基于异步整形机制的智能变电站通信队列调度策略

doi:10.19595/j.cnki.1000-6753.tces.231236

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Abstract The current industrial Ethernet switches in intelligent substations employ a strict priority scheduling strategy to prioritize traffic based on the level of priority. This strategy ensures the real-time performance of high-priority traffic in the substation communication network (SCN). However, when a failure happens in SCN or the network load increases, there may be a situation where low-priority traffic is "starved" due to a significant increase in abnormally high-priority traffic (SV flow). In recent years, time-sensitive networking (TSN) has emerged to provide low-latency and low-jitter transmission for critical commuinication services in intelligent substations. This has been achieved by incorporating key technologies such as clock synchronization, traffic shaping and scheduling, and network configuration. To address the problem of the existing queue scheduling strategy in SCN, which cannot theoretically guarantee the real-time and reliability of critical communication services, a communication queue scheduling strategy based on TSN's asynchronous traffic shaper (ATS) mechanism has been proposed. Firstly, a priority division and shaping queue allocation scheme was proposed for business flows, taking into account the quality of service requirements and transmission characteristics. This scheme aimed to prevent head blocking between traffic in the same shaping queue and minimize interference among different traffic flows.
The configuration problem of traffic shaper parameters was then abstracted as minimizing end-to-end delay for business flows, taking into account multiple constraints. An optimization model for the parameter of an intelligent substation traffic shaper was proposed, and the optimal model was solved using the interior point optimization algorithm based on Matlab software. The calculated optimal traffic shaper parameters ware then configured into the shapers of the TSN switches in the SCN. This shaper shaped the traffic that enters the shaping queue. The shaper adopted a traffic shaping method based on the token bucket algorithm to allocate the eligible time for frame transmission.
Finally, the post-processed business flow entered the corresponding scheduling queue based on the priority division and was scheduled using a strict priority algorithm. The data frames in the scheduling queue were arranged in ascending order based on the eligible time. That is to say, the data frames were scheduled for transmission based on the eligible time and priority parameters. If the eligible time of the data frame at the front of the scheduling queue was earlier than the current time, the data frame could be transmitted. Otherwise, if the current queue data frame couldnot be sent, the data frame from the next priority queue was transmitted. The communication queue scheduling strategy proposed in this paper was implemented based on the INET4.4 framework of the OMNet++ network simulation platform. Additionally, a simplified model of the intelligent substation communication network was constructed for simulation experiments. The experimental results demonstrate that the proposed method can ensure deterministic low-latency transmission of critical communication services and enhance the service quality of business flows, even when high-priority traffic bursts and causes network congestion. This improvement was in comparison to traditional SPQ scheduling strategies. Furthermore, in cases where the time synchronization of the substation communication network failed, the proposed strategy can still guarantee the real-time transmission of business flows.

Key words： Intelligent substation time-sensitive networking asynchronous shaping communication queue scheduling

Received: 31 July 2023

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	Jia Huibin
	Wu Wenrui
	Wu Kun
	Liu Yu
	Chang Naichao

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Jia Huibin,Wu Wenrui,Wu Kun等. A Communication Queue Scheduling Strategy for Intelligent Substations Based on Asynchronous Shaping Mechanism[J]. Transactions of China Electrotechnical Society, 2024, 39(17): 5422-5433.

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