数据优化的意外事件筛选：一种电力系统安全的机器学习方法
Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security
- Federal University of Agriculture（联邦农业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对电力系统安全问题，采用机器学习算法结合SMOTE、PCA等预处理技术，在IEEE-14和IEEE-30系统上评估KNN、RF、SVM的性能，发现RF表现最优，PCA对模型性能的贡献大于SMOTE，为电力系统实时安全评估提供了新方法。
AI中文摘要：
确保电力系统的安全对于稳定性和可靠性至关重要，尤其是在发生中断事件时。对电力系统的意外事件进行有效分类，可实现主动决策，缓解大规模故障与失效。本研究探索使用机器学习算法将电力系统意外事件的安全级别分为安全、中等或严重三类。对于该方法，牛顿-拉夫逊潮流计算法从意外事件场景中提取系统数据，使用综合性能指数（OPI）作为安全度量。数据预处理采用合成少数类过采样技术（SMOTE）和主成分分析（PCA），分别用于解决类别不平衡问题和降低维度。在IEEE-14和IEEE-30节点系统上，针对k=1、2、3的N-k意外事件场景生成的数据集，使用四种混合预处理配置（归一化、SMOTE平衡、PCA变换以及SMOTE与PCA结合变换）对K近邻（KNN）、随机森林（RF）和支持向量机（SVM）进行训练与评估。性能通过精确率、召回率和F1分数评估，优先关注严重意外事件类别。RF在IEEE-30系统中取得最高F1分数0.97，在IEEE-14系统中为0.86；SVM从PCA中显著受益，提升了分类准确率；KNN最适合SMOTE与PCA转换。研究结果表明，PCA对模型整体性能的贡献大于SMOTE；不过SMOTE可提升召回率，但会引入假阳性，因此是准确率的折中方案。本研究强调，机器学习是传统意外事件分析的可扩展且强大的替代方案，可改善实时安全评估。
英文摘要：
Ensuring the security of the power system is essential for stability and reliability, especially in the event of disruption. Effective classification of contingency in power systems enables proactive decision-making and mitigates large-scale breakdowns and failures. This study explores the use of machine learning algorithms to classify security levels of contingencies in power systems into safe, moderate or severe classes. For this approach, Newton-Raphson load flow method extracts system data from contingency scenarios, using Overall Performance Index (OPI) as safety measure. For data pre-processing, Synthetic Minority Over-Sampling Technique (SMOTE) and Principal Component Analysis (PCA) is used to address class imbalance and reduce dimensionality, respectively. K-Nearest Neighbours (KNN), Random Forest (RF) and Support Vector Machines (SVM) is trained and evaluated on datasets generated through N-k contingency scenarios for k equal 1, 2, and 3 on IEEE-14 and IEEE-30 bus systems using four hybrid pre-processing configurations: normalized, SMOTE-balanced, PCA-transformed, and a combined SMOTE PCA-transformed. Performance is assessed by precision, recall and F1 score, with priority given to the severe contingency classes. The RF achieved the highest F1 scores of 0.97 in IEEE-30 and 0.86 in IEEE-14, SVM benefits significantly from PCA and improves the accuracy of the classification, while KNN is best suited for SMOTE and PCA conversion. The findings show that PCA contributes more than SMOTE to the overall performance of the model. However, SMOTE improves recall but can introduce false positives and is therefore a compromise of accuracy. This study highlights machine learning as a scalable and powerful alternative to traditional contingency analysis, which improves the assessment of security in real time.
