Deka et al., 2016 - Google Patents
Estimating distribution grid topologies: A graphical learning based approachDeka et al., 2016
View PDF- Document ID
- 14836207376781418734
- Author
- Deka D
- Backhaus S
- Chertkov M
- Publication year
- Publication venue
- 2016 Power Systems Computation Conference (PSCC)
External Links
Snippet
Distribution grids represent the final tier in electric networks consisting of medium and low voltage lines that connect the distribution substations to the end-users/loads. Traditionally, distribution networks have been operated in a radial topology that may be changed from …
- 238000009826 distribution 0 title abstract description 63
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Deka et al. | Estimating distribution grid topologies: A graphical learning based approach | |
| Zhao et al. | Full-scale distribution system topology identification using Markov random field | |
| Deka et al. | Topology estimation using graphical models in multi-phase power distribution grids | |
| Gao et al. | A physically inspired data-driven model for electricity theft detection with smart meter data | |
| Deka et al. | Learning topology of the power distribution grid with and without missing data | |
| Deka et al. | Structure learning in power distribution networks | |
| Park et al. | Exact topology and parameter estimation in distribution grids with minimal observability | |
| Deka et al. | Learning topology of distribution grids using only terminal node measurements | |
| Gilanifar et al. | Multi-task logistic low-ranked dirty model for fault detection in power distribution system | |
| Jia et al. | Defect prediction of relay protection systems based on LSSVM-BNDT | |
| Luo et al. | Graph convolutional network-based interpretable machine learning scheme in smart grids | |
| Shi et al. | Early anomaly detection and localisation in distribution network: A data‐driven approach | |
| Jacob et al. | Fault diagnostics in shipboard power systems using graph neural networks | |
| Amoateng et al. | Topology detection in power distribution networks: A PMU based deep learning approach | |
| Deka et al. | Structure learning and statistical estimation in distribution networks-part i | |
| Pournabi et al. | Power system transient security assessment based on deep learning considering partial observability | |
| Zhang et al. | Online power system dynamic security assessment with incomplete PMU measurements: A robust white‐box model | |
| Chanda et al. | A heterogeneous graph-based multi-task learning for fault event diagnosis in smart grid | |
| Kaplan et al. | Fault diagnosis of smart grids based on deep learning approach | |
| Aziz et al. | Advanced AI-driven techniques for fault and transient analysis in high-voltage power systems | |
| CN116990631A (en) | A multi-source data fusion microgrid group fault diagnosis method and device | |
| Momtazpour et al. | Analyzing invariants in cyber-physical systems using latent factor regression | |
| Li et al. | PPGN: Physics-preserved graph networks for real-time fault location in distribution systems with limited observation and labels | |
| Bolognani et al. | Grid topology identification via distributed statistical hypothesis testing | |
| Li et al. | Artificial intelligence for real-time topology identification in power distribution systems |