A Temperature Sensor Deviation Calibration Method Based on the Combination of Transformer Monitoring Equipment and the Least Squares Method

Power transformers play a crucial role in the power system. It can be said that the quality and performance of transformers determine the safe and stable operation of the entire power system. With the continuous development and transformation of technology, the accuracy and efficiency of detection have also been constantly improving. However, the detection system of 220 kV substations is still not perfect. There are occasional abnormal situations in temperature measurement, and temperature is one of the most important direct indicators directly reflecting the working state of transformers. Abnormal detection of temperature not only disrupts the normal power supply state of the power grid, Moreover, it will also be accompanied by direct economic losses and social impacts, which should not be underestimated or taken lightly. To this end, this paper proposes a nonlinear correction method for the system through methods such as the negative resistance method and least squares fitting. Firstly, a hardware compensation method based on parallel negative resistance was designed. This method can effectively reduce the nonlinearity of platinum resistance, but it is vulnerable to the influence of the external environment. Secondly, a software compensation method based on the least square method was designed. Through piecewise fitting of the temperature characteristic curve, error correction was achieved. Finally, through experimental comparative analysis of the changes in resistance value, voltage and temperature before and after, it is proved that the proposed method is an effective and feasible deviation calibration method.