Long-form technical writing on PV inverter reliability — failure physics, predictive maintenance economics and the science behind InverterAI.
A practical, non-academic introduction to Physics-Informed Neural Networks (PINN), why they extrapolate where standard deep learning fails, and how InverterAI uses them to predict PV inverter component lifetimes from limited field data.
Read more →A deep technical walkthrough of how junction-temperature swings drive IGBT module fatigue, why utility-scale PV inverters are particularly exposed, and how the Coffin-Manson equation lets you predict failure from SCADA data alone.
Read more →How standard SCADA tags travel from the inverter cabinet to a prioritised work order — the full data pipeline behind InverterAI's OEM-agnostic predictive maintenance, end to end.
Read more →How the Arrhenius equation governs electrolytic DC-link capacitor lifetime, why "10°C halves life" is only part of the story, and how InverterAI uses SCADA temperatures to forecast capacitor failure in utility-scale PV inverters.
Read more →A side-by-side, numbers-based comparison of reactive, calendar-based and physics-informed predictive O&M for utility-scale PV inverters — including the hidden costs that rarely make it into vendor pitches.
Read more →A data-backed look at why inverters drive the bulk of unplanned downtime and energy losses in utility-scale PV plants, drawing on field reliability studies from NREL, Fraunhofer ISE and IEA-PVPS Task 13.
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