| 基于贝叶斯证据框架的支持向量机负荷建模 |
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| 引用本文： | 王振树,李林川,牛丽.基于贝叶斯证据框架的支持向量机负荷建模[J].电工技术学报,2009,24(8). |
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| 作者姓名： | 王振树 李林川 牛丽 |
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| 作者单位： | 天津大学电力系统仿真控制教育部重点实验室;山东大学电气工程学院;国核电力规划设计研究院; |
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| 基金项目： | 山东省自然科学基金资助项目(Y2007F27) |
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| 摘 要： | 负荷建模一直是电力系统中的难题之一,精确的负荷模型对电力系统数字仿真非常重要。本文提出一种基于贝叶斯证据框架的支持向量机负荷建模方法。根据广域测量的负荷特性数据,利用支持向量机进行负荷建模,选用高斯径向基核函数优化模型结构;用贝叶斯证据框架推断准则1解释了支持向量机的训练,又将贝叶斯证据准则2和3应用到支持向量机。采用贝叶斯证据框架的三个准则对负荷模型进行训练并对参数进行了辨识和优化。通过对支持向量机负荷模型的仿真试验,验证了该方法的正确性和有效性。贝叶斯证据框架下的支持向量机负荷模型具有泛化能力强、结构灵活、计算速度快的特点,能够较准确地描述实际负荷特性。
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| 关 键 词： | 贝叶斯证据框架 负荷建模 支持向量机 参数辨识 广域测量系统 |
Load Modeling Based on Support Vector Machine Based on Bayesian Evidence Framework |
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| Wang Zhenshu, Li Linchuan Niu Li.Load Modeling Based on Support Vector Machine Based on Bayesian Evidence Framework[J].Transactions of China Electrotechnical Society,2009,24(8). |
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| Authors: | Wang Zhenshu Li Linchuan Niu Li |
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| Affiliation: | 1. Key Laboratory of Power System Simulation and Control of Ministry of Education Tianjin University Tianjin 300072 China 2. Shandong University Jinan 250061 China 3. State Nuclear Electric Power Design & Research Institute Beijing 100032 China |
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| Abstract: | Load modeling is still one of the difficult problems in power system. Accurate load model plays a very important role in power system digital simulation. This paper presents a support vector machine (SVM) load modeling method which bases on Bayesian evidence framework. According to the load characteristic data acquired from wide area measurement system(WAMS),the load model based on SVM method is founded,and it chooses Gaussian radial basis function (RBF) to optimize the structure of the model. Among three l... |
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| Keywords: | Bayesian evidence framework load modeling support vector machine parameter identification wide-area measurement system |
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