Intelligent Substation Switch Traffic Scheduling Model Based on Time-Sensitive Network

Smart substations are an important trend in substation construction. With increasing data traffic, it is difficult for the traditional Ethernet network to meet the real-time requirements of control information in smart substations. Hence, in this paper, a deterministic network architecture for substations based on time-sensitive networks (TSN) has been developed in order to realize the domain-wide time synchronization and efficient real-time communication of the “three-layer and two-network” model in smart substations. Furthermore, a design scheme for substation automation equipment based on TSN is proposed. The proposed device realizes the timely transmission of real-time control information packets by utilizing the Earliest TxTime First (ETF) Qdisc technology of Linux and the timing sending capability of Intel 210 NIC. Furthermore, it collaborates with the time-aware shaper (TAS) traffic scheduling mechanism of TSN switches to ensure the end-to-end deterministic delay of time-sensitive traffic. As a result, it provides efficient real-time communication services with low latency and jitter for smart substation automation systems.

The intelligent substation realizes the digitization of information in the whole substation and thus time synchronization system becomes more and more important. This paper introduces time synchronization technology of intelligent substation and puts forward the principles for designing intelligent substation time synchronization system. According to some relay protection malfunction examples caused by time synchronization system fault, analyze the influence of time synchronization system fault to relay protection and correspondingly put forward some improving measures.

IEEE 802.1 Time-Sensitive Networking (TSN) assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data in handling time-critical traffic. TSN handles different quality of service (QoS) requirements and frame preemption is one of the key features of TSN. In the healthcare sector networking technology preferred by large organizations uses an enormous number of nodes, and thereby, the complexity of the network increases. Since the priority of the medical data varies at times based on the patient's health, dynamic traffic scheduling mechanisms are preferred. To improve the efficiency of the network, the software-defined access mechanism is used to control the network switches and bridges in the time-sensitive network. This work uses reinforcement learning to identify and eliminate the bridges dropping packets, and the alternative path is used to schedule the real-time data traffic. It is perceived that it performs well for the time-critical data in congestion network, increases the throughput, and reduces latency.

Substations play an important role in power systems. The application of the IEC61850 standard changes the situation of the traditional substation greatly. The information in the intelligent substation is rich, but it is difficult for the operators to digest them in a short time without an effective assistant tool. A fault diagnosis method which uses the alarming information of the primary and secondary system of the intelligent substation is proposed in this paper. This method first uses the Bayesian based algorithm to find the possible faulty set H1. And at the same time, the information entropy difference algorithm is used to obtain the fault assumption set H2. Then the fault assumption set H2 is used to calculate the fault correctness and a decision fusion algorithm can locate the fault components accurately. Finally, a fault scenario is served for demonstrating the feasibility and validity of the method presented.

The convergence of 5G and Time-Sensitive Networking (TSN) offers a powerful foundation for enabling ultra-reliable, low-latency, and deterministic communication across a wide spectrum of emerging applications. While prior surveys primarily concentrate on industrial automation, this work presents, to the best of our knowledge, the first comprehensive survey that systematically investigates the potential of integrated 5G-TSN in four critical domains: industrial automation, intelligent transportation systems, smart energy systems, and digital health. We begin by providing a detailed background and comparative analysis of 5G and TSN technologies, highlighting recent advancements and complementary capabilities. The paper then presents a deep dive into an integrated 5G-TSN system architecture, with particular focus on time synchronization, traffic scheduling, QoS mapping, and cross-domain resource coordination. Building on this technical foundation, we introduce a structured, application-specific analysis that maps the communication requirements, challenges, and domain-specific integration strategies to the corresponding enabling 5G-TSN mechanisms. Finally, we synthesize key technical challenges such as interoperability, end-to-end synchronization, heterogeneous QoS alignment, and unified security, and propose targeted research directions to support the practical, scalable deployment of integrated 5G-TSN for emerging applications.

