DOE SETO, Photovoltaic Plant Predictive Maintenance Optimization under Uncertainties Using Probabilistic Information Fusion
Student Visit to the solar PV plant in ArizonaOverall Information
This project uses artificial intelligence and machine learning methods to develop algorithms that will optimize operation and maintenance of photovoltaic (PV) power plants by detecting and classifying anomalies, predicting failures, and scheduling maintenance activities. Predictive maintenance is important to maintain the long-term financial performance of solar PV plants and reduce downtime. Real-time monitoring data such as power output, temperature, and weather information can be used to identify the common fault class patterns using a hierarchical generative model and probabilistic information fusion framework in the sensor level and system level. This project will use the power plant operated at Arizona State University and Arizona Public Service as the case study to demonstrate the proposed technology for predictive maintenance.
Hao Yan
Associate Professor in School of Computing and Augmented Intelligence
My research develops machine learning methods for high-dimensional, spatio-temporal, and sequential data, including reinforcement learning, generative models, anomaly detection, and causal and tensor-based learning.