Service Areas

Solar

We improve the reliability and performance of solar technologies. Our team supports Photovoltaic Monitoring and Diagnostics (M&D) for plants ranging from 1MW to 100MW+. Using physics-based models and advanced AI, we detect minor issues before they escalate into critical performance or safety concerns. We seamlessly integrate our solutions into your existing M&D infrastructure.

We are excited to announce that Turbine Logic has been selected by the US Department of Energy SBIR/STTR Programs to continue developing new virtual reliability software for real-time failure detection at solar plants.

SunSpotter: AI for Solar Plant Anomaly Detection

This technology leverages existing instrumentation to remotely detect common PV issues like string and tracker faults, improving reliability, reducing maintenance costs, and enhancing power production. We’re looking forward to working on this with our project partner, the Electric Power Research Institute (EPRI).

Turbine Logic will be working with US Department of Energy and EPRI to enhance development of advanced algorithms for solar plant monitoring and diagnostics.

Our project is awarded through the Solar Energy Technologies Office (SETO) Fiscal Year 2019 Funding Program in Photovoltaics Research and Development area.

ELECTRIC POWER RESEARCH INSTITUTE

Project Name: Automating Detection and Diagnosis of Faults, Failures, and Underperformance in PV Plants
Location: Palo Alto, CA
DOE Award Amount: $2 million
Cost Share: $500,000
Project Summary: Using machine learning and developing algorithms, this project team will identify reasons for unplanned maintenance events at utility-scale solar photovoltaic (PV) plants and differentiate them from power fluctuations due to causes that do not require on-site maintenance, like weather or module degradation. By analyzing the continuous energy-production data stream coming from utility-scale PV arrays, this technology can eliminate false alarms that are sent to PV system owners and operations and maintenance firms. This would decrease the labor required to review underperformance, lower the levelized cost of PV electricity, and increase energy output.

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