[go: up one dir, main page]

Fellner, 2024 - Google Patents

Data Driven Detection of Misconfigurations in Power Distribution Systems

Fellner, 2024

View PDF
Document ID
12064648214228140175
Author
Fellner D
Publication year

External Links

Snippet

The modern power system is undergoing fundamental changes to adapt to new requirements, caused by the need of sustainable provision and consumption of energy. These changes especially affect power distributions systems, as they have been designed to …
Continue reading at repositum.tuwien.at (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Similar Documents

Publication Publication Date Title
Zheng et al. A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids
US8751421B2 (en) Machine learning for power grid
Shen et al. Artificial intelligence and digital twins in power systems: Trends, synergies and opportunities: [version 2; peer review: 2 approved]
Goyal et al. Asset health management using predictive and prescriptive analytics for the electric power grid
US11892830B2 (en) Risk assessment at power substations
Kaymakci et al. Energy anomaly detection in industrial applications with long short-term memory-based autoencoders
Zhu et al. Energy forecasting with robust, flexible, and explainable machine learning algorithms
Bessa Future trends for big data application in power systems
Fellner et al. Applying deep learning-based concepts for the detection of device misconfigurations in power systems
Wadi et al. Reliability and sensitivity analysis for closed-ring distribution power systems
US20250183660A1 (en) Method and system for monitoring a voltage grid, method for training an artificial intelligence to predict a future state of a voltage grid, computer program, and computer-readable data carrier
Hojjatinia et al. Peak shaving impact on load forecasting: A strategy for mitigation
Safhi et al. Energy load forecasting in big data context
Zheng et al. Psml: A multi-scale time-series dataset for machine learning in decarbonized energy grids
Dedić et al. Fuzzy C-means clustering applied to load profiling of industrial customers
Fellner Data Driven Detection of Misconfigurations in Power Distribution Systems
Udhaya Shankar et al. Deep Learning Based Effective Technique for Smart Grid Contingency Analysis Using RNN with LSTM
Bagheri et al. Big data from smart grids
Pandya et al. Time-Series Forecasting of Energy Consumption Using LSTM Networks for Optimized Microgrid Management
Okoye et al. A theoretical framework for enhanced forecasting of electrical loads
Zhang et al. OccuEMBED: Occupancy Extraction Merged with Building Energy Disaggregation for Occupant-Responsive Operation at Scale
Nimma et al. Big data analytics for predictive maintenance in smart grids and energy management systems
Flath et al. Smart grid analytics
Essallah et al. Long-term load forecasting in the smart grid framework using time series and econometric approaches
Nastac et al. Large data analysis for advanced grids