Application of SVD and LS-SVM in power quality disturbances classification

作者：Li Tian Yun^*; Chen Chang Lei; Zhou Bo; Wang Jing; Yang Hui

来源：Proceedings of the CSEE, 2008, 28(34): 124-128.

• Coefficient matrixes
• Fundamental frequencies
• Input parameters
• Least square support vector machines
• Noise energies
• Power quality disturbances
• Simulation results
• Sub spaces
• Voltage signals
• Wavelet package

Based on singular value decomposition (SVD) and least square support vector machines (LS-SVM), the paper proposed a new method of identifying the type of power quality disturbance. The method decomposed the voltage signal with components of fundamental frequency, the faulty frequency and the noises into different orthogonal characteristics subspace by SVD of the coefficient matrix from the wavelet package transformation; then used them to make devision with singular value of normal voltage to cancel the effect of noise energy, displayed the tiny differences among different disturbance types as much as possible. And then it used them as the input parameters of the LS-SVM to realize the identification of the power quality disturbance type. Simulation results indicate that the method has good performance of accuracy and stability, and is immune to noises.

• 出版日期2008