Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Vassilis Kekatos

Vassilis Kekatos
Alert button

Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks


Jan 04, 2023
Sarthak Gupta, Ali Mehrizi-Sani, Spyros Chatzivasileiadis, Vassilis Kekatos

Add code
Alert button

Figure 1 for Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks
Figure 2 for Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks
Figure 3 for Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks
Figure 4 for Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Deep Learning for Optimal Volt/VAR Control using Distributed Energy Resources


Nov 17, 2022
Sarthak Gupta, Spyros Chatzivasileiadis, Vassilis Kekatos

Add code
Alert button

Figure 1 for Deep Learning for Optimal Volt/VAR Control using Distributed Energy Resources
Figure 2 for Deep Learning for Optimal Volt/VAR Control using Distributed Energy Resources
Figure 3 for Deep Learning for Optimal Volt/VAR Control using Distributed Energy Resources
Figure 4 for Deep Learning for Optimal Volt/VAR Control using Distributed Energy Resources

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study


Sep 22, 2022
Shaohui Liu, Hao Zhu, Vassilis Kekatos

Add code
Alert button

Figure 1 for Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study
Figure 2 for Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study
Figure 3 for Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study
Figure 4 for Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study
* arXiv admin note: text overlap with arXiv:2104.05614 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Learning Distribution Grid Topologies: A Tutorial


Jun 22, 2022
Deepjyoti Deka, Vassilis Kekatos, Guido Cavraro

Add code
Alert button

Figure 1 for Learning Distribution Grid Topologies: A Tutorial
Figure 2 for Learning Distribution Grid Topologies: A Tutorial
Figure 3 for Learning Distribution Grid Topologies: A Tutorial
Figure 4 for Learning Distribution Grid Topologies: A Tutorial
* 13 pages, 6 figures 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Learning Neural Networks under Input-Output Specifications


Feb 23, 2022
Zain ul Abdeen, He Yin, Vassilis Kekatos, Ming Jin

Add code
Alert button

Figure 1 for Learning Neural Networks under Input-Output Specifications
Figure 2 for Learning Neural Networks under Input-Output Specifications
Figure 3 for Learning Neural Networks under Input-Output Specifications

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes


Feb 15, 2022
Mana Jalali, Manish K. Singh, Vassilis Kekatos, Georgios B. Giannakis, Chen-Ching Liu

Add code
Alert button

Figure 1 for Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes
Figure 2 for Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes
Figure 3 for Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes
Figure 4 for Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

DNN-based Policies for Stochastic AC OPF


Dec 04, 2021
Sarthak Gupta, Sidhant Misra, Deepjyoti Deka, Vassilis Kekatos

Add code
Alert button

Figure 1 for DNN-based Policies for Stochastic AC OPF
Figure 2 for DNN-based Policies for Stochastic AC OPF
Figure 3 for DNN-based Policies for Stochastic AC OPF
Figure 4 for DNN-based Policies for Stochastic AC OPF

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach


May 02, 2021
Sarthak Gupta, Vassilis Kekatos, Ming Jin

Add code
Alert button

Figure 1 for Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach
Figure 2 for Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach
Figure 3 for Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach
Figure 4 for Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach
* Submitted to IEEE Transactions on Smart Grid 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data


Apr 12, 2021
Shaohui Liu, Hao Zhu, Vassilis Kekatos

Add code
Alert button

Figure 1 for A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data
Figure 2 for A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data
Figure 3 for A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data
Figure 4 for A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks


Mar 27, 2021
Manish K. Singh, Vassilis Kekatos, Georgios B. Giannakis

Add code
Alert button

Figure 1 for Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks
Figure 2 for Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks
Figure 3 for Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks
Figure 4 for Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks
* submitted 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email
1
2
3
>>