Imagen de Portada

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

Guardado en:
Publicado en:Processes vol. 13, no. 2 (2025), p. 549
Autor principal: Ye Tao
Otros Autores: Jiongcheng Yan, Niu, Enquan, Zhai, Pengming, Zhang, Shuolin
Publicado:
MDPI AG
Materias:
Skewness
Accuracy
Machine learning
Particle swarm optimization
Research methodology
Swarm intelligence
Deep learning
Genetic algorithms
Artificial intelligence
Forecasting
Algorithms
Support vector machines
Electricity distribution
Optimization
Electric power
Multidimensional data
Anomalies
Multidimensional methods
Parameters
Embedding
Electric power systems
Statistical analysis
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen: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 system case study, the accuracy for normal data reaches 97.58%, the accuracy for load step change detection reaches 99.52%, the accuracy for bad data detection reaches 99.92%, and the accuracy under fault conditions reaches 100%.
ISSN:2227-9717
DOI:10.3390/pr13020549
Fuente:Materials Science Database