Data analysis method for prescriptive O&M strategies of photovoltaic power plants
APOLLO develops next-generation data analysis methods for automated operation and prescriptive maintenance of PV power plants. Using an innovative approach and the early involvement of relevant stakeholders, APOLLO develops methods that automatically identify, classify and evaluate potential problems based on error patterns and then provide recommendations for action. The rapid implementation of these new digital solutions is ensured by a close cooperation between business and science as well as by the development of competence within the companies involved in the fields of digitalization.
Project goals
The primary objective of APOLLO is to develop methods based on statistical modelling and machine learning in order to improve data preparation and processing for more economical operation and prescriptive maintenance of PV systems, and power plants - and subsequently for the digitization of the energy system - to enable.
The specific project objectives can be summarised as follows:
- Development of efficient data analysis methods for prescriptive O&M strategies of PV power plants.
- Optimization of the cost/benefit ratio of the O&M portfolio for PV power plants.
- Development of simple and cost-efficient analysis methods that do not require additional, expensive monitoring electronics.
- Use of fault analysis and classification for more targeted and efficient O&M strategies of PV power plants
Innovation and targeted results
In APOLLO, the focus is on the analysis of PV power plants and their overall performance: project innovations concern
- Connection of field measurements and data-driven remote maintenance
- Improved digital evaluation algorithms for plant monitoring
- Early detection of relevant errors in operation
- Increase in the reliability of power plants
- Optimized operation and maintenance through digitalization
Role of AIT
The AIT plays a central role in the FFG-funded APOLLO project, which aims to optimize and digitize the operation and maintenance of photovoltaic power plants. Tasks include digitalization, implementing machine learning, developing new algorithms for performance and error evaluation.
Project results
In the project, results were obtained on each of the innovations mentioned. In addition, results, tools and methods have been developed for the following areas:
- Automatic analyses for operations management
- New system and methods for classifying errors
- GIS and drone-based 3D reconstruction of the plants
Funding
This project is funded by the FFG within the funding programme “Fast Track Digital”.
