Collaborative Intelligent Infrastructure Lab
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Cities traditionally have been undertaken with static snapshots of infrastructure, population, and technology. Recent advancements in the internet of things (IoT), social networks, and data analytics bring a revolution in the citizen-centric smart grid and smart city management. By integrating real-time sociotechnical data from a variety of sources inside and outside of cities’ systems, we make informed choices about the present and future of cities.
Dr. Reza Arghandeh is the director of CI2 Lab. He is a Professor in the Department of Computer Science, Electrical Engineering, and Mathematical Sciences at the Western Norway University of Applied Sciences, Bergen, Norway. He is also a Research Professor in the Electrical and Computer Department at FAMU-FSU College of Engineering, Florida State University, Tallahassee, USA. He was an assistant professor in the ECE Department, Center for Advanced Power Systems, Florida State University, 2015-2018. Prior to FSU, He was a postdoctoral scholar at the University of California, Berkeley, EECS Dept 2013-2015. He completed his Ph.D. in Electrical Engineering with a specialization in Power Systems at Virginia Tech. He holds Master’s degrees in Industrial and System Engineering from Virginia Tech (2013) and in Energy Systems from the University of Manchester (2008).
Dr. Eren E. Ozguven is the co-director of CI2 Lab. He is an Associate Professor at the Dept of Civil & Environmental Eng at FAMU-FSU College of Engineering, Florida State University, Tallahassee, USA. Dr. Ozguven holds a Ph.D. degree in Civil and Environmental Engineering from Rutgers University. His research interests include smart cities, urban mobility, traffic safety and reliability, emergency transportation, and intelligent transportation systems.
Data Science for Electricity & Transportation Networks
Transportation, Social and Electricity Networks Data Fusion
As more of the world's cities suffer from congestion, pollution, and energy exploitation, urban mobility remains one of the toughest challenges that cities face as the process of population growth and urbanization continues. So far, the most common approach for urban mobility characterization focuses on vehicle's spatial and temporal positions. However, urban mobility is a multidimensional character of the city life, experienced as tangled layers of interconnected infrastructures and information networks around people and their needs in a spatiotemporal frame. As a result, the study of mobility should go beyond transportation systems, be customer-centered and merged into other physical systems and cyber networks such as electricity networks.
In a collaboration with multiple cities such as Tallahassee, FL, we use data fusion, machine learning, and information theory to provide a mathematical foundation for real-time decision-making tools for urban mobility.
Data-Driven Diagnostics for Electric Grids
Electric Grid Event Detection Using Heterogeneous and Spatiotemporal Data
Traditionally electrical networks have been treated mainly as physical entities that connect electricity suppliers to consumers. However, a modern grid is empowered by the internet of things, distributed generation, and networked computational subsystems to support the incorporation of renewable energy resources, electric vehicles, and energy markets. The induced dynamic and stochastic due to the new paradigm in smart grids require high-resolution measurement and agile decision support techniques for system diagnosis and control. Where muli-stream measurement data come from various sensors in various locations of the network, classical machine learning methods are not the ultimate solution. We are developing multi-task and scalable learning machines for event detection in societal scale systems such electric grids.
Data-Driven Resilience in Smart Cities
Characterizing Interoperability of Multi Networks for Resilience
Adversery impacts of large-scale natural and man-made disasters are primarily the result of the inability of city's components such as trasnposration and electricity networks to efficiently cope with random and dynamic changes, which translated into resilience deficiencies while exposing gaps in data availability and data analytics. Therefore, providing optimal and fast infrastructure restoration solutions based on lessons learned and data gathered are critical for governments and emergency responders. However, lack of data due to the shortcoming of monitoring systems in cities and the scarce nature of disasters remains a big challenge for data-driven approaches. We are developing inference and estimation algorithms that can fill the gap in data sparsity and provide integrated resilience for urban infrastructure.
