Zhang et al., 2022 - Google Patents
Model and Data Driven Machine Learning Approach for Analyzing the Vulnerability to Cascading Outages With Random Initial States in Power SystemsZhang et al., 2022
View PDF- Document ID
- 899438154695546093
- Author
- Zhang H
- Ding T
- Qi J
- Wei W
- Catalão J
- Shahidehpour M
- Publication year
- Publication venue
- IEEE Transactions on Automation Science and Engineering
External Links
Snippet
In this paper, a hybrid machine learning model is applied to evaluate the relationship between random initial states and the power system's vulnerability to cascading outages. A cascading outage simulator (CS), which uses off-line AC power flows, is proposed for …
- 238000010801 machine learning 0 title abstract description 56
Classifications
-
- 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
- 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
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- 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
-
- 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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | A hierarchical self-adaptive data-analytics method for real-time power system short-term voltage stability assessment | |
Li et al. | A hierarchical data-driven method for event-based load shedding against fault-induced delayed voltage recovery in power systems | |
US20200119556A1 (en) | Autonomous Voltage Control for Power System Using Deep Reinforcement Learning Considering N-1 Contingency | |
Liu et al. | A systematic approach for dynamic security assessment and the corresponding preventive control scheme based on decision trees | |
Wang et al. | Resilience enhancement with sequentially proactive operation strategies | |
Zhao et al. | Resilient unit commitment for day-ahead market considering probabilistic impacts of hurricanes | |
Wang et al. | On machine learning-based techniques for future sustainable and resilient energy systems | |
CN106026092B (en) | It is a kind of for the power distribution network isolated island division methods containing distributed generation resource | |
Hamilton et al. | Using SHAP values and machine learning to understand trends in the transient stability limit | |
JP7573819B2 (en) | Method and computer system for generating decision logic for a controller - Patents.com | |
Al Karim et al. | A machine learning based optimized energy dispatching scheme for restoring a hybrid microgrid | |
Xie et al. | Massively digitized power grid: opportunities and challenges of use-inspired AI | |
Ren et al. | A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements | |
CN114077809A (en) | Method and monitoring system for monitoring performance of decision logic of controller | |
Oladeji et al. | Density-based clustering and probabilistic classification for integrated transmission-distribution network security state prediction | |
Wang et al. | Scenario-based line switching for enhancing static voltage stability with uncertainty of renewables and loads | |
Shahzad | Application of machine learning for optimal wind farm location | |
Makanju et al. | Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review | |
Zhang et al. | Model and Data Driven Machine Learning Approach for Analyzing the Vulnerability to Cascading Outages With Random Initial States in Power Systems | |
Pakdel | Intelligent instability detection for islanding prediction | |
Qin et al. | An integrated situational awareness tool for resilience-driven restoration with sustainable energy resources | |
Zhang et al. | CNN‐LSTM based power grid voltage stability emergency control coordination strategy | |
Hossain et al. | Multi-agent voltage control in distribution systems using GAN-DRL-based approach | |
Kumar et al. | Power system resilience quantification and enhancement strategy for real-time operation | |
Mu et al. | An optimization method to boost the resilience of power networks with high penetration of renewable energies |