## 4 Conclusions  
Various methods have been applied, such as insulation resistance measurement, field measurement, temperature measurement, ultrasonic wave method, image analysis, and CT, as conventional methods for evaluating the integrity of insulators. These methods show high reliability in the assessment only under certain conditions, and there are significant deviations from the results depending on the environmental conditions and needs for complicated test equipment. However, the proposed method has the advantage of being less affected by the surrounding environment and being able to judge with high sensitivity and accuracy regardless of the types of insulator and damage. However, this application method also has disadvantages, e.g., it should be applied by proximity to the insulator on site or by sampling in the laboratory. Furthermore, it requires somewhat complicated interpretation and an expert to analyze it.  
This paper proposes basic FRA, the PCA of time and frequency response data to distinguish materials and damage type among porcelain insulators. A total of 67 ceramic insulators were collected from 154 kV transmission towers in various locations, and test specimens were constructed to mimic three possible defects at the site. Based on the experimental results, the following conclusions can be drawn:  
\(\bullet\) Frequency response analysis  
The basic frequency response analysis shows that the frequency response function varied depending on the type of material and whether the insulator was intact or damaged. The frequency  
Figure 15: Two-dimensional plot using two eigenvectors extracted from frequency response of all data.  
response of an intact porcelain insulator in cristobalite material was shown in a total of eight natural modes below 10 kHz; in the case of alumina material, the natural modes were shifted to high frequencies. Porcelain damage lost some of the natural modes. Cap damage was similar to the intact response; however, a new natural mode was created in the 1 kHz segment, and the second and third natural modes were moved to a lower frequency. Internal damage was observed as the disappearance of natural modes (such as porcelain damage) or the creation of new modes (such as cap damage).  
\(\bullet\) PCA using time data  
Eleven features were extracted using time data. Only porcelain damage was distinguished through PCA, using kurtosis and average, which are the principal components of a large contribution. With the use of skewness, porcelain damage and materials could be distinguished; however, there was no distinction between cap and internal damage.  
\(\bullet\) PCA using frequency response data  
Ten features were extracted using frequency response data. Through the analysis of the main components, intact, porcelain damage, cap damage, internal damage, and materials were all distinguishable using the moment of real value with considerable variance and the moment of imaginary value. If the third vector of the area of real value with a large variance was included, the distinction became more accurate.  
The proposed PCA method requires modification to enhance its accuracy; other optimization features can be applied to determine the methods required in damage identification. Moreover, it has to be generalized to be applicable to various cases for easy and fast damage classification. The foregoing could be areas of future investigations. PCA is expected to perform an important function in determining damage in porcelain insulators for future machine learning analyses.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; I.H.C. contributed device/analysis tools; [PERSON] and [PERSON] wrote the paper.  
This research was funded by Korea Electric Power Corporation (KEPCO).  
The authors declare no conflict of interest.