An Integrated Framework for Condition Monitoring and Fault Diagnosis of Electric Machine Drive Systems
Funded by NSF EPCN program (Single PI, 360K, 06/2021-5/2024)
This NSF project aims to design and demonstrate an innovative physics-guided signature-based approach for condition monitoring and fault diagnosis (CMFD) of electric machine networks. As the number of electric machines grows rapidly in response to less carbon dioxide emissions, a large number of electric machines are connected to form electric machine networks. However, traditional CMFD were developed based on individual machine sensors, which requires a large number of sensors and do not comprehensively consider fault and degradation propagation. This limitation will be in part resolved by coordinated monitoring and analysis of strategically-placed electrical waveform sensors in power networks. The intellectual merits of the project include integrating high-fidelity physical model of electric machine networks to signature-based CMFD for improved accuracy and robustness. The broader impacts of the project include advancing the research experiences for K-12 and undergraduate students including underrepresented students. The research will be integrated into the undergraduate and graduate electric power engineering curriculum to educate future engineers who will have the skills and knowledge to meet the emerging needs of the industry.
The proposed physics-guided signature-based approach for condition monitoring and fault diagnosis (CMFD) of electric machine networks will advance the literature in a new direction compared to the traditional approach based on individual machine sensors. Four specific objectives will be pursued: (1) Assess the electrical waveform signature due to faults and degradation that will build a technical foundation for the proposed CMFD approach. (2) Create a high-fidelity physical model of electric machine networks that reflects not only the fault, degradation and nonlinearity of the machine, but also waveform propagation due to faults and degradation. (3) Design a physics-guided signature-based method that leverages the waveform propagation model to more effectively and efficiently identify and locate faults or degradation source in electric machine networks. (4) Build a semi-virtual testbed of electric machine networks to evaluate the performance of the proposed CMFD approach. The proposed CMFD approach can be generally used in a variety of applications, including manufacturing and industrial systems, smart buildings, wind farms, and electrified transportation.
Multilevel Cybersecurity for Photovoltaic Systems
Funded by DOE SETO program (UGA PI, $3.5M, 4/2020-4/2023)
The project goal is to devise a multilevel cybersecurity solution to address PV security gaps at the inverter and system levels, and field test the solution under the supervision and review of a US-based solar inverter manufacturer (GE) and PV installer/operator (TPI). The primary objectives are to:
- Create an inverter framework within which multiple new security capabilities can be deployed. These include supply chain security, hot patching, firmware protection, safety protocols, side-channel monitoring, controller security and control resilience.
- Deliver a self-consistent set of complementary system security methods that will work with data routinely available in PV solar farms.
Data-Driven Monitoring and Diagnosis of Industrial Machines via Electrical Waveform Auditing
Funded by DOD Airforce program, (UGA PI, $900K, 5/2020 – 5/2023)
The goal of this project is to design an Electrical Waveform Fault and Attack Detection System (EWFADS). The goal of the system is to monitor the condition of manufacturing machines to detect faults and cyberattacks by analyzing the electrical waveforms of the electrical power connection to the machine at the point of common coupling. In Phase II the team will perform additional research and development to:
- Refine and expand comprehensive mathematics models; case studies for physical fault and cyber-attack scenarios.
- Refine features and machine learning (ML) algorithms for detection and diagnosis.
- Design and construct Hardware-In-The-Loop (HIL) real-time testbed and hardware Testbed.
- Testing, evaluation, and improvement of the developed detection and diagnosis algorithms.
Collaborative Research: SaTC: CORE: Medium: Cyber-threat Detection and Diagnosis in Multistage Manufacturing Systems through Cyber and Physical Data Analytics
Funded by NSF SATC program (Co-PI, 1.2M, 10/2020-10/2024)
In modern multistage manufacturing systems, with increased software-defined automation and control as well as monitoring of manufacturing assets across networks, exposure to cyber-attacks also grows. The cyber-threats may compromise the integrity of manufacturing assets (manufacturing systems and processes, machine tools, fabricated parts), reduce manufacturing productivity, and increase costs. Some cyber-threats including integrity attacks are only partially observable in cyberspace alone, and therefore need to be detected and diagnosed through inter-dependency analysis of both cyber and physical signals. Thus, there is a significant opportunity in exploring physical signals, together with cyber signals, to advance trustworthy manufacturing system research and design.
This project brings together leading researchers from manufacturing systems, computer security, and electrical drives to develop and demonstrate a new methodology and tool for cyber-threat detection and diagnosis in multistage manufacturing systems. The cyber-security tool will monitor a variety of cyber and physical signals and perform cyber-threat detection and root cause diagnosis through advanced cyber-physical data fusion and taint analysis. The goal is to enable the prevention and mitigation of potential harms at the early stage, proactive and predictive maintenance, and countermeasures. This project attempts to integrate and analyze the process and quality signals, and the signals from the power networks and cyber networks of multistage manufacturing systems to detect and diagnose cyber-threats. This new systematic approach expects to capture new cyber-threats, especially data integrity attacks, that traditional cyber-security approaches may not capture. The proposed data analytics and methodology for integrating cyber and physical signals will advance a fundamental understanding of cyber-threat detection and diagnosis in multistage manufacturing systems and can broadly apply to other cyber-physical systems.
