Open Access

ARTICLE

Adaptive Load Control Model for Wind Turbines under Cold Front Conditions

Zhixiang Zhang¹, Chao Luo², Chen Zhang^1,*, Zheng Li¹, Yihua Zhu², Xu Cai¹

1 School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
2 Electric Power Research Institute, China Southern Power Grid, Guangzhou, 510663, China

* Corresponding Author: Chen Zhang. Email:

(This article belongs to the Special Issue: Trends of Offshore Wind Technologies: Support Structure Design, Health monitoring, HVDC transmission, Control and Optimization)

Energy Engineering 2026, 123(4), 19 https://doi.org/10.32604/ee.2025.072678

Received 01 September 2025; Accepted 27 October 2025; Issue published 27 March 2026

Abstract

Fatigue loads on wind turbines are critical factors that significantly influence operational lifespan and reliability. The passive yaw control of wind turbines often fails to capture the dynamic gradient changes of wind speed and direction in the wind field, leading to an increased risk of load overload, severely affecting operational lifespan and reducing power generation efficiency. This impact is even more pronounced during the passage of a cold front. To address this issue, this paper proposes an independent variable-pitch control method that optimizes predictions by utilizing the spatiotemporal relationship between pre-observed cold front patterns and their dynamic propagation. First, a cold front and cold front propagation model suitable for engineering applications was derived. And a non-uniform inflow load model of turbine is established, which, combined with tower vibration response and rotor dynamic loads, accurately simulates the force distribution under complex inflow conditions. Subsequently, a pre-observation-based active cyclic pitch control method is presented, dynamically computing optimal pitch angle sequences by predicting wind field trends. This method eliminates the need for iterative optimization algorithms and reduces control latency to achieve proactive load management. Simulation verification shows that the proposed control strategy can effectively reduce key structural loads and increase power generation without relying on complex optimization algorithms. This method provides a practical solution for improving the economic benefits and operational reliability of wind farms under special wind conditions.

---

Keywords

---