Gradient-enhanced physics-informed neural networks for power systems operational support
Gradient-enhanced physics-informed neural networks for power systems operational support
复制标题
用于电力系统运行支持的梯度增强物理信息神经网络
DOI：
10.1016/j.epsr.2023.109551
复制
发表时间：
2023
影响因子：
3.9
通讯作者：
Fioretto, Ferdinando
中科院分区：
文献类型：
--
作者：
Mohammadian, Mostafa;Baker, Kyri;Fioretto, Ferdinando
应用深度学习方法加速电力系统具有挑战性的问题，最近取得了非常令人鼓舞的结果。然而，电力系统动态不是快照、稳态运行。必须考虑这些动态，以确保这些模型提供的最优解符合实际约束，以避免频率波动和电网不稳定。遗憾的是，基于常微分方程组或偏微分方程组的动态系统模型往往不适合直接应用于控制或状态估计，因为它们的计算成本很高。为了应对这些挑战，本文引入了一种机器学习方法，以接近实时地逼近电力系统动态行为。该框架基于梯度增强型物理信息神经网络(GPINN)，对电力系统的基本物理规律进行编码。建议的gPINN的一个关键特征是它能够在不需要生成昂贵的训练数据的情况下进行训练。文中以单机无穷大系统和三节点电网为例说明了该方法在预测转子转角和频率以及惯性和阻尼等不确定参数方面的潜力，从而展示了该方法在电力系统中的应用潜力。与Pinn模型相比，该模型具有较高的预测精度，预测范围为0.533-4.092，平均相对误差改善达13.30倍。将所提出的gPINN模型的计算性能与传统的求解器进行了比较，结果表明，在求解电力系统中的微分-代数方程组时，gPINN模型的计算速度有31~171倍的显著提高。
The application of deep learning methods to speed up the challenging power system problems has recently shown very encouraging results. However, power system dynamics are not snapshot, steady-state operations. These dynamics must be considered to ensure that the optimal solutions provided by these models adhere to practical constraints to avoid frequency fluctuations and grid instabilities. Unfortunately, dynamic system models based on ordinary or partial differential equations are frequently unsuitable for direct application in control or state estimates due to their high computational costs. To address these challenges, this paper introduces a machine learning method to approximate the behavior of power systems dynamics in near real-time. The proposed framework is based on gradient-enhanced physics-informed neural networks (gPINNs) and encodes the underlying physical laws governing power systems. A key characteristic of the proposed gPINN is its ability to train without the need of generating expensive training data. The paper illustrates the potential of the proposed approach in both forward and inverse problems in a single-machine infinite bus system and a three-bus power network for predicting rotor angles and frequency, and uncertain parameters such as inertia and damping to showcase its potential for a range of power systems applications. The model exhibited high accuracy in predicting the variables, achieving a range of 0.533–4.092 and an average L 2 relative error improvement of up to 13. 30× compared to the PINN model. The computational performance of the proposed gPINN model was compared to a conventional solver, revealing a remarkable speed-up of 31 to 171 times faster in solving differential–algebraic systems of equations in power systems.