Low-Torque-Ripple Sensorless Control of Mutually Coupled Switched Reluctance Machines (MCSRMs)
Funded by NSF EPCN program (Single PI, 360K, 7/2018-7/2021)
The proposed research will advance motor drive technologies by designing a novel control scheme for mutually coupled switched reluctance machines. Such machines are of great importance to satisfy the increasing demand for cost-effective, highly reliable and efficient motor drive systems in electrified transportation, industrial applications, and home appliances. Although induction and permanent magnet synchronous machines are currently dominating the market, due to the soaring prices and rapid depletion of rare-earth materials, researchers in the U.S. and world-wide are searching for rare-earth-free alternatives. Switched reluctance machines belong to the group of such alternatives. They are increasing in popularity due to their simple and rigid structure, fault-tolerant capability, and extended-speed constant-power range. However, conventional switched reluctance machines suffer from high torque ripples, acoustic noise, vibration, and non-standard asymmetric bridge power converters. Mutually coupled switched reluctance machines that are the focus of the proposed research are outperforming conventional switched reluctance machines as they can be driven by a standard six-switch converter. The proposed research will address these technical challenges impeding the widespread utilization of mutually coupled switched reluctance machines. The work will greatly advance the research in power electronics and motor drive technology and will promote research, teaching, training, and learning. The research will be integrated into the undergraduate and graduate electric power engineering curriculum to educate future engineers who will have the skills and knowledge to meet the emerging needs of the industry.
The goal of the proposed research is to develop a novel control scheme for mutually coupled switched reluctance machines using a standard six-switch converter to minimize torque ripples and enable position sensorless control. Three specific objectives will be pursued: (1) Reduce torque ripples through the use of a two-stage current profiling scheme. (2) Attain position sensorless control. (3) Use a six-switch standard converter to develop a low-torque-ripple sensorless control. Accomplishing the objectives of the proposed research will develop mutually coupled switched reluctance machines into the next generation of rare-earth-free electric machines by overcoming key obstacles high torque ripples and non-sensorless control. To date, the modeling of mutually coupled switched reluctance machines has originated from conventional switched reluctance machines; however, due to the unique torque production mechanism, this modeling approach will complicate control system developments. This work will also investigate nonlinear models of mutually coupled switched reluctance machines and integrate nonlinear models into the design of the two-stage torque ripple reduction scheme and position sensorless control, thereby bridging the gap between the modeling and control in the field of mutually coupled switched reluctance machines. In addition, a six-switch standard converter will be used to replace the asymmetric bridge converter to increase cost effectiveness and improve its suitability in electrified transportation, industrial applications, and home appliances.
MRI: Acquisition of a Power-Hardware-in-the-Loop (PHIL) System to Enhance Research and Student Research Training in Engineering and Computer Science
Funded by NSF ECCS program (PI, 297K, 9/2017-8/2020)
Major Research Instrumentation award will enable the acquisition of a state-of-the-art Power-Hardware-in-the-Loop system that is currently revolutionizing test engineering on many levels, including power/smart grids, vehicle and communication systems, civil structures, robotics, and aerospace. The acquisition of this highly scalable and configurable, computationally efficient simulation and experimental testing platform will enable the diverse multi-user community of engineers, computer scientists, and student researchers at San Francisco State University and San Jose State University to develop and evaluate complex systems and/or physical components in an integrated fashion. This system will significantly enhance the research capability at the two participating Hispanic-serving, non-Ph.D-granting institutions that rank among the leaders in ethnic diversity in the U.S. It will create well-equipped research environments that integrate research and research training and provide crucial research infrastructure needed to catalyze cross-disciplinary collaborations among faculty members and initiate and/or strengthen their collaborations with other research institutions and industrial partners. It will enrich the education of Hispanic, female, and African-American students by providing them hands-on research training in frontier technology. It will advance the careers of junior female faculty members enabling them to serve as role models for female students who remain underrepresented in Science, Technology, Engineering, and Mathematics. It will also lead to the creation of new teaching and research laboratories that will be integrated into the engineering and computer science curricula.
The Power-Hardware-in-the-Loop system will become an indispensable tool to stimulate new research in four key sectors of engineering and computer science research: vehicle system, power/smart grid, prosthetic robotic arm development, and structural analysis. In vehicle system research, it will be used to catalyze progress in the following areas: (1) improvements of the reliability, cost-effectiveness, and efficiency of key electric drive components in electrified vehicles; (2) development of hybrid energy storage systems to accelerate the introduction of electrified vehicles in the transportation sector; and (3) development of in-cylinder control strategies for advanced combustion modes in compression-ignition engines. In the power/smart grid research sector, it will be used to enhance research opportunities in the following areas: (1) investigation of the grid integration impacts of electric vehicle charging and renewable energy sources; (2) experimental evaluation of power electronic interfaces in smart grids; (3) failure analyses of power equipment in power grids; and (4) development of communication and/or smart sensing infrastructure in smart grids. The instrument will also enhance the computational capacity for high-fidelity simulation of analytical substructure and facilitate the developments of advanced control interfaces for the next-generation of myoelectric prosthetic robotic arms.