Applied IT & Engineering

1. Introduction

High-voltage transmission and distribution networks form one of the most critical layers of modern power infrastructure. Their reliability depends not only on generation capacity or grid design but also, in a very practical sense, on the long-term performance of electrical insulation. Insulation systems are expected to prevent leakage currents, flashovers, and unintended electrical discharges while operating under complex environmental, electrical, thermal, and mechanical stresses. When insulation begins to deteriorate, the consequences can extend beyond local equipment damage; failures may interrupt power delivery, increase maintenance costs, reduce system availability, and, in severe cases, create safety risks for workers and surrounding communities (Sierra et al., 2015; Su et al., 2020). For this reason, understanding the factors that weaken insulation performance remains a central concern in high-voltage engineering.

The degradation of insulation is rarely caused by a single isolated factor. In actual field conditions, environmental contamination, moisture, electrical overstress, partial discharge, and material aging often interact in ways that gradually reduce dielectric strength. Outdoor insulators, for example, are continuously exposed to dust, industrial pollutants, salt deposits, humidity, rainfall, and temperature variation. These contaminants may accumulate on insulation surfaces and, when combined with moisture, increase surface conductivity and raise the likelihood of leakage current and flashover events (Hussain et al., 2017; Rafiq et al., 2020; Rahman et al., 2021). This process is especially important in coastal, industrial, or highly polluted regions, where contamination and moisture can act together rather than independently. Although the physical mechanisms are well recognized, utilities still face difficulty in translating these risks into practical maintenance priorities.

Electrical stress is another important pathway through which insulation systems lose their functional integrity. Partial discharge is particularly concerning because it may begin as a localized phenomenon but gradually erode the insulation structure, creating channels of deterioration that can eventually lead to breakdown. Previous studies have shown that electrical treeing, transient overvoltage, and repeated switching stress may accelerate insulation damage and shorten service life in high-voltage equipment (Christodoulou et al., 2009; Li & Li, 2017; Ma et al., 2020; Su et al., 2020). Aging further complicates this process. Over time, insulation materials experience chemical, thermal, mechanical, and electrical changes that weaken their original protective properties (Taghvaei et al., 2020). Thus, insulation failure should be viewed as a cumulative process, shaped by both operating conditions and long-term exposure.

Because these degradation mechanisms are interconnected, maintenance strategies need to move beyond reactive repair. Preventive maintenance, including regular inspection, cleaning, testing, and timely replacement, remains widely used because it is practical, familiar, and relatively cost-effective (Mosalam & Günay, 2013). However, conventional maintenance alone may not be sufficient for modern power systems that require higher reliability and faster fault detection. Condition monitoring technologies, infrared thermography, partial discharge detection, optical sensing, hydrophobic coatings, and AI-supported diagnostic systems are increasingly being explored to identify early warning signs before visible failure occurs (Fofana & Hadjadj, 2016; Liang et al., 2018; Liu et al., 2021; Ma et al., 2020; Maraaba et al., 2017). These approaches offer a more data-driven path for insulation management, although their adoption may still be limited by cost, technical expertise, and institutional readiness.

Despite the growing literature on insulation materials, diagnostic techniques, and high-voltage equipment reliability, there remains a need for applied studies that integrate professional field experience with quantitative analysis. Many studies focus on specific materials, laboratory tests, or individual diagnostic tools, but fewer examine how engineers and field professionals perceive the relative importance of environmental, electrical, aging-related, and maintenance-related factors in operational systems. A structured assessment can therefore help identify which factors are considered most influential in practice and which improvement strategies are most commonly adopted.

Accordingly, this study assesses insulation performance in high-voltage transmission and distribution systems using survey-based quantitative data from professionals working in relevant technical roles. It examines key degradation factors, including environmental contamination, partial discharge, insulation aging, moisture influence, mechanical stress, and electrical overloading. It also evaluates the adoption of improvement techniques such as preventive maintenance, condition monitoring, hydrophobic coatings, AI-based fault detection, and nanomaterial insulation. By applying descriptive analysis, Pearson correlation, and multiple regression analysis, the study aims to clarify how these factors relate to insulation performance and to provide practical guidance for improving the reliability, safety, and operational stability of high-voltage power networks.

2. Materials and Methods

2.1 Study Design

This study used a quantitative, cross-sectional analytical design to assess the factors influencing insulation performance in high-voltage transmission and distribution systems. The approach was selected because insulation performance in operational power networks is shaped by several interacting technical, environmental, and maintenance-related factors, many of which are best understood through both field experience and structured numerical assessment. The study focused on six major degradation-related variables: environmental contamination, partial discharge, insulation aging, moisture influence, mechanical stress, and electrical overloading. In addition, the study examined the adoption of improvement techniques, including preventive maintenance, condition monitoring, hydrophobic coatings, AI-based fault detection, and nanomaterial-based insulation.

The methodological framework was informed by previous work on high-voltage insulation condition assessment, insulation degradation, partial discharge monitoring, environmental contamination, and diagnostic technologies (Fofana & Hadjadj, 2016; Hussain et al., 2017; Li & Li, 2017; Rafiq et al., 2020; Su et al., 2020; Tamma et al., 2021). Although the study was not laboratory-based in the strict experimental sense, it was designed to generate a structured and reproducible assessment of how professionals working with high-voltage systems evaluate insulation-related risks and improvement strategies.

