The Evolution of the Smart Grid 🗓

— underlying causes, changing technologies, business models, adverse effects, disruptions, evolution …

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Webinar Date: Thursday, January 10, 2019
Time: 8:00 AM (PT)
Speaker: Steven E. Collier, Director, Smart Grid Strategies
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: smartgrid.ieee.org
Summary: Carl Sagan said, “You have to know the past to understand the present”. Most everyone knows something about the emerging smart grid. However, not everyone knows the whole story about how and why the smart grid began. It is not only fascinating, but also useful to understand the underlying causes that led to the emergence and continuing development of a smart grid. It’s all about changing technologies and business models. For a variety of reasons, the foundations of the century old legacy electric grid began to erode in the 1970s during the aftermath of the OPEC oil embargo. Longstanding favorable economics, acceptable reliability, stable monopoly business model, and standard utility operations were adversely affected. During this time, disruptive new technologies began to emerge to produce, store, and manage energy, both on the supply side and the demand side. New business models and new market participants emerged as well. The smart grid will continue to evolve as technology and business models continue to change.
Bio: Steve Collier writes, speaks and consults widely on issues and technologies related to the smart grid. He has worked for more than forty years as a professional engineer, executive, consultant, board member for energy, telecommunications, and consulting companies in the US and abroad, including Houston Lighting & Power, Power Technologies, Inc., Sandia National Labs, C. H. Guernsey & Company, Cap Rock Electric Cooperative, the Institute for Management Development and Change, Util-LINK LLC, and the National Rural Telecommunications Cooperative. He has BS and MS degrees in electrical engineering from the University of Houston and Purdue University respectively. He has served as chairman of the IEEE IAS Rural Electric Power Committee, a member of the board of directors of IAS, chairman of the IEEE Smart Grid Education and Operations Committees.

Smart Buildings: Approaches to Promoting Reliability of Smart Grid 🗓

— demand-response, incentives, large office buildings, loads, self-healing, resource-responsive …

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Webinar Date: Thursday, January 31, 2019
Time: 10:00 AM (PT)
Speaker: Dr. Raj Gopal, Smart Buildings and Smart Grid, Research Studies, Sustainable Energy and Smart Grid
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: smartgrid.ieee.org
Summary: To ensure meeting the reliability goals of the Smart Grid, Demand Response programs are offered by electric power utilities with incentives to participating customers in order to match power generation to demand and prevent network instability during peak demand periods. According to the Energy Information Administration’s (EIA) 2012 commercial building energy consumption survey (CBECS), large office buildings in the USA with floor area > 9,000 m2 consume annually 180 billion kWh. This comprises of HVAC (cooling 17%, ventilation 25%), lighting (17%) and plug loads comprising of computers, monitors, printers, servers and other electrical loads associated with occupant productivity (17%) and the rest miscellaneous loads. These loads mostly occur, given the occupancy schedule, during the on-peak periods for a summer peaking utility. The need to address Automatic Fault Detection, Diagnosis and System Restoration (AFDDS) becomes important when implementing demand response (DR) strategies whether it is price responsive or resource responsive in office buildings. Should faults occur in the building HVAC system, the kWh energy consumption and KW demand will increase negating the objectives of the Demand Response program. This presentation will cover: definitions for Smart Building HVAC System; Smart Building Facility Management System (SBFMS) Architecture; development of algorithms for AFDDS for an example HVAC system with self-healing and resiliency feature and discuss the results of ‘Smart Voice Activated Speaker’ experiments with lighting and Plug loads and opportunities for its integration with SBFMS.
Bio: Dr. Raj Gopal’s current interest is performing Research Studies as a R&D Specialist in Sustainable Energy and Smart Grid. He is a member of ASHRAE and IEEE. He has served in ASHRAE Energy Calculations and Building Operations Dynamics technical committees, as a member of Standards Committee on Liquid Chilling Packages and as a Forum Chairman on Demand Side Management (DSM). His work experience includes working as a Scientist for a leading Building Automation System company and as a DSM engineer for leading Electric Power utilities and as a full time Consultant for Building Automation System companies. He has taught HVAC, Heat Transfer and Thermodynamics at UW Milwaukee and Milwaukee School of Engineering and holds patents in Thermal Energy Storage and Solar Energy. Has published and presented papers in peer reviewed conferences and publications including presentations on Smart Buildings as the main speaker at UW Madison’s ‘Wednesday Nite@the Lab’ lecture series in December 2017 and at ASHRAE Madison Chapter meeting in September 2018. Also served as an Editor for ASME’s Symposium volumes on “Heat Transfer in Energy Conservation” and “Energy Conservation in Building HVAC Systems”. He has worked for 4 years as a Maintenance Engineer for a Multinational company. Dr. Gopal has a Ph.D. in Mechanical Engineering from the University of Akron and MS from IIT Madras, India.

