Abstract：This paper proposes a novel transient stability assessment (TSA) method using support vector machine with pinball loss (Pin-SVM). A group of system-level and projection energy function classification features are first extracted from the power system operation parameters to build the original feature set of the transient stability index, such as average mechanical power, initial rotor acceleration, system shock, projection speed, projection acceleration and projection kinetic energy (PKE) and so on. Furthermore, a feature selection approach based on maximal relevance and minimal redundancy (mRMR) criterion is employed to evaluate the feature subset which has high sensitivity to transient changes. Then, feature sets are mapped to a higher dimensional space and the TSA problem is then transformed into a linear classification problem and use the quantile to change the closest point position between system stability class and unstable class, converts transient stability classification to the problem of finding the optimal quantile distance in Pin-SVM, which can reduce the influence of boundary interference samples. Finally, the simulated results of classic IEEE 39-bus system, IEEE 145-bus system and practical power grid demonstrate the feasibility and the validity of this proposed method.
陈厚合, 王长江, 姜涛, 李雪, 李国庆. 基于投影能量函数和Pin-SVM的电力系统暂态稳定评估[J]. 电工技术学报, 2017, 32(11): 67-76.
Chen Houhe, Wang Changjiang, Jiang Tao, Li Xue, Li Guoqing. Transient Stability Assessment in Bulk Power Grid Using Projection Energy Function and Support Vector Machine with Pinball Loss. Transactions of China Electrotechnical Society, 2017, 32(11): 67-76.
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2017, Vol. 32 
