State estimation is the cornerstone of the power system control center since
it provides the operating condition of the system in consecutive time
intervals. This work investigates the application of physics-informed neural
networks (PINNs) for accelerating power systems state estimation in monitoring
the operation of power systems. Traditional state estimation techniques often
rely on iterative algorithms that can be computationally intensive,
particularly for large-scale power systems. In this paper, a novel approach
that leverages the inherent physical knowledge of power systems through the
integration of PINNs is proposed. By incorporating physical laws as prior
knowledge, the proposed method significantly reduces the computational
complexity associated with state estimation while maintaining high accuracy.
The proposed method achieves up to 11% increase in accuracy, 75% reduction in
standard deviation of results, and 30% faster convergence, as demonstrated by
comprehensive experiments on the IEEE 14-bus system.

通过物理信息神经网络（PINNs）整合物理规律的先验知识，本研究提出了一种新颖的方法用于加速电力系统状态估计，以监测电力系统的运行情况。该方法在保持高准确性的同时，显著降低了状态估计的计算复杂性，实验证明其准确度提高了 11％，结果的标准偏差降低了 75％，收敛速度提高了 30％。

基于物理信息的神经网络加速电力系统状态估计

Physics-Informed Neural Networks for Accelerating Power System State  Estimation

Due to the proliferation of renewable energy and its intrinsic intermittency
and stochasticity, current power systems face severe operational challenges.
Data-driven decision-making algorithms from reinforcement learning (RL) offer a
solution towards efficiently operating a clean energy system. Although RL
algorithms achieve promising performance compared to model-based control
models, there has been limited investigation of RL robustness in
safety-critical physical systems. In this work, we first show that several
competition-winning, state-of-the-art RL agents proposed for power system
control are vulnerable to adversarial attacks. Specifically, we use an
adversary Markov Decision Process to learn an attack policy, and demonstrate
the potency of our attack by successfully attacking multiple winning agents
from the Learning To Run a Power Network (L2RPN) challenge, under both
white-box and black-box attack settings. We then propose to use adversarial
training to increase the robustness of RL agent against attacks and avoid
infeasible operational decisions. To the best of our knowledge, our work is the
first to highlight the fragility of grid control RL algorithms, and contribute
an effective defense scheme towards improving their robustness and security.

本文针对电网控制系统的弱点进行研究，发现现有的强化学习算法存在敏感性，容易受到恶意攻击，提出了一种基于对抗训练的解决方案，以增强算法的安全性和鲁棒性。