Simulation Analysis of Time-frequency based on Waveform Detection Technique for Power Quality Application
The automatic detection and classification of power quality disturbances has become a significant issue in modern power industry, because of electric load sensitive to power transient signal. This paper presents a novel approach for detection and location of power quality disturbances based on wavelet transform and artificial neural network. The wavelet transform is the projection of a discrete signal into two spaces: the approximation space and a series of detail spaces. The implementation of the projection operation is done by discrete-time subband decomposition of input signals using filtering followed by downsampling. The wavelet transform is utilized to produce representative feature vectors that can accurately capture the characteristics of power quality disturbance, exploring feature extraction of disturbance signal to obtain dynamic parameters. The feature vector obtained from wavelet decomposition coefficients are utilized as input variables of neural network for pattern classification of power quality disturbances. The training algorithm shows great potential for automatic power quality monitoring technique with on-line detection and classification capabilities. The combination performance of wavelet transform with neural network is evaluated by simulation results, approving that the proposed method is effective for analysis of power quality signal.
Automatic detection and classification power quality disturbance feature vector artificial neural network dynamic parameter
Shanlin Kang Huanzhen Zhang Yuzhe Kang
Hebei University of Engineering, Handan 056038, China Beijing University of Chemical Technology, Beijing 100029, China
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
2529-2532
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）