InverterAI
Predict. Maintain. Extend.
AI-Powered Predictive Maintenance for Solar Inverters
Physics-informed machine learning that predicts inverter degradation, estimates Remaining Useful Life, and transitions O&M from reactive to predictive
The Challenge
Inverters are the reliability bottleneck in utility-scale solar plants
70% of O&M
Inverter-related events dominate plant operations
36% Energy Loss
Inverter failures cause the largest share of energy losses
10-15 Years
Inverter lifespan vs 25-30 years for PV modules
Reactive O&M
Most plants still rely on corrective maintenance
The Solution
InverterAI delivers physics-informed predictive intelligence for solar inverters
Physics-Informed Models
Coffin-Manson IGBT fatigue and Arrhenius capacitor degradation grounded in real physics
RUL Estimation
Remaining Useful Life prediction per component: IGBT, capacitors, fans, contactors
PI-NN Deep Learning
Physics-Informed Neural Networks with 10 constraint terms for physically consistent predictions
Explainable AI
SHAP + LIME explanations with role-based summaries for operators, engineers, and auditors
Digital Twin (xDT)
Executable digital twin for real vs simulated comparison and anomaly detection
Root Cause Analysis
FFT waveform analysis with 11 fault codes and automatic work order generation
How It Works
SCADA Data Ingestion
Real-time SCADA data collection: power, temperature, THD, voltage, and weather integration via NREL NSRDB
Physics Engine
Foster/Cauer thermal models estimate junction temperature. Rainflow counting extracts thermal cycles for fatigue analysis
Hybrid Prediction
Gradient Boosting + Random Forest ensemble blended with physics corrections delivers +96% accuracy RUL predictions
Actionable Insights
Risk-ranked maintenance plans, fleet dashboards, and compliance reports for operators, engineers, and auditors
Traditional O&M vs InverterAI
| Aspect | Traditional O&M | InverterAI |
|---|---|---|
| Maintenance Approach | Reactive / Calendar-based | Predictive / Condition-based |
| Failure Detection | After failure occurs | 30+ days early warning |
| Prediction Accuracy | Not applicable | +96% accuracy |
| Component Visibility | Black-box inverter | Per-component RUL (IGBT, caps, fans) |
| O&M Cost | High (unplanned downtime) | 35% reduction |
+96%
Prediction Accuracy
-70%
Downtime Reduction
+20%
Lifetime Extension
Frequently asked questions
What is InverterAI?
InverterAI is a predictive maintenance system for solar inverters. It predicts component degradation and estimates Remaining Useful Life, so operation and maintenance can move from corrective to condition-based.
What physics does it use?
Coffin-Manson fatigue for IGBT modules and Arrhenius degradation for capacitors, with Foster and Cauer thermal models to estimate junction temperature and rainflow counting to extract the thermal cycles that drive fatigue.
What data does it need?
Real-time SCADA data, including power, temperature, total harmonic distortion and voltage, combined with weather data through NREL NSRDB. No additional instrumentation on the inverter is assumed.
Which components get their own prediction?
Remaining Useful Life is estimated per component: IGBT, capacitors, fans and contactors. A single figure for the whole inverter does not tell a maintenance planner what to order or when.
Are the predictions explainable?
Yes. SHAP and LIME explanations are presented as role-based summaries for operators, engineers and auditors, alongside an executable digital twin for real versus simulated comparison and FFT waveform analysis for root cause.