Jimenez et al., 2015 - Google Patents
Steady state signatures in the time domain for nonintrusive appliance identificationJimenez et al., 2015
View HTML- Document ID
- 9139235421201983170
- Author
- Jimenez Y
- Duarte C
- Petit J
- Meyer J
- Schegner P
- Carrillo G
- Publication year
- Publication venue
- Ingeniería e Investigación
External Links
Snippet
Smart Grid paradigm promotes advanced load monitoring applications to support demand side management and energy savings. Recently, considerable attention has been paid to Non-Intrusive Load Monitoring to estimate the individual operation and power consumption …
- 238000005070 sampling 0 abstract description 15
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/25—Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
- G01R19/2513—Arrangements for monitoring electric power systems, e.g. power lines or loads; Logging
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Morales-Velazquez et al. | Smart sensor network for power quality monitoring in electrical installations | |
Ahmadi et al. | Load decomposition at smart meters level using eigenloads approach | |
CN107025365B (en) | A kind of non-intruding load discrimination method for user side | |
US8694291B2 (en) | System and method of waveform analysis to identify and characterize power-consuming devices on electrical circuits | |
Teshome et al. | Distinctive load feature extraction based on Fryze’s time-domain power theory | |
CN111242391B (en) | Machine learning model training method and system for power load identification | |
CN109073704A (en) | Trend function for shelf depreciation | |
Guillén-García et al. | Identification of the electrical load by C-means from non-intrusive monitoring of electrical signals in non-residential buildings | |
Yin et al. | New methods exploration for harmonic source identification technologies | |
Wiczyński | Determining location of voltage fluctuation source in radial power grid | |
Luan et al. | Arc fault detection and identification via non-intrusive current disaggregation | |
Ferracuti et al. | Arc fault detection and appliances classification in AC home electrical networks using recurrence quantification plots and image analysis | |
Matthews et al. | Automatically disaggregating the total electrical load in residential buildings: a profile of the required solution | |
Jimenez et al. | Steady state signatures in the time domain for nonintrusive appliance identification | |
Luo et al. | Waveform abnormality detection method for distribution system equipment condition monitoring | |
EP4005047B1 (en) | Anomaly detection in energy systems | |
Guzel et al. | Principal components null space analysis based non-intrusive load monitoring | |
Tyler et al. | Direct, Instantanious Identification of Home Appliances | |
Bernard et al. | Combining several distinct electrical features to enhance nonintrusive load monitoring | |
Czarnek et al. | Performance comparison framework for energy disaggregation systems | |
Lima et al. | Hardware and software architecture for power quality analysis | |
US9678120B2 (en) | Electrical load identification using system line voltage | |
Adabi et al. | Seads: A modifiable platform for real time monitoring of residential appliance energy consumption | |
Gao et al. | A model for identifying the feeder-transformer relationship in distribution grids using a data-driven machine-learning algorithm | |
Zhao et al. | Series arc fault diagnosis based on variational mode decomposition and random forest |