2.2 Study Population and Sampling

The target population included professionals with practical or technical experience in high-voltage transmission, distribution, substation operation, insulation maintenance, or field-level electrical system supervision. Respondents were eligible for inclusion if they had direct or indirect professional involvement with high-voltage equipment, insulation systems, maintenance activities, condition monitoring, or power system operation. Participants who had no relevant technical experience with high-voltage systems were excluded to reduce the risk of non-specialist responses.

A total of 180 valid responses were included in the final analysis. The respondent group consisted of electrical engineers, substation operators, maintenance technicians, field supervisors, and other professionals involved in high-voltage system operation. This respondent composition was considered suitable because insulation performance is not evaluated only from a design perspective; it is also shaped by field inspection, maintenance practice, and operational decision-making. The sample therefore provided a reasonably balanced view of both engineering and practical maintenance experience.

2.3 Data Collection Procedure

Primary data were collected using a structured questionnaire developed from the major factors reported in the high-voltage insulation literature. The questionnaire was designed to capture professional perceptions of the relative importance of insulation degradation factors, the current adoption level of improvement techniques, and the perceived effectiveness of maintenance and monitoring strategies. The content of the questionnaire was guided by previous studies on environmental stress, insulation aging, partial discharge, transformer condition assessment, high-voltage monitoring, and insulation material performance (Beroual & Haddad, 2017; Liang et al., 2018; Ma et al., 2020; Pleşa et al., 2016; Zhou et al., 2017).

The questionnaire included three main sections. The first section collected demographic and professional information, including job role and educational background. The second section assessed the perceived influence of major degradation factors on insulation performance. The third section examined the adoption and perceived utility of improvement techniques, such as preventive maintenance, condition monitoring systems, hydrophobic coatings, AI-based fault detection, and nanomaterial insulation. Responses related to insulation factors were measured using a five-point Likert scale, where higher scores indicated stronger perceived influence on insulation performance. Likert-type scaling was used because it allows technical judgments to be transformed into comparable quantitative data for statistical analysis (Chronis et al., 2020).

Secondary information was also reviewed from relevant technical reports, operational records, and peer-reviewed literature to support interpretation of the findings. This additional material was not used to replace the primary questionnaire data but helped contextualize the observed patterns in relation to established insulation failure mechanisms and diagnostic practices (Pernebayeva et al., 2019; Rahman et al., 2021).

2.4 Study Variables

The dependent variable of the study was insulation performance, represented through an Insulation Performance Index. The independent degradation-related variables included environmental contamination, partial discharge, insulation aging, moisture influence, mechanical stress, and electrical overloading. Environmental contamination referred to the accumulation of dust, salt, industrial pollutants, and other surface deposits that may increase leakage current and flashover risk. Partial discharge referred to localized electrical discharge activity within or across insulation defects. Insulation aging represented the gradual deterioration of insulation materials under long-term electrical, thermal, chemical, and environmental stress. Moisture influence referred to the effect of humidity, rainfall, or condensation on insulation surfaces and internal dielectric behavior. Mechanical stress and electrical overloading represented physical and operational stresses that may weaken insulation reliability over time.

Maintenance and improvement-related variables included preventive maintenance and condition monitoring. Preventive maintenance included scheduled inspection, cleaning, testing, and replacement practices. Condition monitoring referred to the use of diagnostic systems, such as partial discharge detectors, infrared thermography, optical sensors, and other monitoring tools used to detect early signs of insulation deterioration (Fofana & Hadjadj, 2016; Li & Li, 2017; Ma et al., 2020).

2.5 Insulation Performance Index

To provide a structured measure of insulation system condition, an Insulation Performance Index was calculated using weighted normalized scores of selected insulation factors. The index was expressed as:

IPI = Σwi xi

where IPI represents the Insulation Performance Index, xi is the normalized score of the ith insulation factor, wi is the weight assigned to the ith factor, and n is the total number of factors included in the index. Each factor score was normalized before calculation to ensure comparability across variables. The weighting procedure was based on the relative importance of each factor as derived from expert response patterns and literature-supported relevance. A higher IPI value indicated better insulation condition, while a lower value suggested increased insulation vulnerability or reduced system reliability. This index-based approach follows the broader logic of condition and health-index assessment used in high-voltage equipment evaluation (Tamma et al., 2021).

2.6 Materials, Instruments, and Diagnostic Context

The study considered common high-voltage insulation materials and systems, including porcelain, polymeric, and composite insulators used in transmission, distribution, and substation environments. Although the primary dataset was survey-based, the questionnaire and interpretation framework were developed around diagnostic tools commonly used in high-voltage insulation assessment. These tools included partial discharge detectors, infrared thermography cameras, moisture analyzers, and condition monitoring systems. Such instruments are widely used to detect insulation deterioration, thermal anomalies, moisture-related defects, and early-stage electrical stress (Liang et al., 2018; Ma et al., 2020).

