基于新型半对半多类支持向量机的电力系统静态安全在线评估

Lei Li, Zhigao Zhu
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引用次数: 3

摘要

电力系统静态安全评估是关系到电力系统安全稳定性能的重要问题之一。使用人工智能技术可以快速评估静态安全性。本文比较了人工神经网络(ANN)和支持向量机(SVM)算法的优缺点,选择了支持向量机算法。提出了一种新的基于半对半支持向量机的多分类方法。提出的ha - svm算法已应用于ieee57总线电力系统。仿真结果验证了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On-Line Static Security Assessment of Power System Based on a New Half-Against-Half Multi-Class Support Vector Machine
Power system static security assessment is one of the most important problems which relate power system secure-stable performance. Static security can be rapidly assessed using the artificial intelligence technology. This paper compares the advantages and disadvantages of Artificial Neural Network (ANN) and Support Vector Machines (SVM) and then selects the SVM algorithm. A new multi-classification method based on Half-Against-Half (HAH) SVM has been proposed in this article. The proposed HAH-SVM algorithm has been applied to IEEE 57-bus power system. The simulation results demonstrate the effectiveness of the proposed algorithm.
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