Apparatus and method for predicting transformer state in consideration of whether oil filtering is performed

A method of predicting a state of a power transformer, based on machine learning, in consideration of whether oil filtering of the power transformer is performed, the method comprising:

• generating prediction models including an oil filtering prediction model and an oil non-filtering prediction model, the oil filtering and oil non-filtering prediction models being long short term models, the oil filtering and oil non-filtering prediction models each including a plurality of sub-prediction models, the prediction models generated via:
• receiving, by a transformer data input receiver of a transformer state prediction apparatus, transformer training data of a transformer, the transformer training data including dissolved gas data of the transformer measured at n previous time points, n being greater than or equal to a threshold number of times for predicting the state of the transformer,
• separating the transformer training data into pieces of training data based on a determination, by a processor of the transformer state prediction apparatus, whether oil of the transformer has been filtered on the basis of the transformer training data, the pieces of training data including oil filtering training data and oil non-filtering training data,
• training the oil filtering prediction model with the oil filtering training data including dissolved gas data and transformer state data of a case in which oil filtering has been performed, the oil filtering prediction model trained to predict a transformer state of the transformer in the case in which oil filtering has been performed for the transformer,
• training the oil non-filtering prediction model with the oil non-filtering training data including dissolved gas data and transformer state data of a case in which oil filtering has not been performed, the oil non-filtering prediction model trained to predict a transformer state of the transformer in the case in which oil filtering has not been performed for the transformer, and
• training each sub-prediction model individually to distinguish transformer states and learn weights for distinguishing the transformer states, the transformer states including a normal state, a warning state, a critical state, and a fault state,
• wherein the oil filtering prediction model includes a first layer to which first dissolved gas data corresponding to year t−3 is input as an input value and first transformer state data corresponding up to year t−2 is input as an output value, a second layer to which second dissolved gas data corresponding to the year t−2 is input as an input value and second transformer state data corresponding up to year t−1 is input as an output value, a third layer to which third dissolved gas data corresponding to the year t−1 is input as an input value and the third transformer state data corresponding up to year t is input as an output value, and a fourth layer to which fourth dissolved gas data corresponding to the year t is input as an input value and fourth transformer state data corresponding up to year t+1 is input as an output value, and
• wherein the oil non-filtering prediction model includes a first layer to which first dissolved gas data corresponding to year t−3 is input as an input value and first transformer state data corresponding up to year t−2 is input as an output value, a second layer to which second dissolved gas data corresponding to the year t−2 is input as an input value and second transformer state data corresponding up to year t−1 is input as an output value, a third layer to which third dissolved gas data corresponding to the year t−1 is input as an input value and the third transformer state data corresponding up to year t is input as an output value, and a fourth layer to which fourth dissolved gas data corresponding to the year t is input as an input value and fourth transformer state data corresponding up to year t+1 is input as an output value;
• and
• generating a prediction, by the processor of the transformer state prediction apparatus, of the transformer state at an n+1 time point on the basis of the determination and the prediction models via:
• receiving, by the transformer data input receiver of the transformer state prediction apparatus, transformer data of the transformer comprising dissolved gas data of n previous time points,
• determining, by the processor of the transformer state prediction apparatus, an oil filtering determination of whether oil of the transformer has been filtered on the basis of the transformer data,
• determining a selected model of the prediction models to make the prediction based on the oil filtering determination by:
• selecting the oil filtering prediction model as the selected model responsive to the oil filtering determination indicating oil filtering has been performed for the transformer, and
• selecting the oil non-filtering prediction model as the selected model responsive to the oil filtering determination indicating oil filtering has not been performed for the transformer, and
• providing the dissolved gas data of n previous time points as input to the selected model to generate the prediction of the transformer state at the n+1 time point.