A method to assess the performance of small wind turbines

W. G. Früh

doi:10.24084/repqj21.436

A method to assess the performance of small wind turbines

W. G. Früh^*

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

Abstract

While large wind farms are well monitored, with a wealth of data provided through a SCADA system, the only information about the behaviour of small wind turbines is often only through the metered electricity production. Given the variable electricity output, it is difficult to ascertain whether a particular electricity production in a metering period is the result of the turbine operating normally, or if a fault is resulting in a production less than possible. This paper presents a method to correlate metered electricity output from a set of 5 kW wind turbines with weather information from a weather station some distance from the turbine. That correlation will then be classified into ‘expected’ and ‘unusual’ performance using Principal Component Analysis.

Original language	English
Pages (from-to)	644-649
Number of pages	6
Journal	Renewable Energy and Power Quality Journal
Volume	21
DOIs	https://doi.org/10.24084/repqj21.436
Publication status	Published - Jul 2023
Event	21st International Conference on Renewable Energies and Power Quality 2023 - Madrid, Spain Duration: 24 May 2023 → 26 May 2023

Keywords

Performance Assessment
Principal Component Analysis
Small Wind Turbines

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.24084/repqj21.436

Cite this

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title = "A method to assess the performance of small wind turbines",

abstract = "While large wind farms are well monitored, with a wealth of data provided through a SCADA system, the only information about the behaviour of small wind turbines is often only through the metered electricity production. Given the variable electricity output, it is difficult to ascertain whether a particular electricity production in a metering period is the result of the turbine operating normally, or if a fault is resulting in a production less than possible. This paper presents a method to correlate metered electricity output from a set of 5 kW wind turbines with weather information from a weather station some distance from the turbine. That correlation will then be classified into {\textquoteleft}expected{\textquoteright} and {\textquoteleft}unusual{\textquoteright} performance using Principal Component Analysis.",

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N2 - While large wind farms are well monitored, with a wealth of data provided through a SCADA system, the only information about the behaviour of small wind turbines is often only through the metered electricity production. Given the variable electricity output, it is difficult to ascertain whether a particular electricity production in a metering period is the result of the turbine operating normally, or if a fault is resulting in a production less than possible. This paper presents a method to correlate metered electricity output from a set of 5 kW wind turbines with weather information from a weather station some distance from the turbine. That correlation will then be classified into ‘expected’ and ‘unusual’ performance using Principal Component Analysis.

AB - While large wind farms are well monitored, with a wealth of data provided through a SCADA system, the only information about the behaviour of small wind turbines is often only through the metered electricity production. Given the variable electricity output, it is difficult to ascertain whether a particular electricity production in a metering period is the result of the turbine operating normally, or if a fault is resulting in a production less than possible. This paper presents a method to correlate metered electricity output from a set of 5 kW wind turbines with weather information from a weather station some distance from the turbine. That correlation will then be classified into ‘expected’ and ‘unusual’ performance using Principal Component Analysis.

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KW - Principal Component Analysis

KW - Small Wind Turbines

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EP - 649

JO - Renewable Energy and Power Quality Journal

JF - Renewable Energy and Power Quality Journal

T2 - 21st International Conference on Renewable Energies and Power Quality 2023

Y2 - 24 May 2023 through 26 May 2023

ER -

A method to assess the performance of small wind turbines

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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