Predicting Dynamic Stability from Static Features in Power Grid Models using Machine Learning

• URL: http://arxiv.org/abs/2210.09266v1
• Date: Mon, 17 Oct 2022 17:16:48 GMT
• Title: Predicting Dynamic Stability from Static Features in Power Grid Models using Machine Learning
• Authors: Maurizio Titz, Franz Kaiser, Johannes Kruse, Dirk Witthaut
• Abstract summary: We propose a combination of network science metrics and machine learning models to predict the risk of desynchronisation events. We train and test such models on simulated data from several synthetic test grids. We find that the integrated models are capable of predicting desynchronisation events with an average precision greater than $0.996$ when averaging over all data sets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A reliable supply with electric power is vital for our society. Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article we propose a combination of network science metrics and machine learning models to predict the risk of desynchronisation events. Network science provides metrics for essential properties of transmission lines such as their redundancy or centrality. Machine learning models perform inherent feature selection and thus reveal key factors that determine network robustness and vulnerability. As a case study, we train and test such models on simulated data from several synthetic test grids. We find that the integrated models are capable of predicting desynchronisation events after line failures with an average precision greater than $0.996$ when averaging over all data sets. Learning transfer between different data sets is generally possible, at a slight loss of prediction performance. Our results suggest that power grid desynchronisation is essentially governed by only a few network metrics that quantify the networks ability to reroute flow without creating exceedingly high static line loadings.

Related papers

• Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning [0.0]
  We analyze the potential of Machine Learning for predicting dynamic stability of future power grids with large shares of inverters. We demonstrate that ML models accurately predict the fault-ride-through probability of synthetic power grids. We also show that the ML models generalize to an IEEE-96 Test System.
  arXiv  Detail & Related papers  (2024-06-13T08:28:14Z)
• Predicting Cascading Failures with a Hyperparametric Diffusion Model [66.89499978864741]
  We study cascading failures in power grids through the lens of diffusion models. Our model integrates viral diffusion principles with physics-based concepts. We show that this diffusion model can be learned from traces of cascading failures.
  arXiv  Detail & Related papers  (2024-06-12T02:34:24Z)
• Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs [0.0]
  This paper proposes a physics-informed convolutional autoencoder (PIConvAE) model to detect cyber anomalies in power distribution systems with unbalanced configurations and high penetration of DERs. The performance of the proposed model is evaluated on two unbalanced power distribution grids, IEEE 123-bus system and a real-world feeder in Riverside, CA.
  arXiv  Detail & Related papers  (2024-06-05T04:28:57Z)
• Diffusion-based Neural Network Weights Generation [85.6725307453325]
  We propose an efficient and adaptive transfer learning scheme through dataset-conditioned pretrained weights sampling. Specifically, we use a latent diffusion model with a variational autoencoder that can reconstruct the neural network weights.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design [68.1682448368636]
  We present a supervised pretraining approach to learn circuit representations that can be adapted to new unseen topologies or unseen prediction tasks. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties.
  arXiv  Detail & Related papers  (2022-03-29T21:18:47Z)
• Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks [6.09170287691728]
  The present thesis first develops principled methods to make generic machine learning models robust against distributional uncertainties and adversarial data. We then build on this robust framework to design robust semi-supervised learning over graph methods. The second part of this thesis aspires to fully unleash the potential of next-generation wired and wireless networks.
  arXiv  Detail & Related papers  (2022-02-08T05:49:06Z)
• Data-Driven Interaction Analysis of Line Failure Cascading in Power Grid Networks [0.0]
  We use machine learning tools to model the line interaction of failure cascading in power grid networks. We first collect data sets of simulated trajectories of possible consecutive line failure following an initial random failure. We then consider actual constraints in a model power network until the system settles at a steady state.
  arXiv  Detail & Related papers  (2021-12-02T09:04:01Z)
• Power-grid stability prediction using transferable machine learnings [2.1640200483378953]
  We investigate machine learning techniques to estimate the stability of power grid synchronization. We train three different machine learning algorithms with two different types of synthetic power grids. We find that the three machine learning models better predict the synchronization stability of power-grid nodes when they are trained with the heterogeneous input-power distribution.
  arXiv  Detail & Related papers  (2021-05-17T01:19:01Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.