Mo-Yuen Chow earned his degree in Electrical and Computer Engineering from the University of Wisconsin-Madison (B.S., 1982); and Cornell University (M. Eng., 1983; Ph.D., 1987). Dr. Chow is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. Dr. Chow was a Changjiang Scholar at Zhejiang University.
Dr. Chow’s recent research focuses on distributed control and management, smart micro-grids, batteries, and mechatronics systems. Dr. Chow has established the Advanced Diagnosis, Automation, and Control Laboratory. He is an IEEE Fellow, the Co-Editor-in-Chief of IEEE Trans. on Industrial Informatics 2014-2018, Editor-in-Chief of IEEE Transactions on Industrial Electronics 2010-2012. He has received the IEEE Region-3 Joseph M. Biedenbach Outstanding Engineering Educator Award, the IEEE ENCS Outstanding Engineering Educator Award, the IEEE ENCS Service Award, the IEEE Industrial Electronics Society Anthony J Hornfeck Service Award, and the IEEE Industrial Electronics Society Dr.-Ing. Eugene Mittelmann Achievement Award. He is a Distinguished Lecturer of IEEE Industrial Electronics Society.
SHORT DESCRIPTION OF INTERESTS:
energy systems, batteries, modeling, control.
- CASL: A Novel Collusion Attack Against Distributed Energy Management Systems , IEEE TRANSACTIONS ON SMART GRID (2023)
- Collaborative Distributed Optimal Control of Pure and Hybrid Active Power Filters in Active Distribution Network , IEEE TRANSACTIONS ON POWER DELIVERY (2023)
- Noninvasive liquid level sensing with laser generated ultrasonic waves , Ultrasonics (2023)
- Physics-Constrained Robustness Evaluation of Intelligent Security Assessment for Power Systems , IEEE TRANSACTIONS ON POWER SYSTEMS (2023)
- Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems , IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2023)
- Resilient Collaborative Distributed AC Optimal Power Flow Against False Data Injection Attacks: A Theoretical Framework , IEEE TRANSACTIONS ON SMART GRID (2022)
- Strategic Protection Against FDI Attacks With Moving Target Defense in Power Grids , IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS (2022)
- A Homomorphic Encryption-Based Private Collaborative Distributed Energy Management System , IEEE TRANSACTIONS ON SMART GRID (2021)
- A Multi-Agent System Based Hierarchical Control Framework for Microgrids , 2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM) (2021)
- A Random-Weight Privacy-Preserving Algorithm With Error Compensation for Microgrid Distributed Energy Management , IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2021)
Through multidisciplinary doctoral education in Cybersecurity for Electric Power Systems (CEPSE), North Carolina State University (NCSU) will increase its commitment to graduate training in two areas designated by the GAANN Program as critical to national need: Cybersecurity and Electrical Engineering. The goal of is to enlarge the pool of U.S. citizens and permanent residents who will pursue teaching and research careers in cybersecurity for electric power systems, thereby promoting workforce development and technological innovation impacting, national security, energy security, and environmental sustainability.
In this project, we will develop high temperature (> 600 C) embedded/integrated sensors (HiTEIS) for wireless monitoring of reactor and fuel cycle systems. HT pressure sensors, vibration sensors and liquid level sensors will be designed, fabricated, embedded and characterized, followed by nuclear structure integration and evaluations. The proposed technique will likely be used to enhance the safety and efficiency of nuclear power systems.
To enhance the microgridÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s scalability, reliability, and resilience, this project aims to develop a collaborative and distributed energy management system (CoDEMS) that can determine globally optimal control commands without the need for a central coordinator. This phase of the project will develop a 2-node system that can provide an optimal charging and discharging schedule of the energy storage system using information from the grid support point and load forecast.
