While frequency response analysis (FRA) is a well matured technique widely used by current industry practice to detect the mechanical integrity of power transformers, interpretation of FRA signatures is still challenging, regardless of the research efforts in this area. This paper presents a method for reliable quantitative and qualitative analysis to the transformer FRA signatures based on a decision tree classification model and a fully connected neural network. Several levels of different six fault types are obtained using a lumped parameter-based transformer model. Results show that the proposed model performs well in the training and the validation stages, and is of good generalization ability.

With the deepening of the “source-load-storage” interaction and the development of demand response technology, the emergence of prosumers has led to new vitality and potential for the optimal operation of microgrids. By implementing a demand response mechanism for prosumers, peak shaving and valley filling are realized, and load fluctuations are balanced. However, the high costs of investing and operating energy storage system (ESS) restrict their ability to participate in the scheduling of microgrids. In this paper, for the objectives of obtaining the lowest comprehensive costs and the smallest load fluctuations, an INSGA-II (Improved Fast Nondominated Sorting Genetic Algorithm) algorithm is proposed for the multiobjective configuration optimization model of a prosumer's ESS. To ensure the diversity of the population and improve the search ability of the algorithm in space, based on the original NSGA-II algorithm, the proportion factor set in the selection strategy is improved. The normal distribution crossover operator is introduced in the crossover process, and the local chaotic search strategy is added after the formation of the next generation of the population. An example of a science and technology park with five users is simulated and analyzed. Upon comparison with various typical intelligent algorithms, the results show that the performance of the improved NSGA-II algorithm is the best. At the same time, multiple calculation results show that the improved NSGA-II algorithm has strong algorithmic stability.

Periodical calibration is necessary to ensure the accuracy and reliability of voltage transformers. The traditional calibration method requires the power to be off, and the calibration period for this method is too long, meaning that problems with the transformer cannot be found in time. In this paper, a voltage transformer error online calibration system based on open–closed capacitors is proposed. Two open–closed capacitors and other auxiliary devices are utilized to construct the standard voltage sensor. The outputs of the open–closed capacitors are compared with each other to realize accurate self-checking. The average value of the output is used as the final output, which can improve the system’s accuracy and reliability. An improved algorithm based on a hybrid convolution window is proposed to extract the fundamental and harmonic signals. Test results show that the variation of the ratio error is less than 0.037%, and the variation of the angle error is less than 0.45’.

Air-core coil current transformer is a key piece of equipment in the digital substation development. However, it is more vulnerable to various faults when compared with the traditional electromagnetic current transformer. Aiming at understanding the effect of various parameters on the performance of the air-core coil current transformer, this paper investigates the influence of these factors using the maximum information coefficient. The interference mechanism of influencing factors on the transformer error is also analyzed. Finally, the Stacking model fusion algorithm is used to predict transformer errors. The developed base model consists of deep learning, integrated learning and traditional learning algorithms. Compared with gated recurrent units and extreme gradient boosting algorithms, the prediction model based on stacking model fusion algorithm proposed in this paper features higher accuracy and reliability which helps improve the performance and safety of future digital substations.

A new type of electronic voltage transformer is proposed in this study for big data background. By using the conventional inverted SF_6 transformer insulation structure, a coaxial capacitor sensor was constructed by designing a middle coaxial electrode between the high-voltage electrode and the ground electrode. The measurement of the voltage signal could be obtained by detecting the capacitance current i of the SF_6 coaxial capacitor. To improve the accuracy of the integrator, a high-precision digital integrator based on the Romberg algorithm is proposed in this study. This can not only guarantee the accuracy of computation, but also reduce the consumption time; in addition, the sampling point can be reused. By adopting the double shielding effect of the high-voltage shell and the grounding metal shield, the ability and stability of the coaxial capacitor divide could be effectively improved to resist the interference of stray electric fields. The factors that affect the coaxial capacitor were studied, such as position, temperature, and pressure, which will influence the value of the coaxial capacitor. Tests were carried out to verify the performance. The results showed that the voltage transformer based on the SF_6 coaxial capacitor satisfies the requirements of the 0.2 accuracy class. This study can promote the use of new high-performance products for data transmission in the era of big data and specific test analyses.

When the single-phase reclosing of the high-voltage circuit, there will be tremendous negative sequence current generation. The connection of inverter-type new energy sources may increase the negative sequence current of the power system. The risks will be greatly increased if a large negative sequence current invades the generator, and it will cause the general stator damaged and offline. Based on the chain STATCOM compensation, a new strategy of negative sequence current coordinated control protection on the generator extremity is put forward. This strategy can predict the operation time of inverse time negative sequence current protection by monitoring the negative sequence current on the generator extremity. Once it is determined that the negative sequence current has an effect on the power system, according to the relationship between the STATCOM capacity and the magnitude of negative sequence current, two schemes of full compensation and incomplete compensation are proposed. The negative sequence current output by STATCOM suppresses the magnitude of the negative sequence current invading the generator. It eliminates or prolongs the operation time of the negative sequence current protection and gets more time for power system security and stability control. Finally, the simulation results verify the correctness and feasibility of the proposed strategy.