In this paper, a new testing method for the diagnose of winding faults based on the resonant frequency of the transformer is proposed. The system which consists of the high-voltage DC power and high-voltage IGBT could charge the transformer periodically. The response measured from the bushing of the winding is the oscillating wave that can be used for evaluating the status. Carrying out the new test on 220kV three winding, three-phase transformer in the field, the tests of insulation and the winding have been completed in one test. The calculated frequency verifies the feasibility and applicability of the method in theory and the voltage does not affect the resonant frequency in the test. The three winding, three-phase model transformer was built to simulate the various parameters of the power supply, which proves that the features of the oscillating waves are brought about by the properties of the transformer. And that, the new testing method provide a new opportunity to do parameter identification of axial displacement and short circuit (SC) fault. The above research shows that the proposed method can conduct the test of winding and insulation in one test for a short time, which proves a reference in the field.

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.

Frequency response analysis (FRA) is an effective approach for detecting mechanical damage in the windings of a transformer. However, the use of FRA to estimate the winding condition of autotransformers (ATs) employed in China High-Speed Railway is problematic because the split windings of the ATs are connected inside the tank, such that it is impossible to measure the FRA curves for each split winding outside the tank directly. Therefore, it is difficult to identify fault winding and effectively diagnose the overall condition of the windings. This paper explores the features of FRA curves under different fault conditions to help distinguish fault winding. A verified distributed circuit model with turn unit is built based on split windings, and the electrical parameters of the windings are determined using finite element method. The model is then employed to simulate typical axial displacement faults. The results show that the first anti-resonance and resonance of the frequency response for connections involving series windings or series and common windings are visible features for diagnosing winding faults and the fault level. Specifically, the first and second resonances of the frequency response for the common winding connection can be regarded as the primary feature for identifying fault winding.

Frequency-domain dielectric spectrum (FDS) is an effective testing method to reflect the changes of internal insulation status of oil-impregnated paper (OIP) bushing. In field application, the results of FDS test can be both affected by aging defects and damp defects, then the internal insulation defects of OIP bushing cannot be diagnosed in detail, which is an issue of discrimination of multiclass classification. To solve this problem, a method to diagnose the internal insulation defects of OIP bushing is proposed based on multiclass least square support vector machines (LS-SVMs) optimized by cuckoo search (CS) algorithm. First, the multiclass LS-SVM parameters are optimized by the CS algorithm. Then, the training data set is used to train the multiclass LS-SVM model, and the test data set is used for model testing. The experimental results show that the proposed method can effectively diagnose the internal insulation defects in detail, i.e., aging defects and damp defects. In addition, in the proposed method, the CS algorithm is better than the genetic algorithm and particle swarm optimization algorithm, and the convergence rate of the CS algorithm is faster than the other two algorithms.