Niels Bohr Room\, Schuster Building
 \, University of Manchester\, Manchester\, UK\, M13 9PL &amp; Online\n\n(Onlin
 e link will be sent to registrants before event)\n\nSummary: Electrical po
 wer systems are undergoing unprecedented changes mainly driven by decarbon
 isation targets and climate change as well as other technical\, economic a
 nd social reasons. This leads to the integration of new technologies such 
 as renewable generation\, electric vehicles\, HVDC links\, etc. These devi
 ces are mostly connected via power electronics with very different dynamic
  behaviour\, leading to increasing complexity of power system dynamics. In
  addition\, uncertainty is also increasing due to intermittent nature of r
 enewable generation but also because of how society will use energy on the
  way to decarbonization (e.g. electrification of transport or possibly hea
 ting). At the same time\, advanced measurement and communication infrastru
 cture is being integrated in modern power systems. Such technologies\, esp
 ecially couple with machine learning\, offer opportunities for advanced si
 tuational awareness\, decision support tools and automated control methods
 .\n\nThis presentation will highlight the challenges faced in future power
  systems with high penetration of converter connected units\, in terms of 
 their dynamic behaviour\, and discuss methods and tools to tackle them\, i
 nspired to a large extent by machine learning. Such methods can enable sys
 tem operators to consider detailed dynamics in cases where the computation
 al effort needed is otherwise prohibitive. Two main aspects will be discus
 sed related to: i) how do we model and characterize the complex and uncert
 ain dynamic behaviour in power systems with high converter penetration and
  ii) how machine learning can enable fast and informative dynamic security
  assessment with focus on how to build trust in such methods going beyond 
 the notion that machine learning is simply a powerful black-box predictor.
 \n\nProbabilistic stability assessment methods to quantify the impact and 
 characterize new types of interactions of power systems with converter con
 nected units at both transmission and distribution level will be discussed
 . Machine learning based methods to enable fast stability assessment in op
 erational and planning timescales will be presented with focus on explaina
 bility for improved decision support\, graph-based methods that take into 
 consideration dynamics\, and physics informed reinforcement learning. Meth
 ods enabling the ability to account for detailed dynamics in optimisation 
 will also be presented.\n\nThis event is embedded in our AGM. If you would
  like to explore more about our achievements and visions of the IEEE PES U
 K&amp;I Chapter\, you are welcome to registrer with this link https://events.v
 tools.ieee.org/m/447455 to attend our AGM on the same day!\n\nLooking forw
 ard to meeting you there!\n\nRegards\,\n\nPES Chapter Committee Team\n\n[E
 mail](mailto:n.chen@ieee.org) [Website](https://www.ieee-ukandireland.org/
 chapters/power-and-energy/) [LinkedIn](https://www.linkedin.com/company/ie
 ee-uk-and-ireland-power-and-energy-society-chapter/) [Twitter](https://twi
 tter.com/IEEE_PES_UKRI) [YouTube](https://www.youtube.com/channel/UCz74Ixx
 2wmHHyHoaQ3h5VMA)\n\nCo-sponsored by: The University of Manchester\n\nSpea
 ker(s): Panagiotis Papadopoulos\, \n\nBldg: Schuster Building\, Niels Bohr
  Room\, University of Manchester\, England\, United Kingdom\, M13 9PL\, Vi
 rtual: https://events.vtools.ieee.org/m/448631
LOCATION:Bldg: Schuster Building\, Niels Bohr Room\, University of Manchest
 er\, England\, United Kingdom\, M13 9PL\, Virtual: https://events.vtools.i
 eee.org/m/448631
ORGANIZER:audiche@ieee.org
SEQUENCE:31
SUMMARY:IEEE PES UK&amp;I Seminar on 4th Dec 2024: Can machine learning help se
 cure power systems?
