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Components|Energy|Financial|Measurement|PROJECT|Projects|Service|Solar|System|Systems|Technology|Testing|Contracting|Equipment|Maintenance|Operations
components|energy|financial|measurement|project|projects|service|solar|system|systems|technology|testing|contracting|equipment|maintenance|operations

Risk reduction and decision support for financial investments in solar PV projects

13th May 2022

     

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The CSIR Energy Centre commissioned a quality and reliability laboratory for testing solar photovoltaic (PV) module reliability in accelerated timeframes to support local manufacturers, project developers, system owners, and lenders in the PV value chain.

The lab features accelerated stress test and measurement equipment purchased with funds provided by the Technology Localisation and Implementation Unit (TLIU) and the CSIR. The accelerated stress tests are designed to provoke real-world failure mechanisms in low quality PV modules within a few months in the lab rather than over a few years in the field.

The test protocols reduce the likelihood of costly PV module replacements in the future when these tests are specified during the procurement and contracting because the suppliers will send only the best PV modules for the project. The lab conducts research and testing for domestic and imported PV module manufacturers, module mounting hardware manufacturers, operations and maintenance service providers, and PV plant developers.

The test protocols help reduce risk for PV investments by ensuring the PV system components are held to the highest standards based on international testing and acceptance protocols. 

The CSIR Energy Centre also conducts research and development on PV systems in real-time based on several outdoor systems. The CSIR hosts 2 MW of the grid-connected PV for self-consumption, enabling research on the actual performance of commercial-scale PV systems installed on singe axis trackers, dual-axis trackers, and fixed tilt rooftop system.

The research topics include PV plant performance monitoring and optimisation, outdoor performance monitoring of multiple PV technologies, system performance model verification, pre-feasibility studies, and defect detection and classification in electroluminescence images using the latest in machine learning architecture. 

Edited by Creamer Media Reporter

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