A Reliable Grid is a Smart Grid 🗓

— design, redesign, tech advances, integration, rel principles, machine learning, monitoring …

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Webinar Date: Thursday, December 20, 2018
Time: 10:00 AM (PT)
Speaker: Alan M Ross, Vice President of Reliability, SD Myers
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: smartgrid.ieee.org
Summary: The application of Reliability Engineering disciplines and principles provides a unique perspective to a Smart Grid. In this webinar we will look at how technology, UIoT, Machine Learning and Condition Based Monitoring can positively affect the long-term reliability of the Grid.
While reliability engineering starts at the design phase for asset management decisions, an even greater impact will be on the system those assets comprise. For the most part we are redesigning systems, not designing from scratch, adding technological advances while integrating wide-scale DER and DR into the grid.
Bio: Alan Ross is the Vice President of Reliability for SD Myers. He is a credentialed reliability professional with both the CMRP and CRL certifications and is a member of the IEEE Reliability Society. Alan Is the Chair of the Smart Grid working group for SMRP and the Electrical Power Reliability Summit and on the Planning Committee and Keynote speaker for the Comet Conference. He is a dynamic and frequent presenter or keynote speaker at NETA, Comet, EPRS, SMRP Conference and Symposium, Marcon, Reliability Conference, AIST, IMC and numerous Muni/CoOp regional organizations. Alan has published frequently in AIST Journal, Plant Engineering, Solutions Magazine, Uptime Magazine and on the blog Transformer Reliability, and numerous white papers on the adoption of new technology, reliability and leadership.

Advanced Safety Architecture for Automotive Systems 🗓

— critical components, brakes, steering, redundancy, Steer-By-Wire, Brake-by-Wire …

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Webinar Date: Thursday, December 13, 2018
Time: 11:00 AM (PT)
Speaker: Ramakrishnan Raja, Halla Mechatronics
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: www.ieee-pels.org
Summary: This presentation gives a review of various advanced system architectures deployed for safety-critical components such as brakes and steering. The presentation talks about various type of redundant architectures deployed for autonomous driving conditions. It also discusses about advantages and disadvantages of such architectural changes. An overview of advanced controls strategy for Steer-By-Wire and Brake-by-Wire will be discussed in this presentation.
Bio: Ramakrishnan Raja received his B.Sc. degree from Amrita Institute of technology, India in 2003 and Master’s Degree in electrical engineering from New Jersey Institute of Technology in 2005. He received his Ph.D. degree in automotive system engineering from the University of Michigan-Dearborn. From 2004-2013 he has been working for Delphi steering and Nexteer automotive as Senior Electrical Engineer. Currently he is working at Halla Mechatronics as Chief Scientist-Controls. He is responsible for motor drive control for various automotive applications. His research interests includes electrical machines and variable speed drives including sensorless motor control drives.

