Passive–Active Co-Design for Robust LCL-Filtered Grid-Connected Photovoltaic Inverters

Efficient operation of LCL-filtered grid-connected photovoltaic inverters under uncertain grid conditions requires coordinated treatment of passive-filter and current-controller interactions. This study addresses the interaction mechanism between passive LCL network parameters and active controller parameters through a systematic simultaneous passive–active co-design framework for a 10 kW inverter. A controlled five-strategy M1–M5 comparison separates conventional, control-only, passive-network-only, sequential, and simultaneous designs under a common plant, delay, modulation, switching, FFT, and uncertainty-validation protocol. Multi-objective search is supported by fast physical indicators, while leakage-free Gaussian process regression is used only as a candidate-screening accelerator before direct switching verification. The selected M5 design achieves a switching-model current total harmonic distortion of 0.8004%, passive damping loss of 8.236 W, and nominal phase and gain margins of 59.13° and 8.13 dB, respectively. Across 1000 shared Monte Carlo samples, its strict acceptance ratio is 98.3%, with zero true discrete-pole instability; rejected samples are conservative all-crossover margin anomalies. These simulation-based results indicate that M5 provides a balanced engineering compromise among harmonic performance, damping loss, dynamic response, and robustness rather than a design that dominates every individual metric. Gaussian process regression reduces screening cost but neither defines the objective nor replaces high-fidelity validation. Hardware-in-the-loop and experimental validation remain future work.

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Publication Details

Journal
Energies
Published
2026-10-30
DOI
https://doi.org/10.3390/en19215163
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Passive–Active Co-Design for Robust LCL-Filtered Grid-Connected Photovoltaic Inverters

Yuqi Ao, Kezheng Jiang, Xun Xu, Menghan Xiao et al.
1 links
Energies
Microgrid Control and Optimization
article

Passive–Active Co-Design for Robust LCL-Filtered Grid-Connected Photovoltaic Inverters

Yuqi Ao, Kezheng Jiang, Xun Xu, Menghan Xiao, Jiacheng Li, Lu Zhang
article en

Abstract

Efficient operation of LCL-filtered grid-connected photovoltaic inverters under uncertain grid conditions requires coordinated treatment of passive-filter and current-controller interactions. This study addresses the interaction mechanism between passive LCL network parameters and active controller parameters through a systematic simultaneous passive–active co-design framework for a 10 kW inverter. A controlled five-strategy M1–M5 comparison separates conventional, control-only, passive-network-only, sequential, and simultaneous designs under a common plant, delay, modulation, switching, FFT, and uncertainty-validation protocol. Multi-objective search is supported by fast physical indicators, while leakage-free Gaussian process regression is used only as a candidate-screening accelerator before direct switching verification. The selected M5 design achieves a switching-model current total harmonic distortion of 0.8004%, passive damping loss of 8.236 W, and nominal phase and gain margins of 59.13° and 8.13 dB, respectively. Across 1000 shared Monte Carlo samples, its strict acceptance ratio is 98.3%, with zero true discrete-pole instability; rejected samples are conservative all-crossover margin anomalies. These simulation-based results indicate that M5 provides a balanced engineering compromise among harmonic performance, damping loss, dynamic response, and robustness rather than a design that dominates every individual metric. Gaussian process regression reduces screening cost but neither defines the objective nor replaces high-fidelity validation. Hardware-in-the-loop and experimental validation remain future work.

EnergiesVol. 19(21)
State Grid Hubei Electric Power Company Electric Power Research Institute (CN)
State Grid Hubei Electric Power Co
Affordable and clean energy, Industry, innovation and infrastructure
Openalex Percentile: Top 18%
Microgrid Control and Optimization
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