An anti-power theft method for secondary circuit of energy meter current transformer
At present, power theft ways become increasingly subtle and intelligent. Power theft not only has caused a great loss to the nation and the power sector, but also has brought serious security risks to the equipment and personal health. Although the power theft behavior of the energy meter voltage transformer secondary circuit has been well recognized and effectively suppressed, the anti power theft method for the secondary circuit of the energy meter current transformer is still a weak link. Therefore, this paper proposes a transient method based anti power theft method for the secondary circuit of the energy meter current transformer. This method applied the pulse signal to the secondary circuit of the energy meter current transformer first, then obtained the equivalent resistance and inductance parameter of the circuit by measuring the transient value of the circuit response current. Comparing these parameters with the original parameters of the circuit, we can judge the circuit state and power theft way. The simulation results verify the accuracy and reliability of the proposed method, which is a new anti power theft method of the energy meter.
Judgment of Transformation Rate for Electric Energy Meter Based on BP Neural Network and Adaboost Algorithm
Transformation rate for electric energy meter is the premise of accurate metering. The meter for electric energy measurement in the distribution network provides the basic data for the calculation of the line loss. It is shown in survey that nearly 3% of the energy meters’ transformation rate in the database are different from their actual rate. An inaccuracy transformation rate of electric energy meter will cause errors in calculating power and the line loss rate of low-voltage distribution network. However, it is not easy to find out the inaccuracy transformation rate in a database in daily management. To solve this problem, this paper presents a method based on adaboost and BP neural networks, and proves the feasibility of the method. BP neural networks serve as the weak classifiers. The weights of the sample and the weights of the weak classifiers are calculated based on adaboost algorithm. In the BP neural network for classification, the sample weights are utilized to adjust the error function of BP neural network to optimize the classification. A strong classifier is combined with weighting output of the weak classifiers. Computing results show that this method can precisely pick out electric energy meter with inaccuracy rate based on the power energy data. Accuracy of BP-adaboost classification is higher than that of the BP neural network, and the training time is greatly shortened. This method can improve the efficiency of line loss management in low-voltage distribution networks and greatly reduce consuming the manpower and material resources.
Laminar heat or mass transfer in rectangular channels and in cylindrical tubes for fully developed flow: comparison of solutions obtained for various boundary conditions
Abstract A comparison is made of results obtained with various boundary conditions at the wall of the channels. By comparing blood Sherwood numbers for finite wall resistance with those for constant concentration boundary condition only a small variation is found for various points along the blood channel.