Oil-immersed transformer is one of the most important electrical equipment in power distribution and transmission systems. Dielectric response method is a well-recognized method to diagnose the insulation defect of oil-immersed transformers. However, the applicability of this method is restricted due to the long testing time. Under some special field conditions, the method is not even applicable. In this paper, a novel testing method is proposed based on the following ideas: 1) first, the low frequency dielectric parameters are extracted by using mixing frequency excitation; 2) then, parameters of the extended Debye equivalent circuit are determined based on cuckoo search (CS) optimization algorithm; 3) finally, the specific parameters are used to the established simulation model and obtain the recovery voltage curve. Compared with the traditional method, the testing time of the proposed method has been greatly reduced. Besides, dielectric parameters in both frequency domain and time domain can be obtained simultaneously. The applicability of the proposed method is verified by the dielectric response measurements on a laboratory transformer and a real power transformer in a substation.

Adopting dry-type on-board traction transformer (OBTT) is one of the main ways to reduce the weight of electric traction systems, and its thermal characteristics are crucial for the safe operation of high-speed electric multiple units (EMUs). However, the existing thermal modeling methods either cannot realize the fast calculation of thermal characteristics of dry-type OBTT, or cannot accurately obtain the turbulence heat-transfer intensity when using the high-speed train-induced wind cooling. This article proposes an efficient method for dry-type OBTT thermal characteristics calculation. First, the thermal network of the winding area is proposed considering the thermal developing region of the air duct and the heat flow in the insulation paper between conductors. Then, the effects of key parameters on convective heat-transfer intensity are obtained by computational fluid dynamics (CFD) parametric sweeps, and then a novel local Nusselt number correlation for accurately calculating the turbulent convective thermal resistance is proposed. Finally, validation is performed with an experimental setup. The experimental results show that the computationally efficient thermal network model proposed in this article can quickly obtain the winding temperature distribution and the location of the hot-spot temperature. Moreover, the thermal analysis of a 6.3 MVA dry-type OBTT was conducted using the proposed modeling method.

In order to dig out more potential of design parameters besides nameplate information, a set of pre-physical modelling methods was proposed and integrated into the dynamic thermal model-ling procedure, aiming to optimize the key thermal parameters individually. In this method, a global oil momentum model was created considering the flow resistance, and for the problem of heat transfer between windings, oil flow and radiator, a whole oil flow thermal model was established based on heat transfer principle and energy conservation. Then, the double logarithmic linear regression was applied to quantify the value of the exponents. According to the overload heat run tests, the temperature calculated by the existing models during multiple overloads is obviously different from the measured temperature, while the improved dynamic thermal modelling procedure with optimized thermal parameters attains estimates more accurately.

Traction transformer is a vital equipment in high-speed railway. Insulation status decides its safety and reliability, and frequency domain dielectric spectrum (FDS) test is one of the most effective methods reflecting the changing of insulation status. For field applications, the following problems should be addressed: 1) how to obtain the result of paper insulation from the combined result of oil-paper insulation system; 2) how to discriminate the defects of insulation paper between aging and damp. In this paper, the first problem was transferred to a non-linear equation set, and a CS-DQ algorithm (cuckoo search algorithm optimized by the differential evolution algorithm and the quadratic interpolation method) was proposed to solve it. Then, the insulation states were discriminated by establishing a multi-class LS-SVM (least square support vector machines) model, in which CS-DQ algorithm was also used. Finally, a diagnostic approach for the insulation paper in traction transformer was proposed. The results in laboratory show that the pure result of insulation paper can be obtained and the insulation states can be discriminated effectively by using the proposed approach. Meanwhile the proposed CS-DQ algorithm has better performance than the conventional CS algorithm. The results of field testing also verify the proposed approach.

Real-time thermal capacity estimation is not only part of prognostics and health management (PHM) but also determines the dynamic loading capability of the traction transformer. The hot-spot temperature is the vital parameter for real-time thermal capacity estimation. In high-speed railway, the unbalanced loads of the two phases of the traction transformer cause unbalanced winding losses and therefore affect the top-oil temperature and further influence the hot-spot temperature in each phase. In order to explore the dynamic thermal performance of the transformer under unbalanced load (current), a thermal model for calculating the hot-spot temperature was proposed incorporating thermal interaction between two power supply phases. This model treats the winding loss of each phase independently, considering the temperature dependence of winding ohmic losses and eddy current losses. A thermal circuit topology was presented with independent phase windings sharing the same top-oil temperature, where an improved heat transfer correlation was used to account for the nonlinear thermal convection. The proposed model was verified by the factory temperature rise tests and field operation data for the same batch of transformers.