US 12,224,587 B2
Extraction of instantaneous renewable generation from net load measurements of power distribution systems
Hongbo Sun, Lexington, MA (US); Ashwin Shirsat, Raleigh, NC (US); Kyeong Jin Kim, Lexington, MA (US); and Jianlin Guo, Newton, MA (US)
Assigned to Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed by Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed on Mar. 25, 2022, as Appl. No. 17/656,465.
Prior Publication US 2023/0307908 A1, Sep. 28, 2023
Int. Cl. G06N 3/049 (2023.01); G06N 3/045 (2023.01); G06Q 50/06 (2012.01); H02J 3/00 (2006.01); H02J 3/38 (2006.01)
CPC H02J 3/004 (2020.01) [G06N 3/045 (2023.01); G06N 3/049 (2013.01); G06Q 50/06 (2013.01); H02J 3/003 (2020.01); H02J 3/381 (2013.01); H02J 2203/20 (2020.01); H02J 2300/24 (2020.01)] 24 Claims
OG exemplary drawing
 
1. A computer-implemented method for performing energy disaggregation of a distribution system-level net-load measurements on a distribution system using continuous-point-on-wave (CPOW) measurement units, wherein the method uses a processor coupled with memory stored instructions implementing the method using neural networks including an encoder network, a feature extractor, a separator network, a decoder network stored in the memory, wherein the instructions, when executed by the processor carry out steps of the method, comprising:
generating net-load time-series data from voltage and current measurements via the CPOW measurement units;
generating, using the encoder network, a compressed latent space representation from the net-load time series;
converting the net-load time series into the time-frequency domain using a short-time Fourier transform (STFT) method;
passing time-domain cotextual information with the converted time-frequency domain representation of net-load time series to the feature extractor trained to accurately extract harmonic features;
estimating, using the separator network trained to assign weight to the compressed latent space representation, two weight matrices to be multiplied with an output from the encoder network and learning temporal features of a native load and a photovoltaic (PV) generation;
transforming, using the decoder network, the weighted compressed latent representation corresponding the native load and the PV generation into time-domain representations from outputs of the encoder network and the separator network; and
predicting, using a post-disaggregation adjustment method, the native load and the PV generation at distribution system-level from the transformed time-domain representations corresponding to the native load and the PV generation.