Where dielectric strength was conceptually assessed, it was defined using the standard relationship:

E = V/d

where E represents dielectric strength, V is the applied voltage, and d is insulation thickness. This relationship was used to support the theoretical interpretation of insulation reliability under electrical stress, particularly when discussing how material aging, moisture, and partial discharge may reduce the ability of insulation to withstand voltage stress.

2.7 Statistical Analysis

Data were entered, cleaned, coded, and analyzed using statistical software. Descriptive statistics were first calculated to summarize respondent characteristics, factor rankings, and adoption levels of improvement techniques. Frequencies and percentages were used for categorical variables, while mean scores and rankings were used for Likert-scale variables. This step helped identify which insulation degradation factors were perceived as most influential by professionals working with high-voltage systems.

Pearson correlation analysis was then performed to examine the strength and direction of relationships among insulation degradation factors and improvement-related variables. The Pearson correlation coefficient was calculated as:

r = Σ[(Xi − X̄)(Yi − Ȳ)] / √[Σ(Xi − X̄)² Σ(Yi − Ȳ)²]

where r represents the correlation coefficient, Xi and Yi represent paired values of two variables, and X̄ and Ȳ represent their respective means. Positive correlations indicated that two variables increased together, while negative correlations suggested an inverse relationship. For example, a positive correlation between environmental contamination and partial discharge would suggest that higher contamination is associated with greater discharge-related risk. By contrast, a negative correlation between preventive maintenance and insulation aging would suggest that stronger maintenance practice may be associated with reduced deterioration.

Multiple regression analysis was conducted to evaluate the combined influence of several independent variables on insulation performance. The regression model was specified as:

Y = β0 + β1X1 + β2X2 + β3X3 + … + βnXn + ε

where Y represents insulation performance, β0 is the intercept, β1 to βn are regression coefficients, X1 to Xn represent independent variables, and ε represents the error term. The regression model included environmental contamination, partial discharge, insulation aging, moisture influence, mechanical stress, preventive maintenance, and condition monitoring as predictors. The statistical significance of each predictor was evaluated using t-values and p-values, with p < 0.05 considered statistically significant. This analysis allowed the study to identify which factors had the strongest independent association with insulation performance when other variables were considered simultaneously.

2.8 Assessment of Improvement Techniques

The study also evaluated the adoption of insulation improvement techniques. These included preventive maintenance, condition monitoring, hydrophobic coatings, AI-based fault detection, and nanomaterial insulation. Preventive maintenance was assessed because it remains one of the most widely used strategies for reducing insulation deterioration and preventing unexpected equipment failure. Condition monitoring was included because real-time and sensor-supported diagnostics can help identify faults before they develop into severe breakdowns (Fofana & Hadjadj, 2016; Li & Li, 2017). Hydrophobic coatings were considered because they may reduce the effect of surface contamination and moisture on outdoor insulators (Taghvaei et al., 2020). AI-based detection and image-supported inspection methods were included because recent studies have shown growing interest in intelligent diagnosis of high-voltage equipment and insulator defects (Liu et al., 2021; Pernebayeva et al., 2019).

The efficiency of improvement techniques was conceptually assessed using the following expression:

Improvement Efficiency (%) = [(IPafter − IPbefore) / IPbefore] × 100

where IPbefore represents insulation performance before implementation of an improvement technique and IPafter represents insulation performance after implementation. This formula provides a reproducible way to estimate relative performance improvement when pre- and post-intervention data are available.

2.9 Reliability, Validity, and Quality Control

To improve reliability, the questionnaire was structured around clearly defined variables derived from established insulation-performance literature. Internal consistency of the questionnaire items was assessed using Cronbach’s alpha. A Cronbach’s alpha value of 0.70 or higher was considered acceptable for internal consistency. Content validity was supported through alignment of questionnaire domains with previously reported insulation degradation and diagnostic factors, including environmental contamination, partial discharge, aging, moisture, and maintenance practice (Hussain et al., 2017; Rafiq et al., 2020; Su et al., 2020).

Before final analysis, the dataset was screened for incomplete responses, inconsistent entries, and missing values. Only complete and valid questionnaires were retained. Variable coding was checked to ensure that higher scores consistently represented stronger influence or greater adoption, depending on the item type. Statistical outputs were reviewed for logical consistency between descriptive findings, correlation patterns, and regression coefficients.

2.10 Ethical Considerations

The study used professional survey responses and did not involve clinical participants, patients, or biological specimens. Participation was voluntary, and responses were treated confidentially. No personally identifying information was used in the analysis. Respondents were informed about the academic purpose of the study, and only aggregated data were reported. This approach helped protect respondent privacy while allowing the study to examine professional judgments related to high-voltage insulation performance.

2.11 Methodological Limitations

Although the methodology provides a structured and reproducible framework, several limitations should be acknowledged. First, the study relied mainly on professional perceptions rather than direct experimental measurements from insulation samples or real-time grid failure records. Second, because the design was cross-sectional, the findings show associations rather than definitive causal relationships. Third, the weighting of the Insulation Performance Index may vary depending on system type, operating environment, voltage class, and organizational maintenance practice. Future studies should therefore combine professional survey data with field measurements, laboratory testing, and longitudinal monitoring to strengthen causal interpretation and improve generalizability.

