| With the development of distributed energy system and the demand response management or utilization of plug-in electric vehicles may shift the peak load of a residential transformer,the substation equipments(transformer,circuit breaker,isolating switch,etc.)which affecting the dynamic capacity have been further demanded.As one of the most expensive equipment in the power system,power transformers play a vital role in transforming voltage,transmitting power energy,and load capacity of which is the main factor restricting the capacity of power grid.Meanwhile,its operating state is important to determine the stability and reliability of power systems.Loading capability of an oil-immersed power transformer is closely related to oil temperature and hot-spot temperature(HST).To obtain an accurate calculation of top-oil temperatures(TOT)and HST so as to aid the evaluation of loading capability of an oil-immersed transformer,on the basis of the principle of heat transfer and circuit laws,an improved thermal-electrical analogy model for transformers is developed,by considering the oil viscosity which varies with oil temperatures for the redefinition of nonlinear thermal conductance and taking account of the heat transfer between a transformer and its ambient environment.A hot-spot temperature node is introduced into the improved model,so that the general process of heat transfer in the transformer can be truely reflected.A Genetic Algorithm(GA)is employed to optimize the parameters of the proposed thermal model.Taking a 180 MVA oil-immersed power transformer for example,calculated results of HSTs,TOTs and bottom-oil temperatures of both the original and the improved models are compared with on-line data and the results calculated by the recommended empirical formulas in the transformer loading guide,and the better accuracy of the improved thermal model is verified.Finally,for the first time the improved model is used to estimate the relative daily loss of life,maximum TOT and HST of a large oil-immersed power transformer in given working conditions,so as to analyze the loading capability of the test transformer,which can provide a reference for increasing its load rating.This paper also develops an approach to investigating the effect of a particular parameter on the output accuracy of transformer thermal models,i.e.sensitivity analysis,which can not only reveal the most sensitive parameter of a thermal model but also improve model output accuracies.For the first time,the nonlinear time constant(NTC)of transformer oil is proposed based on an expression of nonlinear thermal conductance to reshape three practical top-oil temperature models: the modified IEEE clause 7 model,Swift’s model,and Susa’s model.Then,multiparametric sensitivity analysis(MPSA)is undertaken to reveal the effect of each parameter on the model output accuracy.Through onsite data validation,the results show that the accuracy performance of the proposed NTC thermal models are improved significantly by considering the nonlinear effect of oil time constant.Moreover,the derived sensitivity performances can clearly reveal the most dominant parameter of the model,so as to simplify it by reducing the number of insensitive parameters.Finally,the heat-run test data is used as a reference to validate parameters optimized through a group search optimizer(GSO),which demonstrates that the proposed NTC IEEE model has not only one sensitive parameter but also superior accuracy performance. |
