Call for Presentation

Postdoc, Ph.D./M.Sc./U.G. students are encouraged to register and submit a title and a 300-word abstract to present their latest research results at the event. Registration Link

Awards: Best Presentation Awards and Certificate of Merits will be awarded to outstanding presenters.

Program Schedule (Times in EST)

9:00 AM - 9:05 AM

Plenary Talk: Dr. Brian Fricke (Oak Ridge National Laboratory)

Title: Automated fault detection and diagnosis deployment Internet of Things solution for building energy system

Abstract: The building sector is the highest energy consumer and ranks first in terms of carbon emissions among all sectors. To address these issues, decarbonization and electrification in the building energy sector are two critical missions of the new US administration. For decarbonization, the new administration has set a target to reduce greenhouse gas emissions by 50–52% by 2030 and targeting a carbon-neutral economy by 2050. For electrification, the goal is to achieve 100% clean electricity by 2035. Such decarbonization and electrification in the building sector require that energy consumption in buildings be reduced significantly. Therefore, the building sector must continuously adopt new technologies to achieve its energy and carbon emission goals. One of the most fundamental technologies is the Internet of Things (IoT). IoT has proven to be an effective solution for the building domain, including building information/energy modeling, smart buildings, etc. Although much progress has been made in the development of IoT-based building energy systems, there is still a lack of reliable, scalable, and affordable IoT-based automated fault and degradation diagnostic (AFDD) solutions. Such solutions would enable deployment of advanced algorithms into real systems to archive the projected energy benefits. This study reviews existing IoT solutions developed for building energy–related application and developed a simple but effective AFDD IoT deployment solution, including developing a suitable IoT architecture and conducting easy and scalable deployment by leveraging a common cloud-based IoT service.

9:55 AM - 10:30 AM

Keynote Speech: Dr. Panayiotis (Panos) Moutis (Carnegie Mellon University) 

Title: Advanced sensing with Digital Twins for the distribution grid

Abstract: Black-outs which are widely spread throughout a transmission system are relatively rare phenomena. On the contrary, faults and disconnections affecting smaller scales in multiple parts of Distribution Networks (DNs) are notably more frequent and add up to service interruptions of – at least – a few hours per customer every year. Additionally, and in light of climate change effects, it is typical utilities’ practice to preemptively de-energize thousands of customers in the path or vicinity of forest fires and floods to reduce further damages and avoid loss of life. All these supply disruptions are strongly related to the limited, if not zero, visibility into the medium and low voltage DNs. Monitoring DNs, however, is not trivial. DNs are geographically dispersed with hundreds of buses over dozens of feeders. It is, thus, important to prioritize the criticality of DN events and understand the diversity of DN characteristics. This talk discusses, first, the value of the digital twin of distribution transformers in identifying DN grid faults with limited and non-disruptive sensing infrastructure. This digital twin methodology also captures the harmonic distortion of the supply that may affect the operation of numerous devices. Secondly, a novel methodology for the detection of forest fires approaching overhead conductors is presented. The methodology relies on the effect of temperature variation to the characteristics of a power line. Both the digital twin of a transformer and the detection of forest fires approaching overhead conductors are enabled thanks to synchrophasor measurements that have been gaining much traction for DN applications.

Short Bio: Panayiotis (Panos) Moutis, PhD, has been Special Faculty with the Scott Institute for Energy Innovation at Carnegie Mellon University (CMU) since August 2018 (postdoc at Electrical & Computer Engineering – ECE, CMU, 2016-18). Panos researches data-driven optimization, control and planning of electrical grids with high shares of renewables. He has recently been working with the grid operator of Portugal, REN, the moonshot factory of Google, X, and the grid operator of NY, NYISO. Between 2018-20 he served as a Marie Curie Research Fellow with DEPsys, Switzerland, on distribution grid awareness. In 2014 he was awarded a fellowship by Arup, UK (through the University of Greenwich), to study microgrids for residential communities. During 2007-15, as part of Prof. Nikos Hatziargyriou’s research group he contributed to over a dozen R&D projects funded by the European Commission. Panos received both his diploma (2007) and his PhD (2015) degrees in ECE at the National Technical University of Athens, Greece, and has published more than 30 papers and contributed to 4 book chapters. He has over 10 years of industry experience on Renewable Energy and Energy Efficiency, and serves as advisor and executive in energy start-ups. He is a senior member of the IEEE, member of the IEEE-USA Energy Policy Committee, co-lead of the Distribution Task Team at NASPI and of the Power & Energy Community of the Climate Change AI initiative, senior editor of IEEE & IET scientific journals, member of IEEE standards working groups, and chair of the IEEE Smart Grid Publications Committee. Personal Website for more information: https://panay1ot1s.com/

