Introduction

Electricity is a foundational industry that is closely tied to the national economy and people’s livelihoods, while power grid companies are one of the country’s essential infrastructures. Personal safety accidents in power grid companies have a significant impact on the safe production of these companies1,2,3. Due to the unique nature of the power industry, personal safety accidents in power grid companies tend to follow certain patterns. Therefore, conducting statistical analysis on past personal safety accidents in power grid companies and summarizing the patterns of these accidents is of great importance for improving the safety production level of power grid companies and ensuring the life and property safety of workers.

Currently, many scholars both domestically and internationally have conducted statistical studies on personal injury and fatal accidents in power companies. Cawley et al.4 conducted a statistical analysis of power line electrocution accidents in the United States from 1992 to 2002 and found that 99.1% of fatal cases were caused by electrocution, with contact with overhead power lines being the primary cause. He Danxin5 performed a statistical analysis on accidents occurring over a ten-year period in a certain power company and concluded that human errors were the main cause of safety accidents in power production. Fan Yunxiao et al.6 classified both the direct and indirect causes of 333 production safety accidents in power supply companies between 1961 and 2008, finding that the accidents primarily reflected a lack of systematic safety management within the company. Fan Xianxin7 conducted a statistical analysis on accidents occurring between 1996 and 2010 in a certain power grid construction company, considering factors such as accident types, causes, and job positions. Based on the main problems identified in the company’s safety production management, he proposed preventive management strategies and recommendations to avoid personal safety accidents. Liang Zhixiang8 explored the causes and preventive measures for personal safety accidents in power companies. Yan Yuqiong et al.9 used statistical methods to study the personal accidents in power companies nationwide from 2016 to 2021, analyzing accident time and space distribution, accident types, companies involved, and operational processes to identify the characteristics and patterns of such incidents.

These studies reveal the basic patterns of personal accidents in power grid companies, analyzing factors such as unsafe behaviors and management deficiencies. However, research on personal accidents in China’s power grid companies lacks depth in areas such as time span and analytical dimensions, and systematic analysis is insufficient. In response, this study conducts a statistical analysis of personal safety accidents in China’s power grid companies from 2014 to 2024, based on six dimensions: overall accident situation, accident types, time distribution, geographic distribution, professional fields, and causes. By deeply investigating the patterns of accident occurrence, this study aims to provide useful references for improving the safety management level and accident prevention and control capabilities of power grid companies.

Overview of personal safety accidents in power grid companies in recent years

To ensure the authenticity and accuracy of the analysis, the statistical data is sourced from the official website of the National Energy Administration, the “Compilation of Power Safety Accident Events” book, the CNKI (China National Knowledge Infrastructure) database, media official websites, and other publicly available resources10,11,12,13 (the statistical data excludes Hong Kong, Macau, and Taiwan, unless otherwise stated). The statistical data used in this study is relatively complete, with broad coverage, and can serve as a data support for studying the patterns and characteristics of personal accidents in power grid companies.

From 2014 to 2024, a total of 125 personal safety accidents occurred in power grid companies nationwide, resulting in 178 deaths. Among them, there were 13 major accidents, accounting for 10.4% of the total number of accidents, with 47 deaths, accounting for 26.4% of the total fatalities. No major or catastrophic accidents occurred during this period. The accident data for the past decade is shown in Figs. 1 and 2.

As shown in Fig. 1, the average number of accidents per year during 2014–2024 was 11.36, with an average of 16.18 deaths per year. The highest number of accidents occurred in 2016, with 19 accidents and 26 deaths. The fewest accidents occurred in 2022, with 6 incidents and 8 deaths. Overall, the year with the highest number of accidents had 3.17 times the number of incidents as the year with the fewest accidents, and the year with the highest number of deaths had 3.38 times the number of fatalities as the year with the fewest deaths.

Fig. 1
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Number of accidents and fatalities by year.

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As shown in Fig. 2, in the past 11 years, there were significant accidents in 6 of those years. No major accidents occurred in 2014, 2019, 2021, 2022,and 2024. The years 2015, 2016, and 2017 experienced the highest number of major accidents, with 3 incidents each.

