Artificial intelligence (AI)-enabled predictive maintenance is now becoming a key component in the pursuit of enhanced reliability and cost-effectiveness in the power industry, and as it continues to evolve, the technique is increasingly emerging as a pivotal driver of innovation and efficiency, according to a study from GlobalData.
The report, Predictive maintenance in power: Strategic intelligence, sets out to provide industry insight on how predictive maintenance drives proactive maintenance strategy and can deliver efficient power generation.
Its topline finding is that AI has become a crucial innovation in the predictive maintenance of electrical infrastructure, set to revolutionise operations in power plants by improving the predictability of equipment upkeep, optimising the allocation of resources and enhancing overall plant efficiency. Global Data believes it has the potential to decrease maintenance expenses by as much as 30% and boost equipment availability by 20%.
By combining data analytics, machine learning and real-time monitoring, utilities can now predict the future condition of their equipment more accurately, said the report.
Rehaan Shiledar, power analyst at GlobalData, said the reasons for this boost are clear. “The recent technological trends, including digital twin technology, the internet of things (IoT) and edge computing, are increasingly being leveraged in predictive maintenance,” he said. “These advancements are proving instrumental in enhancing the accuracy and efficiency of maintenance strategies across the power industry.
“Wind turbines and solar panels are frequently situated in remote or harsh environments, which can make repairs both challenging and costly,” said Shiledar. “Predictive maintenance is playing a crucial role in ensuring these systems operate efficiently, thereby reducing the risk of unexpected breakdowns and the associated expenses.”
The report also highlighted the companies that were offering what it called “sophisticated” predictive maintenance solutions to the power industry. Leading the charge were GE Vernova, Siemens and Schneider Electric, while it also noted that firms 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.
GlobalData says this enhancement allows for precise data collection, facilitating improved performance and maintenance of the turbines. RWE has deployed a condition monitoring system across its network of wind turbines. Enel Green Power, in collaboration with Raptor Maps, has implemented a diagnostics software service 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. The report observed that Enel Green Power has implemented a predictive maintenance strategy to enhance the efficiency and safety of its battery systems. It has also partnered with German battery diagnostics leader Volytica Diagnostics to enhance the efficiency and safety of energy storage systems.
In terms of the other elements driving technologies in the industry, GlobalData called out the European Commission’s Horizon Europe 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.
In addition, it highlighted companies such as Montel Energy using IoT-based predictive maintenance through IoT sensors to monitor the condition of energy assets, such as transformers and converters, in real time.
Edge computing was also cited in the study as no less than revolutionising the field of predictive maintenance by enabling real-time data processing at the source of data generation. Microsoft was highlighted through its programmes to enhance traditional cloud services with edge computing through offerings such as Azure IoT Edge, Azure Stack Edge and Azure Edge Zones. ABB delivers edge computing-based condition monitoring services, integrating real-time analytics and automation into industrial workflows.
“As the power market continues to evolve, predictive maintenance emerges as a pivotal driver of innovation and efficiency,” Shiledar concluded in the report. “It not only bolsters the industry’s shift toward digitisation 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.”
