Online Measurement Method of Intelligent Energy Meter Based on Heuristic Q-Learning Algorithm
DOI:
https://doi.org/10.13052/dgaej2156-3306.40410Keywords:
Smart energy meters, online measurement, heuristic Q-learning, smart grid, abnormal data detectionAbstract
With China’s rapid smart grid development, smart energy meters have been widely applied in the power system. To solve large measurement errors and poor stability in traditional electric energy meters, an online electricity metering method based on heuristic Q-learning algorithm is designed. Based on prior knowledge, a heuristic action learning module is designed to guide the action output. A parallel hybrid deep neural network model is constructed to explore the relationship between electric energy data, line energy loss values, and line loss sequences at different time periods from multiple dimensions. The results showed that the accuracy in analyzing abnormal data was higher than 95%. The accuracy of the online error estimation algorithm was higher than 90%, which was significantly improved when compared with previous algorithms. When the line loss rate was less than 7.5%, the accuracy of the online error estimation algorithm was higher than 90%, greatly reducing the interference of line energy loss on online error estimation. This method can effectively solve the problems such as large error, poor stability, and insufficient adaptability of traditional electric meters, especially in the complex cases such as changes in line energy loss and abnormal data types. The proposed method can effectively improve the online measurement accuracy of smart energy meters, accurately estimating the errors of cluster smart energy meters in real-time.
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