Systems and methods for time-synchronized topology and state estimation in real-time unobservable distribution systems
AssigneesArizona Board of Regents on behalf of Arizona State University, Cornell University
InventorsAnamitra Pal, Behrouz Azimian, Lang Tong
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Abstract
Time-synchronized state estimation for reconfigurable distribution systems is challenging because of limited real-time observability. A system 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 developed.
Claims20
A method, comprising:
- accessing a set of synchrophasor measurement device (SMD) observation data for a distribution network;
- identifying, at a first deep neural network (DNN-TI), a present topology of the distribution network based on the set of SMD observation data;
- and
- estimating, at a second deep neural network (DNN-DSSE) in communication with the first deep neural network (DNN-TI), an estimated state vector for a plurality of nodes of the distribution network.
A system, comprising:
- a processor in communication with a memory, the memory including instructions executable by the processor to:
- access a set of synchrophasor measurement device (SMD) observation data for a distribution network;
- identify, at a first deep neural network (DNN-TI), a present topology of the distribution network based on the set of SMD observation data;
- and
- estimate, at a second deep neural network (DNN-DSSE) in communication with the first deep neural network (DNN-TI), an estimated state vector for a plurality of nodes of the distribution network.
A non-transitory, computer-readable medium storing instructions encoded thereon, the instructions, when executed by one or more processors, cause the one or more processors to perform operations to:
- access a set of synchrophasor measurement device (SMD) observation data for a distribution network;
- identify, at a first deep neural network (DNN-TI), a present topology of the distribution network based on the set of SMD observation data;
- and
- estimate, at a second deep neural network (DNN-DSSE) in communication with the first deep neural network (DNN-TI), an estimated state vector for a plurality of nodes of the distribution network.
Dependencies are inferred from the claim text.