Dr. Reza Arghandeh
CI2 Founding Director
Dr. Eren Erman Ozguven
Civil and Environmental Engineering Department
Florida State University
FSU Webpage: https://www.eng.famu.fsu.edu/~eozguven/
Visiting MS Student
Dept of Electrical Engineering
University of Padova, Italy
Visiting PhD Student
Dept of Electrical Engineering
Technical University of Vienna,
Information Mining with a Span of Fuzzy and Causality Approaches
Dr. Arghandeh Co-PI, the University and College Network for Western Norway, 2019-2020
GridEyeS - Smart Grid Eye, from Space to Sky
Dr. Arghandeh PI, European Space Agency, 2019-2020
IBM Faculty Award, IoT Technology for Smart Buildings
Dr. Arghandeh PI, IBM, 2018-2019
User-Centered Heterogeneous Data Fusion for Multi-Networked City Mobility UHDNetCity
Dr. Arghandeh PI and Dr. Ozguven Co-PI, U.S. National Science Foundation, 2016-2019
Resilient Alaskan Distribution System Improvements using Automation and Energy Storage
Dr. Arghandeh Co-PI, U.S. Department of Energy, 2017-2020
Bridging the Digital Divide for the Well-Being of Aging Populations in Smart Cities
Dr. Ozguven Co-PI and Dr. Arghandeh Co-PI, U.S. National Science Foundation, 2017-2018
Open-source Distributed Control Platform for HIL-based Testing Advanced Ship Power Systems
Dr. Arghandeh Co-PI, U.S. Office of Naval Research, 2016-2017
High Resolution Monitoring System for Distribution Networks with Micro-Synchrophasors
Dr. Arghandeh PI, Florida State University Office of Research, FYA Program, 2016
Data and Model Integration for Multidimensional System Observability
Dr. Arghandeh PI, Florida State University Office of Research, 2015
The Smart Grid Eye, from Space to Sky (GridEyeS) is funded by the European Space Agency. The overall goal of this study is to create an end to end electric grid monitoring platform using satellite images combined with high-resolution drone data to create multi-layer situational awareness, especially in the case of extreme weather events in the electric grid.
Dr. Ozguven, published an article in Tampa Bay Times
Dr. Ozguven, published an article in Tallahassee Democrat
Resilient Distribution Systems Lab Call Awards, Office Of The Under Secretary For Science And Energy, U.S. Department of Energy
Radio Interview," the progression and development of DigiTally", Tallahassee Talk Radio Show.
Featured Research,"FSU Team Tackles Urban Mobility In Smart City Era" FSU News.
Featured Research," FSU Researchers Transforming Tallahassee Into a Smart City,” Florida Flambeau.
News, "ASME Power Award for FSU New Faculty" FSU College of Engineering Website.
News, " IEEE PES Award for Paper on Data Accuracy for Inverters Control" FSU College of Engineering Website.
For the updated list of publications, please check:
von Meier Alexandra and Arghandeh Reza. Chapter 34-Every Moment Counts: Synchrophasors for Distribution Networks with Variable Resources, Renewable Energy Integration 1st Edition, pp 429–438. Elsevier, Boston, USA,2014
Will be updated, please check Google Scholar...
Will be updated, please check Google Scholar...
Ferhat Uçar, Ömer Faruk Alçin, Beşir Dandıl, Fikret Ata, Jose Cordova, Reza Arghandeh. Online Power Quality Events Detection Using Weighted Extreme Learning. 6th International Istanbul Smart Grid and Cities Congress (ICSG 2018), 2018
M. Baran Ulak, Ayberk Kocatepe, L. Madhavi Konila Sriram, E. Erman Ozguven, Reza Arghandeh. Assessment of the hurricane-induced power outages from a demographic, socioeconomic, and transportation perspective. Natural Hazards, Springer 2018
Yuxun Zhou, Reza Arghandeh. Moving Toward Agile Machine Learning for Data Analytics in Power Systems. Elsevier, 2018.
Reza Arghandeh, Kyle Brady, Merwin Brown, George R. Cotter, Deepjyoti Deka. Synchrophasor Monitoring for Distribution Systems: Technical Foundations and Applications. North American SynchroPhasor Initiative, 2018
Yuxun Zhou, Reza Arghandeh. Non-parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) 2018
Ayberk Kocatepe, Mehmet Baran Ulak, Grzegorz Kakareko, Davide Pinzan, Jose Cordova, Eren Erman Ozguven, Sungmoon Jung, John Olusegun Sobanjo, Reza Arghandeh. Assessment of Emergency Facility Accessibility in the Presence of Hurricane-Related Roadway Closures and Prediction of Future Roadway Disruptions. Transportation Research Board 97th Annual Meeting 2018.
