1C4PV is an industry-driven demonstration project that will contribute to achieve the reduction of the total costs of photovoltaic (PV) generation and the Levelized Cost of Electricity (LCoE), providing advanced and automated functions for data analysis for the early fault diagnosis (detection and classification) and maintenance planning for PV assets. Those functions will be part of a cloud platform that collects data from Supervisory Control and Data Acquisition (SCADA), Internet of Things (IoT), sensors and information systems, such as maintenance management or inspections and facilitates the decision making for Operations and Maintenance (O&M). Machine learning algorithms and other artificial intelligence techniques are the black-bone of early fault diagnosis.
As a result, 1C4PV will face main challenges of the PV industry (LCoE reduction) through the optimization of O&M processes in PV plants while maximizing production from the available resource. The main KPIs to measure the project success are O&M costs reduction by 10% and the increase of the Performance Ratio (PR) indicator by 3-4%. To achieve the project's objectives, the partners will bring on board their extensive expertise in the field, starting the project from a leading position. The working plan includes the standardization of a prototype solution, the testing phase in laboratory and the validation in real operation. The plan covers the following technical actions: analyze technologies and application, modeling PV plants and data characterization for multi topologies, algorithms development for problem diagnosis and maintenance decision support systems.
The project consortium is well balanced with three actors covering the value chain: a specialist in information systems for monitoring and control of renewables (Isotrol), an O&M company for solar plants (Tegnatia) and a Research Centre for PV generation optimization (Foss, University of Cyprus).