Enterprise AI Analysis
Forecast-Guided KAN-Adaptive FS-MPC for Resilient Power Conversion in Grid-Forming BESS Inverters
This paper presents a novel Forecast-Guided KAN-Adaptive Finite-Set Model Predictive Control (FS-MPC) framework designed to enhance the resilience of Grid-Forming (GFM) Battery Energy Storage System (BESS) inverters, particularly under severe grid disturbances like voltage sags and abrupt load changes.
By integrating load forecasts and reserve margins into an Operating Stress Index (OSI) and leveraging a lightweight Kolmogorov-Arnold Network (KAN) for adaptive weight tuning, the system significantly improves voltage regulation, reduces recovery times, and enhances overall power quality during transient events, ensuring reliable microgrid operation with real-time computational feasibility.
Executive Impact
Quantified Resilience Improvements
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Worst-Case Voltage Deviation Reduction
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Recovery Time Reduction (S1)
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Degradation Area Reduction (S1)
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DSP Timing Margin
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The controller operates in the stationary αβ frame, eliminating reliance on a Phase-Locked Loop (PLL). This is crucial for maintaining stable control under asymmetrical faults and distorted voltage conditions, mitigating loss-of-lock risk and preserving physically meaningful control representations during deep sags.
OSI fuses load forecasts and reserve margins into a single vulnerability signal. It acts as a supervisory context, updated on a slow timescale (min-hour), to bias adaptive weight tuning in the fast FS-MPC loop, enabling resilience-oriented control posture under stressed regimes.
A lightweight Kolmogorov-Arnold Network (KAN) with learnable B-spline edge functions acts as an online weight governor. It dynamically adjusts FS-MPC weighting factors (λv, λsw) based on OSI and fast electrical features, providing interpretable, bounded, and rate-limited adaptation for robustness.
Enterprise Process Flow: Forecast-Guided Adaptive Control Flow
Forecast Load & Reserve
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Compute Operating Stress Index (OSI)
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Determine Operating Mode
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Acquire Electrical Features
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KAN Weight Inference (λv, λsw)
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FS-MPC Prediction & Optimization
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Switching Command
| Metric | Static FS-MPC | MLP-Adaptive | Proposed KAN-Adaptive |
|---|---|---|---|
| Worst-Case Deviation (p.u.) | 0.85 | 0.55 | 0.30 |
| Recovery Time (ms) | >100 | 45 | 18 |
| Degradation Area (p.u.-ms) | 25.0 | 12.4 | 4.2 |
| Switching Effort (kHz) | 12.5 | 12.0 | 11.0 |
20.7 µs
Worst-case control-step time on DSP
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