Substation communication network (SCN) provides real-time, high-speed, and reliable data transmissions for the advanced monitoring and control functionalities, which are facing increasing cyberspace threats and attacks. Efficient threat perception and cyber situational awareness are essential to enhance secure and reliable SCN operations. This article explores multiscale SCN traffic pattern characteristics with holistic network traffic, separated network traffic for included devices (especially IoT devices) and separated network traffic of certain types of protocol. The proposed online traffic-oriented SCN traffic anomaly detection and cyber situational awareness models are designed for the network anomalies and cyber-attacks that could cause network traffic pattern variations. We leverage a fractional autoregressive integration moving average (FARIMA)-based dynamic threshold model to detect abnormal traffic patterns without sophisticated computations or deep packet inspection. The SCN real-time operation conditions are timely quantified through the statistical methods with the alliance of SCN topology and protocols. The cyber situational awareness model is further carried out to evaluate the most affected protocol and security risks of various devices in SCN using Grubbs’ test. The experiment results are carried out based on a real 110-kV intelligent power substation. The numerical results confirm the comparative low mean square error (MSE) and low complexity of the online traffic characterization when forecasting holistic network traffic and separated network traffics. Furthermore, the timely and quantified cybersecurity risk analysis is conducted based on the SCN traffic with varying scales to detect cyberspace threats and identify the high-risk SCN devices and the most affected protocol.

Time-sensitive networking (TSN) technologies have garnered attention for supporting time-sensitive communication services, with recent interest extending to the wireless domain. However, adapting TSN to wireless areas faces challenges due to the competitive channel utilization in IEEE 802.11, necessitating exclusive channels for low-latency services. Additionally, traditional TSN scheduling algorithms may cause significant transmission delays due to dynamic wireless characteristics, which must be addressed. This paper proposes a wireless TSN model of IEEE 802.11 networks for the exclusive channel access and a novel time-sensitive traffic scheduler, named the wireless intelligent scheduler (WISE), based on deep reinforcement learning. We designed a deep reinforcement learning (DRL) framework to learn the repetitive transmission patterns of time-sensitive traffic and address potential latency issues from changing wireless conditions. Within this framework, we identified the most suitable DRL model, presenting the WISE algorithm with the best performance. Experimental results indicate that the proposed mechanisms meet up to 99.9% under the various wireless communication scenarios. In addition, they show that the processing delay is successfully limited within the specific time requirements and the scalability of TSN streams is guaranteed by the proposed mechanisms.

Time-Sensitive Networking (TSN) is a cluster of protocols working in the data link layer of the OSI reference model, which has been introduced into the industrial Internet due to its ability to ensure the deterministic transmission of data. Through IEEE 802.1AS clock synchronization and IEEE 802.1 Qbv gating mechanism, TSN networks allocate separate transmission time slots for periodic signals to ensure low delay and jitter, but this increases the delay of aperiodic stream to some extent. In this paper, a scheduling scheme of Grouping transmission is proposed. By changing the sending time of periodic data stream, the periodic data stream which is originally continuously transmitted is divided into several groups to be transmitted in different time periods in a cycle, so as to reduce the real-time aperiodic data stream delay. Experimental results show that the proposed scheduling scheme can reduce the transmission delay of the non-periodic traffic without affecting the quality of service (QoS) of the periodic traffic.

In daily life, the switch is an essential part of life and manufacturing. Among recent years, with the continuous development of high-tech technologies such as computers, networks, and microprocessors, the intelligentization of switches has also made tremendous progress. The traditional switch has great inconvenience in application, such as few functions, low flexibility, and almost zero intelligence. The emerging intelligent switch has attracted people's attention and is widely used in all aspects. Among the many types of emerging switches, smart switches that can be controlled remotely have many irreplaceable advantages. This article mainly studied a remotely controllable intelligent switch based on STC15W4K48S4. The use of this system can increase the flexibility and safety of equipment control, realize remote control of equipment, save manpower, and improve production efficiency.

With the wide application of TSN technology in smart substations, it is necessary to study the test technology of power time-sensitive network switches in terms of performance, function, security and reliability. Based on the principle and mechanism specified in TSN series standards, this paper proposes a test and verification scheme for performance, function, security and reliability. It is verified that the test scheme proposed in this paper can effectively achieve the test goal, which has reference significance for the development and testing of TSN network equipment.

