Abstract: In modern power systems, with the increasing application of renewable energy, direct current transmission technology has put forward new requirements for energy metering. In order to solve the accuracy problem of traditional electric energy metering under DC energy, the research is based on the classical empirical modal decomposition (EEMD), and introduces the artificial chemical reaction optimization algorithm (ACROA) to enhance the global search capability and decomposition accuracy of the original algorithm, and at the same time safeguards the accuracy of metering equipment under extreme conditions through the wide quantitative constraints, and ultimately puts forward a new type of optimization model for the accuracy of DC electric energy metering. The highest measurement accuracy of this model could reach 90%, and it performed better in power signal decomposition and accuracy optimization. Especially under high-frequency interference and complex signal conditions, the measurement error could be reduced to 6.87%, the highest decomposition stability was 94.02%, and the shortest measurement time was 1.12 seconds. Therefore, the model constructed in this study exhibits excellent decomposition accuracy and robustness in complex energy environments, solving the shortcomings of traditional energy metering methods and providing new ideas for future optimization of DC energy metering.

Xiaoyu Wang, Xin Yin, Xinggang Li, Jiangxue Man, Yanhe Liang and Fan Xu, “Accuracy Optimization and Wide Limit Constraints of DC Energy Measurement Based on Improved EEMD” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01512100

@article{Wang2024,
title = {Accuracy Optimization and Wide Limit Constraints of DC Energy Measurement Based on Improved EEMD},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01512100},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01512100},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Xiaoyu Wang and Xin Yin and Xinggang Li and Jiangxue Man and Yanhe Liang and Fan Xu}
}