Increasing the use of predictive analytics in wind farms is an essential step toward cementing wind power’s status as one of the primary energy generation forms.
FREMONT, CA: IoT and predictive analytics are among IT solutions for the renewable energy industry that have already proven to create tangible benefits. In the past, wind turbines were mere unintelligent monoliths monitored periodically through on-site technician checks, but wind farms are now generating vast oceans of data filled with potential. Most wind farm operators leverage data for remote monitoring and management. Here is how predictive analytics is redefining wind farm reliability.
Preventative maintenance depends on regularly scheduled replacements and repairs. It can be complex to offer significant savings with this model because of the recurring component costs and the resources used for routine maintenance work. Laboratory results from oil analysis can take a major amount of time from the sample collection to having results in-hand, avoiding many potential monitoring benefits. This complex process means that operations and maintenance teams do not have the opportunity to proactively handle their wind turbines' performance and thus reduce potential damage to each turbine’s components.
A large majority of the cost of offshore wind O&M is due to accessibility, illustrating the logistical hurdles of transporting technicians to offshore sites. Through digitalization and a predictive maintenance r, these costs can be considerably decreased by making turbine health data available remotely and ensuring that visits by technicians can be utilized more effectively to repair multiple components at the same time and lessen the frequency of these visits accordingly.
The wind industry must ensure that the Levelized cost stays competitive with alternative means of energy generation. This is important in light of moving to a merchant market in recent years, 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. 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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