Core Capabilities
Advanced features that deliver measurable results across your inverter fleet
PINN-based RUL
Physics-Informed Neural Networks forecast remaining useful life of IGBT modules, capacitors and cooling systems with confidence intervals spanning 1 to 20 years.
Coffin-Manson Fatigue Model
Quantifies IGBT thermal cycling fatigue using Coffin-Manson kinetics and rainflow cycle counting to predict solder and bond-wire degradation.
Arrhenius Capacitor Aging
Models electrolytic capacitor degradation with Arrhenius kinetics — every 10°C rise halves remaining life. Tracks ESR drift and capacitance decay continuously.
Virtual Tj Sensing
Estimates IGBT junction temperature without direct sensors using a Foster/Cauer thermal network constrained by physics — the most critical and unmeasurable variable.
Physics-Aware Risk Scoring
PINN-driven risk score (0-100) with admin override capability, SHAP + Integrated Gradients attribution, and full audit trail per inverter.
Role-Based Dashboards
Three purpose-built dashboards — Operator (fleet semaphore), Engineer (RUL trends and scenarios), and Auditor (compliance and traceability) — each surfacing the right metrics.
Business Impact
Tangible benefits that transform your PV inverter asset health management
Cut O&M Cost 35%
Shift from reactive to predictive maintenance, reducing unplanned downtime by up to 70% and cutting total O&M expenditure by 35%.
Extend Inverter Life 20%
Optimize maintenance intervals and operating conditions to maximize the useful life of every inverter in your fleet.
Reduce Downtime 70%
Detect pre-failure signatures weeks in advance — THD anomalies, ESR drift, ΔR_th rise — before they become unplanned outages.
From Reactive to Predictive
Get RCM/CMMS-ready maintenance work orders in real time, tailored to the Operator, Engineer and Auditor roles.
Application Areas
InverterAI adapts to every solar segment and inverter topology