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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.

The modular multilevel converter (MMC) station connected to the islanded renewable energy generation system needs to adopt the voltage frequency (VF) control to provide AC voltage. The single-pole converter fault will unbalance the input and output power of the DC power grid, which causes the DC voltage or the bridge arm current of the non-fault pole to exceed the protection value in the time scale of tens to hundreds of milliseconds, leading to cascading failures. To realize the fault ride-through (FRT) of single-pole converter fault, this paper analyzes the electrical characteristic of the system. Based on the analysis, the existing topology is optimized and the reasonable operation reserved margin is designed. Furthermore, the corresponding control strategy is proposed, which can not only ensure the single-pole converter block fault ride-through but can also realize economic, stable, and resilient power supply and address asymmetrical problems. Finally, the simulation model is built in PSCAD/EMTDC and the simulation results validate the effectiveness of the proposed control strategy.

The power system on the offshore platform is of great importance since it is the power source for oil and gas exploitation, procession and transportation. Transformers constitute key equipment in the power system, and partial discharge (PD) is its most common fault that should be monitored and identified ın a timely and accurate manner. However, the existing PD classifiers cannot meet the demand for real-time online monitoring due to their disadvantages of high memory consumption and poor timeliness. Therefore, a new MobileNets convolutional neural network (MCNN) model is proposed to identify the PD pattern of transformers based on the phase resolved partial discharge (PRPD) spectrum. The model has the advantages of low computational complexity, fast reasoning speed and excellent classification performance. Firstly, we make four typical defect models of PD and conduct a test in a laboratory to collect the PRPD spectra as the data sample. In order to further improve the feature expression ability and recognition accuracy of the model, the lightweight attention mechanism Squeeze-and-Excitation (SE) module and the nonlinear function hard-swish (h-swish) are added after constructing the MCNN model to eliminate the potential accuracy loss in PD pattern recognition. The MCNN model is trained and tested with the pre-processed PRPD spectrum, and a variety of methods are used to visualize the model to verify the effectiveness of the model. Finally, the performance of MCNN is compared with many existing PD pattern recognition models based on convolutional neural network (CNN), the results show that the proposed MCNN can further reduce the number of parameters of the model and improve the calculation speed to achieve the best performance on the premise of good recognition accuracy.

The characteristics of short-circuit current provided by modular multilevel converter (MMC) is analyzed after three-phase short-circuit fault occurs at PCC (Point of Common Coupling) of AC system, based on the operation control principle and short-circuit grounding modes of the converter in this paper. First of all, this paper introduced the structure and control principle of MMC. Furthermore, the calculation method of dq component of short-circuit current is proposed, and then the short-circuit current characteristics provided by MMC were studied. When there is a short-circuit near the flexible DC converter bus and the converter bus maintains a certain residual voltage, the contribution of MMC circulating current to short-circuit current mainly depends on the reactive short-circuit current component of MMC during short-circuit fault. Finally, the influence of MMC control modes, short-circuit grounding modes and fault time on the transient peak value of reactive short-circuit current is analyzed and the model is built on PSCAD for simulation verification. This research provides a reference for the control parameter setting of MMC, short-circuit current evaluation and selection of AC circuit breaker in future flexible DC project.

For denoising of partial discharge (PD) signals in electrical equipment, the traditional wavelet threshold method is affected by the empirical and subjective selection methods of wavelet base and decomposition layer, which will cause the waveform to be distorted and produce large errors in the process of denoising. In order to reduce the influence of the above factors, this paper proposes a new adaptive wavelet multilevel soft threshold denoising algorithm based on waveform similarity parameters and energy distribution analysis. Firstly, the waveform similarity method is used to determine the optimal wavelet base. Secondly, multi-scale wavelet decomposition is performed on the simulated ideal partial discharge signal and noise, and the signal is processed by the multilevel threshold method. Finally, the energy analysis of the PD signal and the noise signal is performed to determine the optimal number of decomposition layers. The proposed method and the traditional wavelet threshold method are used to denoise the simulated discharge signal, and the denoising effects of the two algorithms are compared quantitatively from four indexes: signal-to-noise ratio, root mean square error, waveform similarity coefficient and ascending trend parameter. After calculation, the index of the new algorithm is improved, which indicates that the new algorithm has certain practicability and provides a new reference for the denoising of PD signals.