An SVM-Based Anomaly Detection Method for Power System Security Analysis Using Particle Swarm Optimization and t-SNE for High-Dimensional Data Classification

作者：Tao Ye, Jiongcheng Yan, Enquan Niu, Pengfei Zhai, Shuolin Zhang · 发表于：Processes · 年份：2025 · DOI：10.3390/pr13020549 · 被引用次数：12 · 研究领域：Network Security and Intrusion Detection、Smart Grid Security and Resilience、Smart Grid and Power Systems

Research on the detection and identification of anomalies in electric power systems is crucial for ensuring their secure and stable operation. Anomaly detection models based on Support Vector Machines (SVMs) effectively process high-dimensional data while maintaining strong generalization capabilities. However, the performance of SVMs significantly depends on the choice of parameters, where improper parameter settings can lead to overfitting or underfitting, consequently decreasing the accuracy of anomaly detection. Furthermore, the dimensions of anomaly data in electric power systems are often unknown, making it difficult for existing methods to maintain a high precision in multidimensional data detection, and the segmentation of such data lacks intuitive display. In response, this article proposes an improved SVM model for electric power system anomaly detection, enhanced by parameter optimization algorithms, alongside a method for nonlinear dimension reduction and visualization using t-Distributed Stochastic Neighbor Embedding (t-SNE). Initially, traditional SVM parameters are optimized using the following four algorithms: Grid Search (GS), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Artificial Bee Colony (ABC) algorithms, in order to establish the optimized SVM model for electric power system anomaly detection. Finally, the effectiveness of the proposed method is verified through simulations. The simulation results indicate that, in the IEEE-14 node s...