Fig. 2
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Accident level statistics.

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Overall, from 2014 to 2024, the number of personal safety accidents and fatalities in power grid enterprises showed a fluctuating trend, but remained within a relatively stable range.

Statistical analysis of personal safety accidents in power grid enterprises

Statistical analysis of accident types

According to the “Classification Standard for Employee Casualty Accidents” (GB6441-1986)14, the types of accidents relevant to power grid enterprises include seven categories: falls from heights, electric shocks, being struck by objects, mechanical injuries, roof collapse, poisoning and suffocation, and explosions. Based on the accident data and characteristics of power grid enterprises, two additional categories—pole toppling and tower tilting—have been included, bringing the total number of accident types to nine, as shown in Table 1.

Table 1 Classification of accident types and accidents.
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The number of accidents and fatalities for each accident type are shown in Fig. 3. As seen in the figure, electric shock accidents occurred the most, with 64 incidents, accounting for 51.2% of the total number of accidents, and resulting in 78 deaths, or 43.82% of the total fatalities. Falls from heights ranked second, with 31 incidents, accounting for 24.8% of the total accidents, and causing 43 deaths, or 24.16% of the total fatalities. Struck by objects ranked third, with 13 incidents, representing 10.4% of the total accidents, and resulting in 18 deaths, or 10.11% of the total fatalities. Electric shock, falls from heights, and struck by objects together account for 86.4% of all accidents and 78.09% of all fatalities, making these three types of accidents the primary focus for safety prevention in power grid enterprises.

Fig. 3
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Statistics on the occurrence of accidents of different accident types. (a) Number of accidents, (b) Number of deaths.

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Fig. 4
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Statistics on the number of fatalities in a single accident for different types of accidents.

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The number of deaths per accident for different types of incidents is shown in Fig. 4. As seen in Fig. 4, the highest number of fatalities per incident occurs in tower collapse accidents, with an average of 3.33 deaths per accident. This is followed by poisoning and asphyxiation accidents, with 3 deaths per accident, and roof collapse accidents, with 2.5 deaths per accident. Although the occurrences of tower collapse, poisoning/asphyxiation, and roof collapse accidents are relatively few, they result in significant damage, high casualties, and severe consequences, which warrant considerable attention.

Statistical analysis of accident regional distribution

Statistical analysis by province

The statistics and analysis of personal accidents in power grid enterprises across different provinces in China from 2014 to 2024 are shown in Figs. 4 and 5. It is noteworthy that no personal safety accidents in power grid enterprises were reported in Beijing, Shanghai, and Qinghai during the study period. This may be attributed to a combination of factors, including stricter regulatory enforcement, higher levels of safety awareness and training, better infrastructure conditions, and relatively fewer high-risk operations in these regions. Additionally, the possibility of underreporting or classification differences in administrative records cannot be entirely ruled out.

As shown in Fig. 5, the national average number of accidents per province is 4.03. Ten provinces (autonomous regions) had accident numbers above the national average, namely: Guangxi (16 accidents), Inner Mongolia (13 accidents), Yunnan (10 accidents), Shaanxi (9 accidents), Guangdong (9 accidents), Sichuan (9 accidents), Anhui (6 accidents), Hebei (6 accidents), Fujian (5 accidents), Hunan (5 accidents) and Hainan (5 accidents). Among these, Guangxi, Yunnan, and Inner Mongolia had the highest number of accidents, with each province (autonomous region) reporting 10 or more accidents, accounting for 31.2% of the total accidents nationwide. Conversely, five provinces—Tianjin, Liaoning, Shanxi, Henan, Hubei, and Jiangsu—had relatively fewer accidents, each with only one incident.

Fig. 5
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Number of accidents by province.

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Fig. 6
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Fatality rate by province.

