A method of optimized neural network by L-M algorithm to transformer winding hot spot temperature forecasting

2017 International Conference on New Energy and Future Energy System

Wei, B.G.^1,2 ; Wu, X.Y.^3 ; Yao, Z.F.^1 ; Huang, H.^1

Shanghai Municipal Electric Power Company Electric Power Research Institute, Hongkou Shanghai

200437, China^1

Shanghai SEPRI Power Technology Co. LTD., Hongkou Shanghai

200437, China^2

State Grid Shanghai Municipal Electric Power Company, Pudong Shanghai

200120, China^3

关键词: Accurate computations;    Calculated values;    Heat transfer process;    Highest temperature;    Neural network algorithm;    Oil immersed transformers;    Thermal characteristics;    Winding hot spot temperatures;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/93/1/012030/pdf
DOI  :  10.1088/1755-1315/93/1/012030

来源: IOP

【 摘 要 】

Transformers are essential devices of the power system. The accurate computation of the highest temperature (HST) of a transformer's windings is very significant, as for the HST is a fundamental parameter in controlling the load operation mode and influencing the life time of the insulation. Based on the analysis of the heat transfer processes and the thermal characteristics inside transformers, there is taken into consideration the influence of factors like the sunshine, external wind speed etc. on the oil-immersed transformers. Experimental data and the neural network are used for modeling and protesting of the HST, and furthermore, investigations are conducted on the optimization of the structure and algorithms of neutral network are conducted. Comparison is made between the measured values and calculated values by using the recommended algorithm of IEC60076 and by using the neural network algorithm proposed by the authors; comparison that shows that the value computed with the neural network algorithm approximates better the measured value than the value computed with the algorithm proposed by IEC60076.