Voltage stability assessment of complex power system based on GA-SVM

The dynamic stability assessment and prediction of a complex power system is a precondition to take the action of protecting control. This paper presents the four support vector machines (SVMs) with an improved genetic algorithm (GA) to compute their parameters automatically, that one SVM is used to simulate the tangent vector and the others for identifying the instable area. Besides, the GA was initialized by Meta-Learning method to enhance the performance and its optimal solution was selected by last test. Furthermore, a large network simplification was taken for reducing the amount of calculation and responding in real time. Study with the IEEE 118-bus test system indicated that the system status of a complex power system subjected a fault could be predicted based on this technique of the GA-SVM for simulating the tangent vector accurately. Besides, three binary SVM classifiers were trained to locate the instable area, and ranking the levels by the analysis of critical bus is help to management. Based on the test on the networks, the suggested approach can predict accurately with 98.87 % success rate and identify the fault area with 94.91 % success rate.