Scope

The Working Group will cover:

  • Augmented Reality (AR) solutions identification to improve field operation (MWM – Mobile Workforce Management e WFM – Work Force Management) and training solutions to increase network’s operation and maintenance efficiency;
  • Assess Machine learning, Artificial Intelligence (AI) and Advanced Analytics to improve:
    • network management functions;
    • asset condition monitoring and health evaluation;
    • adopting predictive models in terms of modernization (replacement) and maintenance strategies, assuming an ownership strategy more focused on reliability, cost and risk;
    • predictive models, assessing which are the best methodologies and sensing requirements;
  • To Identify asset Digital Twin performance with real impact on asset management, using Machine learning, AI, AR and Advanced Analytics
  • Benchmarking of new and existing demonstration projects using Machine learning, AI, AR and Advanced Analytics

Members

Youssef Abdelaziz Almoataz

Ain Shams University - Egypt

Patrick Brito Mendes

E-Redes - Portugal

Carlos Cândido

E-Redes - Portugal

Sung-Min Cho

Korea Electric Power Research Institute (KEPRI) - Korea

Sreten Davidov

Elektro Ljubljana - Slovenia

Vincent Debusschere

G2ELab - Grenoble INP - France

Matthias Duckheim

Siemens AG - Germany

Heba Mohammed Beder El Bermawy

North Delta Electricity Distribution Company - Egypt

Alireza Fereidunian

K. N. Toosi University of Technology - Iran

David Gopp

Omicron Electronics GmbH - Austria

Mohamed Hasanien Hany

Ain Shams University - Egypt

Lukas Lenz

Stromnetz Hamburg - Germany

Johannes Menzel

Schneider Electric - France

Michael Messner

Energie Steiermark Technik GmbH - Austria

Thomas Pilaud

Enedis - France

Pedro P. Vergara

TU Delft - Netherlands

Erik Winkelmann

Dekra - Germany

Dongjun Won

Inha University - Korea

Sung-Guk Yoon

Soongsil University - Korea

Hong Zhu

State Grid Nanjing Power Supply Company - China