A digital twin solution for floating offshore wind turbines validated using a full-scale prototype

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
E. Branlard, J. Jonkman, Cameron Brown, Jiatian Zhang
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引用次数: 1

Abstract

Abstract. In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The digital twin is validated using measurement data from the full-scale TetraSpar prototype. We focus on the estimation of the aerodynamic loads, wind speed, and section loads along the tower, with the aim of estimating the fatigue lifetime of the tower. Our digital twin solution integrates (1) a Kalman filter to estimate the structural states based on a linear model of the structure and measurements from the turbine, (2) an aerodynamic estimator, and (3) a physics-based virtual sensing procedure to obtain the loads along the tower. The digital twin relies on a set of measurements that are expected to be available on any existing wind turbine (power, pitch, rotor speed, and tower acceleration) and motion sensors that are likely to be standard measurements for a floating platform (inclinometers and GPS sensors). We explore two different pathways to obtain physics-based models: a suite of dedicated Python tools implemented as part of this work and the OpenFAST linearization feature. In our final version of the digital twin, we use components from both approaches. We perform different numerical experiments to verify the individual models of the digital twin. In this simulation realm, we obtain estimated damage equivalent loads of the tower fore–aft bending moment with an accuracy of approximately 5 % to 10 %. When comparing the digital twin estimations with the measurements from the TetraSpar prototype, the errors increased to 10 %–15 % on average. Overall, the accuracy of the results is promising and demonstrates the possibility of using digital twin solutions to estimate fatigue loads on floating offshore wind turbines. A natural continuation of this work would be to implement the monitoring and diagnostics aspect of the digital twin to inform operation and maintenance decisions. The digital twin solution is provided with examples as part of an open-source repository.
利用全尺寸原型验证浮式海上风力涡轮机的数字孪生解决方案
摘要在这项工作中,我们实施、验证了基于物理的数字孪生解决方案,并将其应用于浮式海上风力涡轮机。利用全尺寸 TetraSpar 原型机的测量数据对数字孪生方案进行了验证。我们重点估算了塔架沿线的空气动力载荷、风速和截面载荷,目的是估算塔架的疲劳寿命。我们的数字孪生解决方案集成了:(1)卡尔曼滤波器,用于根据结构的线性模型和涡轮机的测量结果估算结构状态;(2)空气动力估算器;(3)基于物理的虚拟传感程序,用于获取塔架沿线的载荷。数字孪生依赖于现有风力涡轮机上的一系列测量数据(功率、变桨、转子速度和塔架加速度),以及可能成为浮动平台标准测量数据的运动传感器(倾角仪和 GPS 传感器)。我们探索了两种不同的途径来获得基于物理的模型:一套专门的 Python 工具和 OpenFAST 线性化功能。在数字孪生的最终版本中,我们使用了这两种方法的组件。我们进行了不同的数值实验来验证数字孪生的各个模型。在这一模拟领域中,我们获得了塔架前后弯矩的损坏等效载荷估算值,精确度约为 5 % 到 10 %。在将数字孪生估算结果与 TetraSpar 原型的测量结果进行比较时,误差平均增加到 10 %-15 %。总之,结果的准确性很有希望,并证明了使用数字孪生解决方案估算浮式海上风力涡轮机疲劳载荷的可能性。这项工作的自然延续是实施数字孪生的监测和诊断方面,为运行和维护决策提供信息。数字孪生解决方案作为开放源代码库的一部分提供了示例。
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来源期刊
Wind Energy Science
Wind Energy Science GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
6.90
自引率
27.50%
发文量
115
审稿时长
28 weeks
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