Research On Partial Discharge Pattern Recogoniton Of GIS Based On Ultrasonic Detection

Gas insulated switchgear is one of the main equipments in the high voltage substation. Its insulating performance directly influences the reliability of power supply. It plays an important role in the power system. Due to deterioration of insulation in the GIS, partial discharge will occur. Through the partial discharge detection can evaluate the running status of GIS, find hidden dangers in time and ensure reliable operation of device. Ultrasonic detection method uses ultrasonic which is generated from partial discharge to determine whether the partial discharge occurs. Ultrasonic detection method has high detection sensitivity and is not affected by the electromagnetic environment. It has been widely used for GIS detection in the field. Practice has proved that ultrasonic signals generated from different insulation defects are different. If we can identify the types of defects before open GIS, this will be helpful to determine the location of defects and arrange the repair work.Firstly, according to the main six kinds of insulation defects in the GIS, the insulation defect models were designed. The six kinds of defects are high voltage conductor protrusions, spikes on ground electrode, suspended metal particles, free metal particles, fixed metal particles on the surface of the insulator and bubble in the insulator. In the experiment, the insulation defect models were put into the GIS cavity. By using the insulation defect models, the voltage used in the test was lower. This could reduce the noise interference and enable the characteristics of partial discharge more obvious.Characteristic parameter extraction of partial discharge combines the time domain and the frequency domain. In the time domain, RMS, VAR, absolute integral mean value, skewness and kurtosis are extracted. In the frequency domain, the maximum of power spectrum, median frequency and mean power frequency are extracted. These parameters can reflect the characteristics of the partial discharge ultrasonic signals that grasped in different defects very well.Support vector machine is established based on statistical learning theory. It is a new machine learning method and uses the VC dimension theory, structural risk minimization principle and optimization method. In practical application, support vector machine is always one of the best algorithms. So, in this paper, we chose SVM as the pattern recognition classifier and used LIBSVM toolbox in MATLAB to realize the algorithm. Recognition results show that when SVM was used to partial discharge pattern recognition, the recognition rate was high.The method of wavelet analysis was used to do the denoising ultrasonic signals of partial discharge. The signals after denoising were used for pattern recognition with SVM classifier. The results show that compared with original signals, denoising can improve the recognition rate of pattern recognition.