3. Results

3.1 Profile of Respondents

A total of 180 valid responses were included in the analysis. The respondent profile suggests that the dataset was largely informed by professionals who had direct technical or operational exposure to high-voltage transmission and distribution systems. Electrical engineers represented the largest group, accounting for 36.1% of the respondents, followed by substation operators at 22.2% and maintenance technicians at 21.1%. Field supervisors contributed 12.2%, while respondents from other related professional roles accounted for 8.4% [Table 1]. This distribution is important because insulation performance is not only a design issue; it is also shaped by routine operation, inspection, maintenance, and field-level decision-making.

The educational profile also indicated a reasonably strong technical background among the participants. Nearly half of the respondents held a bachelor’s degree (45.6%), while 27.8% had a master’s degree. Diploma holders represented 19.4% of the sample, and 7.2% belonged to other educational categories [Table 1]. Taken together, these characteristics suggest that the responses reflected both academic training and practical field experience. This balance adds value to the interpretation of the findings, particularly because insulation problems in high-voltage systems often require both theoretical understanding and operational judgment (Gökçe et al., 2018).

3.2 Ranking of Factors Affecting Insulation Performance

The analysis showed that environmental contamination was perceived as the most influential factor affecting insulation performance, with the highest mean score of 4.40 [Table 2]. This finding is consistent with the broader understanding that dust, salt deposits, industrial pollutants, and other surface contaminants can increase leakage current, reduce surface resistance, and raise the probability of flashover under high-voltage operating conditions (Hussain et al., 2017; Rafiq et al., 2020; Rahman et al., 2021). In practical terms, this result suggests that environmental exposure remains one of the most pressing concerns for utilities and maintenance teams, especially in coastal, industrial, and high-humidity regions.

Partial discharge ranked second, with a mean score of 4.30 [Table 2]. This result was expected, although still important, because partial discharge is widely recognized as an early and progressive indicator of insulation deterioration. Even when discharge activity begins locally, it may gradually weaken the dielectric structure, accelerate electrical treeing, and contribute to eventual insulation breakdown (Li & Li, 2017; Su et al., 2020). Insulation aging followed closely, with a mean score of 4.20, indicating that respondents also regarded long-term material degradation as a major threat to insulation reliability [Table 2]. Aging is not a single process; rather, it develops through the combined effects of electrical, thermal, chemical, and environmental stress over time (Taghvaei et al., 2020).

Moisture influence was ranked fourth, with a mean score of 4.10 [Table 2]. Although slightly lower than environmental contamination and partial discharge, its score remained high, suggesting that moisture is still perceived as a major contributor to insulation risk. This is technically reasonable because moisture can intensify the effect of surface contamination and may alter dielectric behavior under operating stress. Mechanical stress and electrical overloading received mean scores of 3.95 and 3.85, respectively [Table 2]. These values indicate moderate but still meaningful influence. Overall, the ranking pattern suggests that insulation degradation is viewed as a multi-factorial process, with environmental and electrical stressors carrying the strongest perceived impact.

3.3 Adoption of Insulation Improvement Techniques

The adoption pattern of improvement techniques showed a clear preference for traditional maintenance-based strategies. Preventive maintenance was the most frequently adopted approach, reported by 52.8% of respondents [Figure 1]. This result suggests that scheduled inspection, cleaning, testing, and replacement remain the dominant tools for managing insulation reliability. Such preference is understandable, as preventive maintenance is relatively familiar, operationally feasible, and less dependent on advanced infrastructure compared with sensor-driven or AI-based systems (Mosalam & Günay, 2013).

Condition monitoring systems were adopted by 21.1% of respondents [Figure 1]. Although this proportion is much lower than preventive maintenance, it still indicates a gradual shift toward more data-supported insulation management. Condition monitoring is particularly valuable because it can detect early signs of insulation deterioration before visible or catastrophic failure occurs (Fofana & Hadjadj, 2016; Li & Li, 2017). Hydrophobic coatings were reported by 15.0% of respondents, reflecting their practical relevance in reducing moisture retention and contamination-related surface conduction [Figure 1]. This finding aligns with previous work showing that coating technologies can improve the contamination performance of high-voltage insulators under challenging environmental conditions (Taghvaei et al., 2020).

By contrast, the adoption of AI-based fault detection and nanomaterial insulation remained limited, at 6.7% and 4.4%, respectively [Figure 1]. This lower uptake may reflect cost barriers, limited technical expertise, lack of organizational readiness, or uncertainty regarding long-term field performance. Even so, these technologies remain promising. AI-supported image and fault detection methods, for example, are increasingly being explored for high-voltage insulator inspection and defect recognition (Liu et al., 2021; Pernebayeva et al., 2019). The results therefore point to a transitional stage in insulation management: conventional maintenance remains dominant, while advanced diagnostic and material-based technologies are beginning to emerge.

