Yan et al., 2018 - Google Patents
Deep learning based total transfer capability calculation modelYan et al., 2018
- Document ID
- 9675817582442686883
- Author
- Yan J
- Li C
- Liu Y
- Publication year
- Publication venue
- 2018 International Conference on Power System Technology (POWERCON)
External Links
Snippet
A total transfer capability (TTC) calculation model based on stacked denoising autoencoder (SDAE) is proposed in this paper, considering static security, static voltage stability and transient stability constraints. The TTC calculation model consists of feature pre-screening …
- 238000004364 calculation method 0 title abstract description 33
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/70—Systems integrating technologies related to power network operation and communication or information technologies mediating in the improvement of the carbon footprint of electrical power generation, transmission or distribution, i.e. smart grids as enabling technology in the energy generation sector not used, see subgroups
- Y02E60/72—Systems characterised by the monitored, controlled or operated power network elements or equipments not used, see subgroups
- Y02E60/723—Systems characterised by the monitored, controlled or operated power network elements or equipments not used, see subgroups the elements or equipments being or involving electric power substations
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/70—Systems integrating technologies related to power network operation and communication or information technologies mediating in the improvement of the carbon footprint of electrical power generation, transmission or distribution, i.e. smart grids as enabling technology in the energy generation sector not used, see subgroups
- Y02E60/76—Computer aided design [CAD]; Simulation; Modelling
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Communication or information technology specific aspects supporting electrical power generation, transmission, distribution or end-user application management
- Y04S40/20—Information technology specific aspects
- Y04S40/22—Computer aided design [CAD]; Simulation; Modelling
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/54—Management of operational aspects, e.g. planning, load or production forecast, maintenance, construction, extension
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhu et al. | Hierarchical deep learning machine for power system online transient stability prediction | |
Luo et al. | Data-driven short-term voltage stability assessment based on spatial-temporal graph convolutional network | |
Zhu et al. | Intelligent short-term voltage stability assessment via spatial attention rectified RNN learning | |
Wu et al. | Improved deep belief network and model interpretation method for power system transient stability assessment | |
Wang et al. | Power system transient stability assessment based on big data and the core vector machine | |
Hou et al. | Sparse oblique decision tree for power system security rules extraction and embedding | |
Yan et al. | Deep learning based total transfer capability calculation model | |
Liu et al. | Data-driven transient stability assessment model considering network topology changes via mahalanobis kernel regression and ensemble learning | |
CN104502795A (en) | Intelligent fault diagnosis method suitable for microgrid | |
CN105354643A (en) | Risk prediction evaluation method for wind power grid integration | |
Wang et al. | Wind farm dynamic equivalent modeling method for power system probabilistic stability assessment | |
Zhang et al. | Online power system dynamic security assessment with incomplete PMU measurements: A robust white‐box model | |
Wang et al. | Data-driven transient stability assessment using sparse PMU sampling and online self-check function | |
Ren et al. | A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements | |
Bhuiyan et al. | A deep learning through DBN enabled transmission line fault transient classification framework for multimachine microgrid systems | |
Hong et al. | Deep‐belief‐Networks based fault classification in power distribution networks | |
Wang et al. | SVM based imbalanced correction method for Power Systems Transient stability evaluation | |
Cao et al. | A Review of Data‐Driven Short‐Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges | |
Wang et al. | Cluster division in wind farm through ensemble modelling | |
Ramirez-Gonzalez et al. | Power system inertia estimation using a residual neural network based approach | |
Liu et al. | A Complex Fault Diagnostic Approach of Active Distribution Network Based on SBS‐SFS Optimized Multi‐SVM | |
Zhao et al. | A fast and accurate transient stability assessment method based on deep learning: Wecc case study | |
Bi et al. | Explainable Artificial Intelligence for Power System Security Assessment: A Case Study on Short-Term Voltage Stability | |
Samanta et al. | Prediction of Cryptocurrency Mining Load Tripping Through Learning-Based Fault Classification | |
Wehenkel | A statistical approach to the identification of electrical regions in power systems |