Phase angles as predictors of network dynamic security limits and further implications
| Title | Phase angles as predictors of network dynamic security limits and further implications |
| Publication Type | Conference Paper |
| Year of Publication | 2014 |
| Authors | Kaci, A., Kamwa, I., Dessaint, L.-A., Guillon, S. |
| Conference Name | PES General Meeting | Conference Exposition, 2014 IEEE |
| Date Published | July |
| Keywords | data mining, dynamic security assessment (DSA), dynamic security margins, dynamic security monitoring, energy management systems, generalized linear model, GLM, historical phase angles values, load forecast, load forecasting, Monitoring, network dynamic security limits, phasor measurement, phasor measurement unit (PMU), phasor measurement units, PMU, power system stability, power transfer, Predictive models, R-squares accuracy, Radio frequency, random forest (RF), random forest mapping, random forest models, random processes, security, Stability analysis, synchrophasor, system reliability, Wide-Area Situational Awareness (WASA) |
| Abstract | In the United States, the number of Phasor Measurement Units (PMU) will increase from 166 networked devices in 2010 to 1043 in 2014. According to the Department of Energy, they are being installed in order to "evaluate and visualize reliability margin (which describes how close the system is to the edge of its stability boundary)." However, there is still a lot of debate in academia and industry around the usefulness of phase angles as unambiguous predictors of dynamic stability. In this paper, using 4-year of actual data from Hydro-Quebec EMS, it is shown that phase angles enable satisfactory predictions of power transfer and dynamic security margins across critical interface using random forest models, with both explanation level and R-squares accuracy exceeding 99%. A generalized linear model (GLM) is next implemented to predict phase angles from day-ahead to hour-ahead time frames, using historical phase angles values and load forecast. Combining GLM based angles forecast with random forest mapping of phase angles to power transfers result in a new data-driven approach for dynamic security monitoring. |
| URL | http://ieeexplore.ieee.org/document/6939281/ |
| DOI | 10.1109/PESGM.2014.6939281 |
| Citation Key | 6939281 |
- PMU
- Wide-Area Situational Awareness (WASA)
- system reliability
- synchrophasor
- Stability analysis
- security
- random processes
- random forest models
- random forest mapping
- random forest (RF)
- Radio frequency
- R-squares accuracy
- Predictive models
- power transfer
- power system stability
- Data mining
- Phasor measurement units
- phasor measurement unit (PMU)
- phasor measurement
- network dynamic security limits
- Monitoring
- load forecasting
- load forecast
- historical phase angles values
- GLM
- generalized linear model
- energy management systems
- dynamic security monitoring
- dynamic security margins
- dynamic security assessment (DSA)