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The short-term prediction of the electricity consumption of the distribution network during the peak summer period is of great significance to the safe and stable operation of the distribution network and to reduce the burden on the grid staff. This paper obtains and organizes the operating data of distribution transformers in a city in southern China, takes the active load rate as the electricity consumption index, and uses ARIMA and PCR models to make short-term electricity consumption prediction. The results show that ARIMA prediction results are significantly better than PCR. The research results of this paper have certain guiding significance for the actual work of distribution network.

Nowadays extreme disaster weather have a great impact on the safe operation of distribution transformer. In this paper, an analytic hierarchy process(AHP)-fuzzy comprehensive evaluation method combined with a data mining method based on Apriori algorithm is proposed to build a new disaster weather evaluation system and construct the correlation model between extreme weather conditions and transformer fault types. For the proposed method, the analytic hierarchy process-fuzzy comprehensive evaluation method is firstly used to processed the real data of a certain power grid, and then the disaster weather evaluation system is established to to determine the impact degree of the disaster weather and classify the disaster grade. Lastly the Apriori algorithm is used to mine the real data to obtain the strong association rules and build the correlation model. The proposed model is performed on the field data obtained from a real power grid in South China and the final experimental results prove that the evaluation system and the correlation model are effective and comprehensive.

Adjustable speed drives (ASD) are widely used in modern industrial process, which are sensitive to transient low voltage disturbances. The loss caused by failures of ASD can be avoided by improving its immunity to these disturbances. This paper proposed a practical and cost-effective scheme for industrial users to improve the immunity of ASD to transient voltage disturbances based on battery energy storage system (BESS). By modifying the original ASD structure and introduce a BESS-based DC power supply system, the proposed scheme can provide voltage support for the DC link of the ASD during the transient voltage disturbances. Simulation and experimental test are conducted to verify the effectiveness of the proposed scheme.

This paper presents a coordination scheme of superconducting magnetic energy storage (SMES) and distributed generation (DG) system for regulating the DC bus voltage in the low voltage direct current (LVDC) distribution systems under symmetrical alternative current (AC) grid fault. The scheme can regulate DC bus voltage in milliseconds by taking advantage of SMES device including high power injection and fast response. On the other hand, the active control of DG system is introduced to increase the fully compensation time of the SMES under serious faults. By evaluating the reason of DC bus voltage sag, the control of SMES is proposed in cooperative with the DG system. A LVDC distribution system is modelled in PSCAD/EMTDC. Simulation results have demonstrated the correctness and feasibility of the proposed scheme.

When transformers are energized under no-load or lightly loaded condition, the inrush current may occur on the primary circuit of the transformers. The magnetizing inrush current has an adverse effect on both the transformer itself (especially on the high-voltage built-in high-impedance transformer) and the power quality of the power system. The deprivation of effective magnetizing inrush suppression technology could lead to transformer trip and equipment damage. Since the inrush current of high-voltage built-in high-impedance transformer is large and decays slowly, the study proposes a technique combing Convolutional Neural Network (CNN) and inrush suppression technology. This new technique can adaptively correct the proper transformer switching angle by taking into account various facts, like temperature, operating voltage, oil pressure and times of control action. Based on PSCAD software, it simulates the theoretical effects of magnetizing inrush suppression technology, and verifies its practical benefit through building the actual system.

Voltage tolerance curve (VTC) ofsensitive equipment to voltage sag is important for user to apply the mitigation equipment. Test on the equipment is the straight method to get VTC. The accuracy of VTC and the test workload depend on the test method. This paper compares the current five test methods and proposes a new test method for sensitive single-phase equipment. This paper builds a test platform to test the tolerance of a PLC by the six methods, the test results verify the advantage of the proposed method.

The secondary equipment such as protection under complicated electromagnetic condition of substation may mal-operate due to false data caused by electromagnetic interference. However, conventional transformer magnetizing inrush identification method have not considered the influence of false data. By analyzing the influence of false data on different methods, it can be concluded that existing methods may slow-operate even mal-operate. To solve the problem, a new magnetizing inrush identification method based on similarity identification is proposed to prevent this sort of maloperation, and this method has advantage to trip quickly.