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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Data-driven condition monitoring approaches to improving power output of wind turbines
AU - Qian, Peng
AU - Ma, Xiandong
AU - Zhang, Dahai
AU - Wang, Junheng
N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2019/8/1
Y1 - 2019/8/1
N2 - This paper presents data-driven approaches to improving active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust active power output of individual turbines according to their health condition and can thus optimize the total energy output of wind farm. In the paper, extreme learning machine (ELM) algorithm and bonferroni interval are applied to estimate fault degree while analytic hierarchy process (AHP) is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reduce the fatigue loading on the faulty wind turbines.
AB - This paper presents data-driven approaches to improving active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust active power output of individual turbines according to their health condition and can thus optimize the total energy output of wind farm. In the paper, extreme learning machine (ELM) algorithm and bonferroni interval are applied to estimate fault degree while analytic hierarchy process (AHP) is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reduce the fatigue loading on the faulty wind turbines.
KW - Extreme learning machine (ELM)
KW - Health condition estimation
KW - Bonferroni interval
KW - Analytic Hierarchy Process (AHP)
KW - Condition monitoring
KW - Wind turbines
U2 - 10.1109/TIE.2018.2873519
DO - 10.1109/TIE.2018.2873519
M3 - Journal article
VL - 66
SP - 6012
EP - 6020
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
SN - 0278-0046
IS - 8
ER -