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As shown in Fig. 6, the average fatality rate across all provinces in China is 36%, calculated as the number of deaths divided by the total number of workers involved. A total of 12 provinces (or autonomous regions) exhibited fatality rates higher than the national average, namely: Liaoning (100.00%), Jiangxi (88.89%), Henan (66.67%), Hebei (50.00%), Gansu (50.00%), Tianjin (50.00%), Jiangsu (50.00%), Hunan (46.15%), Zhejiang (46.15%), Guangdong (39.47%), Guizhou (37.50%), and Heilongjiang (37.50%).Among them, seven provinces (Liaoning, Jiangxi, Henan, Hebei, Gansu, Tianjin, and Jiangsu) had particularly high fatality rates, all exceeding or equal to 50.00%. In contrast, Shanxi recorded the lowest fatality rate at 16.67%.

It is also worth noting that Guangxi, Inner Mongolia, and Yunnan reported the highest number of accident cases nationwide, yet their corresponding fatality rates remained relatively low, ranging from 15% to 30%. Conversely, provinces such as Liaoning, Henan, Jiangsu, and Tianjin, despite having fewer accident cases, exhibited fatality rates above 50%.Therefore, while it is crucial to strengthen safety supervision in provinces with high accident frequency, greater attention should also be given to regions with disproportionately high fatality rates, in order to enhance accident prevention capabilities and reduce the severity of outcomes when incidents do occur.

Regional statistics

China is divided into seven geographical regions: East China, South China, North China, Central China, Southwest China, Northwest China, and Northeast China. The statistics for accident numbers and fatalities across these seven regions are presented in Table 2.

Table 2 Accidents by region.
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As shown in Table 2, in terms of the number of accidents, the South China region reported the highest number of incidents, with 27 cases, while the Northeast region recorded the lowest, with only 5 cases. On a per-province basis, South China also exhibited the highest average number of accidents at 9 cases per province, whereas the Northeast had the lowest average at 1.67 cases per province.Regarding the number of fatalities, South China again ranked highest with 37 deaths, while the Northeast had the fewest fatalities, with 7 deaths. The Central China region showed the highest fatality rate, at 56.10%, whereas the North China region had the lowest fatality rate, at 25.00%.Overall, the South China region experienced the highest frequency of personal safety accidents, the highest average number of accidents per province, and the largest number of fatalities, indicating a high-risk pattern. Thus, further efforts are needed to strengthen accident prevention and control in this region. Additionally, given the elevated fatality rate in Central China, enhanced supervision and control of on-site personnel are urgently required to reduce the likelihood of fatal outcomes.

Statistical analysis of accident time distribution

Statistical analysis by quarter

According to the statistical analysis of personal accidents in power grid enterprises from 2014 to 2024, based on the quarters in which the accidents occurred, the accident situation for each quarter is shown in Fig. 7. As seen in the figure, the overall situation of personal accidents in power grid enterprises follows a unimodal distribution.The second quarter is the peak period for accidents, with 52 accidents occurring and 82 fatalities. The third quarter follows, with 32 accidents and 45 fatalities. The fourth quarter ranks third, with 25 accidents and 30 fatalities. The first quarter has the fewest incidents, with 16 accidents and 21 fatalities.

Fig. 7
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Number of accidents and fatalities by quarter.

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Statistical analysis by month

According to the statistical analysis of personal accidents in power grid enterprises from 2014 to 2024, based on the months in which the accidents occurred, the accident situation for each month is shown in Fig. 8. As seen in the figure, the average number of accidents per month from 2014 to 2024 is 10.42, with an average of 14.83 fatalities per month. The number of accidents in April through July and in August is higher than the monthly average, and the number of fatalities from April to August is also above the monthly average.Among these months, April and May have peak accident numbers and fatalities, coinciding with the critical spring inspection period, during which the workload in power grid operations is high, safety risks are elevated, and personal accidents occur frequently. To effectively prevent accidents, power grid enterprises should strengthen safety supervision during the spring inspection period. Conversely, the number of accidents and fatalities is at its lowest in January and February due to the winter shutdown and the reduced workload during the Chinese New Year holiday, which lowers safety risks and leads to fewer personal accidents.