The coupling of microscopic traffic simulation models with emission models offers a powerful tool for assessing and optimising traffic control strategies to reduce fuel consumption and vehicle emissions. Although many studies use traffic simulation for emission analysis and designing traffic control measures, most focus on calibrating a selected traffic model to replicate observed traffic flow. This raises a critical question: are the resulting optimal emission control strategies adequately designed to account for the sensitivity of traffic models in capturing vehicle dynamics and emissions? To address this issue, we compared three car-following models—the Krauss model, the Intelligent Driver Model (IDM), and the Wiedemann model—each rooted in distinct theoretical frameworks to understand traffic dynamics. We evaluated their performance in optimising road speed limits to minimise (PMx) emissions in a school case study. A school was selected as the case because children are highly vulnerable and particularly exposed to pollutants during their school commute, and their exposure can be mitigated through optimal traffic control. Our findings reveal that, even when tuned to achieve comparable levels of traffic flow, the models displayed significant differences in their objective functions for traffic control optimisation. These discrepancies stemmed from variations in fuel consumption and particulate matter (PMx) emission patterns resulting from the traffic dynamics captured by the selected traffic model. At a macroscopic level (e.g., average speed, flow, and density), the models exhibited minimal differences. However, at a microscopic level (e.g., acceleration, deceleration rates, and deviations from the mean), pronounced differences became evident. These results highlight that while certain traffic control strategies appeared less effective, revisiting and critically examining the limitations of the models is essential to ensure robust and tailored solutions for emission reduction.

With the widespread popularity of smart home devices and the emergence of smart home integration platforms such as Google, Amazon, and Xiaomi, the smart home industry is in a stage of vigorous development. While smart homes provide users with convenient and intelligent living, the problem of smart home devices leaking user privacy has become increasingly prominent. Smart home devices give users the ability to remotely control home devices, but they also reflect user home activities in traffic data, which brings the risk of privacy leaks. Potential attackers can use traffic classification technology to analyze traffic characteristics during traffic transmission (e.g., at the traffic exit of a smart home gateway) and infer users’ private information, such as their home activities, causing serious consequences of privacy leaks. To address the above problems, this paper focuses on research on privacy protection technology based on traffic obfuscation. By using traffic obfuscation technology to obscure the true traffic of smart home devices, it can prevent malicious traffic listeners from analyzing user privacy information based on traffic characteristics. We propose an enhanced smart home traffic obfuscation method called SHTObfuscator (Smart Home Traffic Obfuscator) based on the virtual user technology concept and a virtual user behavior construction method based on logical integrity. By injecting traffic fingerprints of different device activities into the real traffic environment of smart homes as obfuscating traffic, attackers cannot distinguish between the real device working status and user behavior privacy in the current home, effectively reducing the effect of traffic classification attack models. The protection level can be manually or automatically adjusted, achieving a balance between privacy protection and bandwidth overhead. The experimental results show that under the highest obfuscation level, the obfuscation method proposed in this paper can effectively reduce the classification effect of the attack model from 95% to 25%.

This article first proposes an adaptive traffic scheduling strategy for optoelectronic hybrid data centers. The strategy is composed of a deep bidirectional LSTM-based traffic prediction model and a prediction-assisted traffic scheduling method. The simulation results confirm that the presented method can achieve non-congested intra-data center traffic scheduling and higher network performance even under heavy traffic conditions.

Recent advances in wireless multimedia sensor networks have led to disjoint multipath routing protocols specifically designed for transmitting large multimedia traffic. Towards the coexistence of different kinds of multimedia traffics, a packet scheduling scheme is proposed for disjoint multipath routing based wireless multimedia sensor networks. The scheme classifies the packets into four classes based on their real-time and reliability requirements and assigns different priorities for them. Non-preemptive queuing model with multiple priorities and routing path condition based traffic scheduling model are presented and employed in traffic scheduling algorithm. And the model proposed in this paper is analyzed. The simulation results show that the performance of proposed traffic scheduling scheme is better than FIFO.

With the complexity and heterogeneity of multi-level networks in energy storage systems (such as station control layer equipment, energy storage compartment battery monitoring networks, etc.), traditional traffic monitoring technologies are unable to effectively cope with challenges such as encrypted traffic covert attacks and dynamic changes in business scenarios. This article proposes a full traffic intelligent analysis model for complex heterogeneous networks, which integrates multi granularity traffic modeling and deep behavior analysis to achieve fine-grained monitoring of network layer and application layer traffic. Firstly, a network traffic baseline model based on spatiotemporal features is constructed, combined with unsupervised learning to dynamically identify traffic patterns in business scenarios; Secondly, design an encrypted traffic metadata association analysis algorithm to detect Advanced Persistent Threat (APT) malicious heartbeat connections without cracking Secure Sockets Layer/Transport Layer Security (SSL/TLS); Finally, the performance of the model is validated based on real energy storage system traffic and simulation attack data. The experiment shows that the model has a detection accuracy of 96.8% for dangerous control instructions, an F1 score of 93.7% for identifying abnormal external communication behavior of encrypted flow, and a false alarm rate of less than 2.5%, which is significantly better than traditional methods. It provides an innovative solution for full flow safety monitoring of energy storage systems.