Authors: Lang Tong, Xinyi Wang, Qing Zhao

Published on: March 11, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2403.06942

Summary

• What is new: Leveraging AI for grid monitoring by using continuous point-on-wave (CPOW) measurements for advanced data compression and fault detection.
• Why this is important: Current grid monitoring systems, relying on SCADA and PMU technologies, are inadequate for future grids with extensive renewable energy and distributed resources.
• What the research proposes: A new monitoring system using CPOW data and AI for better data analytics, capable of detecting unknown faults and trends.
• Results: The proposed system offers a more efficient and robust framework for situational awareness in dynamic grid environments.

Technical Details

Technological frameworks used: Wiener-Kallianpur innovation representation

Models used: Generative AI, Autoencoder, Nonparametric Sequential Hypothesis Testing

Data used: Continuous point-on-wave (CPOW) time series

Potential Impact

Utility companies, renewable energy sector, AI analytics companies

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