Davide Pinzan, Ayberk Kocatepe, Mostafa Gilanifar, Mehmet Baran Ulak, Eren Erman Ozguven, Reza Arghandeh. Data-Driven and Hurricane-Focused Metrics for Combined Transportation and Power Networks Resilience. Transportation Research Board 97th Annual Meeting 2018.
Lalitha Madhavi K. S., Jose Cordova, Mehmet Baran Ulak, Michael Ohlsen, Eren E. Ozguven, Reza Arghandeh, Ayberk Kocatepe. Advanced electricity load forecasting combining electricity and transportation network. IEEE North American Power Symposium (NAPS), 2017.
Y. Zhou, R. Arghandeh, and C. Spanos. Partial knowledge data-driven event detection for power distribution networks. IEEE Transactions on Smart Grid, PP(99), 2017.
A. Tbaileh, H. Jain, R. Broadwater, M. Dilek, J. Cordova, and R. Arghandeh. Graph trace analysis: An object-oriented power flow, verification and comparisons. Electric Power Systems Research, PP(147):145–153, 2017.
A. Kocatepe, M. Ulak, E. Ozguven, M. Horner, and R. Arghandeh. Socioeconomic characteristics and crash injury exposure: A case study in Florida using two-step floating catchment area method. Applied Geography, 2017.
A. Kocatepe, M. Ulak, E. Ozguven, M. Horner, and R Arghandeh. Socioeconomic characteristics and crash proneness a case study in Florida using two-step floating catchment area method. In Annual Meeting Transportation Research Board 2017, 2017.
J. Cordova, R. Arghandeh, Y. Zhou, M. Stiffer, and W. Wu. Shape-based data analysis for event classification in power systems. In IEEE PES PowerTech 2017, 2017.
M. Jacobson, M. Delucchi, R. Arghandeh, and et. al. A 100% wind, water, sunlight (wws) all-sector energy plan for Washington state. Renewable Energy, 86:75–88, 2016.
R. Arghandeh, A..vonMeier, L. Mehrmanesh, and L. Mili. On the deﬁnition of cyber-physical resilience in power systems. Renewable and Sustainable Energy Reviews, 58(2016-5):1060–1069, 2016.
Y. Zhou, R. Arghandeh, and C. J. Spanos. Online learning of contextual hidden markov models for temporal-spatial data analysis. In IEEE Conference on Decision and Control (CDC 2016), 2016.
Y. Zhou, R. Arghandeh, I. C. Konstantakopoulos, S. Abdullah, A. von Meier, and C. J. Spanos. Abnormal event detection with high-resolution micro-pmu measurement. In IEEE Power Systems Computation Conference. IEEE, 2016.
Y. Zhou, R. Arghandeh, I. C. Konstantakopoulos, S Abdullah, and C. J. Spanos. Data-driven event detection with partial knowledge: A hidden structure semi-supervised learning method. In IEEE, editor, American Control Conference (ACC16), 2016.
K. Leahy, I. C. Konstantakopoulos, Y. Zhou, and R. Arghandeh. Wind turbine performance monitoring and fault detection using scada data. In ASME Power and Energy Conference 2016, 2016.
2015 and past:
L. Wang, A. Onen, J. Woyak, R. Arghandeh, R. P Broadwater, and C. Scirbona. Electric power distribution feeder performance evaluation and distributed computation. EC Journal of Science and Engineering, 2(1):59–67,2015.
D. Cheng, A. Onen, D. Zhu, D. Kleppinger, R. Arghandeh, and R.P Broadwater. Automation eﬀects on reliability and operation costs in storm restoration. Electric Power Components and Systems, 43(6):656–664,2015.