URL;VALUE=URI:https://events.vtools.ieee.org/m/448631
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;img src=&quot;https://events.vtools.ieee.org/v
 tools_ui/media/display/81fa0127-a48b-48fa-9173-6c736da55f3a&quot; alt=&quot;&quot; width=
 &quot;900&quot; height=&quot;456&quot;&gt;&lt;/p&gt;\n&lt;p&gt;Dear All PES members\,&lt;/p&gt;\n&lt;p&gt;This year\, the
  IEEE PES UK&amp;amp\;I Chapter will host a technical talk &quot;&lt;span style=&quot;font-
 size: 12.0pt\; font-family: &#39;Aptos&#39;\,sans-serif\; mso-ascii-theme-font: mi
 nor-latin\; mso-fareast-font-family: Aptos\; mso-fareast-theme-font: minor
 -latin\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family: &#39;Times 
 New Roman&#39;\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-GB\; 
 mso-fareast-language: EN-US\; mso-bidi-language: AR-SA\;&quot;&gt;Can machine lear
 ning help secure power systems?&lt;/span&gt;&quot; given by Dr &lt;span style=&quot;font-size
 : 12.0pt\; font-family: &#39;Aptos&#39;\,sans-serif\; mso-ascii-theme-font: minor-
 latin\; mso-fareast-font-family: Aptos\; mso-fareast-theme-font: minor-lat
 in\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family: &#39;Times New 
 Roman&#39;\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-GB\; mso-
 fareast-language: EN-US\; mso-bidi-language: AR-SA\;&quot;&gt;Panagiotis Papadopou
 los&amp;nbsp\;&lt;/span&gt;from 10:30 am -11:30 am (GMT) on 4th Dec 2024 at the Univ
 ersity of Manchester &amp;amp\; Online.&lt;/p&gt;\n&lt;p&gt;&lt;span style=&quot;mso-bookmark: _Hl
 k149689948\;&quot;&gt;This event is co-hosted by&amp;nbsp\;&lt;/span&gt;&lt;span style=&quot;mso-boo
 kmark: _Hlk149689948\;&quot;&gt;&lt;span style=&quot;mso-fareast-font-family: DengXian\; m
 so-fareast-theme-font: minor-fareast\;&quot;&gt;the &lt;/span&gt;PES Student Branch Chap
 ter&lt;/span&gt;&lt;span style=&quot;mso-bookmark: _Hlk149689948\;&quot;&gt;&lt;span style=&quot;mso-far
 east-font-family: DengXian\; mso-fareast-theme-font: minor-fareast\;&quot;&gt; at 
 The University of Manchester and all other PES Student Branch Chapters &lt;/s
 pan&gt;in UK and Ireland\, and is sponsored by &lt;/span&gt;&lt;span style=&quot;mso-bookma
 rk: _Hlk149689948\;&quot;&gt;&lt;span style=&quot;mso-fareast-font-family: DengXian\; mso-
 fareast-theme-font: minor-fareast\;&quot;&gt;T&lt;/span&gt;he University of &lt;/span&gt;&lt;span
  style=&quot;mso-bookmark: _Hlk149689948\;&quot;&gt;&lt;span style=&quot;mso-fareast-font-famil
 y: DengXian\; mso-fareast-theme-font: minor-fareast\;&quot;&gt;Manchester&lt;/span&gt;. 
 All PES members based in UK and Ireland are welcome to attend!&lt;/span&gt;&lt;/p&gt;\
 n&lt;p&gt;&lt;span style=&quot;mso-bookmark: _Hlk149689948\;&quot;&gt;&lt;strong&gt;Time &amp;amp\; Date:&lt;
 /strong&gt; 10:&lt;/span&gt;&lt;span style=&quot;mso-bookmark: _Hlk149689948\;&quot;&gt;&lt;span style
 =&quot;mso-fareast-font-family: DengXian\; mso-fareast-theme-font: minor-fareas
 t\;&quot;&gt;30&lt;/span&gt; am - &lt;/span&gt;&lt;span style=&quot;mso-bookmark: _Hlk149689948\;&quot;&gt;&lt;sp
 an style=&quot;mso-fareast-font-family: DengXian\; mso-fareast-theme-font: mino
 r-fareast\;&quot;&gt;11&lt;/span&gt;:30&lt;/span&gt;&lt;span style=&quot;mso-bookmark: _Hlk149689948\;
 &quot;&gt; am\, &lt;/span&gt;&lt;span style=&quot;mso-bookmark: _Hlk149689948\;&quot;&gt;&lt;span style=&quot;ms
 o-fareast-font-family: DengXian\; mso-fareast-theme-font: minor-fareast\;&quot;
 &gt;Wedne&lt;/span&gt;sday\, &lt;/span&gt;&lt;span style=&quot;mso-bookmark: _Hlk149689948\;&quot;&gt;&lt;sp
 an style=&quot;mso-fareast-font-family: DengXian\; mso-fareast-theme-font: mino
 r-fareast\;&quot;&gt;4&lt;/span&gt;th of December&lt;/span&gt;&lt;span style=&quot;mso-bookmark: _Hlk1
 49689948\;&quot;&gt;&lt;span style=&quot;mso-fareast-font-family: DengXian\; mso-fareast-t
 heme-font: minor-fareast\;&quot;&gt; 2024&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span style=&quot;mso-b
 ookmark: _Hlk149689948\;&quot;&gt;&lt;strong&gt;Venue&lt;/strong&gt;: Niels Bohr Room\, Schust
 er Building\, University of Manchester\, Manchester\, UK\,&lt;span style=&quot;mso
 -spacerun: yes\;&quot;&gt; &amp;nbsp\;M13 9PL &lt;/span&gt;&lt;/span&gt;&lt;span style=&quot;mso-bookmark:
  _Hlk149689948\;&quot;&gt;&lt;span style=&quot;mso-fareast-font-family: DengXian\; mso-far
 east-theme-font: minor-fareast\;&quot;&gt;&amp;amp\;&lt;span style=&quot;mso-spacerun: yes\;&quot;&gt;
