Abstract
The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the transformer panorama information are analyzed. The degree of relative deterioration is used to characterize the deterioration of the transformer state. The membership relationship between the relative deterioration degree of each indicator and the transformer state is obtained through fuzzy processing. Through the long short-term memory (LSTM) network, the evolution of the transformer status is extracted, and a data-driven state prediction model is constructed to realize preliminary warning of a potential fault of the equipment. Through the LSTM network, the quantitative index and qualitative index are organically combined in order to perceive the corresponding relationship between the characteristic parameters and the operating state of the transformer. The results of different time-scale prediction cases show that the proposed method can effectively predict the operation status of power transformers and accurately reflect their status.
Highlights
Power transformers suffer from the long-term effects of high-voltage electric, thermal, and mechanical stresses during operation [1]
Some studies have focused on predicting specific state parameters, such as gas concentration dissolved in the oil [2,3], top oil temperature [4], residual flux [5,6], inrush current [7], moisture in the insulating cellulose [8], and furan [9], to characterize the development of the transformer’s status
Based on the key parameters of the operating state, this paper proposes a method for predicting the running conditions of power transformers based on the long short-term memory (LSTM) network
Summary
Power transformers suffer from the long-term effects of high-voltage electric, thermal, and mechanical stresses during operation [1]. The state prediction models proposed in these studies include the neural network [4], support vector machine regression [2,3], fuzzy logic [14], nonparametric regression [10], and probabilistic graph [16] These methods have demonstrated their effectiveness in a number of circumstances, and some research results have been obtained. An LSTM approach for the estimation of remaining useful life was proposed by Zheng et al [22] This method can make full use of the sensor sequence information and expose hidden patterns within the sensor data with multiple operating conditions, faults, and degradation models. Based on the key parameters of the operating state, this paper proposes a method for predicting the running conditions of power transformers based on the LSTM network.
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