Core Concepts
Deep Neural network-based State Estimator (DeNSE) addresses challenges in time-synchronized state estimation for power systems.
Abstract
The article discusses the challenges of time-synchronized state estimation in power systems due to PMU placement trade-offs. It introduces the DeNSE, a Deep Neural network-based State Estimator, to overcome these challenges. The DeNSE combines SCADA and PMU data to achieve sub-second situational awareness. It addresses scalability, non-Gaussian noise, bad data detection, and correction issues. Results show its superiority over traditional methods.
Introduction
State estimation importance for power utilities.
Traditional methods vs. new challenges with rapid fluctuations.
Motivations
Issues with SCADA-only and hybrid state estimators.
Need for high-speed state estimation due to renewable energy integration.
Novel Contributions
Introduction of DeNSE for high-speed state estimation.
Overcoming observability issues with few PMUs.
Proposed Formulation
Bayesian approach to Transmission System State Estimation (TSSE).
Architecture of the DNN in the DeNSE
Feed-forward architecture with hidden layers.
Creation of Training Database
Unique feature of using SCADA data indirectly through MC sampling.
Enhancements to Proposed Formulation and Online Implementation
Transfer learning for topology changes.
Robust Bad Data Detection and Correction (BDDC)
Wald test methodology for bad data detection.
Impact of Topology Changes
Transfer learning efficiency demonstrated after topology changes.
Mitigating Impact of Bad Data
Proposed NOC-based BDDC methodology outperforms mean value replacement.
Tackling Extreme Scenarios
Extreme scenario filter improves performance under extreme conditions.
Investigating Impact of Different Database Sizes
Performance improvement observed with increasing database sizes.
Stats
The DeNSE results in an average magnitude MAPE of 0.1676% and an average angle MAE of 0.0042%.
The proposed methodology for correcting bad data consistently outperformed the mean value replacement method.
Quotes
"The DeNSE employs a Bayesian framework to combine SCADA and PMU data."
"Transfer learning is used to update the DNN after topology changes."