The authors further delve into the use of data acquisition systems, data aggregation, and performance analysis at different levels. They discuss the challenges of managing large-scale projects and the benefits of digital solutions. They also touch on the use of AI-driven data models for fault detection and the importance of data preprocessing. The article concludes by emphasizing the need for an effective approach to improve underperformance identification and reduce resolution time for device issues.
Key takeaways:
- Analysing a plant’s overall performance typically involves a top-down approach, starting from low-performing devices to project-level, inverters, and finally, the string-level. However, this process is time-consuming and relies heavily on subject-matter experts.
- Big Data Analytics can add value at any stage of O&M objectives, from analysing collected information, fault detection and diagnosis to optimisation via advanced monitoring system recommendations.
- With the solar industry aiming at achieving an ambitious global scale, modern technological innovations such as Information Modeling and Digital Twins are being adopted to drive growth and efficiency.
- Artificial Intelligence Data-Driven Models are being applied across data preprocessing, processing and postprocessing techniques to identify faults and perform string-level analysis more efficiently.