At present, the wind energy sector is still very reliant on humans to perform tasks such as service and maintenance. This poses potential challenges to the industry. Wages for staff are increasing, and there is a limited number of qualified professionals available. Consequently, maintaining and operating turbines to optimal levels becomes more challenging. Using the vast volumes of data created by wind turbines and digital solutions offers the opportunity to overcome these issues.
Resistance to big data within the industry
Surprisingly, however, many wind power operators seem resistant to adopting digital and remote technologies to deal with this challenge. In general, many within the industry take a reactive stance to problems rather than a proactive one. This means that an issue is addressed once it has actually become a problem instead of being preempted and dealt with at the earliest possible stage. Ultimately, to reduce the levelized cost of energy (LCOE), and for wind farms to become completely self-sufficient, operators must unleash the potential of big data.
The reason for this generally resistant attitude is that operators often believe that when they buy a new wind turbine, it will function optimally under standard factory settings. However, many operating conditions, such as location, lubrication, and set-up play a significant role in how quickly a turbine wears. This is where big data comes into play.
Unlocking the value of wind turbine data
The large volumes of information generated by wind turbines is often not used to its full potential. In many instances, companies rely on a single analyst in a remote location who may not fully understand the conditions at wind farms with fluctuating weather conditions. This is a problem because they are unfamiliar with the exact operational requirements of the site they are monitoring. By just checking that factory settings are being adhered to, they are not ensuring that turbines are set up to deal with the conditions that they face. Regardless of how skilled service and maintenance technicians are, they will likely be called out more than is necessary due to components wearing prematurely.
If data is correctly analyzed, operators are able to accurately understand when a turbine will fail and how long they can expect it to be in service for. Over years of operation, turbines accumulate vast amounts of data that can indicate where and why a component is failing. This enables maintenance to be planned accordingly. Condition-based maintenance and benchmarking rely heavily on big data.
But it isn’t just in terms of benchmarking turbine performance where big data is so useful. This wealth of information has the potential to optimize supply chain management and logistics. Operators can see how often they have needed certain components and structure a spare parts strategy around this.
A change in approach could yield significant rewards
Overall, big data could revolutionize key aspects of the wind energy industry, such as operation, maintenance, and inventory management. Nonetheless, before the sector can take the huge step forward, companies will have to change their approach and become more proactive instead of reactive. When this happens, operators are likely to achieve a better levelized cost of energy and become completely self-sufficient.