Increasing predictive analytics in wind farms is an important step toward cementing wind power's status while one of the main energy generation forms.
FREMONT, CA: IoT and predictive analytics are a part of IT solutions for the renewable energy industry that have already created tangible benefits. In the olden days, wind turbines were mere unintelligent monoliths monitored regularly through on-site technician checks, but wind farms are now producing vast oceans of data full of potential. Most wind farm operators hold data for remote monitoring and management. Here is how predictive analytics is revising wind farm reliability.
Preventative maintenance leans on regularly scheduled replacements and repairs. It can be complex to produce significant savings with this model because of the recurring component costs and the resources used for routine maintenance work. In addition, laboratory results from oil analysis can take a significant amount of time from the sample collection to having results in hand, avoiding many potential monitoring benefits. This complex process shows that operations and maintenance teams do not have the opportunity to proactively handle their wind turbines' performance and thus lower potential damage to each turbine's components.
A bulk of offshore wind O&M costs is accessibility, depicting the logistical hurdles of transporting technicians to offshore sites. Through digitalization and predictive maintenance, these costs can be considerably decreased by making turbine health data available remotely and ensuring that technicians can utilize visits more effectively to repair multiple components simultaneously and reduce the frequency of these visits correctly.
The wind industry must confirm that the Levelized cost stays competitive with alternative means of energy generation. In recent years, this is important in light of moving to a merchant market, changing financial risk from governments to energy producers. Despite the subsidies enjoyed by conventional energy, well-executed digitalization of wind energy assets can bring the LCOE below-subsidized levels. In addition, by combining engineering expertise with the latest advancements in artificial intelligence and machine learning, wind energy firms can move to predictive maintenance and mitigate O&M costs by up to 30 percent.
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