Abstract
Increase in the proliferation of DERs requires real-time situational awareness for efficient grid operations. State estimation plays an important role for real time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hierarchical spectral clustering-based network partition algorithm followed by a decentralized compressive sensing (DCS) based state estimation. The applicability of the proposed network partitioning algorithm is tested on IEEE-123 bus, IEEE-8500 node, and a 6204-node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6204-node distribution network after dividing the network into 18 optimal partitions. Simulation results show that DCS-based state estimation recovers the system states with high accuracy and low complexity.
Original language | American English |
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Number of pages | 8 |
State | Published - 2023 |
Event | 2023 IEEE Conference on Innovative Smart Grid Technologies North America (ISGT NA) - Washington, D.C. Duration: 16 Jan 2023 → 19 Jan 2023 |
Conference
Conference | 2023 IEEE Conference on Innovative Smart Grid Technologies North America (ISGT NA) |
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City | Washington, D.C. |
Period | 16/01/23 → 19/01/23 |
Bibliographical note
See NREL/CP-5D00-86223 for paper as published in proceedingsNREL Publication Number
- NREL/CP-5D00-83876
Keywords
- ADMM
- compressive sensing
- distributed state estimation
- network partition
- power distribution network
- spectral clustering