Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids

• URL: http://arxiv.org/abs/2303.08300v1
• Date: Wed, 15 Mar 2023 01:21:50 GMT
• Title: Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids
• Authors: Hossein Hassani and Ehsan Hallaji and Roozbeh Razavi-Far and Mehrdad Saif
• Abstract summary: Fault diagnosis in cyber-physical power systems is affected by data quality. These systems generate massive amounts of data that overburden the system with excessive computational costs. This paper presents the effect of feature engineering on mitigating the aforementioned challenges.
• Score: 4.616703548353371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The performance of fault diagnosis systems is highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence of noise in recorded measurements, which prevents building a precise decision model. Furthermore, the diagnostic model is often provided with a mixture of redundant measurements that may deviate it from learning normal and fault distributions. This paper presents the effect of feature engineering on mitigating the aforementioned challenges in cyber-physical systems. Feature selection and dimensionality reduction methods are combined with decision models to simulate data-driven fault diagnosis in a 118-bus power system. A comparative study is enabled accordingly to compare several advanced techniques in both domains. Dimensionality reduction and feature selection methods are compared both jointly and separately. Finally, experiments are concluded, and a setting is suggested that enhances data quality for fault diagnosis.

Related papers

• Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study [49.5374512525016]
  Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. Some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. We propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
  arXiv  Detail & Related papers  (2024-01-18T16:59:27Z)
• A Multimodal Data-driven Framework for Anxiety Screening [15.002401707506941]
  We propose a data-driven anxiety screening framework, namely MMD-AS, and conduct experiments on the collected health data of over 200 seafarers by smartphones. The proposed framework's feature extraction, dimension reduction, feature selection, and anxiety inference are jointly trained to improve the model's performance.
  arXiv  Detail & Related papers  (2023-03-16T02:25:05Z)
• Machine learning-based approach for online fault Diagnosis of Discrete Event System [0.0]
  The problem is the online diagnosis of Automated Production Systems with sensors and actuators delivering discrete binary signals. We propose a Machine Learning-based approach of a diagnostic system.
  arXiv  Detail & Related papers  (2022-10-24T08:56:13Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data [0.41998444721319217]
  Using phasor measurement units (PMUs) to surveil the power system is one of the technologies that have a promising future. The increased cyber-physical interaction offers both benefits and drawbacks, where one of the drawbacks comes in the form of anomalies in the measurement data. This paper aims to develop a hybrid AI-based model that is based on various methods for anomaly detection in PMUs data.
  arXiv  Detail & Related papers  (2022-09-21T11:22:01Z)
• System Resilience through Health Monitoring and Reconfiguration [56.448036299746285]
  We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration.
  arXiv  Detail & Related papers  (2022-08-30T20:16:17Z)
• Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals [0.11184789007828977]
  This work uses a Federated Learning (FL) privacy-preserving methodology to train AI models over heterogeneous sets of high-definition ECG. We demonstrated comparable performance to models trained using CL, IID, and non-IID approaches.
  arXiv  Detail & Related papers  (2022-08-23T14:21:16Z)
• NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization [59.15047491202254]
  symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition. We propose a new approach based on the supervised learning of neural models with logic regularization. Our experiments show that the proposed approach outperforms the best existing methods in the accuracy of diagnosis when the number of diagnoses and symptoms is large.
  arXiv  Detail & Related papers  (2022-06-02T07:57:17Z)
• On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
  A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements. A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
  arXiv  Detail & Related papers  (2021-10-17T13:42:37Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Residual Generation Using Physically-Based Grey-Box Recurrent Neural Networks For Engine Fault Diagnosis [1.0152838128195467]
  Hybrid fault diagnosis methods combining physically-based models and available training data have shown promising results. An automated residual design is developed using a bipartite graph representation of the system model to design grey-box recurrent neural networks. Data from an internal combustion engine test bench is used to illustrate the potentials of combining machine learning and model-based fault diagnosis techniques.
  arXiv  Detail & Related papers  (2020-08-11T11:59:48Z)

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.