基于MCMC方法和油色谱数据的变压器动态故障率模型

对变压器进行合理的运行可靠性评估是其状态检修的基础。首先分析变压器油中气体含量和产气速率、故障时间分布参数对变压器运行可靠性的影响,建立动态故障率模型。然后,针对油浸式变压器故障时间具有的固有随机性,采用马尔可夫链蒙特卡罗方法对变压器故障周期进行场景模拟,并利用统计方法求解变压器的历史故障率作为训练数据,以求解模型相关参数,有效避免解析法直接求解的困难。最后,对变压器短期和中长期的故障率进行预测,并与传统模型进行对比。结果表明,考虑故障时间分布参数对变压器运行可靠性的影响,能够有效提高模型的预测精度。

Transformer dynamic failure rate model based on MCMC method and oil chromatographic data

The reasonable operation reliability evaluation of transformer is the basis of condition based maintenance. Firstly, the influence of gas content, gas production rate and fault time distribution parameters on transformer operation reliability is analyzed, and the dynamic failure rate model is set up. Then, aiming at the inherent randomness of oil-immersed transformer fault time, the Markov Chain Monte Carlo method is used to simulate the transformer failure cycle, and the statistical method is used to solve the historical failure rate of the transformer as the training data, so as to solve the related parameters of the model and effectively avoid the difficulty of direct solution by analytical method. Finally, the short-term and long-term failure rate of the transformer is predicted and compared with the traditional model. The results show that considering the influence of failure time distribution parameters on transformer operation reliability, the prediction accuracy of the model can be effectively improved. This work is supported by National Key Research and Development Program of China (No. 2016YFB0900500).