基于支持向量机增量学习的电力系统暂态稳定评估

摘要

基于传统支持向量机的暂态稳定评估模型,通常将所有的学习样本同时参与学习,如果有新样本加入,则需要对所有样本重新学习.针对传统暂态稳定评估模型不能在线更新的不足,提出了一种支持向量机增量学习的暂态稳定评估方法.该方法利用一种快速支持向量机增量学习方法,构造递归解法将新数据增加到解中,并对模型更新前的训练数据保持Karush-Kuhn-Tucker条件.通过一次1个样本的增量学习更新暂态稳定评估模型.新英格兰39节点测试系统的仿真实验表明:所提出的方法能有效更新评估模型且大幅减少学习时间,为基于机器学习的电力系统暂态稳定在线学习提供了新思路.%In transient stability assessment using the classical support vector machine (SVM), all the training samples are required at the training stage at the same time. If new samples are added, the SVM need to be retrained with all the samples. Considering this limitation, a transient stability assessment method is proposed based on SVM incremental learning. A fast SVM incremental learning method is adopted. It constructs a recursive solution and adds new data to the solution. Karush-Kuhn-Tucker conditions are maintained for all the previous used training data. Consequently, assessment models are updated through incremental learning once a new sample is added. The simulations on the New England 39-bus test system demonstrate the proposed method can effectively update models and reduce training time dramatically. The method provides a new thought for online learning in power system transient stability assessment based on machine learning method.