Detection of impending ramp for improved wind farm power forecasting

Jie Zhao, Xiaomei Chen, Miao He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Detection of impending front-induced ramp events is studied as a new class of change detection problem - change detection for multiple time series with spatial dependency. A critical step to ramp event detection is to capture the spatial dependency between neighbor turbines' power output. To this end, a graphical model is utilized to model the dependency of turbine-level ramp events. Then, change point detection is carried out for the time series of individual turbines' power output, by using the belief from neighbor turbines in the dependency graph. Once an impending ramp is detected, the magnitude of ramp is then forecasted by using current measurement data. A key observation is that due to the movement of front, the best predictors for individual turbines' power output vary across three different regions of the wind farm. With this insight, different predictive models are adopted for forecasting power output from each region. Through numerical experiments, the proposed detection-based wind power forecasting method is proven to outperform conventional methods for wind power ramps.

Original languageEnglish
Title of host publication2019 IEEE Texas Power and Energy Conference, TPEC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538692844
DOIs
StatePublished - Mar 6 2019
Event2019 IEEE Texas Power and Energy Conference, TPEC 2019 - College Station, United States
Duration: Feb 7 2019Feb 8 2019

Publication series

Name2019 IEEE Texas Power and Energy Conference, TPEC 2019

Conference

Conference2019 IEEE Texas Power and Energy Conference, TPEC 2019
CountryUnited States
CityCollege Station
Period02/7/1902/8/19

Keywords

  • Ramp events
  • short-term wind power forecasting
  • wind farm

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