Vehicle Cyber Security: Where the Rubber Meets the Code 🗓

— (IEEE TransElectrification) – safety, entertainment, navigation, autonomous driving, threat vectors, state of security, issues to be addressed …

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Webinar Date: Wednesday, October 17, 2018
Time: 8:00 AM (PT)
Speaker: Stacy Prowell, Oak Ridge National Laboratory
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: tec.ieee.org
Summary: Modern vehicles include an average of 100 million lines of code and 60 control units. With automotive manufacturers adding an increasing array of safety, entertainment, navigation, and autonomous driving features, the potential threat vectors for vehicle cyber attacks are rapidly expanding. In this talk, Dr. Stacy Prowell, Director of the Oak Ridge National Laboratory Vehicle Security Center, will discuss the current state of security, the issues to be addressed, and some of the work being done to address these issues.
Bio: Dr. Stacy Prowell serves as the Chief Cyber Security Research Scientist in the Computational Sciences and Engineering Division at Oak Ridge National Laboratory. Dr. Prowell also leads the Cyber Warfare Research Team, is the Program Manager for the lab’s Cybersecurity for Energy Delivery Systems program, and is the Director of the lab’s Vehicle Security Center. Dr. Prowell’s research focuses on physics-based methods for intrusion detection and semantics-based methods in malware detection and analysis. Dr. Prowell’s work on a system for deep analysis of compiled software led to the Hyperion system which received a 2015 R&D 100 award and two awards for technology transfer. Previously, Dr. Prowell worked in the CERT Program of the Software Engineering Institute on automated analysis of malware. In his spare time Dr. Prowell is an Associate Professor of Electrical Engineering and Computer Science at the University of Tennessee.

Explainable Machine Learning Models for Healthcare AI 🗓

— definitions, interpretable machine learning models, deployment, recent advances, challenges …

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Webinar Date: Wednesday, September 26, 2018
Time: 9:00 AM (PT)
Speakers: Ankur Teredesai, Dr. Carly Eckert, Muhammad Aurangzeb Ahmad, and Vikas Kumar of KenSci.
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: webinar.acm.org
Summary: This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learning algorithm for a given problem in healthcare.
Bio: Ankur M. Teredesai is the co-founder and Chief Technology Officer of KenSci. He also holds a Professorship in Computer Science & Systems at the University of Washington. Ankur’s research spans data science with its applications for societal impact in healthcare. Apart from his academic appointments at RIT and the University of Washington, Teredesai has significant industry experience, having held various positions at C-DAC Pune, Microsoft Research, IBM T.J. Watson Labs, and a variety of technology startups. He has published more than 75 papers on machine learning, managed large teams of data scientists and engineers, and deployed data science solutions in healthcare. His recent applied research contributions include cost and risk prediction for readmission due to chronic conditions such as congestive heart failure. Other applications of his work have enabled predicting lengths of stay and sepsis as well as predicting medication pathways to lower risks of mortality and rehospitalization. He is the Executive Director of Center for Data Science, and serves as the Information Officer for ACM SIGKDD (Special Interest Group in Knowledge Discovery and Data Mining), the leading organization of industry and academic researchers in data science. He is currently an associate editor for ACM SIGKDD Explorations and IEEE Transactions on Big Data and serves on program committees of major international conferences in machine learning and healthcare.
Presenter:
Bio: Carly Eckert, M.D., M.P.H. is the Medical Director of Clinical Informatics at KenSci. In this role, Dr. Eckert leads and works with doctors, data scientists, and developers to identify patterns in patient data to predict risk that can cost-effectively improve care outcomes. Prior to her role at KenSci, Dr. Eckert was the Associate Medical Director for Catastrophic Care at the Department of Labor & Industries for the state of Washington. She trained in General Surgery at Vanderbilt University Medical Center and in Occupational & Environmental Medicine and Preventive Medicine at the University of Washington (UW).
She has also co-authored several publications on topics related to general surgery, occupational health, and occupational injury. She recently co-authored a publication accepted for presentation at AAAI: Death vs Data Science: Predicting End of Life. Dr. Eckert received her Masters of Public Health (M.P.H.) in Epidemiology from the University of Washington School of Public Health where she continues her studies as a doctoral student in the Epidemiology department. She received her Doctor of Medicine (M.D.) from the University of Oklahoma Health Sciences Center.
Bio: Muhammad Aurangzeb Ahmad is the Principal Data Scientist at KenSci. In this role, his work is focused on applying machine learning to solve problems within healthcare. His research at KenSci is focused on interpretable machine learning, fairness in machine learning, and causal machine learning models within the context of healthcare. Before coming to KenSci, Muhammad worked in applied machine learning in various domains, e.g., retail (Groupon), video gaming (Ninja Metrics), population studies (MPC), biomedical devices (Boston Scientific), and the energy sector (Con Edison). After working in different fields, Muhammad found his calling in healthcare, where he saw the great potential in using machine learning to improve the lives of people.
Muhammad holds a Ph.D. in Computer Science from the University of Washington. He has taught machine learning and data science at the University of Washington – Tacoma, and he was a visiting research scientist at the Indian Institute of Technology at Kanpur. He has published more than 50 research papers on machine learning and data science.
Bio: Vikas Kumar is a Data Scientist working at KenSci. In this role, Vikas works with a team of data scientists and clinicians to build consumable and trustable machine learning solutions for healthcare. His focus is in building explainable models in healthcare and application of recommendation systems in clinical settings. Prior to KenSci, Vikas was pursuing his doctorate in Computer Science at the University of Minnesota, Twin Cities.
Vikas holds a Ph.D. with a major in Computer Science and minor in Statistics from the University of Minnesota, Twin Cities. He has worked on modeling and application of recommendation systems in various domains, such as media, location, and healthcare. His focus has been to interpret the balance users seek between known (or familiarity) and unknown (or novel) items to build adaptive recommendations. Prior to his Ph.D., he completed his Bachelor’s at the National Institute of Technology, India and worked as a software engineer in Microsoft India.
Visit webinar.acm.org for our full archive of past webinars.

