Transformer Risk Assessment Method Based on Hidden Markov Model

Transformers, as a key component of the power system, directly affect the reliability and safety of the grid with their health condition. Therefore, accurate risk assessment of transformers is crucial for formulating effective operation and maintenance strategies and preventive maintenance plans. This paper proposes a transformer risk assessment method based on the Hidden Markov Model (HMM), which combines a dynamic weight matrix with online monitoring data to determine the probability of equipment failure and the weight of its resulting losses, thereby quantifying the total risk loss and assessing the overall risk value of the transformer. In this method, the fault state of the transformer is first determined using online monitoring data and an extended fuzzy logic fault diagnosis model, then the HMM theory is applied to construct the equipment failure probability model. Additionally, a dynamic weight calculation model is adopted, where an expert team evaluates the importance of different risk factors to determine the weight of various risk losses. Case analysis shows that this method can effectively reflect the operating condition of the transformer and dynamically adjust the risk assessment results according to changes in the equipment state. An empirical analysis of online monitoring data from phase C of the main transformer at a substation in the northwest verified the practicality and effectiveness of the proposed method. This research provides a strategic basis for the provincial company's main transformer overhaul and replacement plan, significantly reducing potential economic losses and safety risks.