The aging of porcelain insulators is responsible for the failure of power utilities. Porcelain insulators from different places in South Korea, possessing various aging times and years of installation, have been investigated to carry out lifetime statistics. These samples have a mass of 36,000 lbs and are operated at 154 kV. X-ray fluorescence (XRF) and scanning electron microscopy (SEM) were performed on the porcelain bodies of the samples collected. XRF and SEM revealed trends in the weight percentage of SiO₂, Al₂O₃, and Fe₂O₃ in the porcelain bodies of the collected samples. The SiO₂ and Fe₂O₃ weight percentage reduced to 8.93% and 73.17%, respectively, in sample C compared to A. However, the Al₂O₃ weight percentage increased to 16.23% in sample C compared to sample A. This change in weight percentage of SiO₂, Fe₂O₃, and Al₂O₃ contributed toward enhancing the mechanical and electrical properties of the insulators. Mechanical load tests with electrical voltage (M&E), thermal and mechanical load tests (T&M), and hardness tests (HRB) were performed to evaluate these characteristics. Experiments revealed an increase of 90.9% in the inequality factor (K) in sample C compared to A. The impact of constituents of porcelain on the lifetime expectancy of a porcelain insulator was reported by implementing statistical strategies, such as the Weibull distribution.

Insulators comprise only 5% of the capital cost of transmission lines; they are accountable for 70% of line interruptions and 50% of maintenance costs of transmission lines. Major transmission lines situated in different parts of the world were mostly all constructed 30 years ago. These lines have either completed or are approaching the active life at 30 years. It is not possible to replace all insulators at a time in any utility. From a standpoint of consistency, it is quite important to locate insulators that require replacement prior to the occurrence of failure. Recalling these issues, a replacement strategy was modeled on insulator samples, operated at 154 kV, mechanical and electrical rating (M+E) 25,000 lbs and within the 10–50 years (Y) age group, collected in bulk for laboratory evaluation, based on the probability of mechanical failure (P(F)) of insulators. For conducting these studies, tensile load test such as combined electrical and mechanical failing load test was performed on selected 30 new and aged porcelain insulator samples from bulk to access recent condition. It was observed that insulators under service for 50 years manifested a decrease of 89.3% in quality factor (K), as compared to insulators within 10 years of service. A micro-structural study was carried out by using an optical microscope (OM) and a scanning electron microscope (SEM) for the further confirmation of previous evaluations. P(F) was derived by implementing Weibull distribution on the experimental observations. It was observed that insulators with an age of 50 years depicted a 2.7% increase in P(F), as compared to insulators with an age of 10 years. This article discussed a strategy for accessing the recent condition of new, aged bulk samples and the criteria of the replacement of the insulator string based on P(F).

Suspension type porcelain insulators used in overhead transmission lines comprise metal, ceramic, and cement. The deterioration of cement can lead to mechanical separation. For the degradation analysis, varied sizes of pores ranging from a few µm (capillary pores) to tens to hundreds of mm (detectable by naked eyes) were considered. Cracks that were hard to view with naked eyes were identified by staining with a fuchsine solution. The hydrogen ion concentration and pH value indicate the extent to which the cement is deteriorated. The longer the cement is used, the lower its pH value. High mechanical strength is considered an important advantage of porcelain insulators, and it may decline, if the cement is used for a longer period of time. Water ingress may also occur, resulting in expansion, due to the rehydration of the cement. The process and mechanism of expansion of cement, due to infiltration of water were described. As a method of analysis, a universal indicator was employed to evaluate the pH changes in cement. It was observed that the pH value was 12–13 for new products. However, for products that were used for 52 years, the pH value was under 7, which indicated an acidic tendency, due to deterioration.

The deterioration of the cement that bonds the cap, pin, and porcelain shell of the porcelain insulators can be observed by three-dimensional computed tomography (3D-CT), a non-destructive test. When porcelain insulators are used for a long duration, the size of the pores existing in the cement increase as the cement expands due to the alkali-silica reaction (ASR). It is possible to visually confirm the corrosion of caps and pins. The degree of corrosion is divided into four noticeable stages (I–IV), and deterioration of cement includes one of the four stages of corrosion. The standard time of replacement of insulator vs. continued use is presented. As a result of X-ray diffraction (XRD) analysis, Fe is oxidized to Fe₂O₃ at 36, 43, 54, and 70 degrees. The corrosion in cement is caused by factors including the value of pH, oxygen content, and temperature. For the caps and pins, although a zinc coating is applied to prevent the corrosion of iron, corrosion of zinc is caused by substances present in the external environment. When the zinc coating begins to corrode, the corrosion of the Fe component of the cap and pin accelerates, causing mechanical/electrical problems.

Frequency response signals have been used for the non-destructive evaluation of many different structures and for the integrity evaluation of porcelain insulators. However, it is difficult to accurately estimate the integrity of porcelain insulators under various environmental conditions only by using general frequency response signals. Therefore, this study used a method that extracted several features that can be derived from the frequency response signal and reduced their dimensions to select features suitable for the evaluation of the soundness of porcelain insulators. The latest machine learning techniques were used to identify correlations and not for basic feature analyses. Two machine learning models were developed using the support vector machine and ensemble methods in MATLAB. Both models showed high reliability in distinguishing between normal and defective porcelain insulators, and they could visualize the distribution area of the data by extracting quantitative values and applying machine learning, rather than simply verifying the frequency response signal.

The most common method for inspection of insulators is to measure the change of electrical characteristics such as electric resistance and partial discharge. However, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere. In this respect, an alternative to such methods can be the impact response test, and a frequency response function (FRF) obtained from the test has been widely used as a tool for damage detection. In this study the FRF was applied to identify the cap damage of porcelain insulators. In addition, to solve the danger of high voltage and poor field accessibility near the insulator, a device with high field applicability was developed to measure FRF from a long distance using an auto impact hammer and Micro Electro Mechanical Systems (MEMS) technology. Even though the FRF is most suitable for inspection of porcelain insulators, dynamic characteristics such as natural frequencies may vary depending on manufacturing errors, installation conditions, etc., which may cause difficulties in damage identification. To overcome this limitation, the machine learning (ML) method was applied in this study to provide a diagnostic method that ensured consistent and accurate judgment. As a result of predicting the normal and the cap damage data using the support vector machine (SVM), bagging, k-nearest neighbor (kNN), and discriminant analysis (DA) methods, the overall F1 score was over 87% and the bagging method achieved the highest accuracy. In this study, the frequency range and dynamic characteristics that are sensitive to the physical damage of the insulator were derived and, based on this, the optimum ML methods with improved equipment could provide analysis with higher accuracy and consistency than general analysis using the FRF.