Power System Stability Assessment with Supervised Machine Learning

Power system stability assessment has become an important area of research due to the increased penetration of photovoltaics (PV) in modern power systems. This work explores how supervised machine learning can be used to assess power system stability for the Western Electricity Coordinating Council (WECC) service region as part of the Data-driven Security Assessment for the Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS) project. Data-driven methods offer to improve power flow scheduling through machine learning prediction, enabling better energy resource management and reducing demand on real-time time-domain simulations. Frequency, transient, and small signal stability datasets were created using the 240-bus and reduced 18-bus models of the WECC system. Supervised machine learning was performed to predict the system’s frequency nadir, critical clearing time, and damping ratio, respectively. In addition to varying algorithm hyperparameters, experiments were performed to evaluate model prediction performance through various data entry methods, data allocation methods during model development, and preprocessing techniques. This work also begins analysis of Electric Reliability Council of Texas (ERCOT) grid behavior during extreme frequency events, and provides suggestions for potential supervised machine learning applications in the future. Timestamped frequency event data is collected every 100 milliseconds from Frequency Disturbance Recorders (FDRs) installed in the ERCOT service territory by the Power Information Technology Laboratory at the University of Tennessee, Knoxville. The data is filtered, and the maximum Rate of Change of Frequency (ROCOF) is calculated using the windowing technique. Trends in data are evaluated, and ROCOF prediction performance is verified against another ROCOF calculation technique

Design and Testing of Spacecraft Power Systems Using VTB

A study is presented on the design and testing of spacecraft power systems using the virtual test bed (VTB). The interdisciplinary components such as solar array and battery systems were first modeled in native VTB format and validated by experiment data. The shunt regulator and battery charge controller were designed in Simulink according to the system requirements and imported to VTB. Two spacecraft power systems were then designed and tested together with the control systems

A New Tool for Visualization and Animation of Power Component and System Operation

A new tool for computing and visualizing the operation of power system components and systems is presented. One component of the tool is a time domain simulator that operates in the background within a multitasking environment. Visualization and animation objects can retrieve data in real time from the time domain simulator and display a detailed picture of a component or system operation. As the system state evolves, the pictures are updated resulting in an animated visualization of the process. The tool has been implemented in a multitasking environment, thus permitting user interaction in real time with the system parameters and immediate reflection of the results in animations and visualizations. Several examples of visualization and animation are presented, including animation of Mho relay operation, visualization of small signal stability, load dynamics and voltage sensitivity and synchronous machine operation