Warning And Identification Of Transformer Defects Based On Dissolved Gas Analysis In Oil

Nowadays,with the improvement of the safety of power grid,there is a higher demand for the safe and stable operation of power transformers.The current diagnostic method based on on-line monitoring data of oil chromatography fails to consider the temporal characteristics of fault gases.It has difficulty in determining the threshold and lacks typical fault sample data.In this paper,the hidden Markov model and dynamical network marker are introduced into the method of fault warning and identification of transformer based on the on-line monitoring time-series data of dissolved gas in oil is proposed,provide theoretic instruction for early identification of transformer defect.Firstly,the hidden Markov model(HMM)is introduced into transformer defect warning.In this paper,the dynamic modeling is based on the on-line monitoring gas data of the transformer by using the mapping relation between different dynamic characteristics of transformer operation and the variation of dissolved characteristic gas concentration in transformer.The early warning signal of transformer state transition is detected to achieve the purpose of real-time warning of transformer’s early defects through the change of transformer’s gas concentration.The case study shows that the method based on hidden Markov model can detect the early defects of transformer more timely and effectively than the traditional laboratory chromatography.Secondly,the mapping relationship between on-line monitoring system and internal state of transformer was analyzed.The main characteristic gases which have great influence on the transformer state transition process were selected by analyzing the difference between the actual monitoring data and the predicted data.Then,the dynamical network description model of the current state was established.And there is a critical state in the process of transformer transition from healthy state to fault state based on the critical slowing down theory.The dynamic change of the standard deviation and correlation of the characteristic gases in the dynamical network marker was further analyzed to find out the critical point in the phase transition process,and the early defect warning of transformer could be realized.Compared with the traditional threshold and ratio method,the method based on the dynamical network marker can identify the inner abnormality of transformer in time.Finally,based on the on-line monitoring time-series data,the combination model of hidden Markov model and dynamical network marker for transformer is established.First,the hidden Markov model is used to detect the transformer’s possible “critical point”,and then the corresponding dynamical network marker is established according to the main characteristic gas screened at the “critical point”.Then,the dynamic changes of the dynamical network marker are further analyzed to verify the operation state of the transformer and identify the fault.The method was verified by the field cases.The results show that,compared with single early warning model,the combination model can fully grasp the dynamic evolution process of the transformer operation state,and it can make early warning and identification for the cases of abnormal overheating that have not reached the attention threshold.The transformer defect warning and identification method based on dissolved gas in oil in this paper is completely relied on on-line monitoring time-series data of transformer,true and reliable,and have a certain popularization value.