A5027334086- Smart Grid Security and Resilience
- Energy Load and Power Forecasting
- Power System Optimization and Stability
- Computational Physics and Python Applications
- Network Security and Intrusion Detection
- Anomaly Detection Techniques and Applications
- Image and Signal Denoising Methods
- Power Systems and Technologies
- Smart Grid Energy Management
- High-Voltage Power Transmission Systems
- Smart Grid and Power Systems
- Time Series Analysis and Forecasting
- Lightning and Electromagnetic Phenomena
- Magnetic Properties and Applications
- Radiation Effects in Electronics
- Electromagnetic Compatibility and Noise Suppression
- Ionosphere and magnetosphere dynamics
- HVDC Systems and Fault Protection
- Geotechnical and Geomechanical Engineering
- Electrostatic Discharge in Electronics
- Magnetic Field Sensors Techniques
- Earthquake Detection and Analysis
- Distributed and Parallel Computing Systems
- Advanced Manufacturing and Logistics Optimization
- Real-time simulation and control systems
Dominion (United States)
2022-2025
Arizona State University
2018-2023
A nearest-neighbor-based detector against load redistribution attacks is presented. The designed to scale from small-scale very large-scale systems while guaranteeing consistent detection performance. Extensive testing performed on a realistic system evaluate the performance of proposed wide range attacks, simple random noise sophisticated attacks. capability analyzed different attack parameters its sensitivity. statistical test that leverages introduced identify which loads are likely have...
Three detection techniques are presented against a wide class of cyber-attacks that maliciously redistribute loads by modifying measurements. The detectors use different anomaly algorithms based on machine learning techniques: nearest neighbor method, support vector machines, and replicator neural networks. tested using data-driven approach realistic dataset comprised real historical load data in the form publicly available PJM zonal mapped to IEEE 30-bus system. results show all three be...
A generative model for the creation of realistic historical bus-level load data transmission grid models is presented. data-driven approach based on principal component analysis used to learn spatio-temporal correlation between loads in a system and build model. Given topology set base case loads, individual, time-series each can be generated. This technique demonstrated by learning from large proprietary dataset generating 2383-bus Polish test case.
In this study, a machine learning based method is proposed for creating synthetic eventful phasor measurement unit (PMU) data under time-varying load conditions. The leverages generative adversarial networks to create quasi-steady states the power system slowly-varying conditions and incorporates framework of neural ordinary differential equations (ODEs) capture transient behaviors during voltage oscillation events. A numerical example large grid suggests that can realistic PMU measurements...
A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn patterns a real dataset hourly-sampled week-long profiles and generate unique on demand, based season type required. Extensive testing model performed verify that fully captures characteristics loads it can be downstream power system and/or machine learning applications.
Abstract The availability of large datasets is crucial for the development new power system applications and tools; unfortunately, very few are publicly freely available. authors designed an end‐to‐end generative framework creation synthetic bus‐level time‐series load data transmission networks. model trained on a real dataset over 70 Terabytes synchrophasor measurements spanning multiple years. Leveraging combination principal component analysis conditional adversarial network models,...
The electrical power grid is a critical infrastructure, with disruptions in transmission having severe repercussions on daily activities, across multiple sectors. To identify, prevent, and mitigate such events, grids are being refurbished as 'smart' systems that include the widespread deployment of GPS-enabled phasor measurement units (PMUs). PMUs provide fast, precise, time-synchronized measurements voltage current, enabling real-time wide-area monitoring control. However, potential...
Intelligently designed false data injection (FDI) attacks have been shown to be able bypass the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -test based bad detector (BDD), resulting in physical consequences (such as line overloads) power system. In this paper, using synthetic PMU measurements and intelligently FDI attacks, it is that if an attack suddenly injected into system, a predictive filter with sufficient accuracy detect it....
In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS). Within framework, have designed a complete simulation platform to emulate realistic EMS operations: it includes state estimation (SE), real-time contingency analysis (RTCA), security constrained economic dispatch (SCED). This software allowed us achieve two main objectives: 1) study vulnerabilities an understand their on system, 2) formulate implement...
Electric transmission power grids are being revamped with the widespread deployment of GPS-enabled phasor measurement units (PMUs) for real-time wide-area monitoring and control via precise, time-synchronized measurements voltage current. Large, concurrently produced volumes noisy data hinder PMU usability, particularly analysis oscillation load fluctuation events in grid. We examine visualization challenges electric grid develop PMUVis, a platform that supports scalable network topology...
Geomagnetically-induced current (GIC) due to space weather can flow in the power grid causing undesirable effects such as transformer overheating, misoperation of protection devices, and potential blackouts. It is therefore important monitor GIC improve online situational awareness decision-making system operators during a geomagnetic disturbance. To avoid costly installation monitors at transformers' neutrals, it desirable find correlations between already-monitored parameters. Hence, this...
Intelligently designed false data injection (FDI) attacks have been shown to be able bypass the $\chi^2$-test based bad detector (BDD), resulting in physical consequences (such as line overloads) power system. In this paper, it is that if an attack suddenly injected into system, a predictive filter with sufficient accuracy detect it. However, attacker can gradually increase magnitude of avoid detection, and still cause damage
The increasing presence of high-bandwidth inverter-based resources, FACTS devices, and other components with fast dynamic characteristics requires more robust simulation methods to accurately capture the interactions these traditional power system their effects on behavior bulk system. Many phenomena cannot be modeled positive sequence programs alone, such as transformer saturation electronic device switching. Real-time electromagnetic transient (EMT) architectures provide potential dynamics...
Space weather-driven geomagnetic disturbances (GMD) can cause quasi-DC geomagnetically-induced currents (GIC) to flow in the power grid leading transformer overheating, excessive harmonics, and large reactive loss. To protect system reliability, planners are required perform GMD modelling of their grids calculate GIC transformers assess vulnerabilities during extreme events. However, portion neighboring networks outside a study area that should be modelled for accurate calculations is often...
A nearest neighbor-based detection scheme against load redistribution attacks is presented. The detector designed to scale from small very large systems while guaranteeing consistent performance. Extensive testing performed on a realistic, system evaluate the performance of proposed wide range attacks, simple random noise sophisticated attacks. capability analyzed different attack parameters its sensitivity. Finally, statistical test that leverages algorithm introduced identify which loads...
The goal of a system restoration plan is to re-energize the entirety power as quickly possible. Identifying cranking path at 500 kV level can help decrease downtime after blackout and rapidly restore critical loads. Along identified path, energizing high voltage transmission lines requires capacitive/inductive reactive compensation. Restoring loads and/or switching capacitors/inductors provide certain amount power. However, manually these shunt compensation devices could cause...
The electrical power grid is a critical infrastructure, with disruptions in transmission having severe repercussions on daily activities, across multiple sectors. To identify, prevent, and mitigate such events, grids are being refurbished as 'smart' systems that include the widespread deployment of GPS-enabled phasor measurement units (PMUs). PMUs provide fast, precise, time-synchronized measurements voltage current, enabling real-time wide-area monitoring control. However, potential...
A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn patterns a real dataset hourly-sampled week-long profiles and generate unique on demand, based season type required. Extensive testing model performed verify that fully captures characteristics loads it can be downstream power system and/or machine learning applications.