Title

Geographic information systems visualization of wind farm operational data to inform maintenance and planning discussions

Document Type

Article

Publication Date

2-1-2021

Publication Title

Wind Engineering

Volume

45

Issue

1

First Page

3

Keywords

data analysis, data visualization, maintenance, performance, prognostic, simplified technique, Wind turbine

Last Page

10

Abstract

As utility scale wind farms age, maintenance and contingency planning become increasingly important. Decisions about when and how to repair or replace major turbine components can critically influence profitability. Condition monitoring and prognostic reliability modelling are sometimes used to support these decision-making processes. These often resource intensive, sophisticated techniques are frequently administered by third parties and can be black boxes to wind farm stakeholders. Early experience from the YR21 Investment Decision Support Program has highlighted the importance of broad engagement across wind farm teams in maintenance and planning discussions. The utilization of geographic information systems to illustrate data trends across wind farms proved to be a valuable tool in fostering fundamental understanding of an operation’s signature performance characteristics. This graphical representation of the farm provides a useful visualization of the operation’s best and worst performers in terms of power produced, wind speeds experienced, total revolutions, or highest gear box temperature. These transparent representations of the data represent valuable starting points for discussion of performance or potential maintenance issues across farms. In some cases, it can reveal unexpected trends that may raise bigger questions about how the farm is operating in general. Finally, these simple figures can serve as complementary inputs to larger, more complex data-driven decision systems. Geographic information system plots are presented for three wind farms to demonstrate the potential utility in simple, transparent, and accessible data visualization.

DOI

10.1177/0309524X19862757

ISSN

0309524X

E-ISSN

2048402X

Link to Full Text

Share

COinS