Deep optimization of model predictive control performance for wind turbine yaw system based on intelligent fuzzy deduction

Because wind direction has the trait of time varying, yaw is a common state of wind turbine (WT). To improve the energy capture and reduce the yaw actuator usage time, a Model Predictive Control (MPC) method based on the Fuzzy Deduction Weight Coefficient Evaluator (FDWE) is proposed. Specifically,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Expert systems with applications Jg. 221; S. 119705
Hauptverfasser: Song, Dongran, Li, Ziqun, Deng, Xiaofei, Dong, Mi, Huang, Lingxiang, Yang, Jian, Su, Mei, Joo, Younghoon
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.07.2023
Schlagworte:
AGA-MOPSO
Fuzzy rule optimization
Membership function optimization
MPC
Wind turbine
Yaw system
MPC
Membership function optimization
Fuzzy rule optimization
AGA-MOPSO
Wind turbine
Yaw system
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Because wind direction has the trait of time varying, yaw is a common state of wind turbine (WT). To improve the energy capture and reduce the yaw actuator usage time, a Model Predictive Control (MPC) method based on the Fuzzy Deduction Weight Coefficient Evaluator (FDWE) is proposed. Specifically, in view of the two contradictory control objectives of energy capture loss ratio and yaw actuator usage ratio in MPC, FDWE is designed to dynamically adjusts the weight coefficient connecting the two objectives according to the predicted wind direction. On this basis, to fully exert the advantage of FDWE, the fuzzy rule and membership function (MF) are tried to be simultaneously optimized. For this complex optimization problem involving different types of multiple design variables, three different solving strategies are proposed: fuzzy rule-MF ordered optimization, mixed integer optimization, and association optimization. Finally, an improved Adaptive Grid Algorithm Multi-Objective Particle Swarm Optimization is presented to solve the formulated optimization problems. The results indicate that the optimized FDWE-MPC using the three strategies not only improves the energy capture of the WT, but also reduces the use of yaw actuator compared to the baseline MPC. Consequently, the proposed method is promising in reducing the production cost for the WTs.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.119705