SPSO-DBN based compensation algorithm for lackness of electric energy metering in micro-grid

• Published in: Alexandria engineering journal Vol. 61; no. 6; pp. 4585 - 4594
• Main Authors: Gao, Jinggeng, Wang, Xinggui, Yang, Weiman
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2022; Elsevier
• Subjects: Deep belief network; Electric energy metering; Micro-grid; Particle swarm optimization; Micro-grid; Deep belief network; Particle swarm optimization; Electric energy metering
• ISSN: 1110-0168
• DOI: 10.1016/j.aej.2021.10.018

Accurate electric energy metering in micro-grid is one of the most urgent problems in the field of electric energy metering. In order to solve the problem that the lackness of electric energy metering can't be accurately calculated with the existing electric energy compensation algorithm when the electric energy metering device is completely faulty, this paper proposes an electric energy compensation algorithm that is limited in micro-grid and is based on standard particle swarm to optimize the deep belief network. Taking the historical electric energy data of each micro source metering point in micro-grid as an example, it is divided into the training set and the test set, the standard particle swarm optimization algorithm is used to determine the optimal learning layers and learning rate of deep belief network, and the electric energy algorithm model is established. The model verification results show that the proposed algorithm is nearly five times lower than the algorithm based on deep belief network solely. Further engineering case comparison shows that lackness of electric energy metering compensated by the algorithm studied in the paper is closer to the theoretical value, and the error is also less than the traditional average electricity algorithm. The real-time power curve is closer to the theoretical value, and has the higher accuracy.