A new BVM based approach to transient security assessment

Abstract

SUMMARYMachine Learning techniques have been extensively used in Power Systems Analysis during the last years. This paper describes a ball vector machine based algorithm for on-line transient security assessment of large-scale power systems. The proposed ball vector machine based security assessment reduces the training time and space complexities in comparison with support vector machines, artificial neural networks, and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors and therefore is faster than existing algorithms for on-line applications. A new decision tree based feature selection technique has been presented, too. The proposed ball vector machine based algorithm has been applied to New England 39-bus test system. The simulation results show the effectiveness of the proposed algorithm for on-line transient security assessment procedure.

General prefault transient stability estimation index using wide area phasor measurements

“…Furthermore, many papers have been presented based on pattern recognition approach [19][20][21][22][23][24][25][26]. They are based on artificial intelligent methods, such as probabilistic neural network [21], multi-layer perceptron neural network [9], fuzzy neural network [22,23], decision tree (DT) [24], support vector machine [25,26] and Kernel ridge regression [2]. All of them should be trained for a specified test system and do not provide any general solution for power systems stability assessment.…”