Overview of CURENT - Wide Area Power System Control
Dr. Kevin Tomsovic (UTK) - Motivation for wide area control
- Definition of wide area control - extant control structure
- Historical development of wide area control and monitoring
- Underlying research developments
- New monitoring and sensors
- Modern power system communications
- New actuation technologies
- Local control vs. wide area control systems
Actuation and Power Electronics for the Grid
Dr. Fred Wang (UTK)- Introduction to actuation thrust; power system actuator classification; traditional actuators – generators, switchgears, tap changers, capacitor banks; power electronics based actuators – FACTS, HVDC, renewable energy source interface. Power electronics technologies for grid applications.
Electric Machines
Dr. Leila Parsa (RPI)- Reference Frame Theory
- D-Q Domain Analysis of Electric Machines
- Modeling and Control
Three-phase voltage source converters as the basic building blocks for power system applications of power electronics
Dr. Jian Sun (RPI)- Basic Circuits and Operation
- Pulse-Width Modulation
- Current and Voltage Control
Power quality and reactive power
Dr. Aleksander Stankovic (Tufts) and Dr. Hanoch Lev-Ari(NEU) - Early concepts, historical development, multi-component decompositions for polyphase and poly-harmonic systems.
Power system state estimation
Dr. Ali Abur (NEU) - Network model, WLS estimation, observability, bad data processing, pmu measurements. Students will use a free software to run exercises on three-four cases. They will be asked to also work on some problems using Matlab.
Cyber-physical security in power grid
Dr. Hairong Qi(UTK) - requirements for a secure power grid
- event analysis from signal processing perspective
- multiple attack detection, recognition, and localization
High-dimensional Data Analysis in Power System Monitoring
Dr. Meng Wang (RPI)- Recent developments of exploiting low-dimensional models (such as sparsity, low-rankness, etc) in high-dimensional data analysis, as well as the opportunities of applying these data analysis methods in power system monitoring.
Visual Summaries for Power Grid Situation Awareness
Dr. Jian Huang(UTK)- The students are expected to know basics of programming and Unix or Linux based operating system. After the lectures, students will know the basics of ingesting, managing, analyzing and visualization streaming data.
Vulnerability Assessment in Smart Grids
Dr. Jinyuan "Stella" Sun (UTK)- PMU network, Data security, Vulnerability assessment, Penetration testing, Packet sniffing, Packet injection, Fuzzing, IEEE C37.118, Exploit, etc.
Wide-area monitoring and applications
Dr. Yilu Liu (UTK)- FNET/Grideye, Event alert and location, Oscillation detection, Line trip detection, Islanding detection, Audio authentication and other applications
Data Communication
Dr. Jesmin Khan (TU)- General components in communications system
- Performance of any communication system limited by
- Transmission Bandwidth
- Noise
- Review of the characterization of random processes
- Analog communication
- Digital communications
Flywheel energy storage systems
Dr. Hector Pulgar Painemal (UTK) - Description of flywheel technology
- Modeling and control of a flywheel based on surface permanent magnet machines
- Application to a real power system and evaluation of its impact on oscillations
Fire Hazard Assessment of Electrical Power Plants
Dr. David Icove (UTK) Locational marginal pricing in electricity markets
Dr. Fran Li (UTK)- Competitive markets, locational marginal pricing, ancillary service, integrated transmission and generation planning.
Power Electronics for Electric Vehicles
Dr. Daniel Costinett (UTK)- Overview of major components and operation of the electrical power architecture in electric vehicles
Reconfigurable Grid Emulation
Dr. Leon Tolbert (UTK) - Structure of grid emulation; Active rectifier control; Generator emulator development; Motor and load emulator development; and Hardware development and testing
Support vector learning for classification, recognition and modeling
Dr. Zhao Lu (TU)- The kernel-based support vector (SV) learning was originally proposed for solving nonlinear classification and recognition problems, and it marked the beginning of a new era in the computational learning from examples paradigm. This lecture will focus on the principle of support vector learning and its application in pattern recognition and complex systems modeling.
Social Impact of Energy
Dr. Chien-Fei Chen(UTK)