M. H. F. Wen, R. Arghandeh, A. von Meier, K. Poolla, and V. O. K. Li. Phase identification in distribution networks with micro-synchrophasors. In Power & Energy Society General Meeting, 2015 IEEE, pages 1–5, 2015.
E. M. Stewart, S. Kiliccote, D. Arnold, A. von Meier, and R. Arghandeh. Accuracy and validation of measured and modelled data for distributed pv interconnection and control. In Power & Energy Society General Meeting, 2015 IEEE, pages 1–5, 2015.
J. Poon, I. C. Konstantakopoulos, R. Arghandeh, A. von Meier, C. Spanos, and S. Sanders. Failsafe a generalized methodology for converter fault detection, identification, and reme- diation in nanogrids. In International Conference on Building Energy Efficiency and Sustainable Technologies, 2015.
A. Khoshkbar-Sadigh, M. Heydari, M. Tedde, K. Smedley, R. Arghandeh, and A. von Meier. A unified platform enabling power system circuit model data transfer among different software. In Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, pages 1–5, 2015.
G. Cavraro, R. Arghandeh, K. Poolla, and A. von Meier. Data-driven approach for distribution network topology detection. In Power & Energy Society General Meeting, 2015 IEEE, pages 1–5, 2015.
G. Cavraro, R. Arghandeh, G. Barchi, and A. von Meier. Distribution network topology detection with time-series measurements. In Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, pages 1–5, 2015.
R. Arghandeh, M. Gahr, A. von Meier, G. Cavraro, M. Ruh, and G. Andersson. Topology detection in microgrids with micro-synchrophasors. In Power & Energy Society General Meeting, 2015 IEEE, pages 1–5, 2015.
A. Onen, J. Woyak, R. Arghandeh, J. Jung, C. Scirbona, and R. P. Broadwater. Time-varying cost of loss evaluation in distribution networks using market marginal price. International Journal of Electrical Power&Energy Systems, 62(0):712–717, 2014.
J. Jung, A. Onen, R. Arghandeh, and R. Broadwatera. Coordinated control of automated devices and photovoltaic generators for voltage rise mitigation in power distribution circuits. Renewable Energy, 66(June2014):532–540, 2014.
R Arghandeh, J. Woyak, and R. Broadwater. Economic optimal operation of community energy storage systems in competitive energy markets. Applied Energy, 135(2014-12):71–80, 2014.
R. Arghandeh, A. Onen, J. Jung, D. Cheng, R. P. Broadwater, and V. Centeno. Phasor-based assessment for harmonic sources in distribution networks. Electric Power Systems Research, 116(0):94–105, 2014.
R. Arghandeh, M. Brown, A. DelRosso, G. Ghatikar, E. Stewart, A. Vojdani, and A. von Meier. The local team: Leveraging distributed resources to improve resilience. IEEE Power and Energy Magazine, 12(4), 2014.
A. vonMeier, D. Culler, A. McEachen, and R. Arghandeh. Micro-synchrophasors for distribution systems. In IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2014, pages 1–5. IEEE, 2014.
E. M; Stewart, S; Kiliccote, C.M; Shand, A.W; McMorran, R; Arghandeh, and A von Meier. Addressing the challenges for integrating micro-synchrophasor data with operational system applications. In IEEE PES General Meeting 2014, pages 1–5. IEEE, 2014.
L. Schenato, G. Barchi, D. Macii, R. Arghandeh, K. Poolla, and A.VonMeier. Bayesian linear state estimation using smart meters and pmus measurements in distribution grids. In IEEE International Conference on Smart Grid Communications 2014, pages1–6. IEEE, 2014.
D. Cheng, A. Onen, R. Arghandeh, J. Jung, and R. Broadwater. Model centric approach for monte carlo assessment of storm restoration and smart grid automation. In ASME Power2014 Conference. ASME, 2014.
R. Arghandeh, A. vonMeier, and R. Broadwater. Phasor-based approch for harmonic assesment from multiple distributed energy resources. In IEEE PES General Meeting 2014, pages 1–5. IEEE, 2014.
A. Onen, D. Cheng, R. Arghandeh, J. Jung, J. Woyak, M. Dilek, and R.P Broadwater. Smart model based coordinated control based on feeder losses, energy consumption, and voltage violations. Electric Power Components and Systems, 41(16):1686–1696, 2013.
