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The power system loss is a very important technical and economic indicator in power enterprises. In a system, there are two types of losses: technical losses and non-technical losses (commercial losses). Out of the total system losses, technical losses can be reduced more directly and effectively. This paper focuses on the analysis of the variation and contributing factors of technical losses of each voltage class in Hubei power grid. As a result, main reasons and solutions for reducing technical losses are figured that provide a better understanding of weakness and loss composition of Hubei power grid, and are useful for power enterprises that intend to reduce electric losses as a benchmark and as a background in the planning purposes.

The “two-tickets” system of electric power enterprises is an important organizational measure to ensure operation safety of electric power production site. In the actual electric power production, the operation safety accidents caused by “two tickets” signed not in accordance with regulations or by others occur from time to time. At present, electric power enterprises mainly rely on manual inspection to verify the authenticity of “two tickets” signature, which has the shortcomings of more human resources investment and low efficiency. In this paper, a signature identification method of “two-tickets” based on siamese convolutional neural networks is proposed. The proposed method introduces image processing, max-pooling, batch normalization, and dropout technologies, and effectively combines convolutional neural network and siamese network, which can effectively improve the accuracy of “two-tickets” signature identification. The results show that the proposed method is more accurate than k-nearest neighbor method, naive bayes method, decision tree method and support vector machine method. The proposed method can be applied to the “two-tickets” signature identification in electric power enterprises.

In order to meet the higher demand of dynamic reactive power support is put forward in the Ultra High Voltage Direct Current(UHV DC) power transmission project, the new-generation synchronous condensers are applied in the UHV DC-AC. However, in order to provide bidirectional transient reactive power characteristics during AC and DC grid fault condition, especially to reduce the commutation failure phenomenon at the inverter station, there are great changes in the main body and excitation control system of the new-generation synchronous condensers compared with the same-capacity generators. In this paper, the limiting model of excitation system for the new-generation synchronous condensers under low excitation and strong excitation conditions are established. Then, the expression of sub-transient reactive power for the new-generation synchronous condenser is derived and the reactive power characteristics considering the influence of excitation regulation are also obtained. In addition, the transient reactive power demand of the high-voltage direct current(HVDC) transmission system is also analyzed. Finally, the simulation model of the new-generation synchronous condensers access to UHV DC power grids are built based on the PSCAD software. The simulation results verify the support effect of the new-generation synchronous condensers on DC transient reactive power.

Many power utilities have problems with the quality of data about distribution network topology. This affects the operation and maintenance of smart grid, including outage management and line loss. The operation data, such as voltage data whose similarity has been analyzed to verify distribution network connectivity. The most commonly method used to evaluate similarity is correlation analysis. In this paper, correlation analysis and morphology similarity distance (MSD) have been compared when used in distribution network connectivity verification. The results show that the data should be normalized when MSD is used. Four case studies have been carried out in order to compare the performance of the two methods. Results show that when the voltage loss of 10kV feeder is large, the performance of correlation analysis is better than MSD. When the variety range of the voltage curves is small, the performance of MSD is better than correlation analysis. When the voltage curves have catastrophe points, the performance of MSD is better than correlation analysis.