Raymond C Borges Hink R&D Staff Member, Power & Energy Systems Contact borgesrc@ornl.gov All Publications Technical Learning and Integration of Interns in Advanced Protection Lab Space: Enhancements to Testbed and Experiments to Im... Total Power Factor Smart Contract with Cyber Grid Guard Using Distributed Ledger Technology for Electrical Utility Grid with ... Detection of faulted phases in a medium-voltage main feeder using the cyber grid guard system with distributed ledger technology Darknet Cyber Resilience Final Report Electrical Fault and Power Quality Detection Algorithms and Customer-Owned DERs Monitoring with a Cyber Grid Guard System and DLT Final Report: Energy Delivery Systems with Verifiable Trustworthiness... EmSense: A High-Resolution Emulated Sensor for Experiments with the Smart Grid and Distributed Ledger Technology Assessment and Commissioning of Electrical Substation Grid Testbed with a Real-Time Simulator and Protective Relays/Power Meters in the Loop Supply Chain Management in Cyber Grid Guard Framework Electrical substation grid testbed for DLT applications of electrical fault detection, power quality monitoring, DERs use cases and cyber-events Electrical Fault Detection, Power Quality, Distributed Energy Resource Use Cases, and Cyber Event Applications with the Cyber Grid Guard System Using Distributed Ledger Technology Assessment and Commissioning of Electrical Substation Grid Testbed with a Real-Time Simulator and Protective Relays/Power Meters in the Loop Oak Ridge National Laboratory Pilot Demonstration of an Attestation and Anomaly Detection Framework using Distributed Ledger Technology for the Power Grid Infrastructure Assessment of the Electrical Substation-Grid Testbed with Inside/ Outside Devices and Distributed Ledger Technology GMLC 1.4.9 Technical Report: Data Analytics for Electrical Distribution Systems with Micro PMUs Software-defined Intelligent Grid Research Integration and Development Platform... Machine Learning for Power System Disturbance and Cyber-attack Discrimination An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications Key Links Google Scholar ORCID LinkedIn GitHub Organizations Energy Science and Technology Directorate Electrification and Energy Infrastructures Division Energy Systems Integration and Controls Section Grid Communications and Security Group
