【摘要】 变压器作为现代电力系统中最重要的设备之一,承担电能转换与分配的重要任务,为电力系统稳定运行提供了重要保障。在目前的变压器故障诊断中,由于油气数据信息量不足而经常导致诊断效果欠佳,所以文中提出了一种计及继电保护与油气信息的变压器故障推理方法。该方法首先通过变压器的运行状态与继电保护装置的关系建立贝叶斯网络;其次运用油气信息训练概率支持向量机,并计算该油气信息所对应变压器运行状态与故障程度的概率;随后根据历史统计数据,结合蒙特卡洛模拟算法与极大似然估计推算贝叶斯网络节点的条件概率;最后根据3个故障实例验证了该方法的有效性。实例分析表明,该方法有效的解决了油气数据信息量不足的问题,同时为运维检修人员提供了较为准确的检修决策。更多还原

【Abstract】 Power transformer,as one of the most important equipment in the modern power system,undertakes the conversion and distribution of electrical energy,which provides a crucial guarantee for the reliable operation of the power grids.Due to the intricate mapping relationship between the gas dissolved in oil data and transformer fault types,the accuracy of the existing fault diagnosis methods is not high enough so far.In this paper,a transformer fault reasoning method based on relay protection and gas dissolved in oil is proposed.Firstly,the Bayesian networks are established based on the operating condition of the transformer and the characteristics of the relay protection device.Secondly,the probabilistic support vector machines are trained by the dissolved gas data in oil to acquire probability of operation state and fault degree.And then,the Monte Carlo simulation and maximum likelihood estimation are implemented to estimate the conditional probability of nodes in Bayesian networks based on historical statistics.At last,three case studies show that the proposed method can not only solve the drawback of only using dissolved gas data,but also provide more accurate maintenance decision for maintenance personnel.更多还原