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Physics-enhanced Graph Neural Networks for Electrical Distribution System State Estimation
Predictive maintenance is critical in complex industrial systems as it aims to reduce maintenance costs and prevent unplanned downtime by detecting anomalies before they lead to system failure. Anomaly detection is a central component of predictive maintenance and involves identifying deviations from normal system behavior. In electrical distribution systems, one avenue to anomaly detection is to derive an anomaly score from state estimation. State estimation is an existing data processing algorithm for converting redundant phasor meter readings and other available information into an estimate of the state of an electric distribution system. Conventional state estimation (CSE) estimates the state with the iterative Newton-Rhapson method based on a set of power flow equations. CSE is built around a numerical solver for a set of equations that are derived from a schematical representation of the industrial asset.
Implementing accurate distribution system state estimation (DSSE) faces several challenges, among them is the lack of observability and the high density of the distribution system, convergence issues, and computational time. While data-driven alternatives based on machine learning models show to be a better choice, they suffer from the lack of labeled training data and do not inherently learn the underlying system physics. Because of that reason, in this thesis, we focus on the sim-to-real transfer of physics-enhanced neural state estimation. The goal is to improve DSSE by pre-training a physics-enhanced model on data from extensive existing simulator packages.
We aim to use graph neural networks (GNNs) which have emerged as a promising model for DSSE. GNNs encode graph structures by aggregating features of neighboring nodes, which allows them to capture both local and global structures of the graphs. Real-time processing with data-driven state estimators is still a challenge, but GNNs are a promising approach as they perform well with a comparatively small number of neurons and layers.
The thesis will be conducted at the intelligent maintenance and operations lab at EPFL (https://www.epfl.ch/labs/imos/).
The student will investigate the use of physics-enhanced graph neural networks for electrical distribution system state estimation. The focus will be on sim-to-real transfer from conventional state estimation, defining the physics-enhanced model, and on detecting anomalies based on a designed anomaly score.
It is not required that the student has worked with GNNs before, but the student should have basic prior knowledge in deep learning and python programming.
The thesis will be conducted at the intelligent maintenance and operations lab at EPFL (https://www.epfl.ch/labs/imos/).
The student will investigate the use of physics-enhanced graph neural networks for electrical distribution system state estimation. The focus will be on sim-to-real transfer from conventional state estimation, defining the physics-enhanced model, and on detecting anomalies based on a designed anomaly score.
It is not required that the student has worked with GNNs before, but the student should have basic prior knowledge in deep learning and python programming.
The student will investigate the use of physics-enhanced graph neural networks for electrical distribution system state estimation. The focus will be on sim-to-real transfer from conventional state estimation, defining the physics-enhanced model, and on detecting anomalies based on a designed anomaly score. The proposed model will be trained on simulated data, evaluated on real-world benchmark datasets, and compared to state-of-the-art methods.
The student will investigate the use of physics-enhanced graph neural networks for electrical distribution system state estimation. The focus will be on sim-to-real transfer from conventional state estimation, defining the physics-enhanced model, and on detecting anomalies based on a designed anomaly score. The proposed model will be trained on simulated data, evaluated on real-world benchmark datasets, and compared to state-of-the-art methods.
If you have any questions, don't hesitate to contact raffael.theiler@epfl.ch. Please apply through SiROP with your CV and transcript of records.
If you have any questions, don't hesitate to contact raffael.theiler@epfl.ch. Please apply through SiROP with your CV and transcript of records.