This Accelerating Innovation Technology Translation project focuses on translating a novel Smart Battery Gauge technology to meet the increasing need for accurate battery charge level and remaining life estimations for stationary energy storage systems of renewable energy. This global battery market is currently $89B and is expected to reach $132B by 2016. Specifically, propelled by the US renewable portfolio standards (RPS) targets (20% renewables in 2025) and the PresidentÃƒÂ¢Ã¢â€šÂ¬Ã‹Å“s clean energy goals (80% in 2050) to improve the efficiency of power grid and reduce greenhouse gas emissions, the large-scale integration of renewable energy into the power grid is driving great demand in stationary battery energy storage systems. However, the reliability and safety concern remains as the major barrier that prevents its widespread deployment, which not only may cause unplanned system downtime, but also leads to high battery maintenance cost. The Smart Battery Gauge technology is proposed to improve the reliability and safety of energy storage systems. It outperforms the existing solutions with three salient features: 1) Accurate battery state of charge (SOC) estimation using the adaptive battery model parameters, 2) Accurate battery remaining useful life (RUL) estimation using adaptive model predictive approach, and 3) Flexible customizations for different battery chemistries. To demonstrate commercial potential of the proposed Smart Battery Gauge technology, it will be essential for us to prototype our technology and show that it is feasible to be used in stationary energy storage systems. To achieve this goal, we propose to: 1) Validate the battery SOC estimation accuracy by implementing the patent-pending co-estimation algorithm in an ARM microcontroller, and compare it with off-the-shelf products; 2) Extract the relevant data and models that are needed for battery RUL estimation; 3) Design adaptive algorithms to adjust the parameters of the battery, power generation/ consumption, capacity degradation, and internal resistance escalation models with real-time measurement feedback; 4) Implement the adaptive model predictive algorithm in the ARM microcontroller and compare the RUL estimation results with existing literature approaches and off-the-shelf products. The expected outcome of this project is prototyping the Smart Battery Gauge technology with 95% accuracy in SOC estimation and 10 days accuracy in RUL prediction.
The penetration of energy storage into the grid is low due to the inability to accurately and constantly assess the State of Charge (SOC) and State of Health (SOH). The Smart Battery Gauge can constantly monitor the batteries to provide live and accurate updates about the batteryÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s status to alleviate these issues
The objective of this proposal is to formulate a novel methodology for creating secure algorithms in cyber-physical systems and to develop metrics for evaluating the security of composed systems. Cyber-physical systems are composed of interconnected, semi-autonomous devices. The inherently open nature of a CPS implies a susceptibility to attacks that differ fundamentally from conventional cyber attacks. CPS-specific attack vectors exist as purely cyber, cyber-enabled physical attacks, and physically enabled cyber attacks. As such, the endpoints may be fundamentally unsecurable (such as the sensed information from physical resources) or may be compromised (as in computational resources). Creating a secure communications channel between two nodes is inadequate if one of the endpoints of the communication is insecure. Therefore, new methodologies are needed to ensure that the system is protected in the presence of open information flows from physical resources and possibly malicious entities inside the system.
The energy storage systems at Butler Farms are currently being monitored and maintained by NCEMC using the Smart Battery Gauge developed during the Smart Battery Gauge for Continuous Battery Assessment at Butler Farm and Smart Battery Gauge for Continuous Battery Health Assessment at Butler Farm projects. The Smart Battery Gauge was developed to continuously monitor and provide live feedback about the State of Charge and State of Health of the energy storage system at a rack level. This project will develop a Remaining Useful Life (RUL) Assessment algorithm to assist in determining State of Function (SOF).
The energy storage systems at Butler Farms are currently being monitored and maintained by NCEMC using the Smart Battery Gauge developed during Phase I of this project. The Smart Battery Gauge was developed to continuously monitor and provide live feedback about the State of Charge of the energy storage system at a rack level. This project will develop a State of Health (SOH) estimation algorithm that can provide meaningful insights into appropriate energy storage system operation in the microgrid to increase Remaining Useful Life (RUL) and State of Function (SOF).
The energy storage systems at Butler Farms are currently being monitored and maintained by NCEMC. This project is to develop a Smart Battery Gauge to constantly monitor the energy storage setup and provide live and accurate feedback regarding battery State of Charge (SOC), State of Health (SOH), Remaining Useful Life (RUL), and State of Function (SOF).
Online Adaptive Parameter Identification and SoC Co-Estimation This project will develop 1. A battery model for online parameter identification and SOC co-estimation 2. Online parameter identification and SOC co-estimation including battery degradation 3. Online parameter identification and SOC co-estimation algorithm considering noise 4. Online parameter identification and SOC co-estimation algorithm considering temperature 5. The algorithm integration test in battery pack