Online dynamic security assessment of wind integrated power system using SDAE with SVM ensemble boosting learner

Abstract

The increasing trend of decarbonization, particularly high penetration of wind power generation (WPG), has resulted in an increased operational uncertainty of the power system. This emerging trend of wind integration requires novel and effective methods of power system security assessment. This paper proposes a method for dynamic security assessment (DSA) based upon stacked de-noising auto-encoder (SDAE) using support vector machine (SVM) ensemble with boosting learning approach. A two-step feature reduction method is introduced after screening the redundant features among original feature set using mutual information theory. Multi-level features of original input data are extracted using multi-layer SDAE. SVM ensemble classifier is used to perform classification with data from all hidden layers of SDAE. The output of multi-layer SDAE and SVM ensemble are combined together as an input to ensemble boosting learning method which gives the final DSA result. The proposed method is tested on modified New England 39 bus system and extended on simplified AC/DC hybrid power system of Shandong province, China. The effectiveness and accuracy of the proposed method is demonstrated by simulation results and by comparisons with other methods.