State Estimation in Smart Grids Using Temporal Graph Convolution Networks

Hossain, Md. Jakir; Rahnamay-Naeini, Mahshid

doi:10.1109/naps52732.2021.9654642

Cited by 20 publications

(18 citation statements)

References 29 publications

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“…2) Physics-informed Loss Function: Next, the effect of the physics-informed loss term (18), based on the Kirchhoff's law, is examined using model C12, where the PI loss is removed during the training process. Comparing the losses in Table II, it is clear that the PI loss positively contributed to model performance.…”

Section: Ablation Studymentioning

confidence: 99%

See 1 more Smart Citation

PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement

Yin¹,

Jiang²,

Huang³

et al. 2022

Preprint

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A Physics-Informed Dynamic Graph Neural Network (PIDGeuN) is presented to accurately, efficiently and robustly predict the nonlinear transient dynamics of microgrids in the presence of disturbances. The graph-based architecture of PIDGeuN provides a natural representation of the microgrid topology. Using only the state information that is practically measurable, PIDGeuN employs a time delay embedding formulation to fully reproduce the system dynamics, avoiding the dependency of conventional methods on internal dynamic states such as controllers. Based on a judiciously designed message passing mechanism, the PIDGeuN incorporates two physicsinformed techniques to improve its prediction performance, including a physics-data-infusion approach to determining the inter-dependencies between buses, and a loss term to respect the known physical law of the power system, i.e., the Kirchhoff's law, to ensure the feasibility of the model prediction. Extensive tests show that PIDGeuN can provide accurate and robust prediction of transient dynamics for nonlinear microgrids over a long-term time period. Therefore, the PIDGeuN offers a potent tool for the modeling of large scale networked microgrids (NMs), with potential applications to predictive or preventive control in real time applications for the stable and resilient operations of NMs.

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Section: Ablation Studymentioning

confidence: 99%

“…Most of the GNN-oriented studies on power systems focus on static problems, e.g. optimal power flow (OPF) problem [14], power flow approximations [15]- [17], state estimation [18]- [20], and anomaly detection [21]- [23]. For these static problems, the scalable nature of GNN makes it powerful to handle large systems in an efficient manner.…”

Section: Introductionmentioning

confidence: 99%

PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement

Yin¹,

Jiang²,

Huang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent proposals for GNN-based SE [43] [44], the GNN models are trained to predict state variables based on the dataset of power system's measurements annotated with the node voltage values. The centralised implementation of the trained GNN model's inference results in linear computational complexity with the number of nodes in the power system (assuming the constant node degree).…”

Section: Model-based Versus Data-based Distributed Se 1) Model-based ...mentioning

confidence: 99%

Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks

Ognjen¹,

Forcan²,

Cosovic³

et al. 2022

Preprint

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networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays.

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“…An additional feed-forward neural network was trained to learn the solutions that minimise the SE loss function, resulting in accelerated SE solution with O(n 2 ) computational complexity during inference. In [16], state variables are predicted based on a time-series of node voltage measurements, and the SE problem is solved by employing GNNs enriched with gated recurrent units.…”

Section: Introductionmentioning

confidence: 99%

Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networks

Ognjen¹,

Cosovic²,

Vukobratović³

2022

Preprint

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The power system state estimation (SE) algorithm estimates the complex bus voltages based on the available set of measurements. Because phasor measurement units (PMUs) are becoming more widely employed in transmission power systems, a fast SE solver capable of exploiting PMUs' high sample rates is required. To accomplish this, we present a method for training a model based on graph neural networks (GNNs) to learn estimates from PMU voltage and current measurements, which, once it is trained, has a linear computational complexity with respect to the number of nodes in the power system. We propose an original GNN implementation over the power system's factor graph to simplify the incorporation of various types and numbers of measurements both on power system buses and branches. Furthermore, we augment the factor graph to improve the robustness of GNN predictions. Training and test examples were generated by randomly sampling sets of power system measurements and annotated with the exact solutions of linear SE with PMUs. The numerical results demonstrate that the GNN model provides an accurate approximation of the SE solutions. Additionally, errors caused by PMU malfunctions or the communication failures that make the SE problem unobservable have a local effect and do not deteriorate the results in the rest of the power system.

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State Estimation in Smart Grids Using Temporal Graph Convolution Networks

Cited by 20 publications

References 29 publications

PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement

PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement

Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks

Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networks

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