Artificial intelligence (AI) has the potential to revolutionise the wind industry globally, making it more efficient, reliable and cost-effective. AI can change the way wind turbines are designed, operated and maintained by enabling them to learn and adapt to new situations without explicitly programming. In addition, AI can enhance the efficiency of wind turbines by optimising their operations and maintenance (O&M), reducing downtime and improving energy prediction and production. It can also help reduce wind energy expenses by identifying areas for improvement in turbine design and operation. The role of AI and predictive maintenance in the wind O&M industry is becoming increasingly important as wind energy continues to gain significance as a source of electricity generation. While the use of AI in the wind industry is still in its early stages, with wind turbines becoming more advanced and complex, leveraging predictive maintenance and AI is essential to optimise performance and reduce costs.
Use of AI in predictive maintenance
Predictive maintenance is a proactive approach that uses data analysis to predict potential equipment failures before they occur. This approach helps minimise downtime, reduce maintenance costs and improve overall operational efficiency. Wind power companies can leverage AI technology to analyse the vast amounts of data generated by wind turbines and predict potential issues. AI algorithms can analyse data from sensors, weather forecasts and historical maintenance records to predict when maintenance will be required. By identifying potential issues at an early stage, maintenance teams can address them before they cause downtime or damage to the turbines.
AI technology can also help optimise wind turbine performance and minimise downtime by analysing data to determine the best operational parameters. Another critical application of AI in wind power O&M is fault detection. AI can analyse data from wind turbines and detect faults or anomalies that may indicate an impending failure. This allows maintenance teams to respond quickly and proactively to address potential issues, thus reducing downtime and repair costs. Furthermore, AI can be used to perform autonomous inspections of wind turbines, using drones or other remote devices equipped with cameras and sensors. This can reduce the need for human inspections, improve safety, and reduce costs. Some of the applications of AI in predictive maintenance are:
Wind resource evaluation: The most important component of wind farm assessment is site identification, and the project’s economic viability depends heavily on it. In order to maximise the amount of energy produced by a certain wind farm, AI can be used to analyse wind data and optimise the positioning and operation of wind turbines.
Grid connectivity and energy forecasting: One of the most significant issues associated with the limited penetration of wind power in many national grids is its unpredictable nature, which makes it difficult to become a part of the energy mix. AI can forecast the wind power output using previous weather and energy data, allowing utilities to better anticipate energy demand and improve system stability.
Condition monitoring: AI can be used to monitor the condition of wind turbines, including the condition of individual components, such as gearboxes and bearings. It can help detect early signs of wear and tear, allowing for repairs or replacements to be scheduled before the component fails.
Risk management: AI can be used to manage risk in wind O&M. By analysing data on equipment failures, maintenance histories and other factors, AI can help identify potential risks and suggest strategies to mitigate them.
Resource management: Resource planning and personnel management can be optimised during the erection and commissioning of major wind projects, which often involve the deployment of heavy labourers working continuous shifts, resulting in fatal human errors. AI can optimise and manage resource utilisation, ensuring that the health and safety executive standards are upheld at the site and factory.
Smart component interface: Integrating AI with exhaustive specification sheets of various mechanical, electrical and electronic components used in wind turbines can resolve the puzzle of integrating components from different brands to achieve optimal energy output from a wind turbine while minimising costs.
Future outlook
As technology advances, more AI applications are expected to emerge in the wind industry. By leveraging AI, the wind power industry can continue to grow and become an increasingly important source of renewable energy.
By Anusshka Duggal