3.4 Correlation Pattern Among Insulation Factors

Pearson correlation analysis provided further insight into how the major insulation-related factors were associated with one another. Environmental contamination showed a strong positive correlation with moisture influence (r = 0.69) and partial discharge (r = 0.68) [Figure 2]. This pattern suggests that environmental and electrical stressors may not act independently in practical settings. Rather, contamination may create surface conditions that become more harmful when moisture is present, while both factors may contribute to discharge-related deterioration. This interpretation is consistent with earlier studies describing the combined impact of pollution, humidity, and surface conductivity on high-voltage insulator performance (Hussain et al., 2017; Sierra et al., 2015).

The strongest positive relationship was observed between partial discharge and insulation aging (r = 0.71) [Figure 2]. This finding is particularly meaningful because it supports the view that partial discharge is closely linked with progressive material degradation. In other words, discharge activity may not only indicate an existing weakness but may also contribute to further insulation aging over time (Li & Li, 2017; Su et al., 2020). Moisture influence also showed positive correlations with environmental contamination, partial discharge, and insulation aging, reinforcing the idea that moisture can intensify several degradation pathways at once [Figure 2].

Preventive maintenance showed negative correlations with the major degradation factors, especially insulation aging (r = −0.70) and partial discharge (r = −0.67) [Figure 2]. This inverse relationship suggests that stronger preventive maintenance practices may be associated with reduced deterioration. Condition monitoring also showed negative associations with degradation-related variables, although

Table 1. Demographic profile of respondents. This table presents the professional and educational characteristics of the 180 respondents included in the study. The respondents represented major technical roles involved in high-voltage transmission and distribution systems, including electrical engineers, substation operators, maintenance technicians, field supervisors, and other related professionals. Educational background was categorized into diploma, bachelor’s degree, master’s degree, and other qualifications. The distribution indicates that the dataset included both technically trained and field-experienced participants, providing a balanced basis for evaluating insulation performance from operational and engineering perspectives.

Figure 1. Adoption level of improvement techniques for high-voltage insulation systems. This figure illustrates the adoption rate of major insulation improvement techniques reported by respondents. Preventive maintenance was the most widely used strategy, followed by condition monitoring systems and hydrophobic coatings. AI-based fault detection and nanomaterial insulation showed comparatively lower adoption rates. The pattern suggests that conventional maintenance practices remain dominant, although advanced diagnostic and material-based technologies are gradually emerging in high-voltage insulation management.

Table 2. Ranking of factors affecting insulation performance in high-voltage systems. This table summarizes the mean scores and ranking of major factors perceived to affect insulation performance. Environmental contamination received the highest mean score, followed by partial discharge, insulation aging, moisture influence, mechanical stress, and electrical overloading. Higher mean scores indicate stronger perceived influence on insulation deterioration. The ranking suggests that environmental and electrical degradation mechanisms were considered the most critical concerns for high-voltage insulation reliability.

Table 3. Multiple regression analysis of factors associated with insulation performance. This table shows the regression coefficients, standard errors, t-values, and p-values for the predictors included in the insulation performance model. Environmental contamination, partial discharge, insulation aging, and moisture influence showed significant positive associations with insulation degradation, while preventive maintenance and condition monitoring showed significant negative associations, suggesting protective effects. Mechanical stress showed a weaker and statistically non-significant association. Abbreviations: EC, environmental contamination; PD, partial discharge; IA, insulation aging; MI, moisture influence; MS, mechanical stress; PM, preventive maintenance; CM, condition monitoring.

the strength of these relationships was generally moderate [Figure 2]. This may indicate that condition monitoring contributes to early fault identification, but its full benefit depends on how consistently it is implemented and integrated into maintenance decisions. Mechanical stress showed comparatively weaker relationships with other factors, suggesting that it may operate through a more independent pathway than contamination, moisture, or partial discharge.

3.5 Predictors of Insulation Performance

Multiple regression analysis was performed to identify the independent contribution of each factor to insulation performance. The model showed that partial discharge had the strongest positive coefficient among the degradation-related variables (β = 0.29, p = 0.005), followed by insulation aging (β = 0.27, p = 0.016), environmental contamination (β = 0.24, p = 0.009), and moisture influence (β = 0.22, p = 0.030) [Table 3]. These findings suggest that electrical discharge activity, long-term material aging, environmental exposure, and moisture-related effects were all statistically significant contributors to insulation performance variation.

Mechanical stress had a smaller coefficient and was not statistically significant at the 0.05 level (β = 0.14, p = 0.083) [Table 3]. This does not necessarily mean that mechanical stress is unimportant; rather, within this dataset, its independent effect was weaker after other factors were considered. This result may reflect the fact that mechanical stress is often more equipment-specific or event-driven, while environmental contamination, moisture, partial discharge, and aging are more continuous and cumulative in field conditions.

The regression model also showed that preventive maintenance had a significant negative coefficient (β = −0.31, p = 0.001), while condition monitoring also showed a significant negative coefficient (β = −0.26, p = 0.002) [Table 3]. These negative coefficients are meaningful because they suggest that greater adoption of maintenance and monitoring practices was associated with reduced insulation deterioration. Preventive maintenance showed the strongest protective association in the model, which supports the earlier correlation findings and reinforces its continued practical value. Condition monitoring also appeared to contribute to improved insulation management, likely by helping operators identify early-stage defects before they develop into major failures (Fofana & Hadjadj, 2016; Ma et al., 2020).

