Scope of AI & IoT-Based Predictive Maintenance for Wind Turbines
Abstract: Internet of Things allows applications, devices, and machines to interact with one another via a network. It defines how AI and IoT can revolutionise the maintenance strategy plan for stately giants while alleviating downtime and improving efficiency.
The wind has a huge potential to produce energy and the windmill farms are ‘Renewables-ninjas’ for current and future generations to come. According to research by IEA, offshore wind is going to become a large source of electricity in the EU in 2040. The global wind energy council stated that at the end of 2016, there were 341,320 wind turbines spinning around the world.
However, despite witnessing enormous growth in the sector, improving efficiencies and reducing costs for wind power mills are critical issues for most companies. How can you maximise the power production output? What are the challenges to optimise windmills farms’ operation and generate a high return on investment?
Challenges in Reducing Overall Downtime in Wind Turbine Operation
The managed service provider (MSP) for windmill forms could face a plethora of performance and sustainability issues, like, incorrect forecast of power output, non-scalable process management, decreasing energy revenue, limited visibility of the maintenance requirements, and many more.
So, the implementation of preventive and predictive maintenance in the wind turbine’s life cycle is required to reduce the chances of sudden component failure and the tendency for defects to fall outside of the manufacturer’s warranty period. Organisations across the ecosystem are increasingly focusing on enabling all their business units with new-age technologies.
Despite this, the wind energy sector is behind and has some catching up to do. What benefits could AI and IoT offer the wind industry?
Wind Energy and the IoT and AI: A Perfect Amalgamation
Wind farms are usually located in the isolated areas, away from populated areas, or out at sea. It is expensive to deploy a permanent maintenance department at these locations. Suppose, there is an error that resulted in a power outage and it could damage the main engine. Travelling to the site may take many hours, even days to reach the site. This is where AI and IoT could do wonder, and reduce logistic and maintenance cost.
The engineers and service technicians can gather data through IoT and can maintain crucial components like electrical generator, electronic controller, hydraulics system, cooling unit, etc. The data sent from the turbines to a control centre can identify the irregular patterns in the functionality of the machine.
Sometimes, it automatically based on preset parameters. It allows engineers to take action and resolve an issue at the earliest while reducing the requirement for human intervention.
Collecting data perpetually vouchsafes analysts to achieve transparency over conditions that are triggering failure. It empowers them to build a volume of information, needed to identify patterns. As a result, operators maintain a more accurate forecast of when their bearings will fail and why.
Using sophisticated AI mechanisms and predictive analytics, data acquisition, data persistence and data processing aspects of all the data generated continuously by a wind turbine can be determined. For the data acquisition and data processing purposes, Apache Kafka4 and Apache Spark5 can be used.
Predictive maintenance is a cost effective way to keep the overall of whole wind farms under control. The recent advancements in AI and IoT, the new generation of predictive maintenance technology can develop realistic digital models for wind turbine farm and achieve greater predictive accuracy.
Mandira Srivastava is an environment enthusiastic. She is willing to learn and exchange thoughts on green technology. She has written different kinds of technical manual and documents for all kinds of business and marketing communication verticals like blogs, user guides, newsletters, landing pages, e-books, etc. She can be reached at email@example.com