AI-Based Predictive Maintenance Strategies for Improving the Reliability of Green Power Systems

The adoption of AI-based predictive maintenance techniques increases the dependability of green power systems. Using AI, researchers are examining a vast array of potential approaches to revamping the maintenance of renewable energy systems. The use of supervised learning algorithms enables the classification and prediction of faults using historical data from green power systems, enabling the detection and categorization of distinct failure types. In addition, sensor data collected from renewable energy sources can be utilized by unsupervised learning algorithms for anomaly detection and failure identification. Moreover, reinforcement learning algorithms optimize the scheduling and allocation of maintenance resources by utilizing the system's current state. The combination of Internet of Things (IoT) sensors and data analytics tools makes real-time monitoring of renewable power systems feasible. To better predict the likelihood of equipment failure and the remaining useful life, it is necessary to analyze sensor data for patterns, trends, and anomalies that may indicate impending failures or performance degradation. In addition, there is an increase in the use of digital twin technology, which creates digital duplicates of green energy systems to model and foresee their operation in order to facilitate condition monitoring, predictive maintenance planning, and performance enhancement. Integrating real-time sensor data with digital twin models enables continuous system monitoring and diagnostics, providing the groundwork for timely maintenance interventions. Predictive maintenance of hydraulic systems is the primary focus of this paper, which demonstrates the practical application of AI-based predictive maintenance strategies via intriguing case studies and results. The dependability and performance of renewable energy sources illustrate the effectiveness and advantages of AI-driven maintenance procedures.