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: | |
| Otros Autores: | , , , |
| Publicado: |
MDPI AG
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3171221069 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2227-9717 | ||
| 024 | 7 | |a 10.3390/pr13020549 |2 doi | |
| 035 | |a 3171221069 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Ye Tao |u School of Electrical Engineering, Shandong University, Jinan 250061, China; <email>taoye@mail.sdu.edu.cn</email> (Y.T.); <email>zhangsl@mail.sdu.edu.cn</email> (S.Z.) | |
| 245 | 1 | |a An SVM-Based Anomaly Detection Method for Power System Security Analysis Using Particle Swarm Optimization and t-SNE for High-Dimensional Data Classification | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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%. | |
| 653 | |a Skewness | ||
| 653 | |a Accuracy | ||
| 653 | |a Machine learning | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Research methodology | ||
| 653 | |a Swarm intelligence | ||
| 653 | |a Deep learning | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Forecasting | ||
| 653 | |a Algorithms | ||
| 653 | |a Support vector machines | ||
| 653 | |a Electricity distribution | ||
| 653 | |a Optimization | ||
| 653 | |a Electric power | ||
| 653 | |a Multidimensional data | ||
| 653 | |a Anomalies | ||
| 653 | |a Multidimensional methods | ||
| 653 | |a Parameters | ||
| 653 | |a Embedding | ||
| 653 | |a Electric power systems | ||
| 653 | |a Statistical analysis | ||
| 700 | 1 | |a Jiongcheng Yan |u Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, China | |
| 700 | 1 | |a Niu, Enquan |u Shandong Luruan Digital Technology Co., Ltd., Jinan 250101, China; <email>niuenquan369@hotmail.com</email> | |
| 700 | 1 | |a Zhai, Pengming |u Wenshan Power Supply Bureau of Yunnan Power Grid Co., Ltd., Wenshan 663000, China; <email>zhaipm0303@hotmail.com</email> | |
| 700 | 1 | |a Zhang, Shuolin |u School of Electrical Engineering, Shandong University, Jinan 250061, China; <email>taoye@mail.sdu.edu.cn</email> (Y.T.); <email>zhangsl@mail.sdu.edu.cn</email> (S.Z.) | |
| 773 | 0 | |t Processes |g vol. 13, no. 2 (2025), p. 549 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171221069/abstract/embedded/KOLE7RPJVUKQAXRX?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3171221069/fulltextwithgraphics/embedded/KOLE7RPJVUKQAXRX?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171221069/fulltextPDF/embedded/KOLE7RPJVUKQAXRX?source=fedsrch |