Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, Jan Helsen
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引用次数: 2

Abstract

Abstract. Condition monitoring and failure prediction for wind turbines currently comprise a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the normal behavior modeling framework. The first part of the paper consists of an in-depth overview of the current state of the art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, six demonstration experiments are designed. The first five experiments test different techniques for the modeling of normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The selection of the tested techniques is driven by requirements from industrial partners, e.g., a limited number of training data and low training and maintenance costs of the models. The paper concludes with several directions for future work.
基于scada的风力涡轮机状态监测的正常行为建模方法概述,并通过运行风电场的数据进行了演示
摘要风力发电机组的状态监测与故障预测是目前研究的热点。这是因为,由于向可再生能源生产的过渡,风能领域的投资大幅增加。本文回顾并实现了利用SCADA数据和正常行为建模框架进行风力涡轮机状态监测的最新研究中的几种技术。本文的第一部分包括对当前技术状况的深入概述。在第二部分,从概述中实施了几种技术,并使用来自五个运行风电场的数据(SCADA和故障数据)进行了比较。为此,设计了六个示范实验。前五个实验测试了正常行为建模的不同技术。第六项实验比较了几种可用于识别预测误差中异常模式的技术。测试技术的选择是由工业合作伙伴的需求驱动的,例如,有限数量的培训数据和模型的低培训和维护成本。最后,提出了今后工作的几个方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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