TY - GEN
T1 - Detection of impending ramp for improved wind farm power forecasting
AU - Zhao, Jie
AU - Chen, Xiaomei
AU - He, Miao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/6
Y1 - 2019/3/6
N2 - 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.
AB - 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.
KW - Ramp events
KW - short-term wind power forecasting
KW - wind farm
UR - http://www.scopus.com/inward/record.url?scp=85063883199&partnerID=8YFLogxK
U2 - 10.1109/TPEC.2019.8662203
DO - 10.1109/TPEC.2019.8662203
M3 - Conference contribution
AN - SCOPUS:85063883199
T3 - 2019 IEEE Texas Power and Energy Conference, TPEC 2019
BT - 2019 IEEE Texas Power and Energy Conference, TPEC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Texas Power and Energy Conference, TPEC 2019
Y2 - 7 February 2019 through 8 February 2019
ER -