3.6 Integrated Interpretation of the Findings

Overall, the results suggest that insulation performance in high-voltage transmission and distribution systems is shaped by a combination of environmental, electrical, material-aging, and maintenance-related factors. Environmental contamination emerged as the highest-ranked perceived factor, while partial discharge showed the strongest statistical association with aging and the highest degradation-related regression coefficient [Table 2; Figure 2; Table 3]. This combination of findings indicates that insulation deterioration is not a simple linear process. It appears to develop through overlapping pathways in which contamination, moisture, partial discharge, and aging reinforce one another.

At the same time, the results show that preventive maintenance and condition monitoring remain central to insulation reliability. Preventive maintenance was the most widely adopted improvement strategy and also showed the strongest protective association in the regression model [Figure 1; Table 3]. Condition monitoring was less widely adopted, but its negative association with degradation factors suggests practical value, especially for early diagnosis and risk-based maintenance planning. The relatively low adoption of AI-based fault detection and nanomaterial insulation indicates that advanced insulation technologies are still in an early implementation phase. Therefore, the findings point toward a realistic improvement pathway: utilities may continue relying on preventive maintenance, but stronger integration of condition monitoring and intelligent diagnostic tools could further improve reliability in high-voltage insulation systems.

4. Discussion

4.1 Overview of the Main Findings

This study examined the major factors associated with insulation performance in high-voltage transmission and distribution systems, with particular attention to environmental contamination, partial discharge, insulation aging, moisture influence, mechanical stress, electrical overloading, preventive maintenance, and condition monitoring. Taken together, the findings suggest that insulation performance is not shaped by one dominant cause alone. Rather, it appears to be influenced by a cluster of environmental, electrical, material, and

Figure 2. Pearson correlation matrix of insulation-related factors. This figure presents the correlation pattern among major degradation and improvement-related factors. Strong positive correlations were observed between partial discharge and insulation aging, as well as between environmental contamination and moisture influence. Preventive maintenance and condition monitoring showed negative correlations with several degradation factors, indicating their potential role in reducing insulation deterioration. Abbreviations: EC, environmental contamination; PD, partial discharge; IA, insulation aging; MI, moisture influence; MS, mechanical stress; PM, preventive maintenance; CM, condition monitoring. EC = Environmental Contamination; PD = Partial Discharge; IA = Insulation Aging; MI = Moisture Influence; MS = Mechanical Stress; PM = Preventive Maintenance; CM = Condition Monitoring.

maintenance-related conditions that interact gradually over time.

The respondent profile provides a useful basis for interpreting these findings. Electrical engineers formed the largest group of participants, followed by substation operators, maintenance technicians, and field supervisors [Table 1]. This mix is important because high-voltage insulation problems are not confined to design calculations or laboratory theory. They are often first noticed through inspection, operation, maintenance, and fault-response activities. The educational profile of the respondents also suggests that the dataset reflects both technical training and practical field exposure [Table 1]. In that sense, the results can be understood as a professional assessment of insulation-related risks in real operational settings, rather than as a purely theoretical evaluation.

4.2 Environmental Contamination as a Primary Operational Threat

Environmental contamination emerged as the highest-ranked factor affecting insulation performance, with a mean score of 4.40 [Table 2]. This result is technically reasonable and consistent with previous studies showing that outdoor high-voltage insulation is highly vulnerable to dust, salt, industrial pollutants, and other surface deposits (Hussain et al., 2017; Rafiq et al., 2020; Rahman et al., 2021). Under dry conditions, these contaminants may remain relatively inactive. However, when moisture, humidity, rainfall, or condensation is present, the contaminated layer can become conductive and increase leakage current. Over time, this may promote surface discharge, tracking, flashover, and accelerated insulation deterioration.

The strong correlation between environmental contamination and moisture influence further supports this interpretation [Figure 2]. In practical terms, contamination and moisture rarely behave as separate problems in the field. A polluted insulator surface becomes more dangerous when exposed to humidity, and a humid environment can intensify the effect of even moderate contamination. This finding is especially relevant for high-voltage systems located in coastal, industrial, desert, or densely populated urban regions, where airborne particles and moisture may act together. Previous risk-based studies have also emphasized the role of environmental pollution and weather exposure in high-voltage substation and outdoor insulator failure (Sierra et al., 2015; Hussain et al., 2017).

The implication is clear: utilities should not treat environmental contamination as a minor maintenance issue. Regular cleaning, pollution mapping, hydrophobic surface protection, and risk-based inspection scheduling may be necessary in regions where contamination levels are high. This becomes even more important when operating conditions include humidity or frequent rainfall.

4.3 Partial Discharge and Insulation Aging as Linked Degradation Processes

Partial discharge was ranked as the second most influential factor, with a mean score of 4.30 [Table 2]. The regression analysis also identified partial discharge as one of the strongest degradation-related predictors of insulation performance [Table 3]. This finding aligns with the established view that partial discharge is both a symptom and a driver of insulation deterioration. It may begin in small voids, defects, cracks, interfaces, or contaminated regions, but repeated discharge activity can progressively damage insulation material and reduce dielectric strength (Li & Li, 2017; Su et al., 2020).

