Abstract
This paper presents the comparison of three pair-merging methods to reduce the number of Gaussian mixture components used to model non-Gaussian Probabilistic Density Function (PDF) of random power system variables such as power demands, wind power outputs or other intermittent power sources. It also introduces a fine-tuning algorithm to improve the solution of the pair-merging methods to better approximate the original Gaussian mixture. A Gaussian mixture distribution with seven components is used to validate and demonstrate the algorithms.
Original language | English |
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Title of host publication | host publication |
Publisher | IEEE |
Number of pages | 7 |
Publication status | Published - Jul 2012 |
Event | IEEE PES General Meeting 2012 - Duration: 22 Jul 2012 → 26 Jul 2012 |
Conference
Conference | IEEE PES General Meeting 2012 |
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Period | 22/07/12 → 26/07/12 |
Keywords
- Gaussian mixture model
- probabilistic density function
- probabilistic power flow
- wind power