Grid modeling challenge for data-driven research studies
Recently, the integration of inverter-based resources (IBRs) in renewable energy systems has introduced new dynamic challenges to the power grid. These challenges include converter-driven oscillations and the expansion of power outage areas, which pose a significant risk to the reliability of future grids. Grid sensor technology has been developed, enabling the collection of a vast amount of measurement data. This data presents an opportunity for machine-learning experts to leverage big data analytics and tackle various grid reliability analysis challenges specific to the future power system. However, additional technical hurdles are related to data integrity and consistency, particularly regarding labeling and other factors. In power system analysis, time-domain simulation models have traditionally been used for contingency analysis, post-mortem analysis, grid controller design, and grid-wide protection. Synthetic data, which refers to simulated responses, has emerged as a promising approach for imputing missing events and measurements in the data. However, the validation of simulation models, especially for grid-wide events, has been insufficient. This means that synthetic data from numerical simulation models are accurate for limited power system dynamic studies but may not be adequately validated for broader applications. In addition, there is a lack of collaboration between modeling experts specializing in electromagnetic transient modeling, electromechanical modeling, IBR modeling, and grid modeling. Furthermore, no generally accepted model validation procedure or requirements is in place. As a result, well-validated model parameters for simulation studies are scarce, even though the models themselves are readily available. Given these challenges, machine-learning experts must perceive the potential missing dynamics or model errors when utilizing synthetic data for data-driven research studies. This presentation aims to address the current quality of synthetic data and provide insights on navigating the complexities associated with big data in grid reliability analysis. Join us for an engaging discussion on this topic.
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- Co-sponsored by University of California, Riverside
- Starts 15 June 2023 08:29 PM UTC
- Ends 30 June 2023 10:00 PM UTC
- No Admission Charge
Speakers
University of California, Riverside
Dr. Koji Yamashita
Dr. Koji Yamashita received his B.S. and M.S. degrees in Electrical Engineering from Waseda University, Tokyo, Japan, in 1993 and 1995, respectively. He went on to complete his Ph.D. in Electrical and Computer Engineering from Michigan Technological University, Houghton, MI, USA, in 2020.
With over 20 years of industry experience, Dr. Yamashita has made significant contributions to the field of power systems. Notably, his groundbreaking research focused on the derivation of recommended relay settings for an out-of-step relay under unbalanced open circuits. This work, published in a prestigious domestic journal, showcased his expertise in protection systems and set the stage for his subsequent achievements.
Dr. Yamashita's expertise in load modeling has also garnered widespread recognition. Through a long-term project in collaboration with power companies, he developed a load model parameter derivation tool and identified crucial parameters using extensive measured and recorded data from substations in Western Japan. The outcomes of this project were published in a reputable domestic journal and have been cited in review papers, technical committee reports, and Japanese textbooks.
Recognizing the importance of international collaboration and knowledge exchange, Dr. Yamashita actively participated in activities organized by CIGRE (International Council on Large Electric Systems). As a speaker and presenter, he shared his research findings on load modeling and renewable energy integration at international conferences, receiving positive feedback from the audience.
Driven by his dedication to advancing renewable energy integration, Dr. Yamashita proposed the establishment of a new working group within CIGRE focused on renewable energy modeling. As the honorable convener of this working group, he led efforts to explore parameter estimation techniques, system stability analysis, and other critical aspects of renewable energy modeling. The research findings of the working group, disseminated through conference papers and journal articles in multiple languages, have significantly contributed to the global understanding of renewable energy integration.
Dr. Yamashita's influential work and extensive publication record, which includes 24 journal articles and 2 book chapters, have positioned him as an authority in the field of power system engineering. His research outcomes have not only advanced academic and technical knowledge but have also influenced industry practices and decision-making processes. Currently, he serves as an associate editor of IEEE Transactions on Power Systems, further contributing to disseminating cutting-edge research in the field.
Dr. Yamashita's diverse expertise, industry experience, and commitment to driving innovation in power systems make him a highly sought-after speaker and presenter. His engaging presentations provide valuable insights into the challenges and opportunities of renewable energy integration, as well as the application of machine learning techniques using big data in power system analysis. His ability to bridge the gap between research and practical implementation makes him a captivating figure in power system dynamics.
Address:Riverside, California, United States, 92507