基于改进动态贝叶斯网络的110 kV变电站数字化模型可靠性分析OA

[Objective]As the digital transformation of substations accelerates,traditional evaluation methods have limitations in the accuracy and adaptability of reliability analysis,and the reliability of substations is of great significance for the stable operation of the power system.To this end,a reliability analysis method based on the improved dynamic Bayesian network(DBN)for a 110 kV digital substation model was proposed to enable real-time monitoring and accurate evaluation of the system's status.[Methods]Firstly,statistical analysis of the key parameters was conducted,such as failure rates of various types of equipment and components within the substation,thus constructing the basic data for reliability evaluation.Secondly,DBN was introduced as the modeling tool.The network structure was dynamically adjusted and redesigned in response to environmental factors such as temperature,humidity,and load fluctuations to enhance the model's adaptability to a non-stationary operating environment.Finally,fault tree analysis(F TA)was employed to identify logical relationships of system-level faults,and the results were systematically mapped into DBN to build a probabilistic reasoning model with both hierarchical and causal characteristics.By adopting probabilistic reasoning to compensate for information gaps,reasoning robustness and accuracy can be improved by this method under incomplete information or missing data.[Results]Experiments conducted on the 110 kV digital substation model show that the area under the ROC curve of the proposed method is the closest to 1,indicating that the analysis results are the closest to the actual values.Meanwhile,it exhibits the lowest error rates and stronger stability in the reliability analysis of the three substations,with the accuracy,precision,recall,and F1 scores being 0.891,0.875,0.904,and 0.889,respectively.Thus,its overall performance is better than the comparative methods.[Conclusions]The proposed method exhibits significant advantages in terms of accuracy,stability,and adaptability.By integrating the structured modeling capabilities of F TA with the adaptive reasoning mechanism of DBN,it effectively overcomes the limitation of insufficient evaluation accuracy of traditional methods in dynamic environments and information deficiency conditions.This method not only achieves dynamic quantification of reliability indexes for substation digital models but also provides a reliable theoretical support and practical tool for system status monitoring and intelligent operation and maintenance,with promising engineering application prospects.