Design of a Massively Open Online Course on Electrical Microgrids with Real Datasets

Roxana Maria Melendez-Norona; Maria M. Larrondo-Petrie; Eduardo David Sagredo

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Energy Conversion, Conservation and Nuclear Engineering Division (ECCNE) Technical Session 3
Tagged Divisions: Energy Conversion and Conservation and Nuclear Engineering Division (ECCNE)
Tagged Topic: Diversity
Page Count: 22
DOI: 10.18260/1-2--43229
Permanent URL: https://peer.asee.org/43229
Download Count: 121

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Paper Authors

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Roxana Maria Melendez-Norona Florida Atlantic University

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Dr. Roxana M. Melendez-Norona is an electrical engineer and engineering project management specialist with a Master's and Doctoral degree in electrical engineering from Florida Atlantic University (Boca Raton, Florida, United States). As a professional Dr. Melendez-Norona continues being an engineering patroness and advocate since her energy and dedication towards engineering education are her best reference. She has been working in the engineering industry since 2001 and teaching engineering courses since 2008. Renewable Energy Technologies, Electrical Circuits and Electronics, Introduction to Engineering, Electrical Substations, Introduction to AutoCAD, Digital Electronics and Systems and Controls are among the courses Dr Melendez-Norona has taught. She is committed to an engineering education of excellence and to service the community and has participated in a training for cybersecurity issues immersed in smart grids also funded by the NSF. Dr Melendez-Norona is currently a postdoctoral fellow at Florida Atlantic University (Florida, United States), with the support of the ASEE eFellows program, funded by the National Science Foundation (NSF).

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Maria M. Larrondo-Petrie Florida Atlantic University orcid.org/0000-0003-2354-4986

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Dr. Larrondo Petrie has a Ph.D. in Computer Engineering and is a Professor in the College of Engineering and Computer Science of Florida Atlantic University. She is the Executive Director of LACCEI (Latin American and Caribbean Consortium of Engineering Institutions, and a Titular Member of the Pan-American Academy of Engineering.

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Eduardo David Sagredo Asesor Ministerio Energia y Minas Dom. Rep.

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Eduardo David, Sagredo Robles.
Education:
January 1993 - May 1994.
Florida International University. (FIU) Miami Florida.
Student PhD program "Electrical and Computer Engineering" August 1966 - January 1968.
Massachusetts Institute of Technology. (MIT) Cambridge, Mass. Master in Science in Nuclear Engineering. (MSNE)
August 1965 - December 1965.
University of Puerto Rico, Nuclear Center, Mayagüez Campus. (CAAM) Mayagüez, Puerto Rico.
Postgraduate studies in Nuclear Engineering (Nuclear Center of P.R.) August 1963 - May 1965.
University of Puerto Rico, Mayagüez Campus. (CAAM)Mayagüez P.R.
Bachelor in Science in Mechanical Engineering. (BSME) September1960 - August 1963.
Universidad Autónoma de Santo Domingo. Faculty of Engineering. Civil Engineering Student.
Professional Licensees and Exequaturs:
•Register Professional Engineer. (Mar’91) State of Florida No.0043826.
•Graduate Engineer. (Nov'65) Puerto Rico No.04720.
•Exequatur Engineering (Mar'72) Dominican Rep. CODIA No. 1076.

