Today's wind farms produce massive oceans of data brimming with possibilities, whereas wind turbines were previously viewed as dumb monoliths inspected periodically by on-site technicians.
FREMONT, CA: Renewable energy is getting traction quicker than ever before. Due to digitization and data analytics advancements, wind turbines may be depended on as a faithful source of electricity. The wind energy business has traditionally been slow to take on new digital technologies.
Nevertheless, in a post-subsidy market, the imperative for efficiency advancement is stronger than earlier. Wind turbines are gradually equipped with digital sensors that record priceless data on the turbine's performance. Unfortunately, wind farm owners and operators cannot completely exploit this data's potential.
This, still, is changing. Utilizing the huge amounts of data generated by pre-installed sensors for uncovering and monitoring health indicators like drive train vibration, O&M experts can collaborate with data analysts to train algorithms to see problems in wind turbines before their occurrence. When paired with the proficiency of O&M professionals, these algorithms may be trained to diagnose problems with near 99% accuracy.
It is then conceivable to have a complete site of digitalized wind turbines connected to the Internet of Things (IoT) and one another that provide performance and health data to remotely located operations and maintenance teams.
Using AI, data analysis may be automated, enabling the examination of massive data sets beyond a technical team's capability to evaluate promptly. Engineering expertise is crucial to the process because it guarantees that the algorithms are properly trained to recognize which trends are important and what each trend signature signifies for the performance and reliability of a wind turbine.
Cloud computing further eradicates the economic barriers to this modern research by allowing wind farm owner-operators to process huge data and easily access it, letting the wind sector access a scale of computer resources previously impossible. This allows in-house teams to perform predictive maintenance at a low cost while still using engineering skills as necessary.
