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Optimal Temperature-Based Condition Monitoring System for Wind Turbines

机译：基于最佳温度的风力涡轮机状态监测系统

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摘要

With the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.
机译：随着由于挑战性市场条件，由于挑战性的风能项目的效率越来越大，与维护规划有关的挑战正在增加。本文提出了一种基于基于数据驱动建模的风力涡轮机（WTS）的基于条件的监测系统。首先，使用量身定制的人工神经网络估计WTS密钥组分的正常条件。然后，计算从估计值的实时测量数据的偏差，指示异常条件。本文的主要贡献之一是提出用于计算安全频带的优化问题，以最大限度地提高异常情况识别的准确性。在异常条件或WTS的危险条件下，触发警报并更新了建议的风险指标。使用来自德国海上风电场的真实数据来证明模型的有效性。通过对现实世界数据的提出模型进行实验，表明拟议的风险指标与即将到来的风力涡轮机故障完全一致。

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