SVM based imbalanced correction method for Power Systems Transient stability evaluation

Due to the requirement of safety and reliability in power systems, unstable samples in the real system are rarely appeared. The evaluation results of the model trained by these imbalance samples have a certain preference. Generally, the imbalance in quantity is taken into account, while the imbalance in quality is ignored. Faced with such a problem, an imbalanced correction method based on support vector machine (SVM) is proposed. Firstly, the classification hyperplane trained by SVM and the normalized Euclidean distance between each sample and the classification hyperplane are calculated so as to obtain their fault severity. Based on this, training samples can be grouped to multilevel sets. Then, the original stacked sparse auto-encoder (SSAE) are pretrained to quantify the imbalance between two classes of samples in multilevel sets. Subsequently, in order to improve the imbalance of training samples, a cost-sensitive correction matrix is generated according to the imbalanced information of multilevel sets. Finally, the loss function of SSAE is modified by cost-sensitive correction matrix to establish the final classifier. Simulation results in IEEE 39-bus system and the realistic regional power system of Eastern China show the high performance of the proposed imbalanced correction method.