用并行图神经网络快速找电力系统极端运行工况，适合新能源场景在线保护定值计算。
Efficient Extreme Operating Condition Search for Online Relay Setting Calculation in Renewable Power Systems Based on Parallel Graph Neural Network
- 构建四层电网信息图，通过并行图神经网络提取特征
- 在39和118节点系统上验证，预测精度优于传统方法，计算速度显著提升
- 专为高比例新能源系统设计，支持在线实时保护定值计算
极端运行条件搜索（EOCS）是继电保护定值计算中的关键问题，旨在确保保护定值在部署后能适应电力系统运行条件的变化。随着可再生能源渗透率提高及逆变型资源广泛应用，新能源电力系统运行条件更加波动，亟需采用在线继电保护定值计算策略。然而，现有基于局部枚举、启发式算法和数学规划的EOCS方法计算速度难以满足在线计算需求。本文首次提出一种高效深度学习驱动的EOCS方法，适用于在线继电保护定值计算。首先，将电力系统信息建模为四层结构：元件参数层、拓扑连接层、电气距离层和图距离层，并输入并行图神经网络（PGNN）进行特征提取；随后，将每个节点对应的四层特征拼接拉直，输入决策网络以预测系统的极端运行条件。在改进的IEEE 39-bus和118-bus测试系统上验证该方法，其中部分同步发电机被可再生能源单元替代，故障电流计算充分考虑了可再生能源的非线性故障特性。实验结果表明，所提PGNN方法在求解EOCS问题上具有更高精度，同时在在线计算时间方面取得更显著提升。
原文摘要 · Abstract (English)
The Extreme Operating Conditions Search (EOCS) problem is one of the key problems in relay setting calculation, which is used to ensure that the setting values of protection relays can adapt to the changing operating conditions of power systems over a period of time after deployment. The high penetration of renewable energy and the wide application of inverter-based resources make the operating conditions of renewable power systems more volatile, which urges the adoption of the online relay setting calculation strategy. However, the computation speed of existing EOCS methods based on local enumeration, heuristic algorithms, and mathematical programming cannot meet the efficiency requirement of online relay setting calculation. To reduce the time overhead, this paper, for the first time, proposes an efficient deep learning-based EOCS method suitable for online relay setting calculation. First, the power system information is formulated as four layers, i.e., a component parameter layer, a topological connection layer, an electrical distance layer, and a graph distance layer, which are fed into a parallel graph neural network (PGNN) model for feature extraction. Then, the four feature layers corresponding to each node are spliced and stretched, and then fed into the decision network to predict the extreme operating condition of the system. Finally, the proposed PGNN method is validated on the modified IEEE 39-bus and 118-bus test systems, where some of the synchronous generators are replaced by renewable generation units. The nonlinear fault characteristics of renewables are fully considered when computing fault currents. The experiment results show that the proposed PGNN method achieves higher accuracy than the existing methods in solving the EOCS problem. Meanwhile, it also provides greater improvements in online computation time.
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