An Automated Capacity-Allocating-Based Transition Strategy Between Harmonic and Reactive Power Compensation for Multifunctional PAPF

This paper proposes a practical heuristic engineering strategy for automated capacity allocation in a multifunctional parallel active power filter (PAPF) that simultaneously provides harmonic and reactive power compensation. Unlike theoretically optimal methods, our approach prioritizes real-time feasibility and ease of implementation. The key features are: (1) an event-triggered, closed-loop THD-feedback mechanism that dynamically recalculates the minimum active power required for harmonic compensation only when the load harmonic content changes, avoiding periodic computational waste; (2) a strict priority handling that guarantees grid current THD below 5% (IEEE-519 compliant) under all operating conditions, even when capacity is severely insufficient; (3) a closed-loop transition mechanism that uses measured grid current THD and remaining capacity as feedback inputs to continuously adapt power distribution. The proposed rule-based strategy does not claim theoretical optimality but provides a verifiable, ready-to-implement solution with experimental evidence. Simulation and experimental results on a three-level NPC PAPF prototype demonstrate that the strategy maintains grid current THD below 5% while keeping the apparent power within the rated capacity, achieving near-optimal reactive compensation (92–96% of the optimum) without iterative optimization. The experimental validation includes efficiency measurements, switching-loss estimation, DSP timing analysis, and robustness tests under grid disturbances. Future work will extend the concept to multi-inverter systems using multi-objective optimization and AI-based allocation.