Artificial intelligence (AI)-enabled predictive maintenance stands out as a key component in the pursuit of enhanced reliability and cost-effectiveness in the power industry.
According to GlobalData, this advanced approach is revolutionising operations in power plants by improving the predictability of equipment upkeep, optimising the allocation of resources, and enhancing overall plant efficiency.
It has the potential to decrease maintenance expenses by as much as 30% and boost equipment availability by 20%.
GlobalData’s latest report, “Predictive Maintenance in Power: Strategic Intelligence,” reveals that AI has become a crucial innovation in the predictive maintenance of electrical infrastructure. By combining data analytics, machine learning, and real-time monitoring, utilities can now predict the future condition of their equipment more accurately. Companies such as GE Vernova, Siemens, and Schneider Electric are offering sophisticated predictive maintenance solutions to the power industry.
Rehaan Shiledar, Power Analyst at GlobalData, comments: “Wind turbines and solar panels are frequently situated in remote or harsh environments, which can make repairs both challenging and costly. Predictive maintenance is playing a crucial role in ensuring these systems operate efficiently, thereby reducing the risk of unexpected breakdowns and the associated expenses.”
Companies such as E.ON and Enel have equipped their turbines with sensors to monitor a variety of variables, such as temperature, vibration, wind speed, and output. This enhancement allows for precise data collection, facilitating improved performance and maintenance of the turbines. RWE has deployed condition monitoring system across its network of wind turbines. Enel Green Power, in collaboration with Raptor Maps, has implemented a diagnostics software solution to detect irregularities in photovoltaic panels.
Predictive maintenance is also gaining prominence in the realm of energy storage systems, which are pivotal in maintaining the stability, reliability, and efficiency of power grids, as well as playing a significant role in the integration of various renewable energy sources. For instance, Enel Green Power has implemented a predictive maintenance strategy to enhance the efficiency and safety of its battery systems. The company has partnered with the German battery diagnostics leader, Volytica Diagnostics, to enhance the efficiency and safety of energy storage systems.
Shiledar continues: “The recent technological trends, including digital twin technology, the Internet of Things (IoT), and edge computing, are increasingly being leveraged in predictive maintenance. These advancements are proving instrumental in enhancing the accuracy and efficiency of maintenance strategies across the power industry.”
The European Commission’s Horizon Europe program launched the TwinEU project, which aims to create a digital twin of Europe’s electricity system. In June 2024, the WindTwin initiative was granted funding by Innovate UK. The three-year project is aimed at developing digital twins to replicate wind turbines.
Companies such as Montel Energy are utilizing IoT-based predictive maintenance. It employs IoT sensors to monitor the condition of energy assets, such as transformers and converters, in real-time.
Edge computing is revolutionizing the field of predictive maintenance by enabling real-time data processing at the source of data generation. Microsoft is enhancing traditional cloud services with edge computing through solutions like Azure IoT Edge, Azure Stack Edge, and Azure Edge Zones. ABB delivers edge computing-based condition monitoring solutions, integrating real-time analytics and automation into industrial workflows.
Shiledar concludes: “As the power market continues to evolve, predictive maintenance emerges as a pivotal driver of innovation and efficiency. It not only bolsters the industry’s shift toward digitization but also aligns with the increasing focus on sustainability by aiding power companies in managing their assets in an environmentally responsible manner. The adoption of predictive maintenance is poised to rise as stakeholders in the power market acknowledge its capacity to foster operational excellence and propel business success.”
