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.%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 levels of Bayesian evidence framework inference, the level linference is used to explain SVM training, both levels 2 and 3 can also be applied to SVM. Load model parameters are identified and optimized by using three inference levels of Bayesian evidence framework. Simulation tests for SVM load model verify the validity of this method. SVM load model based on Bayesian evidence framework has good generalization ability, flexible structure, and rapid calculation speed, so it can describe the actual load characteristics more accurately.
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