为了解决变电站运维过程中风险因素复杂多样且难以确定的问题，提出了一种基于动态贝叶斯网络（dynamic Bayesian network, DBN）的风险评估模型。首先，通过现场调研和查阅相关文献，结合模糊集理论对风险指标进行筛选和优化，构建了包含5个一级指标和17个二级指标的变电站运维风险评估体系。其次，利用解释结构模型（interpretive structural model, ISM）并引入时间维度构建动态贝叶斯网络，利用模糊集理论（fuzzy set theory, FST）对专家评语进行量化处理，计算网络节点的先验概率和转移概率，并采用Leaky Noisy-or Gate扩展模型修正条件概率，采用DBN的双向推理功能，实现对变电站运维风险的动态评估。结果表明：正向推理得到变电站运维风险发生的概率为0.611，反向推理得到变电站运维风险一级指标中人为、管理和设备因素方面的风险性较大，二级指标中安全监督不到位、疲劳和注力不集中、设备过热因素是导致变电站运维风险发生的关键影响因素。最后，通过实证分析，将案例信息输入模型中，验证该模型的可行性。可见，研究成果为变电站运维风险动态评估提供了新的理论支持与方法借鉴。

In order to address the complex and diverse risk factors in substation operation and maintenance that are difficult to identify, a risk assessment model based on a dynamic Bayesian network (DBN) was proposed. Field research and literature review were used to investigate risk factors in substation operations, and rough set reduction theory was applied to screen and optimize risk indicators. This led to the establishment of a risk assessment system comprising 5 primary indicators and 17 secondary indicators. Subsequently, an interpretive structural model (ISM) was integrated with a temporal dimension to construct the DBN. Fuzzy set theory(FST) was used to investigate the quantification of expert evaluations, enabling the calculation of prior and transition probabilities for network nodes. The Leaky Noisy-or Gate extension model was introduced to refine conditional probabilities, and the bidirectional reasoning capability of the DBN was leveraged to achieve dynamic risk assessment. The results show that the probability of operational risk occurrence, derived from forward reasoning is 0.611, while reverse reasoning identified human, managerial, and equipment-related factors as the most significant primary risk indicators. Among secondary indicators, inadequate safety supervision, worker fatigue, lack of concentration, and equipment overheating are identified as critical contributors to operational risks. It is concluded that empirical case analysis validated the feasibility of the model by inputting real-world data into the framework. This research provides new theoretical and methodological support for the dynamic assessment of substation operation and maintenance risks.