Fig. 8
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Number of accidents and fatalities by month.

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Statistical analysis by time period

The statistics for 125 personal safety accidents in power grid enterprises from 2014 to 2024, categorized by hourly intervals, are shown in Fig. 9.

As seen in Fig. 9, personal safety accidents in power grid enterprises predominantly occur during the daytime, with an overall bimodal distribution. Accidents are relatively fewer between 21:00–01:00 and 02:00–05:00, while accidents occur more frequently between 09:00–10:00 in the morning and 15:00–16:00 in the afternoon.This pattern can be attributed to the high mental concentration and intense physical labor required in power grid operations. After working for a period in the morning and afternoon, workers tend to experience short fatigue periods, during which their energy, attention, and reaction times decrease. This significantly increases the risk of accidents occurring.

Fig. 9
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Number of accidents and fatalities by time period.

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Statistical analysis of specialized areas of accidents

According to the classification standards for power-related personal injury and fatality accidents published by the National Energy Administration, the personal safety accidents in power grid enterprises are categorized into two professional fields: power construction and power production. The statistical analysis of personal safety accidents in power grid enterprises from 2014 to 2024, based on professional fields, is presented in Table 3, showing the number of accidents, the proportion of fatalities in each field, and the number of fatalities per accident for each field.

Table 3 Accidents in different areas of specialization.
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As shown in the table, the number of accidents in power production is the highest, accounting for 59.2% of the total accidents, while the number of accidents in power construction accounts for 40.8% of the total. The number of fatalities in power production is also the highest, representing 51.69% of the total fatalities, while fatalities in power construction account for 48.31% of the total. The average number of fatalities per accident is highest in power construction, with 1.69 fatalities per incident, followed by power production, with 1.24 fatalities per incident.

Statistical analysis of accident causes

Main causes of accidents

According to the 4 M theory, accident causes are categorized into four main factors: unsafe human behavior, unsafe material conditions, management loopholes, and environmental defects15. The basic situation of accidents caused by these different factors is shown in Table 4.

Table 4 Causes of accidents by type of accidents.
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As shown in Table 4, the majority of accidents occur due to unsafe human behavior, with 113 incidents, accounting for 54.85% of the total accidents. The second most common cause is management loopholes, leading to 68 accidents, or 33.01% of the total. Unsafe material conditions caused 36 accidents, accounting for 17.483% of the total accidents. Environmental defects were the cause of 8 accidents, or 3.88% of the total.

Furthermore, as shown in Fig. 10, electric shock accidents caused by unsafe human behavior resulted in the highest number of fatalities, with 78 deaths. Falls from heights followed, with 40 fatalities. Being struck by objects ranked third, with 10 fatalities each. Electric shock accidents caused by unsafe material conditions resulted in 16 fatalities, with falls from heights following at 15 deaths, and being struck by objects ranking third with 12 fatalities. Poisoning/suffocation accidents caused by environmental defects resulted in 7 fatalities, with falls from heights following at 4 deaths, and pole toppling ranking third with 3 fatalities. Finally, management loopholes led to the highest number of electric shock fatalities, with 33 deaths, followed by falls from heights with 28 fatalities, and being struck by objects ranking third with 11 fatalities.

Fig. 10
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Fatalities in different types of accidents caused by different contributing factors.

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Direct causes of accidents

  1. (1)

    Statistics on direct causes of accidents

The four main accident causes—unsafe human behavior, unsafe material conditions, management loopholes, and environmental defects—can be further subdivided. Based on the 125 existing accident investigation reports, the statistics for the number of accidents caused by the subdivided causes are shown in Table 5.

Table 5 Number of accidents by cause of accident.
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As shown in the table, among the human-related factors, the most frequent direct cause of accidents was incorrect safety operating procedures, accounting for 29 cases. This was followed by failure to implement appropriate safety protection and technical measures with 25 cases, and insufficient safety distance from live equipment with 19 cases. Among equipment-related factors, mechanical equipment quality defects led to the highest number of accidents (18 cases), followed by failure of mechanical safety protection devices (12 cases). In terms of management-related factors, failure to implement or communicate safety measures was the leading cause with 28 cases, followed by blind organization of work with 18 cases, and lack of on-site supervision with 16 cases. Regarding environmental factors, strong winds and other adverse weather conditions accounted for 6 accidents, making them the most significant environmental contributor to accident occurrence.

