Research On Key Techniques Of Insulator Pollution Level Detection Using Infrared Thermal Imaging

With the stable and rapid development of economy, on the one hand, the voltage rank is getting increasingly higher and the power system scale is becoming continuously larger; on the other hand, the environmental pollution becomes severer. Pollutants are accumulated on surfaces of insulators for their exposure to the contaminant condition. Under adverse weather conditions, such as heavy fog, drizzle, snow melt, pollution flashover is easily caused within the power grid. Pollution flashover has become one of the most harmful influencing factors on the safety of the power grid. It seriously affects the reliability of the power supply. Realizing the safe and accurate monitoring of insulator pollution severity could enable the transmission lines to change from planned maintenance into condition-based maintenance. It is urgent to solve the secure operation of transmission lines, and is significant to resolve the problem of flashover. Infrared imaging can achieve a non-contact detection of insulator pollution level with many merits, such as safety, thrifty, facility and immunization to electromagnetic interference. Key technical problems—such as heating theory for running insulator, image de-noising, image segmentation, disc image extraction, pollution feature extraction, pollution level classification, visual angle and feature selection—are discussed deeply and systemically in this dissertation. The concrete works are as follows:1. It was a lack of perfect heating theory to support pollution level detection of high voltage insulators using infrared thermal imaging. As the existing heating models of insulators could not handle the problem of dry band or dry band arc on the insulator surface, a heating analytical method of polluted and wetted insulators is proposed on the assumption that water evaporation mainly depends on heat generation on the insulator surface. By introducing contamination layer surface resistivity, humid intensity and arc model, the judging condition of the generation of dry band or dry band arc and the heating model for each running state are developed and solved by numerical analysis method. The simulation results reveal the thermal distribution on the insulator surface and the impact of the dry band or the dry band arc on leakage current and heating. Infrared thermal imaging experiment results of polluted and wetted insulators indicate that the proposed model is reasonable and can give theoretical support to insulator pollution level detection by infrared thermal imaging.2. The insulator infrared thermal image is characteristic of low contrast and big noise, so effective measures must be taken to restore the real temperature distribution on the insulator surface. It is confirmed for the first time that the wavelet transform coefficients of insulator infrared thermal image obey Laplacian distribution. Because the redundancy of stationary wavelet transform coefficients is beneficial to handle the image with the statistical law, a stationary wavelet-domain local adaptive de-noising method for insulator infrared thermal image based on maximum a posteriori (MAP) estimation is developed. The noise variance is estimated using the finest scaling sub-band coefficients. The pointwise signal variance is computed with its circular neighbouring coefficients, and the neighborhood size is adjusted based on the noise-to-signal ratio of image. MAP estimator is adopted to estimate different scaling clean coefficients locally and adaptively. Finally, inverse SWT is applied to gain the de-noised image. Taking the advantage of both approximate shift invariance and good directional selectivity of dual tree complex wavelet transform (DT-CWT), a complex wavelet-domain local adaptive de-noising method for insulator infrared thermal image based on MAP estimation is developed. The author utilizes the finest scaling sub-band coefficients of different filter banks to estimate their respective noise variances, and computes the signal variance of a coefficient using neighboring coefficients within a circular window whose radius varies with resolution, so noise-free coefficients are more accurately estimated by MAP estimation and the quality of the de-noised image is improved. Experimental results demonstrate that the developed methods get higher signal-to-noise rate (SNR), de-noise more effectively and preserve more detail information of the original image than traditional Wiener filtering method,the adaptive Bayesian threshold methods based on wavelet transform, SWT and DT-CWT.3. A single insulator is regarded as analytical object in actual pollution detection. According to the characteristics of the gray histogram of the intercepted infrared thermal image of the single insulator, two image segmentation threshold extracting methods are presented. One extracts the segmentation threshold from the histogram envelope line, and the other gets the segmentation threshold by the method of OTSU in logarithmic transform domain, based on the two threshold extracting methods, a segmentation method integrated threshold segmentation and morphologic post-processing is presented for insulator infrared thermal image. Experiment results indicate that the segmentation quality is eminent, the insulators are intact and their margins are clear.4. Because of the insulator infrared thermal images interlapping with each other in the insulator strings, half of the disc surface of insulator is the region of interest in the research. The validity of the feature extraction directly depends on whether the half of the disc surface could be well segmented from the image or not. The disc surface image of insulator is characteristic of ellipse. The edge points of the disc surface of insulator are sampled through different angle's straight line extending from the barycentric coordinates which are computed from the segmented image. The elliptic equation of the disc surface edge is fitted by the least square method. The ellipse image region above its long axis is abstracted, which is the half of the disc surface of insulator. Experiment results show that the presented method can obtain the half of the disc surface of insulator uniformly and normatively.5. The difference of the real temperature and the measured temperature is unpredictable by reason of the temperature measurement error of the infrared imaging system. To avoid the error effect of the measured temperature and utilize adequately the measurement precision of the infrared imaging system, a pollution feature extraction method based on relative temperature is put forward to bring the temperature distribution more reliable and accurate. Pollution features are extracted on the basis of the heating distribution on the insulator surface. Pollution level recognition takes the influence of environmental humidity into consideration. Six statistical parameters, namely, the average, the variance, the skewness, the kurtosis, the energy and the entropy of the relative temperature distribution, are extracted as pollution features from the point of view of the difference of whole temperature distribution, and a back-propagation neural network classifier is designed to check the insulator pollution level. The radial mean values of relative temperature are extracted as pollution features for the difference of temperature distribution along the disc diameter, and insulator pollution level is evaluated by minimum distance classifier under the nearest humidity condition. The gray histogram of insulator infrared thermal image indirectly embodying the relative temperature distribution, the normalized gray histogram is extracted as pollution features, and site pollution severity class is evaluated by maximum comparability criteria of grey synthetically relational degrees under the nearest humidity condition. Experiment results prove the feasibility and effectiveness of the three proposed methods.6. The best visual angle is propitious to improve the accuracy of detecting insulator pollution level by infrared imaging. A method to determine the best visual angle is proposed through comparative analysis of the same pollution features abstracted from insulator infrared images with Fisher criterion. Experiment results indicate that the thermal field of insulator surface significantly changes with the angle of view, and the features of lower surface have better classification performance to the uppers. It is recommended that the visual angle should aim at the lower surface for insulator pollution level detection by infrared thermal imaging.7. To acquire pollution features with excellent classification performance and lower characteristic dimension, a pollution feature selection method based on single factor variance analysis is brought forward. Experiment results show that the proposed method is simple and effective, not only decreases the complexity of data processing, but also avoids the undesirable characteristics into the feature subset for classification, improves the accuracy of pollution level classification.To sum up, key techniques of pollution level detection of insulator strings using infrared thermal imaging have been resolved in this paper. It is able to realize an accurate detection of pollution level of insulator strings by infrared thermal imaging.