Preprint
Article
Develop of Methods for an Overhead Cable Asset Soundness Evaluation Considering Economic Feasibility
Altmetrics
Downloads
79
Views
23
Comments
0
A peer-reviewed article of this preprint also exists.
This version is not peer-reviewed
Submitted:
12 September 2023
Posted:
14 September 2023
You are already at the latest version
Alerts
Abstract
To supply stable and high-quality power according to the advancement of industrial growth, electric power companies have performed maintenance of power facilities using various meth-ods. In the case of domestic power distribution facilities, there are limitations in performing di-agnostic management on all facilities owing to the large number of facilities; therefore, old fa-cilities are managed by the health index assessment method. The health index assessment com-prises only facility operation data and external environmental data and is managed only for four types of distribution facilities, including overhead/underground transformers and switchgear. In the case of extra high–voltage overhead wires, there is no standard for old replacement, despite the large number of wires, such as transformers and switchgear, and the large ripple effect of power failure in the event of a power outage. Therefore, in this paper, a health index assessment methodology for extra-high voltage overhead cables was developed. In this paper, we devel-oped an economic health index assessment methodology that additionally considers risk cost, which is different from the existing health index assessment method that uses only operational data and external environmental data to determine facility performance evaluation and aging replacement criteria. Using the health index assessment methodology developed in this paper, it is possible to expect a reduction in facility operating costs and investment costs from the per-spective of electric power companies through the replacement of old extra-high voltage over-head cables. In addition, from the perspective of consumers, it is expected to increase power re-liability and reduce the ripple effect of failure by preferentially replacing equipment with a high probability of failure.
Keywords:
Subject: Engineering - Electrical and Electronic Engineering
1. Introduction
As economic growth develops, consumers continue to demand a stable and high-quality power supply, and power companies continuously develop and maintain power facilities to meet this. Thus, the failure of power facilities causes enormous economic losses to consumers and power companies. For this reason, power companies manage power facilities in various ways, and power facility management technology has been developed accordingly. Conventional power facilities depend on a time-based maintenance (TBM) method with regular cycles before exhausting the life of the facility [1]. In introducing the TBM management method, the life of the power facility must be predicted through an experimental life evaluation. Accordingly, the overall lifespan of power facilities is predicted through life evaluation based on accelerated tests, and the TBM management method is operated accordingly.
Since the 1990s, condition-based maintenance (CBM) methods have been applied to monitor the condition of facilities online by attaching a sensor to the power facility, optimizing maintenance according to abnormal signs of the facility and predicting the facility’s life. As the CBM method is applied, methods for predicting the life of power facilities have become more diverse. However, these have certain limitations, such as limitations in sensing technologies and expensive diagnostic systems. Thus, most countries use the health index to predict the state of power facilities. The health index means expressing the overall state of power facilities as an indicator to establish a strategy for replacing power facilities. The health index defined in the Council on Large Electric Systems (CIGRE) technical document is shown in Table 1 [2,3,4].
In the case of distribution facilities in Korea, old facilities are being replaced through power distribution facility diagnosis and health index assessment to increase the efficiency of facility operation and minimize damage to power companies and consumers. Recently considering the probability of failure of devices and the complex impact on systems, the environment, and the safety of workers, research has been conducted on the risk-Based Maintenance (RBM) method, which is a maintenance direction that can meet the preferences of facility operators and managers [5]. The RBM method determines the priority of facility replacement based on the risk factors affecting a facility. Risk assessment is quantitatively derived by calculating the interaction between the probability of power facility failure and the ripple effect that ensues when failure occurs, where the facility operator analyzes and evaluates a case using calculated risks to establish replacement priorities [6]. Although many research institutes and papers have applied or studied asset management of power equipment using the RBM method, most of them were only for transmission and transformation power facilities [7,8]. In contrast, distribution power facilities lack sufficient diagnostic failure data for asset management using the RBM method, and failure data are insufficient because facilities are demolished as a preventive measure before failure occurs. Therefore, the RBM method is hard to apply to power distribution facilities. In addition, as the risk cost calculation used in RBM is determined by policy decision-making, the health index and diagnosis results are mainly used in the asset management of distribution facilities in Korea. Therefore, in this paper, we propose a new risk-based health index assessment method that applies facility risk costs to health index assessment as a quantitative concept.