  1. (2)

    Apriori analysis of direct causes of accidents

An Apriori algorithm based on association rule mining was applied to analyze the direct causes of personal safety accidents in power grid enterprises. The minimum support threshold was set at 0.1 and the minimum confidence at 0.8, resulting in a total of 22 association rules. Selected results are shown in Table 6. Key insights derived from the table include:

  1. (1)

    The co-occurrence of failure of mechanical equipment safety protection devices, unlicensed or unpermitted operations, failure to implement or communicate safety measures, and absence of pre-task risk and hazard identification is common. Therefore, during hazard inspections, special attention should be paid to equipment condition, worker qualifications, and the implementation of safety measures.

  2. (2)

    The association rule linking lack of on-site supervision with failure to take proper safety and technical protection measures has a confidence level of 84.85% and a lift value of 1.97. When project management is weak, the entire construction process—especially its safety performance—can be significantly compromised. Insufficient safety awareness and poor risk identification among workers may easily lead to a series of safety incidents.

  3. (3)

    Unauthorized expansion of the work scope, unauthorized changes to construction plans, and blind organization of work tasks frequently occur together. The association rule involving these three factors has a confidence of 85% and a lift value of 1.97. These issues typically stem from safety managers failing to organize site operations in accordance with construction standards, resulting in disordered workflows, frequent rule violations, and increased risk of casualties and property loss.

  4. (4)

    In personal safety accidents within power grid enterprises, the probability of occurrence significantly increases when managerial personnel fail to exercise adequate safety control over subordinate departments and on-site workers, and when frontline workers lack the necessary competencies to perform their tasks safely.

Table 6 Partial association rule data with high confidence.
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Conclusion

  1. (1)

    From 2014 to 2024, the number of personal safety accidents in power grid enterprises fluctuated, but remained relatively stable overall. Major accidents accounted for about one-tenth of the total incidents, but the fatalities from these accidents represented about one-fifth of the total fatalities. These incidents remain a key focus for accident prevention and control in the present and future.

  2. (2)

    Fall accidents, electric shock accidents, and being struck by objects are the primary types of accidents requiring focused prevention efforts. While tower tilting, poisoning and suffocation, and roof collapse accidents occur less frequently, they result in significant damage, high casualties, and severe consequences, warranting heightened vigilance.

  3. (3)

    Guangxi, Yunnan, and Inner Mongolia have a higher number of accidents, while Jiangxi, Liaoning, and Henan exhibit relatively high fatality rates. The South China region demonstrates a higher incidence of personal safety accidents, whereas the Central China region records the highest fatality rate.These provinces and regions should prioritize strengthening personal safety management and control.

  4. (4)

    The number of accidents in April through July and August exceeds the monthly average, with fatalities from April to August being above the monthly average. In terms of power grid operation times, accident prevention efforts should focus on the periods from 09:00–10:00 and 15:00–16:00.

  5. (5)

    Unsafe human behaviors and management deficiencies are the primary causes of accidents. Among human-related factors, the highest number of accidents was attributed to incorrect safety operating procedures. In terms of management deficiencies, the main issue lies in the failure of enterprise safety personnel to implement or communicate safety measures effectively to frontline workers. Furthermore, management deficiencies are more likely to trigger unsafe human behaviors, highlighting the critical role of effective safety management in accident prevention.6) This study has several limitations. First, the data are limited to China, which may affect the generalizability of the results. Second, the analysis relies on administrative records, which may involve reporting bias or inconsistencies. Third, the lack of exposure data, such as the total number of workers at risk, limits the ability to conduct relative or rate-based analyses. These limitations should be considered when interpreting the findings.