WIND POWER
Wind Power and Artificial Intelligence
A sustainable partnership by Sandeep Gupta
L
ast November 13th, the COP26 conference in Glasgow, Scotland concluded with the 197 participant nations (just barely) agreeing to continue pursuing the commitments that emerged from the 2015 Paris agreement – specifically, the goal of limiting global climate change induced temperature increases to 1.50C. A significant driver of achieving that ambitious goal will be dramatic reductions in fossil fuel usage and associated increases in the implementation of renewable power sources; in particular, wind power, both onshore and off. And while the pace of wind power installations has risen dramatically in recent years, with a concomitant fall in perMW generation costs, the challenge of keeping wind turbines operating efficiently with minimal O&M spend throughout their asset lifetimes, is never-ending. Artificial intelligence (AI) and machine learning (ML) technologies are uniquely poised to provide the extra “lift” that these expensive and complex systems need to deliver on the promise of renewable energy. In 2020, wind power supplied nearly 1600 TWh of electricity, (about 5 percent of the world’s total). Despite wind’s recent expansion, numerous factors impede an even faster growth rate, including fossil fuel subsidies, manufacturing supply chain issues, and the politics of not-in-my-backyard (NIMBY) syndrome. But there is an equally daunting challenge to that of installing new turbines, and that is efficiently maintaining and operating the 350,000 or so that are already up and running. While the technology is itself relatively mature and well understood, additional difficulties arise simply due to the environments in which turbines operate. Whether in the vast plains of west Texas or off the coast of England in the North Sea, equipment is subjected to hostile settings; offshore wind, which is difficult to access with maintenance personnel and equipment, emphasizes the challenge of clearly understanding how these complex systems are operating, and knowing (as far in advance as possible) when malfunctions are likely to occur.
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