3 citations · 6 across the 8 of their papers we have counts for
9 papers
GPU-Accelerated Verification of Machine Learning Models for Power Systems
Samuel Chevalier, Ilgiz Murzakhanov, Spyros Chatzivasileiadis
Computational tools for rigorously verifying the performance of large-scale machine learning (ML) models have progressed significantly in recent years. The most successful solvers…
Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessment
Rahul Nellikkath, Andreas Venzke, Mohammad Kazem Bakhshizadeh +2
A significant increase in renewable energy production is necessary to achieve the UN's net-zero emission targets for 2050. Using power-electronic controllers, such as Phase Locked…
Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources
Ilgiz Murzakhanov, Sarthak Gupta, Spyros Chatzivasileiadis +1
The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control r…
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations
Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad +5
Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This letter first seeks to…
A Novel Decentralized Inverter Control Algorithm for Loss Minimization and LVRT Improvement
Ilgiz Murzakhanov, Gururaj Mirle Vishwanath, Vemalaiah Kasi +3
Algorithms that adjust the reactive power injection of converter-connected RES to minimize losses may compromise the converters' fault-ride-through capability. This can become cruc…
Neural network interpretability for forecasting of aggregated renewable generation
Yucun Lu, Ilgiz Murzakhanov, Spyros Chatzivasileiadis
With the rapid growth of renewable energy, lots of small photovoltaic (PV) prosumers emerge. Due to the uncertainty of solar power generation, there is a need for aggregated prosum…