The strongest positive correlation in the study was observed between partial discharge and insulation aging [Figure 2]. This relationship deserves careful attention. It suggests that insulation aging and partial discharge may reinforce one another in a progressive deterioration cycle. Aging weakens the material, increases defect formation, and reduces resistance to electrical stress. Partial discharge then exploits these weaknesses and may accelerate further degradation. In high-voltage systems, this type of gradual deterioration is particularly problematic because early-stage damage may not be visible during routine inspection.

Insulation aging also showed a high mean score of 4.20 [Table 2], confirming that respondents viewed long-term material degradation as a major concern. This result is consistent with studies showing that electrical, thermal, chemical, and environmental stresses gradually alter the physical and dielectric properties of insulation materials (Taghvaei et al., 2020; Zhou et al., 2017). The finding reinforces the need for maintenance programs that do not rely only on visible damage or emergency failure response. Instead, utilities may need periodic diagnostic testing, partial discharge measurement, thermal inspection, and insulation health-index evaluation to identify deterioration before it becomes irreversible.

4.4 Moisture, Mechanical Stress, and Electrical Overloading

Moisture influence ranked fourth among the assessed factors, with a mean score of 4.10 [Table 2]. Although it did not rank above environmental contamination, partial discharge, or aging, its contribution should not be underestimated. Moisture can reduce surface resistance, support contaminant conductivity, and affect dielectric behavior in insulation systems. Its positive relationship with environmental contamination and partial discharge suggests that moisture may function as an amplifying factor rather than an isolated cause [Figure 2]. This is particularly important for outdoor insulators, transformer insulation, and equipment exposed to seasonal humidity variation.

Mechanical stress and electrical overloading received somewhat lower mean scores than the other factors, but they still remained relevant [Table 2]. Mechanical stress may occur due to vibration, seismic movement, installation errors, wind load, thermal expansion, or structural deformation. Previous studies have shown that mechanical and seismic stresses can influence the reliability of high-voltage insulators and related equipment (Gökçe et al., 2018; Günay & Mosalam, 2013; Mosalam & Günay, 2013). In the present regression analysis, however, mechanical stress was not statistically significant at the 0.05 level [Table 3]. This may indicate that, within this dataset, mechanical stress had a weaker independent influence when environmental, electrical, and maintenance-related factors were considered together.

Electrical overloading also showed a moderate mean score [Table 2]. While it was not emphasized as strongly as contamination or partial discharge, it remains technically important because repeated overloading, transient voltage events, and switching stresses can contribute to progressive insulation deterioration. These findings suggest that overload-related risk may become more visible when combined with aging materials, environmental stress, or existing insulation defects.

4.5 Preventive Maintenance as the Dominant Protective Strategy

Preventive maintenance was the most widely adopted improvement technique, reported by 52.8% of respondents [Figure 1]. It also showed strong negative correlations with insulation aging and partial discharge [Figure 2], and regression analysis indicated a significant protective association with insulation performance [Table 3]. These findings strongly suggest that preventive maintenance remains central to insulation reliability management.

This is not surprising. Preventive maintenance is practical, familiar, and relatively affordable compared with advanced monitoring or AI-based diagnostic systems. Routine inspection, cleaning, tightening, testing, and replacement can reduce the accumulation of risk before it develops into failure. Previous work on high-voltage equipment performance has similarly emphasized the role of systematic maintenance and reliability-focused inspection in reducing operational vulnerability (Mosalam & Günay, 2013; Zhou et al., 2017).

Still, the findings also suggest a possible limitation. While preventive maintenance is valuable, it may not always detect early internal deterioration, especially partial discharge or hidden material defects. Therefore, preventive maintenance should not be viewed as a complete solution by itself. A more effective strategy may be to combine traditional maintenance with condition monitoring, targeted diagnostics, and risk-based asset management.

4.6 Emerging Role of Condition Monitoring and Advanced Technologies

Condition monitoring was adopted by 21.1% of respondents [Figure 1]. Although this adoption rate was much lower than preventive maintenance, the statistical findings suggest that condition monitoring has meaningful value. It showed negative associations with several degradation factors [Figure 2] and a significant protective coefficient in the regression model [Table 3]. This indicates that monitoring systems may help reduce insulation risk by detecting early-stage faults before they become severe.

This finding is consistent with previous studies describing the usefulness of electrical diagnostic techniques, optical sensors, infrared thermography, and partial discharge monitoring for assessing high-voltage equipment condition (Fofana & Hadjadj, 2016; Li & Li, 2017; Ma et al., 2020). In practice, condition monitoring allows utilities to move from time-based maintenance toward condition-based or predictive maintenance. This transition is important because not all insulation systems age at the same rate. Some assets may remain stable beyond expected service intervals, while others may deteriorate quickly due to local contamination, moisture exposure, manufacturing defects, or operating stress.

