Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements

被引：7
|
作者：
Colone, L. [1 ]
Reder, M. [2 ]
Tautz-Weinert, J. [3 ]
Melero, J. J. [2 ]
Natarajan, A. [1 ]
Watson, S. J. [3 ]
机构：
[1] Tech Univ Denmark, Frederiksborgvej 4000, Roskilde, Denmark
[2] CIRCE Univ Zaragoza, C Mariano Esquillor 15, Zaragoza 50018, Spain
[3] CREST Loughborough Univ, Holywell Pk, Loughborough LE11 3TU, Leics, England
来源：
14TH DEEP SEA OFFSHORE WIND R&D CONFERENCE, EERA DEEPWIND'2017 | 2017年 / 137卷
基金：
英国工程与自然科学研究理事会;
关键词：
Wind Turbine; Condition Monitoring; SCADA; Data Optimisation; Data Mining; Operation and Maintenance (O&M); COST-ANALYSIS;
D O I：
10.1016/j.egypro.2017.10.386
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Operation and Maintenance (O&M) is an important cost driver of modern wind turbines. Condition monitoring (CM) allows the implementation of predictive O&M strategies helping to reduce costs. In this work a novel approach for wind turbine condition monitoring is proposed focusing on synergistic effects of coexisting sensing technologies. The main objective is to understand the predictability of signals using information from other measurements recorded at different locations of the turbine. The approach is based on a multi-step procedure to pre-process data, train a set of conversion functions and evaluate their performance. A subsequent sensitivity analysis measuring the impact of the input variables on the predicted response reveals hidden relationships between signals. The concept feasibility is tested in a case study using Supervisory Control And Data Acquisition (SCADA) data from an offshore turbine. (C) 2017 The Authors. Published by Elsevier Ltd.
引用
收藏
页码：571 / 578
页数：8
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