Machine Learning

We study the optimal control of multiple-input and multiple-output dynamical systems via the design of neural network-based controllers with stability and output tracking guarantees. While neural network-based nonlinear controllers have shown superior performance in various applications, their lack of provable guarantees has restricted their adoption in high-stake real-world applications.

A key barrier to using reinforcement learning (RL) in many real-world applications is the requirement of a large number of system interactions to learn a good control policy. Off-policy and Offline RL methods have been proposed to reduce the number of interactions with the physical environment by learning control policies from historical data.

The increase in penetration of inverter-based resources provides us with more flexibility in frequency regulation compared to conventional linear droop controllers. Using their power electronic interfaces, inverter-based resources can realize almost arbitrary responses to frequency changes.

We study the control of networked systems with the goal of optimizing both transient and steady-state performances while providing stability guarantees. Linear Proportional-Integral (PI) controllers are almost always used in practice, but the linear parameterization of the controller fundamentally limits its performance.

Inverter-based distributed energy resources provide the possibility for fast time-scale voltage control by quickly adjusting their reactive power. The power-electronic interfaces allow these resources to realize almost arbitrary control law, but designing these decentralized controllers is nontrivial.

This is a UW-DIRECT capstone project cooperated with Pacific Northwest National Laboratory (PNNL) and mentored by Marjolein Oostrom, Sarah Akers and Dr. Steven R. Spurgeon. We proposed a few-Shot machine learning algorithum for classification of electron microscopy images and implemented a graphical-user interface for it.

Transient stability of power systems is becoming increasingly important because of the growing integration of renewable resources. These resources lead to a reduction in mechanical inertia but also provide increased flexibility in frequency responses.

Green Seattle is an inter-disciplinary project in Clearn Energy Insitute (CEI) collaborated by seven students majoring in Chemical Sciences, Political Science and Electrical Engineering. The first part of Green Seattle is focused on predicting Seattle Traffic Trends (codes are publicly available here), while the second is focused on Visualizing Seattle Traffic Trends, as well as outputs of a regression model seeking to predict future traffic patterns based on user inputs (codes are publicly available here).

The dynamics of power grids are governed by a large number of nonlinear ordinary differential equations (ODEs). To safely operate the system, operators need to check that the states described by this set of ODEs stay within prescribed limits after various faults.