The lower adoption of AI-based fault detection and nanomaterial insulation is also notable [Figure 1]. AI-based fault detection was reported by only 6.7% of respondents, while nanomaterial insulation was reported by 4.4%. These findings suggest that advanced technologies are still in an early adoption stage. Cost, technical expertise, data availability, and organizational readiness may limit broader implementation. Even so, the literature suggests that intelligent image-based inspection and advanced material systems may become increasingly important in future high-voltage asset management (Liu et al., 2021; Nazir et al., 2019; Pernebayeva et al., 2019; Zhou et al., 2018).

4.7 Practical Implications for High-Voltage System Reliability

The findings have several practical implications. First, utilities should prioritize environmental contamination and partial discharge as key risk indicators because these factors showed strong perceived and statistical relevance [Table 2; Table 3]. Second, moisture should be managed as an amplifying factor, particularly in regions where humidity and surface pollution occur together [Figure 2]. Third, preventive maintenance should remain a core reliability strategy, but it should be strengthened through condition monitoring where possible [Figure 1; Table 3].

A balanced insulation management program may therefore include routine inspection, periodic cleaning, contamination severity mapping, partial discharge testing, thermal imaging, moisture control, and data-supported maintenance scheduling. Hydrophobic coatings may be particularly useful in polluted or humid environments, while AI-supported diagnostics may help utilities analyze visual inspection data and detect defects more efficiently. The broader direction is not to replace traditional maintenance entirely, but to make it more intelligent, targeted, and responsive to real operating conditions.

4.8 Study Limitations and Future Research Directions

This study provides useful insight into insulation performance from the perspective of high-voltage system professionals, but several limitations should be acknowledged. First, the findings are based mainly on questionnaire responses rather than direct laboratory measurements or continuous field-monitoring data. As a result, the study reflects professional perception and operational experience, not direct causal proof. Second, the cross-sectional design means that the associations observed among contamination, partial discharge, aging, maintenance, and monitoring should be interpreted carefully. They indicate relationships, but they do not fully establish temporal or causal pathways.

Third, the study did not separate findings by voltage class, geographic region, insulation material type, equipment age, or environmental severity. These variables may influence insulation performance substantially. For example, porcelain, polymeric, and composite insulators may respond differently to contamination, moisture, and aging. Similarly, coastal and industrial regions may show different risk patterns from dry inland regions. Future studies should therefore combine survey-based assessment with field measurements, laboratory testing, historical fault records, and longitudinal monitoring. Such work would make it possible to validate the Insulation Performance Index more rigorously and refine maintenance recommendations for different operating environments.

4.9 Summary of Discussion

Overall, the study indicates that high-voltage insulation performance is shaped by a complex interaction of contamination, moisture, partial discharge, aging, and maintenance practice. Environmental contamination was identified as the highest-ranked factor, while partial discharge showed strong statistical relevance and a close relationship with insulation aging [Table 2; Figure 2; Table 3]. Preventive maintenance remains the most widely adopted and statistically protective strategy, but condition monitoring also appears to provide important support for early fault detection [Figure 1; Table 3]. These findings support a practical conclusion: improving insulation reliability requires not only better materials or isolated inspection routines but also an integrated maintenance framework that combines environmental risk control, electrical diagnostics, preventive action, and gradually expanding use of intelligent monitoring technologies.

Conclusion

This study indicates that insulation performance in high-voltage transmission and distribution systems is influenced by several interconnected environmental, electrical, material-aging, and maintenance-related factors. Environmental contamination emerged as the most highly ranked concern, while partial discharge and insulation aging showed strong statistical relevance, suggesting that insulation degradation often develops through gradual and mutually reinforcing pathways. Moisture also appeared to intensify insulation vulnerability, particularly when combined with surface contamination. Preventive maintenance remained the dominant and most widely adopted improvement strategy, and its negative association with degradation factors supports its continued practical value. At the same time, condition monitoring showed meaningful potential for early fault detection and more targeted maintenance planning. Overall, the study suggests that improving insulation reliability requires more than routine inspection alone. A balanced framework combining preventive maintenance, environmental control, partial discharge assessment, condition monitoring, and selective adoption of intelligent diagnostic tools may provide a more resilient path for high-voltage system operation.

Author Contributions

M.S.H. conceptualized the study, designed the research methodology, supervised data collection, performed statistical analysis, interpreted the results, and drafted the original manuscript. U.P. contributed to questionnaire development, assisted in data collection and validation, and reviewed and edited the manuscript. F.H.C. supported literature review, contributed to data curation, assisted in result interpretation, and reviewed the final manuscript. All authors have read and agreed to the published version of the manuscript.

Acknowledgements

The authors sincerely thank all professionals who participated in this study, including electrical engineers, substation operators, maintenance technicians, and field supervisors, for their time and valuable insights in completing the structured questionnaire. Their practical expertise and operational experience were instrumental in shaping the findings of this research. The authors also extend their gratitude to the respective organizations and institutions that facilitated access to qualified respondents and supported the data collection process. Special appreciation is extended to the reviewers and editorial team for their constructive feedback, which significantly contributed to improving the quality and clarity of this manuscript. This research received no specific funding from any public, commercial, or not-for-profit funding agency. The authors declare no conflict of interest in relation to this work.

Competing financial interests

The authors M.S.H. et al., have no conflict of interest.

References