Support vector machine model based on glowworm swarm optimization in application of vibrant fault diagnosis for hydro-turbine generating unit

In view of the support vector machine (SVM) model applied in vibrant fault diagnosis for hydro-turbine generating unit, it exists problems of parameter settings and classification-plane incline due to unequal sample, which leads to lower diagnosis accuracy. As a new bionic intelligent optimization algorithm for glowworm swarm optimization(GSO), it has the characteristics of strong versatility and fast convergence speed. In this paper, GSO is first introduced into the field of hydropower unit vibration fault diagnosis, and GSO-SVM diagnosis model was established, which has realized the effective mapping for vibration characteristics into fault sets, and achieve the goal of fault diagnosis. The simulation results show that the established model has better convergence speed and global optimization ability as well as the higher calculation precision, providing an efficient and innovative solutions in the field of vibrant fault diagnosis for hydro-turbine generating unit.