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March 15, 2022

Many countries are relying on wind energy among other renewable resources to tackle climate change and reduce carbon emissions. However, it’s a capital-intensive sector and wind turbines like any other asset require regular operations and maintenance (O&M) to prevent unplanned breakdowns and repairs. The average shelf life of a wind turbine is 20 to 25 years. This means that many onshore wind farms in Europe and the U.S. installed before the year 2000 have already arrived at the end of design (EOD) life. Wind farm operators will need to find more profitable ways to run their business, or risk decommissioning wind assets and writing off the investment.

The proliferation of new digital technologies has given an opportunity for operators to increase the useful life of wind turbines and optimize the power yield. According to WindEurope, out of 22GW of wind power that is coming to its EOD life, 18GW will be eligible for lifetime extension (LTE) projects.

The cost of inaction is high—O&M needs to become more proactive

Traditional O&M activities centered around routine operations and scheduled maintenance, an approach that relied on reactive decision making with the hope that everything was working fine. In worst case scenarios, undetected problems would result in expensive, corrective actions. In fact, O&M accounts for approximately 10 to 20% of the total cost of energy for a wind project. In addition, many wind farm owners signed expensive maintenance contracts to fill the O&M skill gap. While monitoring systems such as SCADA (supervisory control and data acquisition) have been used for a while on wind farms, what’s missing is the sophistication needed to arrive at insightful decisions during the early stages of a problem. The ability to extract information from data and interpret outcomes for early detection of failures is critical. Digital O&M has now made this possible, giving wind operators more control over turbine performance.

Utilizing the power of data with digital twins and AI

Digital technologies use data as a vehicle to tackle O&M business challenges, as opposed to using it to define those challenges. Sensing and capturing accurate raw data is a critical first step. Many windfarms that were commissioned in the last two decades are not equipped with sensors found in modern wind turbines. Unobtrusive computational sensing through radar-based acoustic signature, drone-based imagery, and other related technologies enables multi-sensor fusion for enriching data capture across different components. The next step is employing data management and transformation techniques, including cleansing and merging the data to make it fit for consumption by analytical engines.

Data engineering is just one piece of the puzzle. Data-driven digital twins of the components and its adjoining processes combined with predictive analytics will generate insights on performance to support proactive decision-making and corrective maintenance. When combined with artificial intelligence and machine learning, analytics can mine new parameters and lead indicators from historical data. This data can be tracked and analyzed using advanced algorithms to understand the current and future state of critical wind turbine components.

But analytics will be irrelevant, if the data and insights are not available to the operator, engineer or business stakeholder in the right format, in a timely manner. Many enterprises with a broader O&M vision are using ‘servitization’ by linking analytics with ERP (enterprise resource planning) systems. This ensures a process-driven approach in which information is actionable, and it triggers the right response.

Moving towards reliable, low-cost wind energy

Wind power represents 4.4% of the total generated power and is likely to increase up to 20% by 2030. With governments reducing subsidies, wind farms have to find new ways to cut costs and stay competitive. Digital technologies are the way forward. It will reduce downtime, cut O&M costs and improve the operational efficiency of wind turbines. The result is increased clean energy production at low costs.

Prasenjit Das leads the grid edge initiative within the Manufacturing and Utilities CTO group at TCS. He has an extensive experience of more than two decades in the software industry. In his current role, Prasenjit leads a team comprising of senior scientists and data engineers who are building offerings in the asset, network and trading domains of renewable energy with the help of advanced digital technologies such as artificial intelligence and machine learning.

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