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.