电力系统保护中机器学习的标准化框架
A Standardized Framework for Machine Learning in Power System Protection
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中文总结 AI 辅助
该研究提出电力系统保护机器学习的标准化框架，明确7个评估维度，通过PROTECT-90基准案例验证MLP等模型性能，为机器学习电力系统保护的可复现、可审计评估提供基础。
中文摘要 AI 辅助
基于机器学习的电力系统保护研究越来越多地报告近乎完美的分数，但这些分数的含义在很大程度上取决于评估设置。保护任务、物理范围、测量、时间、目标、预处理和验证通常共同变化，且未完全明确。本文提出了一个面向标准化的框架，将评估设计视为科学贡献的一部分。该框架定义了七个必要的研究维度：保护目标、物理范围、可观测性、时间与决策窗口、目标与样本有效性、验证协议以及评估输出。该框架在公共PROTECT-90电磁暂态基准的有限案例研究中实例化，包含来自90kV双线路拓扑的9022个模拟场景，用于基于故障发生条件的故障分类与定位。在集中式感知、与模拟元数据对齐的20ms窗口以及场景分组验证下，多层感知机（MLP）的分类任务五折平均宏F1分数为0.991±0.001，定位任务的平均绝对误差为线路长度的10.20±0.25%（场景分组折的均值±标准差）。将决策窗口扩展至50ms保留了该任务相关的性能不对称性，而降低可观测性使MLP的定位误差几乎翻倍，但对分类影响很小。同步双端传统定位器在其更丰富的干净信息集下优于学习型定位器，测量退化表明干净的预测性能并不决定鲁棒性。该框架将评估假设转化为明确、可复现的证据，为机器学习保护功能的更具可比性、可审计的评估及未来面向认证的评估提供基础。
英文摘要
Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on the evaluation setting. Protection task, physical scope, measurements, timing, targets, preprocessing, and validation often vary jointly and remain incompletely specified. This paper proposes a standardization-oriented framework that treats evaluation design as part of the scientific contribution. It defines seven required study dimensions: protection objective, physical scope, observability, timing and decision windows, targets and sample validity, validation protocol, and evaluation outputs. The framework is instantiated in a bounded case study on the public PROTECT-90 electromagnetic-transient benchmark, comprising 9022 simulated episodes from a 90 kV double-line topology, for onset-conditioned fault classification and localization. Under centralized sensing, simulation-metadata-aligned 20 ms windows, and episode-grouped validation, a multi-layer perceptron (MLP) achieved a five-fold mean macro-averaged F1 score of 0.991 +/- 0.001 for classification and a localization mean absolute error of 10.20 +/- 0.25% of line length (mean +/- std across episode-grouped folds). Extending the decision horizon to 50 ms preserved this task-dependent performance asymmetry, while reduced observability approximately doubled the MLP localization error but had little effect on classification. A synchronized two-ended conventional locator outperformed the learning locators under its richer clean information set, and measurement degradation showed that clean predictive performance did not determine robustness. The framework turns evaluation assumptions into explicit, reproducible evidence and provides a basis for more comparable, auditable evaluation and future certification-oriented assessment of machine-learning protection functions.
发表机构
- Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）
- Ostbayerische Technische Hochschule Amberg-Weiden（东巴伐利亚应用技术大学安贝格-魏登分校）
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