A Transformer Hot Spot Temperature Prediction Method Based on Multi-working Condition Parameter Identification and Optimization

Abstract

The invention discloses a transformer hot spot temperature prediction method for multi-working condition parameter identification optimization, which includes the following steps: collecting transformer test data in real time, and dividing the test data into a training set and a prediction set according to its operating conditions; selecting an IEEE model in The parameters that change significantly under different working conditions are used as the parameters to be identified and optimized, and the parameter identification optimization objective function of the IEEE model under each working condition is constructed; based on the training set of each operating condition, the search method is used to find the parameter identification optimization under each working condition For the optimal solution of the objective function, the identification and optimization parameters under each working condition are substituted, and the IEEE model is used to predict the test data of the prediction set under each working condition, and the transformer hot spot temperature prediction sequence is obtained. The invention can obtain hot spot temperature prediction value with high precision, can reliably analyze the dynamic change trend of hot spot temperature, and further improves the accuracy of transformer in thermal characteristic measurement.

Description

technical field [0001] The invention relates to a transformer hot spot temperature prediction method for multi-working condition parameter identification and optimization. Background technique [0002] As one of the most important equipment in the power system, the power transformer is worthy of attention for its safety and economy. The transformer is allowed to operate under short-term overload. On the premise of accurately and reliably predicting the temperature of the top layer of transformer oil and hot spots, increasing the temperature rise limit of the transformer by 2% to 3% will bring huge economic benefits. Transformer hot spot temperature is the biggest limiting factor for transformer dynamic operating capacity. If the hot spot temperature exceeds the safety limit, it will damage the insulation and affect the safe operation of the transformer. Therefore, it is very important to accurately and reliably predict the transformer hot spot temperature. [0003] The mech...

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Problems solved by technology

The thermal circuit model requires more transformer heat transfer parameters; the neural network method lacks clear physical meaning; the IEEE Std C57.91 recommended model is a traditional method, and the model parameters are relatively easy to calculate, but there is a problem of low accuracy, especially in Under overload conditions, the prediction accuracy is severely reduced, and the predicted value under overload conditions is lower than the measured value, which increases the risk of transformer operation beyond the nameplate

[0004] Therefore, none of the existing models considered the predicted safety margin of the transformer under overload conditions

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