In order to address the problem of high failure efficiency of power electronic equipment in complex operating conditions in new energy transmission and transformation systems

this article combines key equipment examples of wind farm boosting stations to construct a failure mode and cause analysis model

and uses Monte Carlo simulation method to quantitatively evaluate equipment reliability. The research method not only relies on the historical operation and test data to establish the failure probability distribution

but also introduces the intelligent monitoring concept

emphasizing the use of real-time sensor acquisition and edge computing to modify model parameters to improve the prediction accuracy. The results indicate that the evaluation framework can effectively identify typical fault links and verify its applicability in new energy scenarios. Furthermore

it is proposed that in special environments such as high humidity salt spray and power shock

equipment reliability can be significantly improved by optimizing design

introducing new semiconductor devices

and intelligent monitoring and diagnostic systems. The research provides theoretical and methodological support for the intelligent operation and maintenance of new energy transmission and transformation systems

and has reference value for promoting the safe grid connection of large-scale new energy.