Power Transformer Insulation Monitoring And Partial Discharge Faults Diagnosis

Power transformer is one of the most critical and expensive electrical pieces of equipment in power system,whose safety and reliability are closely related to the operation condition of the whole system.Furthermore,in the process of actual operation,power transformer is unavoidable to subject to such outside factor influence as electricity,machinery and heat,and so forth further causing its winding insulation deterioration to produce partial discharge(PD)phenomena,threatening the safety of operation of the whole system.Therefore,it is essential to monitor the insulation condition and provide a proper maintenance action for in-service power transformers.For this purpose,this paper taking a 330 kV transform substation in Gansu for example,t he aging mechanism of transformer insulation system,on-line monitoring of power transformer insulation and partial discharge fault diagnosis were studied.The main contents are as follows:The concept of entropy is introduced to PD signal feature extraction.Using the difference of each frequency band energy and complexity,signals feature region is established by the multidimensional energy parameters and the multidimensional sample entropy parameters to describe PD signals multidimensional feature information.The approach can effectively remove interference of the background noise,and solve the partial discharge signal frequency dispersion,denoising difficult.Partial discharge faults of the transformer are divided into three typical insulation defects.Taking the multi-dimensional feature region of the PD signal as the input parameter,the Power transformer partial discharge fault diagnosis model is established based on the sphere-structured support vector machine algorithm.The model is applied to the actual fault analysis,and fault recognition accuracy of more than 95%.The above results show that the approach proposed by this paper can identify and classify different partial discharge faults.