Join us for an engaging seminar with Travis W. Grablander as he shares his insights and experiences from the field of electrical engineering at Black Hills Energy. Travis will provide an in-depth overview of key concepts and practical applications in transmission line design, distribution systems, and protection engineering. Drawing on his work in relay protection design and system programming, he will discuss real-world challenges and solutions for ensuring the reliability and safety of modern electrical <a href="http://grids.This" target="_blank" title="grids.This">grids.This session is an excellent opportunity for students, faculty, and professionals to explore the intersection of academic learning and industry practices in power engineering. Don’t miss this chance to learn from an industry expert and connect with professionals in the <a href="http://field.Speaker(s):" target="_blank" title="field.Speaker(s):">field.Speaker(s): Travis W. Grablander , Room: 254, Bldg: EEP, South Dakota School of Mines , Rapid City , South Dakota, United States, 57701

The combined Santa Clara Valley, San Francisco, & Oakland/East Bay IEEE PELS is very pleased to invite you to our upcoming online webinar. We are excited and honored to have Prof. Qianwen Xu of the KTH Royal Institute of Technology, Sweden to speak on the topic of AI and Digitalization as Enablers of the Clean Energy Transition. Here's more about the talk from the Speaker: As we transition towards a climate-neutral society, future energy systems will increasingly feature high shares of renewable energy sources and power electronic converters. However, these advancements introduce significant challenges in maintaining power balance and system stability. This talk will explore how AI helps to address these challenges. Specifically, the talk focuses on transfer learning based modeling for converter interfaced assets, digital twin development of power systems, and safe deep reinforcement learning for grid optimization with numerous converter interfaced renewable energy sources. This talk targets those interested in tech-driven environmental sustainability, offering insights into how digital advances support the clean energy transition. Virtual: https://events.vtools.ieee.org/m/443097

IEEE-AESS-SPS Monthly Excom Meeting for DecemberVirtual: https://events.vtools.ieee.org/m/452035

Generative Adversarial Networks (GANs) implement Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this talk, we discuss their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii) mitigating security attacks. GANs have the following advantages. First, they can learn and synthesize field data, which can be costly, time consuming, and non-repeatable. Second, they enable pre-training classifiers by using semi-supervised data. Third, they facilitate increased resolution. Fourth, they enable recovering corrupted bits in the spectrum. The talk will provide basics of GANs, a comparative discussion on different kinds of GANs, performance measures for GANs in computer vision and image processing as well as wireless applications, a number of datasets for wireless applications, performance measures for general classifiers, a survey of the literature on GANs for i)–iii) above, some simulation results, and future research directions. In the spectrum sharing problem, connections to cognitive wireless networks are established. Simulation results show that a particular GAN implementation is better than a convolutional auto encoder for an outlier detection problem in spectrum <a href="http://sensing.Speaker(s):" target="_blank" title="sensing.Speaker(s):">sensing.Speaker(s): Ender AyanogluVirtual: https://events.vtools.ieee.org/m/446267

Generative Adversarial Networks (GANs) implement Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this talk, we discuss their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii) mitigating security attacks. GANs have the following advantages. First, they can learn and synthesize field data, which can be costly, time consuming, and non-repeatable. Second, they enable pre-training classifiers by using semi-supervised data. Third, they facilitate increased resolution. Fourth, they enable recovering corrupted bits in the spectrum. The talk will provide basics of GANs, a comparative discussion on different kinds of GANs, performance measures for GANs in computer vision and image processing as well as wireless applications, a number of datasets for wireless applications, performance measures for general classifiers, a survey of the literature on GANs for i)–iii) above, some simulation results, and future research directions. In the spectrum sharing problem, connections to cognitive wireless networks are established. Simulation results show that a particular GAN implementation is better than a convolutional auto encoder for an outlier detection problem in spectrum sensing. Speaker(s): Ender Ayanoglu Virtual: https://events.vtools.ieee.org/m/446267