论文标题
用于电源系统动态的非线性系统识别物理信息的神经网络
Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics
论文作者
论文摘要
基于转换器的生成单元的动力输入的不同功率引入了系统参数（例如惯性和阻尼）的极大不确定性。结果，系统操作员在执行动态安全评估和采取实时控制措施方面面临越来越多的挑战。本文利用相量测量单元（PMU）的广泛部署并旨在开发快速的动态状态和参数估计工具，研究了物理知识神经网络（PINN）的性能，以发现未来电力系统的频率动态。 Pinns有可能应对挑战，例如低惯性系统的更强的非线性，增加的测量噪声和数据可用性有限。在几个测试案例中使用4-BUS系统证明了估计量，并将其与最先进的算法（例如Uncented Kalman Filter（UKF））进行了比较，以评估其性能。
Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping. As a consequence, system operators face increasing challenges in performing dynamic security assessment and taking real-time control actions. Exploiting the widespread deployment of phasor measurement units (PMUs) and aiming at developing a fast dynamic state and parameter estimation tool, this paper investigates the performance of Physics-Informed Neural Networks (PINN) for discovering the frequency dynamics of future power systems. PINNs have the potential to address challenges such as the stronger non-linearities of low-inertia systems, increased measurement noise, and limited availability of data. The estimator is demonstrated in several test cases using a 4-bus system, and compared with state of the art algorithms, such as the Unscented Kalman Filter (UKF), to assess its performance.