  &amp;nbsp\;&lt;/span&gt;&lt;strong&gt;Online&lt;/strong&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span style=&quot;
 mso-fareast-font-family: DengXian\; mso-fareast-theme-font: minor-fareast\
 ;&quot;&gt;(Online link will be sent to registrants before event)&lt;/span&gt;&lt;/p&gt;\n&lt;p c
 lass=&quot;MsoNormal&quot; style=&quot;text-align: justify\;&quot;&gt;&lt;strong&gt;&lt;u&gt;Summary:&lt;/u&gt;&lt;/st
 rong&gt; Electrical power systems are undergoing unprecedented changes mainly
  driven by decarbonisation targets and climate change as well as other tec
 hnical\, economic and social reasons. This leads to the integration of new
  technologies such as renewable generation\, electric vehicles\, HVDC link
 s\, etc. These devices are mostly connected via power electronics with ver
 y different dynamic behaviour\, leading to increasing complexity of power 
 system dynamics. In addition\, uncertainty is also increasing due to inter
 mittent nature of renewable generation but also because of how society wil
 l use energy on the way to decarbonization (e.g. electrification of transp
 ort or possibly heating). At the same time\, advanced measurement and comm
 unication infrastructure is being integrated in modern power systems. Such
  technologies\, especially couple with machine learning\, offer opportunit
 ies for advanced situational awareness\, decision support tools and automa
 ted control methods.&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: justify\
 ;&quot;&gt;&lt;span style=&quot;mso-spacerun: yes\;&quot;&gt;&amp;nbsp\;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNorm
 al&quot; style=&quot;text-align: justify\;&quot;&gt;This presentation will highlight the cha
 llenges faced in future power systems with high penetration of converter c
 onnected units\, in terms of their dynamic behaviour\, and discuss methods
  and tools to tackle them\, inspired to a large extent by machine learning
 . Such methods can enable system operators to consider detailed dynamics i
 n cases where the computational effort needed is otherwise prohibitive. Tw
 o main aspects will be discussed related to: i) how do we model and charac
 terize the complex and uncertain dynamic behaviour in power systems with h
 igh converter penetration and ii) how machine learning can enable fast and
  informative dynamic security assessment with focus on how to build trust 
 in such methods going beyond the notion that machine learning is simply a 
 powerful black-box predictor.&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align:
  justify\;&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: justify\;
 &quot;&gt;Probabilistic stability assessment methods to quantify the impact and ch
 aracterize new types of interactions of power systems with converter conne
 cted units at both transmission and distribution level will be discussed. 
 Machine learning based methods to enable fast stability assessment in oper
 ational and planning timescales will be presented with focus on explainabi
 lity for improved decision support\, graph-based methods that take into co
 nsideration dynamics\, and physics informed reinforcement learning. Method
 s enabling the ability to account for detailed dynamics in optimisation wi
 ll also be presented.&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;span style=&quot;mso-bookmark: _
 Hlk149689975\;&quot;&gt;This event is embedded in our AGM. If you would like to ex
 plore more about our achievements and visions of the IEEE PES UK&amp;amp\;I Ch
 apter\, you are welcome to registrer with this link &lt;a href=&quot;https://event
 s.vtools.ieee.org/m/447455&quot;&gt;https://events.vtools.ieee.org/m/447455&lt;/a&gt;&amp;nb
 sp\;to attend our AGM on the same day!&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;Look
 ing forward to meeting you there!&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;Regards\,&lt;/p&gt;\n&lt;
 p&gt;PES Chapter Committee Team&lt;/p&gt;\n&lt;p&gt;&lt;a href=&quot;mailto:n.chen@ieee.org&quot;&gt;Emai
 l&lt;/a&gt;&amp;nbsp\;&lt;a href=&quot;https://www.ieee-ukandireland.org/chapters/power-and-
 energy/&quot;&gt;Website&lt;/a&gt;&amp;nbsp\;&lt;a href=&quot;https://www.linkedin.com/company/ieee-
 uk-and-ireland-power-and-energy-society-chapter/&quot;&gt;LinkedIn&lt;/a&gt;&amp;nbsp\;&lt;a hr
 ef=&quot;https://twitter.com/IEEE_PES_UKRI&quot;&gt;Twitter&lt;/a&gt;&amp;nbsp\;&lt;a href=&quot;https://
 www.youtube.com/channel/UCz74Ixx2wmHHyHoaQ3h5VMA&quot;&gt;YouTube&lt;/a&gt;&lt;/p&gt;
END:VEVENT
END:VCALENDAR