Search

The partial discharge (PD) is the most common fault of transformers, which is the main factor affecting the stable operation of transformers. Therefore, the PD should be monitored and identified timely to improve the reliability of the transformers. In this paper, a transformer PD pattern recognition algorithm based on the gray-level co-occurrence matrix of optimal parameters and support vector machine (GLCMOP-SVM) is proposed. Firstly, the GLCM of optimal parameters (GLCMOP) is proposed to be determined by calculating the proportion of the off-diagonal elements (PODE) in GLCM. The GLCMOP has the advantage of avoiding the subjectivity of parameter selection and simplifying the calculation process. Then, the phase-resolved partial discharge (PRPD) maps are used as the PD samples and are converted into the GLCMOP to extract the PD features. Moreover, the feature space of the GLCMOP is dimensionally reduced by screening out the features with high distinguishability, which can improve the generalization ability and recognition speed of the classifier. Finally, the SVM classifier is trained to sort the PD samples and recognize the PD types, which include the tip discharge, surface discharge, and air discharge PD types. Lab tests are performed to verify the accuracy and validity of the proposed methodology. Compared with the traditional algorithms based on GLCM, XGBoost (eXtreme Gradient Boosting) and artificial neural network (ANN), the performance of GLCMOP-SVM is better. The GLCMOP-SVM has less memory consumption and faster recognition speed, so it is very suitable for the online and real-time monitoring of PD occurred in the transformers.

Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer.

The service life of dry-type transformers and their operating conditions depend largely on the insulation performance of its windings, the heat generation and heat dissipation inside the dry-type transformer and its insulation performance affect each other, together determining the overall working performance of the dry-type transformer. The internal temperature distribution of dry-type transformers, especially the hot spot temperature, has become an important indicator for its stable operation. Therefore, a more accurate research of the temperature field distribution in dry-type transformers is of great significance for transformer life assessment. Based on the analysis of dry-type transformer heat generation and cooling theory, this paper establishes a three-dimensional finite element simulation model of dry-type transformer, and conducts electromagnetic and thermal coupling simulation analysis of the finite element model to obtain the internal temperature field distribution. Compared with the actual situation, the simulation results are verified to be correct, the relationship between the hot spot temperature and the surface temperature is explored based on the existing simulation results and a mew method for calculating the hot spot temperature is proposed.

With the increase of the penetration rate of distributed generation, DC distribution network has gradually attracted attention due to higher power quality, lower losses, and larger distributed energy resource acceptance capability compared with AC distribution network. This paper proposes a new power flow calculation method for DC distribution network considering DC-DC converter control mode. Firstly, the steady-state models of voltage source converter (VSC) and DC-DC converter are established, and their control modes are analyzed in detail. Then power flow equations in DC distribution network considering different DC-DC converter control modes are derived, and the method for solving DC power flow problem is proposed. Finally, the modified IEEE 14-bus system is taken, and the program is implemented in MATLAB. Compared with the PSCAD simulation results, the effectiveness of the proposal is verified.