This project-based workshop introduces students to data-driven reliability analysis of renewable energy systems through hands-on experiments. Students will assemble small-scale photovoltaic, wind energy, or hybrid microgrid systems, collect real-time operational data, and inject controlled faults to extract reliability models from data and investigate the impact of faults on system performance. Student projects will include one of the following experiments:

·       Photovoltaic system under varying irradiance and shading conditions.

·       Wind energy system under changing wind conditions and turbine faults.

·       Hybrid renewable microgrids of integrated PV, wind, battery storage, and electrical loads under varying generation and demand.

Using the collected measurements, students will preprocess and analyze time-series data, detect faults and anomalies, and extract reliability models such as Fault Trees and Markov Chains. These models will be parameterized from experimental data and used to evaluate system reliability and availability.

Objectives

·       Design and conduct controlled fault injection experiments.

·       Collect, preprocess, and analyze time-series measurement data.

·       Detect faults and anomalies using data-driven methods.

·       Develop and parameterize reliability models (e.g., Fault Trees and Markov Chains) from experimental data.

·       Evaluate system reliability and availability through simulation and analysis.

·       Communicate technical findings through scientific reports and presentations.

Required Skills

·       Basic knowledge of data analysis

·       Basic programming skills (Python or equivalent)

·       Basic understanding of probability and statistics

·       Interest in renewable energy systems and reliability engineering

Registration: Please briefly state your motivation for taking this course and attach your CV and Transcript of Records.

Grading Points: 1 Written Report (30%), 2 Presentations (30%), and Implementation (40%)