A Transformer Winding Hot Spot Temperature Prediction and Evaluation System with Adaptive Function

Abstract

The invention discloses a transformer winding hot spot temperature prediction and evaluation system with an self-adaptive function, which mainly includes seven parts such as a transformer top layer oil temperature monitoring system, a substation environment temperature monitoring system, and transformer load current monitoring equipment. The system can monitor the temperature change trend after the load of the transformer is increased, and accurately know the coil temperature of the transformer in real time and predict its temperature change trend. By making full use of the transformer equipment, the temperature effect after the load increase can be measured without damaging the transformer. In the case of equipment insulation performance, it provides an evaluation method for the feasibility of overload operation of the power grid system during peak hours.

Description

technical field [0001] The invention relates to the research and application of state evaluation of transformer equipment in a smart grid, in particular to a hot spot temperature prediction and evaluation system for transformer windings. Background technique [0002] The existing transformer winding temperature monitoring method is to install a thermometer on the top of the oil tank of the transformer body, and convert the winding temperature data by measuring the resistivity of the transformer oil at different temperatures. This data is the value of the winding temperature obtained by simulation, and the winding temperature indicator data obtained by simulation calculation has a deviation from the real winding temperature, which has poor accuracy and poor timeliness. Correlation analysis data shows that there is a lag of about 4 to 5 hours between the hot spot temperature inferred from the traditional measurement of transformer oil temperature and the actual hot spot temper...

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Problems solved by technology

This data is the value of the winding temperature obtained by simulation, and the winding temperature indicator data obtained by simulation has a deviation from the real winding temperature, which has poor accuracy and poor timeliness

Correlation analysis data shows that there is a lag of about 4 to 5 hours between the hot spot temperature inferred from the traditional measurement of transformer oil temperature and the actual hot spot temperature

At the same time, the measured values ​​in the prior art cannot monitor and analyze the temperature change trend after the transformer load increases

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