By focusing on the accuracy limitations of the naive Bayesian classifier in the transient stability assessment of power systems, a tree augmented naive Bayesian (TAN) classifier is adopted for the power system transient stability assessment. The adaptive Boosting (AdaBoost) algorithm is used in the TAN classifier to form an AdaBoost-based tree augmented naive Bayesian (ATAN) classifier for further classification performance improvement. To construct the ATAN classifier, eight attributes that reasonably reflect the transient stability or transient instability of a power system are selected as inputs of the proposed classifier. In addition, the class-attribute interdependence maximization (CAIM) algorithm is used to discretize the attributes. Then, the operating mechanism of the power system is used to obtain the dependencies between the attributes, and the parameters of the ATAN classifier are learned according to the Bayes’ theorem and the criterion of maximizing a posterior estimation. Four evaluation indicators of the ATAN classifier are used, that is, the value of Kappa, the area under the receiver operating characteristic curve (AUC), F₁ score, and the average evaluation indicator. Lastly, experiments are implemented on the IEEE 3-generator 9-bus system and IEEE 10-generator 39-bus system. The simulation results show that the ATAN classifier can significantly improve the classification performance of the transient stability assessment of the power system.

针对朴素贝叶斯分类器在电力系统暂态稳定性评估中的精度局限性，采用树增强朴素贝叶斯（TAN）分类器进行电力系统暂态稳定性评估。在 TAN 分类器中使用自适应 Boosting (AdaBoost) 算法形成基于 AdaBoost 的树增强朴素贝叶斯 (ATAN) 分类器，以进一步提高分类性能。为了构建 ATAN 分类器，选择了八个能够合理反映电力系统暂态稳定性或暂态不稳定性的属性作为所提出的分类器的输入。此外，类属性相互依赖最大化（CAIM）算法用于离散化属性。然后利用电力系统的运行机制获取属性之间的依赖关系，根据贝叶斯定理和最大化后验估计的准则学习ATAN分类器的参数。使用了ATAN分类器的四个评价指标，即Kappa值，受试者工作特征曲线下面积（AUC），F ₁分，平均评价指标。最后，在 IEEE 3 发电机 9 总线系统和 IEEE 10 发电机 39 总线系统上进行了实验。仿真结果表明，ATAN分类器能够显着提高电力系统暂态稳定性评估的分类性能。