2. Health Index in Korea
2.1. Power Facility Operation Environments
For domestic power facilities, health index assessment based on preventive diagnosis is mainly used. In Korea, as the main purpose is to stably provide high-quality power, the high reliability of power systems is maintained through advance replacement before equipment failure occurs [9]. Because transmission and substation facilities have large power facilities, a few facilities, and a large ripple effect due to facility failures, periodic preventive diagnosis is performed on all facilities, and breakdowns are prevented through 24-h monitoring by attaching diagnostic sensors [10]. However, as shown in Table 2, performing activities such as patrols and inspections on all power distribution facilities is difficult because power distribution facilities are numerous but small in size and many facilities are exposed to the outside environment. Thus, attaching diagnostic sensors to and monitoring power distribution facilities are impossible. Therefore, health index assessment for major power distribution facilities has been introduced and operated to prevent the failure of power distribution facilities.
2.2. Health Index Standards
Health index assessment is currently applied only to the management of power distribution facilities and is introduced and operated only for a total of four facility types: overhead transformers, overhead switchgear and underground switchgear, and transformers. Although the health index assessment items for each facility are different, all four types are composed of a perfect score of 100. Meanwhile, a score of 81 or higher is considered a very poor grade, thus requiring replacement. The criteria for health index assessment for each facility are evaluated using the items of operation data and external environmental data, as shown in Table 3.
The Korea Electric Power Corporation (KEPCO) has established and is operating its own standards for old replacement through health index assessment. According to an analysis of the ratio of old replacement by health index assessment among power distribution facilities removed by KEPCO over the past 10 years, about 30% of power distribution facilities were replaced through health index assessment. Table 4 shows the demolition rate of overhead transformers according to the reasons for demolition from January 2013 to December 2022 [13].
2.3. Difficulties and Solutions in the Health Index Evaluation of Distribution Facilities
Domestic health index assessment is evaluated using external environment data and operational data. However, there is a limit to securing diagnostic data, and there is a tendency for the old replacement quantity to be excessively calculated because economic feasibility is not considered. If the allocated replacement budget is insufficient, old facilities may not be replaced. In addition, because health index assessment is performed only for overhead and ground switchgear/transformers among various power distribution facilities, there are no standards for health index assessment for overhead wires, which account for a large number of power distribution facilities, and equipment is instead replaced after an operator’s visual inspection and regular inspection once every 4 years. Overhead wires are among the power distribution facilities installed in external environments, yet there is still no standard for health index assessment for overhead wires despite the high risk of accidents due to corrosion and deterioration caused by the external operating environment.
In this study, we present a methodology for health index evaluations to determine the criteria for replacing aging extra-high-voltage overhead wires. This methodology can evaluate overall conditions by reflecting operational data, external environmental conditions, and economic evaluation items, which can be used as health index assessment items to evaluate the performance of overhead wires. In addition, the existing section-weighted summation methodology is disadvantageous given that the health index assessment score differs greatly depending on the difference in section 1. Therefore, in this study, we propose a methodology for health index assessments based on a linear equation rather than a stepwise weighted summation format.
3. Proposed method
Figure 1 shows the health index assessment method proposed in this paper. Herein, the operation health and risk cost health are calculated to determine the overall health score.
3.1. Operation Health Index Weight
The weights of operation health index assessment items were calculated using survival analysis. The calculation process is shown in Figure 2.
Using the weights calculated for each evaluation item, the operation health index assessment formula was constructed as shown in Equation (1). At this time, the operation health index assessment score was based on a perfect score of 100 points.
In case the ,
3.2. Risk Health Index Weight
In the event of a breakdown of the line, the customer suffers from power failure and the power company incurs costs to solve it. In the event of a facility failure, the cost of sales loss for each line that suffers from the failure to supply power to the power company and the line replacement cost for restoring power facilities in the event of a failure are defined. Therefore, in this paper, two items, the power outage equipment cost and line replacement cost in the event of a line failure, were defined and used as risk costs.
When calculating the risk cost, the failure probability of each line calculated through the survival analysis was applied to the risk cost defined in this paper, and the failure risk cost for each line was calculated using the average annual power consumption per region.
The failure risk cost relational expression defined in this study is shown in Equation (2).
Equation (3) shows the method for calculating the failure risk cost score using the risk cost. Here, the failure risk cost score was based on 100 points.
