Year: 2025
Type: other
Abstract: Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This chapter addresses this challenge by formulating a deep learning (DL)-based approach for topology identification (TI) and unbalanced three-phase distribution system state estimation (DSSE). Two deep neural networks (DNNs) are trained for time-synchronized DNN-based TI and DSSE, respectively, for systems that are incompletely observed by synchrophasor measurement devices (SMDs) in real-time. A data-driven approach for judicious SMD placement to facilitate reliable TI and DSSE is also provided. Robustness of the proposed methodology is demonstrated by considering non-Gaussian noise in the SMD measurements. A comparison of the DNN-based DSSE with more conventional approaches indicates that the DL-based approach gives better accuracy with smaller number of SMDs.
Institutions: GE Vernova (United States), Arizona State University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Awards: CONTROL NUMBER:1858-1631
ARIZONA STATE UNIVERSITY: NEW AWARD.
SENSOR ENABLED MODELING OF FUTURE DISTRIBUTION SYSTEMS WITH DISTRIBUTED ENERGY RESOURCES
THIS COOPERATIVE AGREEMENT IS HEREBY AWARDED TO ARIZONA STATE UNIVERSITY TO DEVELOP CRITICAL DISTRIBUTION SYSTEM ANALYSIS AND CONTROL TOOLS THAT WILL MAINTAIN THE RELIABILITY AND EFFICIENCY OF THE DISTRIBUTION SYSTEM IN PRESENCE OF HIGH LEVELS OF DISTRIBUTED ENERGY RESOURCES (DERS) AND STORAGE., CAREER: Time-Synchronized Estimation in Power Systems: Unique Challenges and Innovative Solutions
OpenAlex ID: W4408383521