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  • PES
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    IEEE Members: $10.00
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    Length: 49:27
Panel Session 03 Aug 2020

The adoption of DER’s power electronics apparatus, advanced sensing, and network management devices have increased the availability of high‐resolution network and load measurements in various spatial and temporal scales. Moreover, the transformation of distribution network to smart grid has resulted in more complexity due to interconnections between the grid’s physical and cyber components grid. Conventional distribution management systems (DMS) have difficulties to integrate massive measurements with various types due to the data heterogeneity. Machine learning and artificial intelligence (AI) offer new opportunities to process these spatiotemporal data and generate data-driven models that can augment the physical principals behind the power distribution networks. In this session, we address how AI can be combined with traditional modeling and analysis approaches and help accelerate the transformation of next-generation DMS systems. Perspectives from research and industry offer a complementary view of how these technologies will come together in futureDMS.

Chairs:
Reza Arghandeh, Hamed Mohsenian-Rad
Primary Committee:
Analytic Methods for Power Systems (AMPS)
Sponsor Committees:
Big Data Analytics

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