Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

• URL: http://arxiv.org/abs/2008.11582v1
• Date: Tue, 25 Aug 2020 01:25:59 GMT
• Title: Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements
• Authors: Iman Niazazari, Hanif Livani, Amir Ghasemkhani, Yunchuan Liu, and Lei Yang
• Abstract summary: This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The proposed method is based on a machine learning method, the convolutional neural network (CNN)
• Score: 2.3780731536926165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real-time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-of-the-art classifiers.

Related papers

• Joint Microseismic Event Detection and Location with a Detection Transformer [8.505271826735118]
  We propose an approach to unify event detection and source location into a single framework. The proposed network is trained on synthetic data simulating multiple microseismic events corresponding to random source locations.
  arXiv  Detail & Related papers  (2023-07-16T10:56:46Z)
• DynamoPMU: A Physics Informed Anomaly Detection and Prediction Methodology using non-linear dynamics from $\mu$PMU Measurement Data [0.0]
  We develop a physics dynamics-based approach to detect anomalies in the $mu$PMU streaming data and simultaneously predict the events using governing equations. We demonstrate the efficacy of our proposed framework through analysis of real $mu$PMU data taken from the LBNL distribution grid.
  arXiv  Detail & Related papers  (2023-03-31T19:32:24Z)
• Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200 [0.13246303154954686]
  A novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy comparable to that of real waveforms.
  arXiv  Detail & Related papers  (2023-03-11T05:16:20Z)
• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Robust Event Classification Using Imperfect Real-world PMU Data [58.26737360525643]
  We study robust event classification using imperfect real-world phasor measurement unit (PMU) data. We develop a novel machine learning framework for training robust event classifiers.
  arXiv  Detail & Related papers  (2021-10-19T17:41:43Z)
• Deep Transformer Networks for Time Series Classification: The NPP Safety Case [59.20947681019466]
  An advanced temporal neural network referred to as the Transformer is used within a supervised learning fashion to model the time-dependent NPP simulation data. The Transformer can learn the characteristics of the sequential data and yield promising performance with approximately 99% classification accuracy on the testing dataset.
  arXiv  Detail & Related papers  (2021-04-09T14:26:25Z)
• Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests [1.0766846340954257]
  We devise a machine learning technique to solve the general problem of inferring network links that have time-delays. We first train a type of machine learning system known as reservoir computing to mimic the dynamics of the unknown network. We formulate and test a technique that uses the trained parameters of the reservoir system output layer to deduce an estimate of the unknown network structure.
  arXiv  Detail & Related papers  (2020-10-29T00:24:13Z)
• Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning [4.666011151359189]
  We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. We propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples.
  arXiv  Detail & Related papers  (2020-10-05T07:48:45Z)
• Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction [79.81193813215872]
  We develop a hybrid (graph) neural network that combines a traditional graph convolutional network with an embedded differentiable fluid dynamics simulator inside the network itself. We show that we can both generalize well to new situations and benefit from the substantial speedup of neural network CFD predictions.
  arXiv  Detail & Related papers  (2020-07-08T21:23:19Z)

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.