A significant increase in renewable energy production is necessary to achieve the UN's net-zero emission targets for 2050. Using power-electronic controllers, such as Phase Locked Loops (PLLs), to keep grid-tied renewable resources in synchronism with the grid can cause fast transient behavior during grid faults leading to instability. However, assessing all the probable scenarios is impractical, so determining the stability boundary or region of attraction (ROA) is necessary. However, using EMT simulations or Reduced-order models (ROMs) to accurately determine the ROA is computationally expensive. Alternatively, Machine Learning (ML) models have been proposed as an efficient method to predict stability. However, traditional ML algorithms require large amounts of labeled data for training, which is computationally expensive. This paper proposes a Physics-Informed Neural Network (PINN) architecture that accurately predicts the nonlinear transient dynamics of a PLL controller under fault with less labeled training data. The proposed PINN algorithm can be incorporated into conventional simulations, accelerating EMT simulations or ROMs by over 100 times. The PINN algorithm's performance is compared against a ROM and an EMT simulation in PSCAD for the CIGRE benchmark model C4.49, demonstrating its ability to accurately approximate trajectories and ROAs of a PLL controller under varying grid impedance.

翻译：为实现联合国2050年净零排放目标，可再生能源产量需大幅提升。使用锁相环（PLL）等电力电子控制器将并网可再生能源与电网保持同步，可能在电网故障期间引发快速暂态行为，进而导致失稳。然而，评估所有可能场景并不现实，因此确定稳定边界或吸引域（ROA）至关重要。但利用电磁暂态（EMT）仿真或降阶模型（ROM）精确计算ROA的计算成本高昂。作为替代方案，机器学习（ML）模型已被提出作为预测稳定性的高效方法。然而，传统ML算法需要大量标注数据进行训练，这同样带来高昂计算成本。本文提出一种物理信息神经网络（PINN）架构，可在较少标注训练数据条件下精确预测PLL控制器在故障下的非线性暂态动力学行为。所提PINN算法可集成至常规仿真中，将EMT仿真或ROM加速超过100倍。基于CIGRE基准模型C4.49，在PSCAD中将PINN算法与ROM及EMT仿真进行性能对比，结果表明该算法能精确逼近不同电网阻抗条件下PLL控制器的轨迹与ROA。