The line replacement cost, defined as a risk factor in this study, was calculated using the KEPCO construction standard unit price and wire length as follows:
The method for calculating the line replacement score using the line replacement cost is shown in Equation (5). Here, the line replacement score was based on 100 points.
Because power supply interruption and line replacement always accompany each other in the event of a failure, the health index of the total risk cost evaluation was calculated by defining the health index score of the failure risk cost and the health index score of the line replacement cost at a ratio of 0.5:0.5. This was calculated on a scale of 100 points.
Therefore, the overall health index assessment formula for the overhead line in this paper was calculated as shown in Equation (8).
4. Case Study
4.1. Health Index Weight
In this paper, the weights of the health index assessment were calculated using the data of about 4.7 million overhead lines installed in Korea. The average values for 2021 were used for the unit price and blackout time in the health index assessment items of risk cost, and the operation health index assessment items were defined as factors that affect the performance of the overhead lines for the operation health index assessment. The operation health index assessment items are listed in Table 5 [12].
In evaluating the health index, the data types must be classified for each evaluation item and the maximum value must be selected for each evaluation item. For this purpose, the results of data type classification and bucket selection of evaluation items are shown in Table 6 [13].
Table 7 shows the average life expectancy at the point where the survival probability is 0.995 using the analysis bucket for each evaluation item. Here, the life expectancy of each bucket is used only for weight calculation, where the failure rate for each evaluation item in the failure data was multiplied by the reciprocal of the average life. The final calculations for the weights for each evaluation item are shown in Table 7. Here, the sum of the weights was calculated out of 100 points.
Here, the salt damage grade and kind of overhead line are categorical data. The weight calculated by multiplying the life expectancy and failure rate for each grade is shown in Table 8.
The final operation health index calculation formula using the calculated weight is shown in Equation (9).
4.2. Result of Overhead Line Total Health Index
Currently, in Korea, health index ratings are distinguished in units of 20 points. The overall health index assessment results by grade are shown in Figure 3 and Table 9. The Gyeongbuk and Gangwon regions apparently have plenty of extremely poor supplies, whereas the Seoul and Namseoul Headquarters have a few.
Table 10 and Table 11 show that the forest rates in the Gangwon and North Gyeongsang regions were higher than those in other regions, whereas the underground rate was low [14]. Given the many long-span lines in mountainous areas and because the construction cost for replacing them is high, these characteristics are reflected by the large number of replacement items of the health index.
Analyzing the number of actual spans for each level of health index and its ratio to the total number of spans, as shown in Table 12, there were many lines with an actual span of 50 m or more in the Gangwon, Chungbuk, and Gyeongbuk regions. In particular, the number of spans in Gangwon and Gyeongbuk was higher than that in other regions; therefore, the number of spans with an actual span of 50 m or more was the highest in Gangwon and Gyeongbuk. This proves that the replacement cost of overhead cables is high because the underground rate is low and there are many long-span sections in the forest area.
In Seoul and South Seoul Headquarters, the Very Poor quantity was calculated to be significantly less. This means the overhead cable operating environment was good because the number of overhead cables was low in the downtown area, the pollution level was good, and the wind speed was not as strong as those in other areas. In the past, we used the replacement method of overhead wires that relied only on instantaneous/diagnostic extraction. However, according to the health evaluation method developed in this paper, a risk-based replacement can prevent consumer life/property damage through a stable power supply and prevent power facility accidents.
4.3. Analysis of Economic Effects
The expected replacement quantity of extra-high-voltage overhead wires in the future was calculated to analyze the economic effects of the health index assessment model proposed in this study. As for the lifetime of the overhead wires, the manufacturer’s warranty period of 30 years was applied. Table 13 shows the calculation of the quantity of extra-high-voltage overhead wires over 30 years for the next 3 years and the analysis of such replacement quantity using the method proposed in this study.
As shown in the above results, it is necessary to replace a much smaller quantity when replacing extra-high-voltage overhead wires using the method proposed in this paper than when replacing a simple old overhead wire. This can reduce the annual replacement cost by more than 400 million won and can increase the accuracy of failure prevention by investing in activities such as precise diagnosis of overhead wires with reduced costs.
5. Conclusion
In this paper, a method for evaluating the health index of overhead wires was proposed to establish criteria for the efficient replacement of power distribution facilities.