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Abstract

Smart Grids represent one of the most suitable and relevant applications in a sustainable scenario, where it is of high importance to provide electricity to millions of customers using advance technology and efficient methods. Microgrids are key subsystems forming the Smart Grid and, as a consequence, the study and analysis of microgrids and the methods to improve its operation are crucial for the optimal performance of Smart Grids. Microgrid infrastructure may be seen as a combination of information technology and equipment, becoming a target for cybersecurity attacks, vulnerable both in software and hardware. The importance of microgrids as part of the smart power system makes them become a target for cybersecurity attacks. Abrupt disconnection from the smart grid, failure in equipment and the technical problems with the control and telecommunication elements are among the multiple issues that a microgrid faces in a cyber secure scenario. Thus, microgrids must become more and more resilient to cyberattacks. In research areas associated with Smart Grids, the U.S. National Science Foundation (NSF) has offered support for projects for workforce development and research in cybersecurity, state estimation and optimization in electrical microgrids through several programs. Further research is needed specially using real dataset. This paper focuses on the design of laboratory experiences on microgrid State Estimation, Optimization and Cybersecurity for a proposed Massively Open Online Course (MOOC). In a post-pandemic scenario, the design and implementation of MOOCs became a valuable tool to reach students and professionals around the world. For the present study, simulations for cybersecurity cover the utilization of real dataset associated with the electrical power system of the Dominican Republic by means of deep learning tools offered by the MATLAB software. Thus, students and instructors may be able to count on a resource to learn and expand their knowledge on microgrids, using real data. This paper presents the main ideas associated with the design of the MOOC, as well as some results obtained from those simulations implemented in MATLAB that are included as part of the MOOC and the results associated with reinforcement learning techniques for optimization in a microgrid that is operating in stand-alone mode. The cybersecurity simulations display the capacity of the MATLAB software to analyze sets of data to classify the different results into a variety of categories, training a neural network and allowing for the analysis of new data points in order to classify whether or not an electrical system is subject to a cyberattack. All this relevant research work has been funded by the Engineering Postdoctoral Fellowship eFellows program, administer by the American Society of Engineering Education, funded by the National Science Foundation (NSF). The MOOC is planned to be offered as a free resource for the community. The real datasets used for the Cybersecurity simulations will be available in an Open Science website.

Citation
Format

Melendez-Norona, R. M., & Larrondo-Petrie, M. M., & Sagredo, E. D. (2023, June), Design of a Massively Open Online Course on Electrical Microgrids with Real Datasets Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43229

TY - CPAPER
AB - Smart Grids represent one of the most suitable and relevant applications in a sustainable scenario, where it is of high importance to provide electricity to millions of customers using advance technology and efficient methods. Microgrids are key subsystems forming the Smart Grid and, as a consequence, the study and analysis of microgrids and the methods to improve its operation are crucial for the optimal performance of Smart Grids. Microgrid infrastructure may be seen as a combination of information technology and equipment, becoming a target for cybersecurity attacks, vulnerable both in software and hardware. The importance of microgrids as part of the smart power system makes them become a target for cybersecurity attacks. Abrupt disconnection from the smart grid, failure in equipment and the technical problems with the control and telecommunication elements are among the multiple issues that a microgrid faces in a cyber secure scenario. Thus, microgrids must become more and more resilient to cyberattacks. In research areas associated with Smart Grids, the U.S. National Science Foundation (NSF) has offered support for projects for workforce development and research in cybersecurity, state estimation and optimization in electrical microgrids through several programs. Further research is needed specially using real dataset. This paper focuses on the design of laboratory experiences on microgrid State Estimation, Optimization and Cybersecurity for a proposed Massively Open Online Course (MOOC). In a post-pandemic scenario, the design and implementation of MOOCs became a valuable tool to reach students and professionals around the world. For the present study, simulations for cybersecurity cover the utilization of real dataset associated with the electrical power system of the Dominican Republic by means of deep learning tools offered by the MATLAB software. Thus, students and instructors may be able to count on a resource to learn and expand their knowledge on microgrids, using real data. This paper presents the main ideas associated with the design of the MOOC, as well as some results obtained from those simulations implemented in MATLAB that are included as part of the MOOC and the results associated with reinforcement learning techniques for optimization in a microgrid that is operating in stand-alone mode. The cybersecurity simulations display the capacity of the MATLAB software to analyze sets of data to classify the different results into a variety of categories, training a neural network and allowing for the analysis of new data points in order to classify whether or not an electrical system is subject to a cyberattack. All this relevant research work has been funded by the Engineering Postdoctoral Fellowship eFellows program, administer by the American Society of Engineering Education, funded by the National Science Foundation (NSF). The MOOC is planned to be offered as a free resource for the community. The real datasets used for the Cybersecurity simulations will be available in an Open Science website.
AU - Roxana Maria Melendez-Norona
AU - Maria M. Larrondo-Petrie
AU - Eduardo David Sagredo Asesor
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - Design of a Massively Open Online Course on Electrical Microgrids with Real Datasets
UR - https://peer.asee.org/43229
DO - 10.18260/1-2--43229
ER -