Recognition And Monitoring Of The Transient Power Quality Disturbance

While the power system is developing quickly and a great deal of nonlinear,impact and unsymmetrical load is taken to the Power system, the Power quality issues are more and more serious.The first step to improve the Power quality is the detection and analysis of Power quality. We should detect the Power quality issues quickly and exactly,and take them into analysis effectively. So we can control and treat the issues,while we confirm the types and areas of the issues.Based on wavelet transformation and neural network,an approach—wavelet network, on Power quality(PQ) disturbance detection and location is presented in this paper. It can recognize the transient PQ effectively. After Recognition, a parallel-composed morphological filter with adaptive multiple structure elements is designed to filter random noise and impulse noise in PQ disturbance signals at first. Then use the strange dots and model max theory of the wavelet transformation to detect the transient PQ. Numerical results under Matlab environments show that the proposed approach is valid and effective. Design a platform of power quality analysis with transferring Matlab codes to LabVIEW. This platform of PQ analysis has a good visualization of operability. Can basically meet the needs of transient PQ detection, location, identification.