This study proposes a crack identification algorithm to analyze the surface condition of porcelain insulators and to efficiently visualize cracks. The proposed image processing algorithm for crack identification consists of two primary steps. In the first step, the brightness is eliminated by converting the image to the lab color space. Then, the background is removed by the K-means clustering method. After that, the optimum image treatment is applied using morphological image processing and median filtering to remove unnecessary noise, such as blobs. In the second step, the preprocessed image is converted to grayscale, and any cracks present in the image are identified. Next, the region properties, such as the number of pixels and the ratio of the major to the minor axis, are used to separate the cracks from the noise. Using this image processing algorithm, the precision of crack identification for all the sample images was approximately 80%, and the F1 score was approximately 70. Thus, this method can be helpful for efficient crack monitoring.

This study examines the feasibility of the image deep learning method using convolution neural networks (CNNs) to maintain a porcelain insulator. Data augmentation is performed to prevent over-fitting, and the classification performance is evaluated by training the age, material, region, and pollution level of the insulator using image data in which the background and labelling are removed. Based on the results, it was difficult to predict the age, but it was possible to classify 76% of the materials, 60% of the pollution level, and more than 90% of the regions. From the results of this study, we identified the potential and limitations of the CNN classification for the four groups currently classified. However, it was possible to detect discoloration of the porcelain insulator resulting from physical, chemical, and climatic factors. Based on this, it will be possible to estimate the corrosion of the cap and discoloration of the porcelain caused by environmental deterioration, abnormal voltage, and lightning.

This paper investigates the soundness of porcelain insulators associated with the acoustic emission (AE) technique. The AE technique is a popular non-destructive method that measures and analyzes the burst energy that occurs mainly when a crack occurs in a high-frequency region. Typical AE methods require continuous monitoring with frequent sensor calibration. However, in this study, the AE technique excites a porcelain insulator using only an impact hammer, and it applies a high-pass filter to the signal frequency range measured only in the AE sensor by comparing the AE and the acceleration sensors. Next, the extracted time-domain signal is analyzed for the damage assessment. In normal signals, the duration is about 2ms, the area of the envelope is about 1,000, and the number of counts is about 20. In the damage signal, the duration exceeds 5ms, the area of the envelope is about 2,000, and the number of counts exceeds 40. In addition, various characteristics in the time and frequency domain for normal and damage cases are analyzed using the short-time Fourier transform (STFT). Based on the results of the STFT analysis, the maximum energy of a normal specimen is less than 0.02, while in the case of the damage specimen, it exceeds 0.02. The extracted high-frequency components can present dynamic behavior of crack regions and eigenmodes of the isolated insulator parts, but the presence, size, and distribution of cracks can be predicted indirectly. In this regard, the characteristics of the surface crack region were derived in this study.

Porcelain insulators are typically exposed to surface discharge and lightning impulse in service. This study investigates the insulation characteristics of the external and internal discharges of a porcelain insulator with respect to its flashover for a 154 kV transmission line. The experiments are also conducted using a wet flashover test and an impulse test based on the external discharge and the internal penetration, to classify the flashover voltage-time curve of the porcelain insulator. When an impulse with a strength of 2,500 kV/μs was applied three times to 6.5 mm ceramic samples, electrical penetration of approximately 70% occurred. The impulse experiment confirmed that the electrical penetration inside the porcelain insulator coincided with the area where the electric field was concentrated. The wet flashover voltage test revealed that the flashover threshold voltage increases by approximately 7% after cleaning of the surface.

Porcelain insulators have been used for a long time in 154 kV power transmission lines. They are likely to be exposed to sudden failure because of product deterioration. This study was conducted to evaluate the quality of porcelain insulators. After stresses were applied, the damaged regions of aged insulators were investigated in terms of chemical composition, material structure, and other properties. For porcelain insulators that were in service for a long time, the mechanical failure load was 126 kN, whereas the average mechanical failure load was 167.3 kN for new products. It was also determined that corrosion occurred at the metal pin part due to the penetration of moisture into the gap between the pin and the ceramic. Statistical analyses of failure were performed to identify the portion of the insulators that were broken. Cristobalite porcelain insulators fabricated without alumina additives had a high failure rate of 54% for the porcelain component. In the case of the addition of Alumina (Al₂O₃) to the porcelain insulators to improve the strength of the ceramic component, a more frequent damage rate of the cap and pin of 73.3% and 27%, respectively, was observed. This study reports on the material component of SiO₂ and the percentage of alumina added, with respect to the mechanical properties of porcelain insulators.

Porcelain insulators have been used mainly for power line fixing and electrical insulation in transmission towers. Porcelain insulators have generally a 30 years desired life, but over 50% exceed their life expectancy. Since the damage to porcelain insulators is usually accompanied by enormous loss of human resource material, their efficient maintenance has emerged as an important issue. In this regard, this study applied a frequency response function (FRF) for integrity assessment of the insulator. The characteristics of the FRF according to damage types were identified and analyzed by the change in natural frequencies, curve shape, attenuation, and Nyquist diagram stability. The results showed significant differences in the FRF according to damage types, which can be used as basic data for the effective integrity assessment of porcelain insulators.

It is extremely important to improve methodologies for the lifetime assessment of porcelain insulators. While there has been a considerable amount of work regarding the phenomena of lifetime distributions, most of the studies assume that aging distributions follow the Weibull distribution. However, the true underlying distribution is unknown, giving rise to unrealistic inferences, such as parameter estimations. In this article, we review several distributions that are commonly used in reliability and survival analysis, such as the exponential, Weibull, log-normal, and gamma distributions. Some properties, including the characteristics of failure rates of these distributions, are presented. We use a Bayesian approach for model selection and parameter estimation procedures. A well-known measure, called the Bayes factor, is used to find the most plausible model among several contending models. The posterior mean can be used as a parameter estimate for unknown parameters, once a model with the highest posterior probability is selected. Extensive simulation studies are performed to demonstrate our methodologies.