This research proposes a machine learning-based attack detection model for power systems, specifically targeting smart grids. By utilizing data and logs collected from Phasor Measuring Devices (PMUs), the model aims to learn system behaviors and effectively identify potential security boundaries. The proposed approach involves crucial stages including dataset pre-processing, feature selection, model creation, and evaluation. To validate our approach, we used a dataset used, consist of 15 separate datasets obtained from different PMUs, relay snort alarms and logs. Three machine learning models: Random Forest, Logistic Regression, and K-Nearest Neighbour were built and evaluated using various performance metrics. The findings indicate that the Random Forest model achieves the highest performance with an accuracy of 90.56% in detecting power system disturbances and has the potential in assisting operators in decision-making processes.

翻译：本研究提出一种基于机器学习的电力系统攻击检测模型，专门针对智能电网设计。通过利用从相量测量装置（Phasor Measuring Devices, PMUs）采集的数据和日志，该模型旨在学习系统行为并有效识别潜在的安全边界。所提出的方法涉及关键阶段，包括数据集预处理、特征选择、模型构建与评估。为验证方法的有效性，我们使用了包含15个独立子数据集的数据集，这些子数据集来自不同的PMU、继电器Snort警报及日志。我们构建了三种机器学习模型——随机森林（Random Forest）、逻辑回归（Logistic Regression）和K近邻（K-Nearest Neighbour），并通过多种性能指标进行评估。结果表明，随机森林模型在检测电力系统扰动时取得了最高性能，准确率达到90.56%，并具备辅助操作员决策过程的潜力。