Domestic Coal-Fired Power Plant Industry according to industrial accidents by industry from 2008 to 2017, the number of workers killed every year is about 6 and the number of injured workers is about 95. The increase in industrial accidents in the coal-fired power plant industry is not leading to an appropriate statistical analysis of industrial accidents occurring in the coal-fired power plant industry. Accordingly, in this study, industrial accidents of five domestic power generation coal-fired power plants were analyzed in various ways from 2008 to 2017, and high-risk accident types and work improvement plans were identified. Based on this, high-risk accident types were identified and suggested improvement measures. For the basic data of the preceding research, the public report from the special investigation committee during the coal-fired power plant in 2019 and the data surveyed from the safety diagnosis joint service of the five coal-fired power plants in 2019 were cited. The statistical analysis results from this study quantified the actual injuries, deaths, injuries and mortality rates of 10 years for each power plant headquarters and partner companies of the 5th coal-fired power plant. Using the statistical analysis method, the severity of industrial accidents was identified by identifying the increase and decrease according to the period, the comparison with the same industry, the correlation of social phenomena, the correlations between the injured and the deaths of the five development companies. As a result of the improvement measures for high risk type of disaster, 4M (man, machine, media, management) method was used to analyze the causes of the disaster, and as a result of the analysis, it was found that the equipment factor and the work factor occupy a large proportion of the causes of the fall. Therefore, improvement measures for falling accident prevention were presented as common matters, and improvement measures that could cover the causes of fall in terms of equipment and work were presented. It is expected to be used as an improvement in statistical analysis of industrial accidents and safety education data for power generation industry.