## 2 The Framework of the Study  
It is well known that China is at the leading edge of power system technology. However, there is still relatively little research on the human factors reliability analysis of power safety production. [PERSON] et al. (2020) identified \"fatigue\" as the main cause of human accidents in power grid enterprises based on the HFACS and grey correlation method. [PERSON] et al. (2020) used correlation rule analysis, exploratory factor analysis, and Pearson correlation analysis to mine eight behavioral formation factors in nuclear power plants and found the correlations among the factors of \"complexity\", \"stress\", and \"available time\". [PERSON] et al. (2020) conducted a chi-square test and concession ratio analysis on the human factors of power production accidents to speculate on their causal relationships. The above scholars, based on accident data, have used traditional statistical methods to analyze the important risk factors for accidents. It is able to partially respond to the causes of accidents, but the traditional statistical approach itself suffers from independent assumptions and requires high data integrity. Other academics are beginning to integrate accident data and expert assessments to make an assessment of accident risk. [PERSON] et al. (2020) proposed a fuzzy weighted cognitive reliability and error analysis method (CREAM) to quantify the probability of human error in power operations. [PERSON] et al. (2020) proposed a quantitative analysis method for the human operational reliability of power grids based on the CREAM model, with expert weights and model input data determined by expert scoring. While these scholars have combined the expert opinion and also used fuzzy set theory methods, they have only quantitatively assessed accident failure probabilities and have not bothered to explore the coupling relationships between accident factors. This work applies the FFTA-HFCAS model to analyze the HOFs in EMAs. The proposed analytical model is the first of its kind to be applied to the field of electrical production safety. This method not only analyses the important risk factors for electrical mishaps from traditional statistical methods, but also combines expert empirical assessment, using fuzzy set theory to generalize the subjectivity of experts and quantify the probability of failure and the interaction between accident risk factors. The framework of this work is shown in Figure 1.  
1. Data collection. Reliable EMAs data forms are the basis of accident analysis studies. In the present study, 135 typical EMAs of China Southern Power Grid from 2003 to 2016 were selected to establish an accident database. The 135 EMAs reports described the background of the accident, the cause of the accident, the exposed problems, and the preventive measures that will be taken in the future, which provide an excellent reference value for the study of this article. 2. HFACS structure. The preliminary HFACS, a model, set up for aviation, originated from the [PERSON]'s \"Swiss Cheese\" model, which emphasizes the organizational dimension of accident causation [27]. The four levels of the HFACS framework including unsafe acts, preconditions for unsafe acts, unsafe supervision, and organizational influences, used today were expanded by [PERSON] et al. [28]. Each level consists of a set of sub-levels, and each sub-level has classification codes, known as nano-codes. Because of its unique features, various versions of HFACS have been used as a valuable and reliable tool for identifying and analyzing human factors in a wide range of other domains, such as HFACS-MI for mining [29; 30], HFACS-RAs for railways [31], HFACS-MMO for marine engineering, HFACS-CSMEs for the chemical industry [32], and so on. In this stage, the accident report, expert knowledge, and literature review can be summarized, and the causes of EMAs are transformed into levels in the HFACS hierarchical structure [33]. 3. Statistical analysis.