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

Lorenzo Colone; M. Reder; J. Tautz-Weinert; J. J. Melero; Anand Natarajan; S. J. Watson

doi:10.1016/j.egypro.2017.10.386

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

Lorenzo Colone^*, M. Reder, J. Tautz-Weinert, J. J. Melero, Anand Natarajan, S. J. Watson

^*Corresponding author for this work

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Abstract

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.

Original language	English
Journal	Energy Procedia
Volume	137
Pages (from-to)	571-578
ISSN	1876-6102
DOIs	https://doi.org/10.1016/j.egypro.2017.10.386
Publication status	Published - 2017

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@inproceedings{2e433294347f477bac6c090e525d6473,

title = "Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements",

abstract = "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.",

author = "Lorenzo Colone and M. Reder and J. Tautz-Weinert and Melero, {J. J.} and Anand Natarajan and Watson, {S. J.}",

year = "2017",

doi = "10.1016/j.egypro.2017.10.386",

language = "English",

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pages = "571--578",

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AU - Colone, Lorenzo

AU - Reder, M.

AU - Tautz-Weinert, J.

AU - Melero, J. J.

AU - Natarajan, Anand

AU - Watson, S. J.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

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DO - 10.1016/j.egypro.2017.10.386

M3 - Conference article

SN - 1876-6102

VL - 137

SP - 571

EP - 578

JO - Energy Procedia

JF - Energy Procedia

ER -

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

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