Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 350148, 9 pages
http://dx.doi.org/10.1155/2015/350148
Research Article

Forecasting Models for Hydropower Unit Stability Using LS-SVM

College of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China

Received 14 January 2015; Revised 30 April 2015; Accepted 7 May 2015

Academic Editor: Michael Small

Copyright © 2015 Liangliang Qiao and Qijuan Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper discusses a least square support vector machine (LS-SVM) approach for forecasting stability parameters of Francis turbine unit. To achieve training and testing data for the models, four field tests were presented, especially for the vibration in -direction of lower generator bearing (LGB) and pressure in draft tube (DT). A heuristic method such as a neural network using Backpropagation (NNBP) is introduced as a comparison model to examine the feasibility of forecasting performance. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to the NNBP, which is of significant importance to better monitor the unit safety and potential faults diagnosis.