For major power distribution facilities, there is an old replacement standard called health index assessment, but there is no old replacement standard for about 4.7 million power distribution overhead wires based on span, which were replaced only through diagnoses and visual inspections. This resulted in excessive diagnostic costs. In addition, by preemptively replacing overhead wires to prevent failure, excessive facility investment has been executed due to preemptive replacement even though the durability of the overhead wires remains. Thus, in this paper, a health index assessment model was proposed to establish cost-effective power distribution facility operating costs and facility replacement standards for overhead cables.
In future works, we plan to apply the algorithm developed in this study to an actual operating environment, perform replacements for more than 1 year, analyze statistical data on failure occurrence and old demolition, and consider machine learning-based algorithm accuracy verification results and expert opinions. In addition, we plan to increase the accuracy of equipment condition estimation by adjusting the weights of operation health and risk cost health using an analytic hierarchy process and calculating the optimal overall health weight.
Author Contributions
Conceptualization, H.L. and B.L.; methodology, H.L. and Y.K; software, H.L.; validation, H.L. and Y.K.; formal analysis, H.L.; investigation, G.H. and Y.K.; resources, H.L. and B.L.; data curation, H.L.; writing—original draft preparation, H.L. and B.L.; writing—review and editing, G.H. and Y.K.; visualization, H.L.; supervision, Y.K.; project administration, H.L. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Dong-Jin Kwon, 2018, “Technology Trends for Asset Management System of Power Facilities”, The Korean Institute of Electrical Engineers, 67(1), pp.
- 2006.
- 2010.
- 2013.
- N.S. Arunraj, J. N.S. Arunraj, J. Maiti, 2007, “Risk-based maintenance—Techniques and applications”, Journal of Hazardous Materials, 142(3), pp.
- Hong-Seok Lee, 2021, "RISK Assessment of Asset Management System for Distribution Transformer", Ph.D. Thesis, Korea National University of Transportation, Republic of Korea.
- Yahaya, M.S.; Azis, N.; Ab Kadir, M.Z.A.; Jasni, J.; Hairi, M.H.; Talib, M.A. Estimation of Transformers Health Index Based on the Markov Chain. Energies 2017, 10, 1824. [Google Scholar] [CrossRef]
- Kadim, E.J.; Azis, N.; Jasni, J.; Ahmad, S.A.; Talib, M.A. Transformers Health Index Assessment Based on Neural-Fuzzy Network. Energies 2018, 11, 710. [Google Scholar] [CrossRef]
- Kim Yu Min, Kim Myungchin, 2021, “Asset Management System Technology Trend Analysis of Power Facilities” Proceedings of Symposium of the Korean Institute of communications and Information Sciences, pp.
- KEPCO, 2022, “The Monthly Report on Major Electric Power Statistic(2022)”, Korea Electric Power Corp.
- KEPCO, 2022, “Statistics of Electric Power in Korea”, Vol.
- 2022.
- 2022.
- 2021.
Figure 1.
The health index assessment method.
Figure 2.
The calculation algorithm process of operation health index weight.
Figure 3.
Result of the overall health index assessment of overhead lines by grade.
Table 1.
Defining the CIGRE health index.
| Tech. Brochure |
Health Index Definition |
|---|---|
| TB309 | To develop an understanding of the overall condition of the asset base and the effect of aging on the ability of equipment to perform its intended function, many utilities have begun to develop and apply indicators, which are representative of asset condition |
| TB422 | The health index is one single overall indicator of the condition of an asset |
| TB541 | The health index is an indicator of the asset’s overall health and is typically given in terms of percentage |
Table 2.
Power distribution facility quantity.
| Facility Classification | Installation Location | Quantity | |
|---|---|---|---|
| Route length (c-km) |
High voltage | Overhead power | 197,996 |
| Underground power | 49,703 | ||
| Underwater power | 147 | ||
| Low voltage | Overhead power | 265,542 | |
| Underground power | 11,783 | ||
| Supporter | Concrete pole | 9,525,065 | |
| Panzer mast | 413,947 | ||
| Wooden pole | 170 | ||
| Steel pole | 177 | ||
| Steel tower | 1,081 | ||
| Transformer number | Overhead power | 2,368,002 | |
| Underground power | 66,978 | ||
| Static condenser number | Overhead power | 123,754 | |
| Underground power | 81,636 | ||
Table 3.
