Active power dynamic interval control based on operation data mining for wind farms to improve regulation performance in AGC

Abstract

With the real‐time changes of wind speed and operating conditions, it is a challenge to fully tap the active power regulation ability and improve the control performance of automatic generation control (AGC) in a wind farm (WF). The essence of tapping the active power regulation ability is to realise the coordination and complementarity of each wind turbine's (WT's) dynamic adjustment performance (DAP). To address this, a novel data mining method is developed to derive the internal relations between WTs’ output power and pitch angle, impeller speed and pitch angle during the power adjustment process, and a unified mechanism model is established to describe DAP of WTs. Based on the discovered relationship between WTs’ DAP and its operating states, an active power distribution algorithm and a dynamic interval control method are proposed. Then, an active power dynamic interval control strategy that has been implemented using Java script in MyEclipse for WFs is further developed. The control strategy has been tested and applied in a 50 MW WF in northwest China. The preliminary results showed that the control strategy has improved the rapidity and accuracy of AGC in the WF.

[Retracted] Quantitative Evaluation and Optimal Decision‐Making Study on the Cultivation under the Environment of Humanistic Literacy Based on Advanced Data Analysis Technologies

“…By mining and exploring this latent knowledge from large amounts of unprocessed data, it is ultimately made available to managers or analysts for decision support. Data mining technique [ 28 ] is based on many disciplines, including ergonomics [ 29 ], pattern recognition, and visual analysis techniques.…”