10:30 AM - 10:50 AM

Title: Towards Efficient Heating, Cooling, and Air Conditioning (HVAC) Systems in the Buildings

Abstract: Demand for electricity is set to increase as a result of rising household demand, with the electrification of transport and increasing demand for digitally connected devices and heating, cooling, and air conditioning (HVAC) systems. The HVAC systems consume approximately half of the total energy consumed in the building and it is estimated to increase annually by more than 1.5% for the next 20 years. Therefore, due to the existing faults, the abnormal operation of HVAC systems can result in a massive increase of energy consumption. In fact, fault detection and diagnostics is an analytic tool that identifies faults in HVAC systems and provides solutions about how to address those problems. Fault detection and diagnostics tools basically monitor the data points in the HVAC system in real-time (e.g., temperatures, flows, pressures, actuator control signals) and then apply a set of rules to resolve the problem or generating alerts for maintenance. Accordingly, the soft faults detection has greater interest because they will often remain intact for an extended time because they are not detected by the occupant and they are more difficult to detect. This work focuses more on soft-faults detection relied on data-mining methods to predict, detect, and diagnose faults in HVAC systems. The developed method should be capable to detect faults and generate automatically alerts to the occupant indicating the degradation of the system performances.

Short Bio: El MOUATAMID Abdellatif is a Postdoctoral research associate in the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, USA. He received the Ph.D. degree in Energy Engineering and Automation from Chouaib Doukkali University of El-Jadida, Morocco. He has a research experience in smart microgrid, transmission and distribution lines, power systems, energy efficiency in buildings, and engaged in numerous collaborative research projects. As a Ph.D. student, he received scholarships from the United States Agency for International Development (USAID) under PEER (Partnership for Enhanced Engagement in Research) project program. His research focuses on the development of “Smart-Grid” concept by integrating the new information and communication technologies (ICTs), renewable energy resources, and energy storage systems.

10:50 AM - 11:10 AM 

Invited Talk II: Adam Kemp (Princeton International School of Mathematics and Science) 

Title: An Education Centered Approach to Space-Based Exploration

Abstract: PRISMSpace-1 is an educational 1U CubeSat designed, built, and tested by students at Princeton International School of Mathematic and Science (PRISMS), Princeton, NJ. As part of PRISMS’ Junior/Senior Research & Development program, a group of students at PRISMS were able to fully develop this satellite payload in the span of 2 years, leveraging resources available in the school and the Princeton area. Led by Adam Kemp, Co-Head of STEAM Department at PRISMS and students’ Applied Engineering Research mentor, each student proposed, designed, and constructed a unique system that made up the satellite payload. PRISMSpace-1 was launched to the International Space Station (ISS) on the April 2nd of 2018 from Cape Canaveral Air Force Station as a part of the CRS-14 Resupply Mission. After 31 days of operation in low-earth orbit (primarily inside the SpaceTango module on board of the ISS), the payload was recovered and later delivered back to PRISMS for further analysis. PRISMSpace-1 is a great educational resource that already has and will continue to provide groundwork for future research projects at PRISMS. Data and the results acquired as part of the experiments are being analyzed by our students, and in the future will be available for the broader educational and scientific community.

Short Bio: Adam Kemp is an Assistant Principal and Research Coordinator at Princeton International School of Mathematics and Science

11:10 AM - 1:00 PM

Student Contributed Talks:

Spotted Lanternfly Detection Using Thermal Imagery and UAVs
Joseph Miller

A Handheld Vehicle Detector
Advait Jadhav 

High Precision Computations of Transcendental Constants
Brandon Feder 

Offshore Wind Farms Data Monitoring Using Optimal IoT Infrastructure
Sindhu Parimi 

Condition monitoring of wind turbine drive train with contactless magnetic sensors
Akhyurna Swain 

Deep Neural Network Acceleration with FPGA
Lucas Ekiert and Nakul Kochar 

A Weather-Based Method for Photovoltaic Power Forecasting Using Artificial Neural Networks approaches
Raaid Kabir 

Offshore Wind farm Power Forecasting Using Machine Learning Algorithms
Salma Alami Yadri 

ReD-LUT: Reconfigurable In-DRAM LUTs Enabling Massive Parallel Computation
Ranyang Zhou 

Organizers

Dr. Philip Pong, Dr. Alex Dytso, Dr. Shaahin Angizi