【摘要】 在变压器绝缘劣化之前,可以进行油中溶解气体分析、局部放电检测、传递函数测量等试验方法对其状态进行评估。所有这些试验现象需要很多实际经验才能正确解释。因此人工智能技术逐渐被应用于提高单一试验数据的分析中。但是,仅使用一种方法,可能难以得到满意的诊断结果,如油中溶解气体分析是不能准确对局部放电进行定位。然而,应用不同的方法可能产生各异的诊断结果,因此文中引入模糊信息融合系统来解决此问题,提出了产生一致性结论和处理不同方法中不确定性的证据推理策略。并在信息融合的帮助下,建立了有机组合多种诊断方法系统框架。通过实例证明,基于信息融合的变压器绝缘故障诊断方法是有效的。更多还原

【Abstract】 Methods used to assess the insulation status of power transformers before they deteriorate to a critical state include dissolved gas analysis(DGA),partial discharge(PD)detection and transfer function techniques,etc.All of these approaches require experience in order to correctly interpret the observations.Artificial Intelligence(AI)is increasingly used to improve interpretation of the individual data sets.However,a satisfactory diagnosis may not be obtained if only one technique is used.For example,the exact location of PD cannot be predicted if only DGA is performed.However,using diverse methods may result in different diagnosis solutions,a problem that is addressed through the introduction of a fuzzy information infusion model.An inference scheme is proposed that yields consistent conclusions and manages the inherent uncertainty in the various methods.With the aid of information fusion,a framework is established that allows different diagnostic tools to be combined in a systematic way.The application of information fusion technique for insulation diagnostics of transformer is effective by means of examples.更多还原