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To measure and assess the energy efficiency level of electric vehicles charging equipment, this paper proposes an energy efficiency measurement scheme for DC charging piles. In this scheme, electricity meters with suitable performance are selected to achieve real-time measurement of energy consumption and overall energy efficiency of each part of the charging process. By establishing the theoretical model of charging equipment, the weakness of the charging pile is found. In order to further improve the accuracy of energy efficiency measurement scheme, the simulation model of three-phase bridge type controllable rectifier DC charging pile is implemented. Finally, the accuracy and feasibility of the proposed energy efficiency measurement scheme are verified by comparing the simulation and measured results of electric energy and the calculation results of the charging pile loss model.

Considering the problems of the uncertainty of the load type in household scenarios and the incompleteness of the load signature database in the non-intrusive load database, which easily leads to decreasing accuracy in load identification, this paper proposes a load identification method to cope with these problems. On the base of electrical signatures, this method also uses time signature which includes the characteristics of the length of operation time, load operation time, working period and vacation features. In this method, firstly, the piece-wise normalization mean-shift clustering method is used to cluster the detected load event features and obtain the number of potential load types. Then the time signatures and power signature of load events are counted to get their probability. And the Bayesian method is used to identify the load by decision-making. Finally, this paper uses the AMPds public dataset to carry out actual tests, the experimental results show that this method has a good identification effect to this scenario.

In order to solve the problem that the smart meter cannot carry out the experiment of radio frequency immunity in the general type of Radio Anechoic Chamber, a virtual load test system filter for smart meters is designed in this paper, which solves the influence on the accuracy caused by electromagnetic leakage through input and output test lines. Based on the filter parameters and the actual measured voltage data of the smart meter, a reactance compensation circuit is designed into the test circuit to solve the problem that the smart meter errors is out of tolerance before radio-frequency radiation immunity test. Finally, the validity of this method is verified by comparing the test data of different specfications of smart meters. The test results show that the introduction of the filters and compensation circuits ensure the basic error of the tested smart meter before conducting the radio-frequency radiation immunity test, and solve the problem that the radiation electromagnetic compatibility test of the smart meter cannot be carried out according to the standard requirements in the general type of Radio Anechoic Chamber.

Focusing on the current impulse waveforms generated when the fixed or variable frequency air conditioners, vacuum cleaner, microwave oven and other electrical appliances are turned on, the characteristic parameters such as impulse amplitude, rising time, dropping amplitude, and falling time are defined, and a multi-feature Bayesian classification model is established. Considering that the classification algorithm works on the smart meter hardware, the computing and storage resources are limited, as a result that the model scale needs to be reduced as much as possible, a meant parameters Bayesian classification method is proposed. The current impulse waveform samples of different appliances are divided into several groups, and the mean value of the characteristic values of the samples is used as the parameters of the classification model. Under the same model capacity, the generalization error of the model is reduced. The simulation results show that the accuracy of Bayesian classifier constructed with meant value parameters is better than that of ordinary Bayesian classifier. In the laboratory, the impulse classification test is carried out on a single-phase smart meter, and the test results verify the feasibility of the proposed method.

At present, as the residential electricity power consumption growth rate is far higher than that of industrial and agricultural electricity power consumption, optimize the residential electricity power consumption behavior is of great significance to reduce the power grid peak-valley gap and comprehensive energy loss. To study the non-intrusive load identification technology, analyze residential electricity usage details and provide electricity usage suggestion to residents is of paramount importance. In this paper, the non-invasive load identification technology is deeply studied and the layered classification algorithm based on multidimensional load characteristic matching is proposed. On the basis of State Grid single-phase carrier intelligent electricity meter, the intelligent power meter with residential load identification function is developed. In order to verify the load identification function of smart electricity meters, the test environment was set up in the laboratory. Test results displayed that the identification accuracy of the electric appliance type can be 100%. In addition to the extremely complicated working conditions, the power consumption identification accuracy of smart electricity meters is more than 90%.