| 基于改进粒子群算法与支持向量机的变压器状态评估 |
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| 引用本文： | 吴米佳,卢锦玲. 基于改进粒子群算法与支持向量机的变压器状态评估[J]. 电力科学与工程, 2011, 27(3): 27-31 |
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| 作者姓名： | 吴米佳 卢锦玲 |
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| 作者单位： | 华北电力大学电气与电子工程学院,河北,保定,071003 |
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| 摘 要： | 支持向量机(SVM)能较好地解决小样本、非线性特征的多分类问题,适用于电力变压器运行状态评佑,但参数选择时分类效果有着显著影响.利用改进的粒子群算法(PSO)对支持向量机(SVM)参数进行寻优,通过引入收敛因子、惯性因子动态化和自适应杜子变异三种方法对传统的PSO算法进行改进,从而获得最佳的分类模型.该模型以变压器油中...
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| 关 键 词： | 电力变压器 状态评估 粒子群算法 支持向量机 参数寻优 |
Condition Assessment for Power Transformer Based on Improved Particle Swarm Optimization and Support Vector Machine |
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| Wu Mijia,Lu Jinling. Condition Assessment for Power Transformer Based on Improved Particle Swarm Optimization and Support Vector Machine[J]. Power Science and Engineering, 2011, 27(3): 27-31 |
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| Authors: | Wu Mijia Lu Jinling |
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| Affiliation: | Wu Mijia,Lu Jinling(Schooling of Electrical and Electronic Engineering,North China Electric Power University,Baoding 071003,China) |
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| Abstract: | The Support Vector Machine(SVM) have a better solution in small sample problem and nonlinear characteristics of the multi-classification.As a result SVM is suitable for condition assessment of power transformer operation;however the parameters of Support Vector Machine(SVM) have significant implications on the classification results.In order to obtain the best classification model,an improved particle swarm optimization(PSO) algorithm is introduced to optimize the parameters of the support vector machine(SV... |
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| Keywords: | power transformer state assessment PSO SVM parameter optimization |
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