Physics-grounded RUL estimation and predictive maintenance for utility-scale photovoltaic inverters.
InverterAI fuses Coffin-Manson, Arrhenius and PINN models with SCADA data to deliver auditable RUL predictions you can act on.
Physics-Informed Neural Networks forecast remaining useful life of IGBT modules, capacitors and cooling systems with confidence intervals spanning 1 to 20 years.
Quantifies IGBT thermal cycling fatigue using Coffin-Manson kinetics and rainflow cycle counting to predict solder and bond-wire degradation.
Models electrolytic capacitor degradation with Arrhenius kinetics — every 10°C rise halves remaining life. Tracks ESR drift and capacitance decay continuously.
Estimates IGBT junction temperature without direct sensors using a Foster/Cauer thermal network constrained by physics — the most critical and unmeasurable variable.
PINN-driven risk score (0-100) with admin override capability, SHAP + Integrated Gradients attribution, and full audit trail per inverter.
Three purpose-built dashboards — Operator (fleet semaphore), Engineer (RUL trends and scenarios), and Auditor (compliance and traceability) — each surfacing the right metrics.
A robust platform designed to integrate seamlessly with multi-OEM SCADA environments while providing the flexibility to grow with your solar fleet
Three role-specific dashboards — fleet semaphore for operators, RUL trends and what-if scenarios for engineers, and compliance traceability for auditors.
Connect with Huawei, Sungrow, SMA, TMEIC and Power Electronics SCADA. NREL NSRDB weather integration for physics-aware feature engineering.
Generate IEC-compliant performance and inverter health reports automatically. Export in PDF and Excel for regulatory submissions and internal audits.
Configurable thresholds for THD > 4.9%, ΔR_th rise and ESR drift — the EPRI-validated early-warning indicators for inverter pre-failure.
AES-256 encryption and SOC 2 Type II compliance with role-based access control and SHA-256 cryptographic audit trails.
Deploy on-premises, in your private cloud, or use our secure SaaS platform. Hybrid options available for multi-site operators.
The failure physics, the models and the economics, written out in full rather than summarised into a claim.
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 →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 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.
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