Title: Multi-task learning neural network for monitoring and diagnosis of smart meters in power IoT systems

Authors: Ming Zhang; Yong Cui; Lei Wang; Shuang Ji

Addresses: State Grid Jilin Electric Power Co., Ltd., Siping Power Supply Company, Siping, Jilin, China ' State Grid Jilin Electric Power Co., Ltd., Siping Power Supply Company, Siping, Jilin, China ' State Grid Jilin Electric Power Co., Ltd., Siping Power Supply Company, Siping, Jilin, China ' State Grid Jilin Electric Power Co., Ltd., Siping Power Supply Company, Siping, Jilin, China

Abstract: With the continuous development of power Internet of Things (IoT) technology, the scale and complexity of power systems are increasing, and the safe operation of power IoT systems is facing challenges. In this paper, an intelligent safety monitoring and diagnosis algorithm is proposed for IoT terminals at all levels in the power system. The algorithm uses deep learning methods to propose a novel multi-task learning Deep Neural Network (DNN), which is used for online perception, monitoring and diagnosis of the operation status of power interconnection terminals. The proposed method can simultaneously target a variety of different running tasks and states of smart meters in power IoT systems, and realise multi-task-oriented deep data mining and intelligent decision-making. The experimental results of this paper verify the accuracy and reliability of the proposed method for a real power IoT system, and validate the effectiveness of the method in a power IoT terminal monitoring program.

Keywords: deep neural network; multi-task learning; computer vision; smart meter; power IoT terminal.

DOI: 10.1504/IJWMC.2024.140269

International Journal of Wireless and Mobile Computing, 2024 Vol.27 No.2, pp.125 - 132

Received: 09 Nov 2023
Accepted: 27 Dec 2023
Published online: 01 Aug 2024 *

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