用可微物理模型精准诊断光伏阵列故障，效率与可解释性双提升。
Fault Diagnosis and Quantification for Photovoltaic Arrays based on Differentiable Physical Models
- 构建可微快算故障模拟模型，支持多故障I-V特性建模
- 基于梯度优化实现故障参数量化，重构误差低于3%
- 适合光伏系统智能运维人员及电力设备研究者参考
准确的故障诊断与量化对光伏（PV）阵列的可靠运行和智能维护至关重要。然而，现有故障量化方法常面临效率低、可解释性差的问题。为此，本文提出一种基于可微快速故障仿真模型（DFFSM）的新型光伏串行故障量化方法。该模型能精确模拟多种故障下的I-V特性，并提供关于故障参数的解析梯度。利用此特性，设计了基于梯度的故障参数识别方法（GFPI），采用Adahessian优化器高效量化局部遮蔽、短路及串联电阻退化等故障。在仿真与实测I-V曲线上的实验结果表明，所提GFPI方法在各类故障下均实现高精度量化，I-V重构误差低于3%，验证了可微物理模拟器在光伏系统故障诊断中的可行性和有效性。
原文摘要 · Abstract (English)
Accurate fault diagnosis and quantification are essential for the reliable operation and intelligent maintenance of photovoltaic (PV) arrays. However, existing fault quantification methods often suffer from limited efficiency and interpretability. To address these challenges, this paper proposes a novel fault quantification approach for PV strings based on a differentiable fast fault simulation model (DFFSM). The proposed DFFSM accurately models I-V characteristics under multiple faults and provides analytical gradients with respect to fault parameters. Leveraging this property, a gradient-based fault parameters identification (GFPI) method using the Adahessian optimizer is developed to efficiently quantify partial shading, short-circuit, and series-resistance degradation. Experimental results on both simulated and measured I-V curves demonstrate that the proposed GFPI achieves high quantification accuracy across different faults, with the I-V reconstruction error below 3%, confirming the feasibility and effectiveness of the application of differentiable physical simulators for PV system fault diagnosis.
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