Dissatisfied With Your Job? 🗓

— (IEEE-USA) – unhappiness, employee surveys, satisfaction, strategies, managing your work life …

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Webinar Date: Thursday, September 27, 2018
Time: 11:00 AM (PT)
Speakers: Richard Feller and Peggy Hutcheson
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: (put shortened chapter/webinar URL here)
Summary: Careers consume so much of our lives that being unhappy at work can lead to feeling pretty unhappy about much of life. If you’ve been part of an employee survey, most likely your company was using this as a tool to see how much the employees there are engaged with their work and satisfied with the company. This is good — it’s important for employers to recognize what is being done to maintain a strong corps of talent for the business and what needs to be done to improve employee satisfaction. Unfortunately, it may also mask some advantages that are associated with EMPLOYEE DISSATISFACTION. Most people are less than fully happy with their jobs at some point in their careers. In this webinar, you will learn more about:
— What contributes most to satisfaction in technical careers
— How to identify the core source of dissatisfaction
— Strategies that those who are most satisfied use to manage their work lives
Presenters Richard Feller and Peggy Hutcheson have experience both personally and professionally in managing their careers and in helping individuals and organizations to develop an understanding of the roots of dissatisfaction and to create positive strategies to use dissatisfaction as a warning flag that leads to positive action and increased satisfaction.

Application of Machine Learning in Power Systems 🗓

— (IEEE SmartGrid) – overview, use of machine learning, deep reinforcement learning, power system emergency control, leverage …

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Webinar Date: Thursday, October 25, 2018
Time: 10:00 AM (PT)
Speaker: Qiuhua Huang, Pacific Northwest National Laboratory
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: smartgrid.ieee.org
Summary: The webinar will begin with a short tutorial of machine learning and provide an overview of application of machine learning in power generation, transmission and distribution systems, including the history and the state of the art. Two projects at Pacific Northwest National Laboratory will be presented. The first project is power system emergency control using deep reinforcement learning, which powered AlphaGo to beat human Go champions. The second project is adaptive Remedial Action Scheme (RAS) settings using machine learning. The ultimate goal of these two projects is to leverage state-of-the-art machine learning technologies to make decision-makings in power system control centers—the “brain” of the grid— adaptive, robust and smart. Lastly, future work and research directions will be discussed.
Bio: Qiuhua Huang received his B.Eng. and M.S. degree in electrical engineering from South China University of Technology, Guangzhou, China, in 2009 and 2012, respectively, and his Ph.D. degree in electrical engineering from Arizona State University, Tempe, AZ, in 2016. Qiuhua Huang is currently a power system research engineer in the Electricity Infrastructure group, Pacific Northwest National Laboratory, Richland, WA, USA. His research interests include power system modeling, simulation and control, transactive energy, and application of advanced computing and machine learning technologies in power systems. Currently, he is the principal investigator/project manager of several DOE funded projects. He is co-chair of the “Deep Learning and Smart Grid Applications” panel session at PES GM 2018. He is an Associated Editor of CSEE Journal of Power and Energy Systems.

