7 research outputs found
Creating Temporally Correlated High-Resolution Power Injection Profiles Using Physics-Aware GAN
Traditional smart meter measurements lack the granularity needed for
real-time decision-making. To address this practical problem, we create a
generative adversarial networks (GAN) model that enforces temporal consistency
on its high-resolution outputs via hard inequality constraints using a convex
optimization layer. A unique feature of our GAN model is that it is trained
solely on slow timescale aggregated power information obtained from historical
smart meter data. The results demonstrate that the model can successfully
create minutely interval temporally-correlated instantaneous power injection
profiles from 15-minute average power consumption information. This innovative
approach, emphasizing inter-neuron constraints, offers a promising avenue for
improved high-speed state estimation in distribution systems and enhances the
applicability of data-driven solutions for monitoring such systems.Comment: 5 page
Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes
Recently, there has been a major emphasis on developing data-driven
approaches involving machine learning (ML) for high-speed static state
estimation (SE) in power systems. The emphasis stems from the ability of ML to
overcome difficulties associated with model-based approaches, such as the
handling of non-Gaussian measurement noise. However, topology changes pose a
stiff challenge for performing ML-based SE because the training and test
environments become different when such changes occur. This paper overcomes
this challenge by formulating a graph neural network-based time-synchronized
state estimator that considers the physical connections of the power system
during the training itself. The superiority of the proposed approach over the
model-based linear state estimator in the presence of non-Gaussian measurement
noise and a regular deep neural network-based state estimator in the presence
of topology changes is demonstrated for the IEEE 118-bus system.Comment: 5 pages, 2 figure
Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges
As phasor measurement units (PMUs) are usually placed on the highest voltage
buses, many lower voltage levels of the bulk power system are not observed by
them. This lack of visibility makes time-synchronized state estimation of the
full system a challenging problem. We propose a Deep Neural network-based State
Estimator (DeNSE) to overcome this problem. The DeNSE employs a Bayesian
framework to indirectly combine inferences drawn from slow timescale but
widespread supervisory control and data acquisition (SCADA) data with fast
timescale but local PMU data to attain sub-second situational awareness of the
entire system. The practical utility of the proposed approach is demonstrated
by considering topology changes, non-Gaussian measurement noise, and bad data
detection and correction. The results obtained using the IEEE 118-bus system
show the superiority of the DeNSE over a purely SCADA state estimator, a
SCADA-PMU hybrid state estimator, and a PMU-only linear state estimator from a
techno-economic viability perspective. Lastly, the scalability of the DeNSE is
proven by performing state estimation on a large and realistic 2000-bus
Synthetic Texas system