Open Access

ARTICLE

RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things

Jiangang Lu, Ruifeng Zhao^*, Zhiwen Yu, Yue Dai, Kaiwen Zeng

Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou, 510600, China

* Corresponding Author: Ruifeng Zhao. Email:

Computers, Materials & Continua 2024, 78(3), 4453-4469. https://doi.org/10.32604/cmc.2024.048020

Received 25 November 2023; Accepted 05 January 2024; Issue published 26 March 2024

Abstract

Time synchronization (TS) is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things (IoT). Multi-clock source time synchronization (MTS) has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales, and the coupling of synchronization latency jitter and pulse phase difference. In this paper, the multi-timescale MTS model is conducted, and the reinforcement learning (RL) and analytic hierarchy process (AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference. Specifically, the multi-clock source selection is optimized based on Softmax in the large timescale, and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale. Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.

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Keywords

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