Scalable Algorithms for Grid Operations: Challenges and Opportunities 🗓

— (IEEE SmartGrid) – monitoring, controlling, optimizing, distributed energy resources, controllable devices, integration …

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Webinar Date: Thursday, October 11, 2018
Time: 10:00 AM (PT)
Speaker: Subhonmesh Bose, Assistant Professor, University of Illinois at Urbana-Champaign
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: smartgrid.ieee.org
Summary: Scalability is the fundamental challenge in the design of algorithms for monitoring, controlling, and optimizing the assets of a power system. Adoption of distributed energy resources (DERs) is adding to the number of controllable devices. Integration of variable renewable production from wind and solar is making it necessary to account for multiple different scenarios in making dispatch decisions with uncertain supply. Addition of phasor measurement units and automated metering infrastructure is leading to the collection of a large amount of data that needs processing to provide meaningful information. Thus, system operations today require algorithms that are able to synthesize large volumes of data, produce actionable decisions within reasonable runtimes, and do so with provable performance guarantees. In this talk, an overview of the design challenges in solving large-scale optimization problems in power system operation will be presented, how these problems are compounded by the evolving landscape of the power grid, and various approaches to address them.
Bio: Mark SiiraSubhonmesh Bose is an Assistant Professor in the Dept. of Electrical and Computer Engineering at the University of Illinois at Urbana Champaign. Prior to joining UIUC, he was an Atkinson Postdoctoral Fellow in Sustainability at Cornell University. He received his MS and PhD at California Institute of Technology in Electrical Engineering in 2012 and 2014, respectively. Also, in 2009 he received his B.Tech degree at Indian Institute of Technology Kanpur. His research interests are in algorithm and market design for the electric power system with renewable supply.

Grid Modernization and Resiliency: Frameworks and Case Study 🗓

— (IEEE SmartGrid) – readiness determination, clear goals, application of resiliency concepts …

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Webinar Date: Thursday, September 20, 2018
Time: 10:00 AM (PT)
Speaker: Aaron F. Snyder, Director of Grid Technology Consulting, EnerNex
Location: on the Web
Cost: none
RSVP: required
Event Details & Registration: smartgrid.ieee.org
Summary: This presentation will describe the origins of Grid Modernization and propose a readiness determination methodology. This methodology will then show how to develop a grid modernization project in a rigorous fashion to meet clearly defined goals. Following this, a framework for one aspect of Grid Modernization, resiliency, will be presented along with a case study to highlight the application of resiliency concepts to meet Grid Modernization goals.
Bio: Mark Snyder obtained his BSEE (1993) and MSEE (1997) from Virginia Polytechnic Institute and State University, and his Diplôme d’Études Approfondies (1996) and Diplôme de Docteur (1999) from the Institut National Polytechnique de Grenoble in Grenoble, France. As the Director of Grid Technology Consulting at EnerNex, Aaron works with utility and vendor clients on metering, AMI, Smart Grid, and Grid Modernization projects. In recent years he has been supporting AMI, DA, Microgrid, and ADMS projects in the USA and Middle East, including enterprise architecture, strategy development, requirements, equipment specifications, procurement support, and pre-deployment activities. He is a Board member of the UCA International Users Group, and participates in standards development activities at national and international levels. He is a Senior Member of IEEE.