Evaluation items for the health index of power distribution facilities.
| Evaluation items |
|---|
| Lifetime loss rate (%) |
| Number of lightning strike days per year |
| Salt damage grade |
| Construction plan |
| Months of usage |
| Monthly average temperature difference |
| Average load |
| Number of operations of DAS switchgear |
| Failure rate by specification |
| Utilization rate |
| Ambient temperature |
| Type of insulation |
| Failure experience |
| Diagnostic inspection |
Table 4.
Ratio of overhead transformer demolition according to the reason for demolition.
| Fault | Burnout | Overload | Old age | Expansion | Relocation | etc |
|---|---|---|---|---|---|---|
| 8.55% | 3.36% | 2.44% | 24.63% | 11.69% | 11.41% | 37.91% |
Table 5.
Evaluation factor.
| Division | Evaluation factor | Note |
|---|---|---|
| Internal factors | Kind of overhead line, elapsed years | Internal characteristics of the lines, such as corrosion resistance and factors that can cause deterioration due to aging |
| External factors | Salt damage grade, number of lightning strike days, fatigue coefficient (wind tunnel impact factor) | Environmental factors that cause deterioration, corrosion, and abrasion of conductors |
Table 6.
Data type classification.
| Data type | Bucket for analysis | |
|---|---|---|
| Kind of overhead line | Static and plain categorical variable | ACSR-OC |
| Elapsed years | Continuous | 18+ years |
| Salt damage grade | Static and plain categorical variable | Grade D |
| Number of lightning strike days | Dynamic and continuous variable | more than 7 days |
| Fatigue coefficient | Dynamic and continuous variable | over 500 |
Table 7.
Life expectancy, failure rate, and weight results for each evaluation item bucket.
| Life expectancy by bucket | Failure rate | Weight | |
|---|---|---|---|
| Kind of overhead line | 30 | 0.287537 | 31 |
| Elapsed years | 39 | 0.359176 | 30 |
| Salt damage grade | 20 | 0.15211 | 25 |
| Number of lightning strike days | 19 | 0.042198 | 7 |
| Fatigue coefficient | 30 | 0.07262 | 8 |
Table 8.
Result of salt damage grade and kind of overhead line weight.
| Grade | Weight | |
|---|---|---|
| Salt damage grade | A | 8 |
| B | 17 | |
| C | 23 | |
| D | 25 | |
| Kind of overhead line | ACSR/AW-TR/OC | 2 |
| etc | 11 | |
| ACSR/AW-OC | 20 | |
| ACSR-OC | 31 |
Table 9.
Results of the overall health index assessment rating of overhead lines.
| Headquarters | Grade | ||||
|---|---|---|---|---|---|
| Very Good | Good | Fair | Poor | Very Poor | |
| Seoul | 2,280 | 43,582 | 25,078 | 1,474 | 64 |
| Namseoul | 2,371 | 40,544 | 25,121 | 1,661 | 36 |
| Incheon | 3,361 | 77,184 | 88,059 | 16,339 | 250 |
| Northern Gyeonggi | 3,551 | 131,494 | 141,668 | 20,947 | 882 |
| Gyeonggi | 17,210 | 234,831 | 200,601 | 25,706 | 763 |
| Gangwon | 5,892 | 160,287 | 190,444 | 32,809 | 4,363 |
| Chungbuk | 3,241 | 108,805 | 131,852 | 32,235 | 3,758 |
| Daejeon Sejong Chungnam | 12,844 | 254,623 | 255,106 | 41,839 | 2,297 |
| Jeonbuk | 7,571 | 196,672 | 174,226 | 21,153 | 1,105 |
| Gwangju Jeonnam | 15,762 | 327,569 | 280,552 | 28,205 | 1,876 |
| Daegu | 8,635 | 210,686 | 166,195 | 12,330 | 683 |
| Gyeongbuk | 5,286 | 186,429 | 160,331 | 33,887 | 5,444 |
| Busan Ulsan | 10,900 | 122,249 | 100,529 | 11,221 | 301 |
| Gyeongnam | 9,233 | 224,809 | 184,719 | 16,667 | 914 |
| Jeju | 1,520 | 34,734 | 46,441 | 11,170 | 193 |
| Total | 109,657 | 2,354,498 | 2,170,922 | 307,643 | 22,929 |
Table 10.
