With installed wind energy capacity now over 539 GW1, many turbines are reaching the mid to late stage of their operational life expectancy. Typically wind turbines have an operational life expectancy of 20 years, but many only operate for the duration of the Power Purchase Agreement (PPA) under which they were constructed, sometimes just 15 years. Creating a strategic plan for asset life extension is critical for efficient management of wind farms. Clir Renewables recently launched a new product feature that allows for variable scenario analysis to determine the best way to operate assets.
As turbines age, owners and operators more urgently need to know how long their turbines are going to last. They often end up setting aside increasing amounts of budget for unplanned maintenance to ensure the asset is operating in a safe and profitable manner as various components approach the end of their design life. However, an in-depth analysis of turbine data allows for the creation of strategic maintenance plans. These maintenance plans are created to prevent unexpected component failures and extend the operational life of the turbines. This is often undertaken as a one-off study assessing existing site data which can’t take into consideration true site conditions of the future. Clir Renewables has tackled the industry issue of life extension analysis by developing detection tools enabling clients to assess and quantify the potential for life extension of their assets beyond 20 years and provide guidance for their maintenance planning.
Clir’s software regularly analyses wind farm data, providing clients clear access to the information they need to make operational decisions in real time. Analytical models consider a wide range of data, including wind flow conditions, component temperatures, and vibrational data. Each data set is then compared to the turbine design and loading parameters to quantify the operational risks turbines and individual components are exposed to. The software takes design loading conditions on a certification and site basis, compares it to real-world conditions derived from turbine data to quantify what risks turbines are exposed to and to what degree the turbines are operating within themselves. This understanding gives Clir's clients actionable information to assist in long-term strategic maintenance planning and avoid unexpected component failures.
With Clir’s software, continual assessment of component health ensures owners and operators are better informed about the condition of their assets and can build operational strategies in line with their long-term plans. The data can be fed into financial models, O&M strategies and assist in deciding whether to ‘sweat’ the assets during the PPA or extend their operational life past the agreement.
“Using automated algorithms and machine learning to assess wind turbine performance is an important advancement in the optimization of the wind industry. Continually assessing multiple data points across a site or portfolio means decisions are based on up-to-date rather than historical data. Better informed decisions reduce risk which can only be positive”, said Selena Farris, Data Scientist at Clir Renewables.
1 Source GWEC: 2017 figure was 539,123MW - http://gwec.net/global-figures/graphs/
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