Pradeep et al., 2024 - Google Patents

AI-Driven Fault Detection in Smart Grids

Pradeep et al., 2024

Document ID
4359137938864954531
Author
Pradeep S
Rex C
Kalaiyarasi D
Dhandapani R
Sakthivel M
Vijaipriya K
Publication year
Publication venue
2024 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON)

External Links

Snippet

The use of artificial intelligence (AI) for fault finding in smart grids, with the end goal of improving the reliability and efficiency of ultramodern power systems. Smart grids, which are distinguished by their intricate architectural design and massive data collection, are …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
    • G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
US7080290B2 (en) Exception analysis for multimissions
Tan et al. A new sensor fault diagnosis method for gas leakage monitoring based on the naive Bayes and probabilistic neural network classifier
Wachla et al. A method of leakage location in water distribution networks using artificial neuro-fuzzy system
AU2002252259A1 (en) Real-time spatio-temporal coherence estimation for autonomous mode identification and invariance tracking
Liu et al. Technology development and commercial applications of industrial fault diagnosis system: a review
AU2002248549A1 (en) Real-time spatio-temporal coherence estimation for autonomous mode identification and invariance tracking
De Simone et al. LSTM-based failure prediction for railway rolling stock equipment
Zideh et al. Physics-informed machine learning for data anomaly detection, classification, localization, and mitigation: A review, challenges, and path forward
Djelloul et al. Fault diagnosis of manufacturing systems using data mining techniques
EP1752898A2 (en) Exception analysis for multimissions
Mei et al. Machinery condition monitoring in the era of industry 4.0: A relative degree of contribution feature selection and deep residual network combined approach
EP1724717A2 (en) Real-time spatio-temporal coherence estimation for autonomous mode identification and invariance tracking
Zhang et al. A novel architecture for an integrated fault diagnostic/prognostic system
CN105426665A (en) Dynamic reliability determination method based on state monitoring
Dang et al. seq2graph: Discovering dynamic non-linear dependencies from multivariate time series
Pradeep et al. AI-Driven Fault Detection in Smart Grids
Shahzad Artificial neural network for transient stability assessment: A review
WO2022175767A1 (en) Equipment fault detection system using artificial intelligence(icms)
Aftabi et al. A Variational Autoencoder Framework for Robust, Physics-Informed Cyberattack Recognition in Industrial Cyber-Physical Systems
He et al. Probabilistic model based algorithms for prognostics
Azhar et al. Transient Stability Assessment Considering Number and Location of PMUs Using CNN-LSTM
Cao et al. Study on Prognostics and Health Management of Fluid Loop System for Space Application
Xu et al. Multivariable correlation feature network construction and health condition assessment for unlabeled single-sample data
Nannan et al. A Review on Prognostics and Health Management
Jie et al. Investigation on Power System Dynamic Monitoring System Using Artificial Neural Networks