| Popis: |
Smart meter is a key component in the energy grid. In order to detect the abnormal smart meter in a topological low voltage energy system, the orthogonal matching pursuit and Bollinger Band followed by the recursive model are proposed in this paper. Our model consists of two major components, including a data filter and an error estimation module. The data filter employs orthogonal matching pursuit and Bollinger Band to identify abnormal data from the meter reading data set. The data, which is significantly different from the orthogonal matching pursuit recover estimate and Bollinger bands, will be classified as abnormal data and removed from the data set. Then a recursive model is proposed to calculate the meter error. The meter error can be obtained by solving a linear equation constructed from the meter reading data. The experimental results present the performance under different scenarios. When the number of submeters is less than 100 and the line loss rate in the system is less than 8%, the accuracy of error estimation is higher than 90%. Overall, the proposed error estimation method provides a new idea to detect a set of smart meters by estimating the meter error. In the experimental results, the average absolute error and root mean square error obtained by our method are 1.30% and 1.09%, respectively, which are the lowest values compared with the classical methods. This suggests that our method has a distinct advantage, which provides higher practicability and efficiency, compared with the traditional on-site inspection and the machine learning techniques. |
