The Study And Application Of Dual Tree Complex Wavelet In Transformer PD Signals Denoising And Pattern Recognition

The Power Transformer is one of the most important electrical equipments in Pow-er System, whether does it ruan safely and stably will affect the power reliable supplyand system normal operation.The insulation accidents of high voltage electrical equipment can be defendedthrough the course of patial discharge (PD) characteristics of the parameters of theanalysis and the early found of signs of abnormality with the use of PD monitoringon-line and patten recognition technology. The partial discharges monitoring systemon-line can be divided into the measurement of the PD signal, the suppression ofexternal disturbance,atlas analyse, extract eigencector and indentify classify.An in-depth study of transformer partial discharge monitoring and patter recogniti-on technology has been researched. In this paper, based on the study, a new denoisingalgorithm was adopted, which is a method of transformer partial discharge monitoringsystem for pretreatment of the input signal denoising based on dual tree complex wavel-et. Dual tree complex wavelet reflects the non-steady signal sensitivly, apt to measurethe unusual different signal. This method is practical and effective, even under thestrong environment that interfers, can be made the great filtering effectiveness. And itwas combined with the BP neural network commonly used in pattern recognition toidentify the type of transformer partial discharge.Based on the above methods and techniques, a software measuring system on-linefor transformer partial discharge based on LABVIEW platform was developed in thispaper. And in the laboratory the characteristics of PD signals are analyzed and studied.Experiments show that the partial discharge monitoring software system has goodusability and stability.