Y. Jung, J. and Cho, D. Cheng, A. Onen, R. Arghandeh, and R. P. Dilek, M.and Broadwater. Monte carlo analysis of plug-in hybrid vehicles and distributed energy resource growth with residential energy storage in michigan. Applied Energy, 108:218–235, 2013.
R. Arghandeh, A. Onen, J. Jung, and R. P. Broadwater. Harmonic interactions of multiple distributed energy resources in power distribution networks. Electric Power Systems Research, 105(December 2013):124–133, 2013.
M. Petrov and R. Arghandeh. Concept and application of distributed compressed air energy storage systems integrated in utility networks. In American Society of Mechanical Engineers Power Conference 2013, (ASME Power2013), Boston, MA, USA, page 8. American Society of Mechanical Engineers, 2013.
M. Perov, R. Arghandeh, and R. Broadwater. Control strategy for distributed compressed-air energy storage in grid-tied small-scale renewable energy systems. In Solar Power International 2013. SEIA, 2013.
R. Arghandeh, A. Onen, J. Jung, D. Cheng, and V. Broadwater, R.and Centeno. Harmonic impact study for distributed energy resources integrated into power distribution networks. In American Society of Mechanical Engineers Power Conference 2013, (ASME Power2013), Boston, MA, USA, page 8. American Society of Mechanical Engineers, 2013.
R. Arghandehand S. Provencher. A need for power & energy engineers, today and tomorrow. IEEE GoldRush, 8(4):17–18, 2012.
R. Arghandeh, M. Pipattanasomporn, and S.Rahman. Flywheel energy storage systems for ride-through applications in a facility microgrid. IEEE Transactions on Smart Grid, 3(4):1955– 1962, 2012.
R. Arghandeh, A. Onen, and R. Broadwater. Distributed energy storage system control for optimal adoption of electric vehicles. In Power and Energy Society General Meeting, 2012 IEEE, pages 1–8. IEEE, 2012.
H. Khodaei, R. Taheri, and R. Arghandeh. Exergy analysis of the steam network in tehran oil refinery and evaluation with new scenario. In Industrial Energy Technology Conference, 2009.
R. Arghandeh, N. Schulz, and A. Srivastava. Intelligent operation and management system for shipboard mvdc networks. In Second Mississippi State University Energy Workshop. Mississippi State University, Mech Eng Dept, 2009.
R. Arghandeh and B. Rashidian. Gas refinery steam network thermal modeling and optimization by pinch method. In International Conference on Modeling, Simulation, and Applied Optimization, 2009.
R. Arghandeh and B. Rashidian. Distributed generation chp system with heat pipes: A novel approach to more energy-efficient buildings. In International Colloquium on Environmentally Preferred Advanced Power Generation 2009. American Society of Mechanical Engineers (ASME), 2009.
R. Arghandeh, M. Amidpour, and H. Khodaei. Steam network modeling and simulation in gas refinery by considering pinch technology. In Southeastcon, 2009. SOUTHEASTCON’09. IEEE, pages 428–433. IEEE, 2009.
M. R. Jaffari Nasr, R. Arghandeh, and M. Gharae. Analysis of refinery steam network by pinch technology approach. In International Chemical Engineering Congress, 2008.
R. Arghandeh, R. Parvizi, M. Amidpour, and A. Chaibakhsh. Gas based distributed generation systems, a key to iran buildings growing energy demand. In Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International, pages 1592–1596. IEEE, 2008.
R. Arghandeh, A. Ghaffari, and M. Amidpour. Distributed generation based on micro-turbines usage in rural areas. In Iranian Conference of Electrical Engineering. IAEE, 2008.
R. Arghandeh, M. Amidpour, and A. Ghaffari. Hierarchical approach in steam network modeling. In Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International, pages 839–843. IEEE, 2008.
R. Arghandeh and M. Amidpour. Energy sustainability in buildings by chp distributed generation systems. In International World Energy System Conference (WES 08), , Iasi, Romania, July 2008, page A17. World Energy System Consortium, 2008.