Wind O&M Trends

Investing in advanced technologies for better monitoring and maintenance

The global wind turbine operations and maintenance (O&M) market is ex­pected to reach $27.6 billion by 2027 from $17.9 billion in 2020, growing at a compound annual growth rate (CAGR) of 6.4 per cent over this period, according to a report by Research and Markets. The onshore wind turbine O&M market, one of the segments analysed in the report, is pro­jected to record a CAGR of 4.7 per cent and reach $22.3 billion by the end of the analysis period. Meanwhile, growth in the offshore wind turbine O&M segment has been readjusted to a revised CAGR of 17.6 per cent for the next seven-year period.

The Indian wind O&M market too holds immense potential, but has been facing several challenges. A key problem is the financial distress of original equipment ma­nufacturers (OEMs), which is leading to de­lays in accessing valuable data. This, al­ong with the unavailability of spare parts, impacts power generation and revenues of plant owners. Moreover, complying with the forecasting and scheduling regulations across states is cumbersome. To eliminate these bottlenecks and overdependence on OEMs, the wind industry is looking at alternatives, deploying advanced monitoring sy­stems and exploring the role of artificial intelligence (AI) in O&M.

Key O&M challenges

The foremost challenge in the wind O&M space in India pertains to OEMs. The financial distress of OEMs adversely im­pacts the O&M of wind projects. Hence, developers have started looking for alternatives. The adverse situation at the OEMs’ end leads to another challenge – the un­availability of spare parts. This lea­ds to significant revenue losses due to de­lays in finding a replacement when a critical component fails.

Amongst the states, there are significant O&M challenges in Rajasthan where developers fa­ce issues of blade tip erosion because of sandstorms, leading to wind generation losses. Also, because of this issue, the blades of wind projects in this region often have to be replaced. As wind assets age, challenges related to their updates and upgrades of supervisory control and data acquisition systems also start to surface. With respect to policies and regulations, a key concern for O&M players pertains to fo­recasting and scheduling, as these re­gu­lations are different in different states across India.

Trends in O&M research

The research paper titled, “New Tenden­ci­es in Wind Energy Operation and Mainte­nance”, authored by Á.M. Costa, J.A. Or­o­sa, D. Vergara, P. Fernández-Arias, from Ap­p­lied Sciences (MDPI), provides in­si­ghts into key trends in wind O&M resear­ch and highlights the reduction in wind O&M costs over the years.

The research paper concludes that the tr­end in research into gearboxes and transmission parts is towards increased reliability. Moreover, in terms of maintenance, broadly, research has been focusing on improving the reliability of developing pre­dictive maintenance of different tech­ni­ques. To achieve this objective, the condition-monitoring is one of the most im­portant techniques. In addition to the de­velopment of a condition monitoring system at a reasonable cost, research work sh­o­uld focus on improving the operability un­der faulty conditions. This could be done with respect to redundant systems or other solutions, even prior to the desi­gn of wind turbines. Fur­thermore, there is a trend towards increasing scientific interest in failure analysis, while interest in different techniques for the O&M of wind farms is decreasing.

According to the paper cited above, as wind power technology has somewhat re­ached technical maturity, research is now focused on studying different cases and models of failure of various equipment used in wind farms. It has highlighted the fact that bet­ween 2007 and 2018, the levellised cost of energy (LCOE) of onshore wind projects decreased by an annual rate of ar­ound 3.6 per cent. For offshore projects, it decreas­ed by an annual rate of nearly 3.4 per cent. During the same period, O&M costs of onshore wind projects decreased by an annual rate of 2.3 per cent and for offshore projects by 2.9 per cent.

Overall, during the same period, the LCOE in onshore wind projects has de­creased by 45 per cent, while in offshore projects it has decreased by 28 per cent. The O&M cost of onshore wind projects fell by 52 per cent and for offshore projects, it declined by 45 per cent.

Going forward, it is important to continue working on increasing the reliability of on­shore wind turbines and optimising ma­intenance costs. It is crucial to limit the ma­­ximum faults for offshore wind turbi­nes, since the maintenance of these wind farms is more complex, both technically and logistically, especially for large-scale corrective maintenance.

The development of more advanced algorithms for predicting wind conditions is an­other important issue in terms of reducing maintenance costs and faults in wind turbines. These issues continue to pose a big challenge for the optimisation of wind power projects, with a huge impact on en­vironmental sustainability, even though it is estimated that research work in these fields has declined.

Role of AI and the way forward

To match the actual energy generated with the predicted generation, proper O&M pr­actices need to be adopted with the support of advanced technology tools and te­chniques. Thus, a high degree of coordination and flexibility is required between di­fferent systems in the power sector value chain as well as in the O&M of individual wind power generation assets.

AI has emerged as a key enabler in this respect, offering a host of benefits such as efficient integration of intermittent wind power into the grid, enabling automation of various construction and O&M activities and better grid management. A recent wh­ite paper by the World Economic For­um titled, “Harnessing Artificial Intelligen­ce to Accelerate the Energy Transition”, produced in collaboration with Bloomberg NEF and German energy agency Deut­sche Energie Agentur (dena), discusses the need for and applications of AI in en­ergy transition and provides recommendations for the future.

According to the white paper, the most promising AI applications can be categorised into four focus areas: renewable power generation and demand forecasting, grid operation and optimisation, management of energy demand and distributed resources, and materials discovery and innovation. A variety of input data is used in AI to serve these applications in all these focus areas. From a wind energy perspective, this input data could be as given below.

Market, commodity and weather data: Su­ch data series are often collected at regular time intervals through various sour­c­es to identify patterns or outcomes. In wi­nd po­­­wer plants, electricity data and we­ather data might be useful for project operations.

Images and videos: Images and videos are used and observed to predict irregularities or outcomes. Images can be used for inspection and prediction of faults in wind turbines.

Equipment and sensor data: Sensor data from equipment with advanced communication techniques can enable accurate real-time monitoring of wind turbines and help in improving the energy output and preventing costly repairs.

O&M of wind power projects is a costly and time-consuming task and becomes even more complicated in the case of offshore wind assets. AI can help predict faul­ts before they occur so as to prevent project downtime and repairing costs. Further, with the use of sensors and re­mote monitoring systems, alarms can be raised if a po­ssible fault is diagnosed, so that timely corrective action can be taken. AI can also be an effective tool for the O&M team to keep track of maintenance schedules.

Wind O&M can also be improved through the use of AI as it can help in accurate fo­recasting of the energy output, based on data on weather, wind speeds and historical wind generation in specific conditio­ns. The wind power supply has to be met with equal demand so as to prevent generation curtailment. Thus, AI can help as­certain the power demand based on consumer data and load profiles, and help in better integration of wind power into the grid.

AI can play an important role in ensuring proper O&M of equipment and in monitoring grid performance. Further, AI can help in maintaining and ensuring grid stability and security, especially with the increasing integration of intermittent renewables such as wind power and greater adoption of electric vehicles.

The Indian wind O&M industry has to over­come twin challenges going forward. One, to resolve legacy issues in traditional O&M practices and two, to have the bandwidth to invest more in digitalisation and automation practices, including AI.

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