Forest rate by administrative district.
| Administrative district | 2020 | ||
|---|---|---|---|
| National land area | Forest area | Forest rate | |
| Nationwide | 10,041,260 | 6,298,134 | 62.72 |
| Seoul | 60,523 | 15,323 | 25.32 |
| Busan | 77,007 | 34,926 | 45.35 |
| Daegu | 88,349 | 48,338 | 54.71 |
| Incheon | 106,523 | 39,373 | 36.96 |
| Gwangju | 50,113 | 18,944 | 37.8 |
| Daejeon | 53,966 | 29,764 | 55.15 |
| Ulsan | 106,209 | 68,001 | 64.03 |
| Sejong | 46,491 | 24,849 | 53.45 |
| Gyeonggido | 1,019,527 | 512,105 | 50.23 |
| Gangwondo | 1,682,968 | 1,366,644 | 81.20 |
| Chung-cheong bukdo | 740,695 | 488,337 | 65.93 |
| Chungcheongnam-do | 824,617 | 404,097 | 49 |
| Jeollabuk do | 806,984 | 440,746 | 54.62 |
| Jeollanam-do | 1,234,809 | 686,852 | 55.62 |
| Gyeongsangbuk-do | 1,903,403 | 1,333,691 | 70.07 |
| Gyeongsangnam-do | 1,054,055 | 698,810 | 66.3 |
| Jeju | 185,021 | 87,334 | 47.2 |
Table 11.
Underground rate of national headquarters.
| Headquarters | Underground rate |
|---|---|
| Seoul | 57.87% |
| Namseoul | 64.58% |
| Incheon | 46.72% |
| Northern Gyeonggi | 24.89% |
| Gyeonggi | 32.67% |
| Gangwon | 10.63% |
| Chungbuk | 12.33% |
| Daejeon Sejong Chungnam | 19.34% |
| Jeonbuk | 11.94% |
| Gwangju Jeonnam | 11.95% |
| Daegu | 16.10% |
| Gyeongbuk | 5.63% |
| Busan Ulsan | 35.42% |
| Gyeongnam | 10.33% |
| Jeju | 20.52% |
| Total | 20.67% |
Table 12.
Number of spans over 50 m for each level of the health index.
| Very Good | Good | Fair | Poor | Very Poor | Percentage of the total span | |
|---|---|---|---|---|---|---|
| Seoul | 0 | 861 | 1,277 | 694 | 120 | 4.07% |
| Namseoul | 0 | 1,309 | 968 | 723 | 66 | 4.40% |
| Incheon | 0 | 8,904 | 4,522 | 4,279 | 256 | 9.70% |
| Northern Gyeonggi | 0 | 29,365 | 15,256 | 14,158 | 1,373 | 20.15% |
| Gyeonggi | 0 | 35,954 | 14,581 | 15,558 | 1,030 | 14.01% |
| Gangwon | 0 | 43,141 | 29,780 | 22,314 | 7,105 | 25.99% |
| Chungbuk | 0 | 38,602 | 26,801 | 20,842 | 6,013 | 32.96% |
| Daejeon Sejong Chungnam | 0 | 71,110 | 33,440 | 25,612 | 3,901 | 23.66% |
| Jeonbuk | 0 | 50,283 | 23,392 | 15,032 | 2,093 | 22.66% |
| Gwangju Jeonnam | 0 | 72,229 | 29,524 | 20,487 | 3,343 | 19.20% |
| Daegu | 0 | 27,912 | 13,462 | 8,281 | 1,233 | 12.77% |
| Gyeongbuk | 0 | 49,351 | 25,906 | 19,853 | 9,237 | 26.66% |
| Busan Ulsan | 0 | 8,070 | 5,582 | 5,516 | 517 | 8.03% |
| Gyeongnam | 0 | 45,781 | 21,448 | 10,994 | 1,905 | 18.36% |
| Jeju | 0 | 6,885 | 2,484 | 3,796 | 387 | 14.41% |
| Total | 0 | 489,757 | 248,423 | 188,139 | 38,579 | 19.43% |
Table 13.
Estimated replacement quantity of extra-high voltage overhead wires for the next 3 years.
| Classification | 2022.12 | 2023.12 | 2024.12 | 2025.12 |
|---|---|---|---|---|
| Quantity of wires over 30 years | 64,550 | 8,047 | 10,397 | 18,549 |
| The health index model in this study Very Poor quantity |
22,929 | 1,001 | 991 | 817 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
MDPI Initiatives
Important Links
© 2024 MDPI (Basel, Switzerland) unless otherwise stated