The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions, exacerbating security risks, such as unauthorized access, data tampering, and forgery. In response to these challenges, this study introduces a novel framework that enhances the protection of data assets. It incorporates a multi-dimensional knowledge graph (MDKG) to refine access control and overcome current limitations by integrating a comprehensive set of data asset attributes, roles, policies, and permissions. This approach fosters the development of a nuanced and adaptable access-control mechanism. Furthermore, the framework integrates multiple topology (MTP) for holistic security risk detection, leveraging attention mechanisms, and cross-fusion to adapt to the dynamic data security landscape. Empirical evaluations affirm the effectiveness of MDKG-based access control, whereas comparative experiments demonstrate the superiority of the MTP-based security risk model over existing models. The framework was proven to be effective in countering security risks. This study provides innovative perspectives on data asset protection and establishes a solid foundation for the advancement of smart grid technology.

The 5G network provides an appealing communication platform for the cyber-physical power system (CPPS), enabling the transmission of tremendous sensing data from remote terminal units (RTUs). To address the issue of energy scarcity faced by RTUs, this paper presents an energy-harvesting RTU model for data sensing and transmission in 5G-enabled CPPS. A hybrid energy source scenario is investigated, where renewable energy is harvested from the surroundings while grid energy is used as supplementary. Then the RTU power scheduling issue is formulated to minimize the energy cost under the constraints of long-term queuing delay and average energy storage. Given the problem is NP-hard due to information uncertainty, Lyapunov optimization is adopted to transform it into a series of short-term power scheduling subproblems. Furthermore, a deep reinforcement learning (DRL) based power scheduling algorithm (TD3-PER-OPSA) is developed to facilitate online power scheduling decisions. Extensive experimental evaluations using real-world solar power generation data demonstrate that the proposed approach achieves the optimal energy-communication tradeoff and outperforms benchmark methods in terms of training convergence, energy cost, and transmission performance.

Aiming at the problem of urban electricity load anomaly risk identification, this paper proposes a Gaussian Mixture Model (GMM) clustering-based method for identifying abnormal electricity load risks in urban areas. The method first collects user electricity parameters such as voltage and current, constructs a data table after preprocessing, and then employs the GMM clustering algorithm to analyze the processed data. This clustering identifies normal samples and abnormal load samples, categorizing the load state into three types: normal, approaching overload, and overload. Furthermore, it classifies the abnormal risks into specific types such as over-voltage, steady-state overload, and harmonic overload. Experimental results demonstrate that, compared with DBSCAN clustering, the proposed method significantly enhances anomaly detection capability: the harmonic overload detection rate increases by 24.14%, and the overall accuracy reaches 92.49%. This method can effectively identify abnormal risks in user electricity loads, providing robust support for the safe and stable operation of urban power systems.

As the effects of global climate change become more pronounced and the need for sustainable development increases, conventional power systems face unprecedented challenges. As the world's largest carbon emitter, China has proposed a dual carbon target of carbon peaking by 2030 and carbon neutrality by 2060. This not only requires the power system to operate efficiently and safely, but also requires technological innovations for intelligent and automated management. To address this challenge, this study designs and implements an intelligent and cooperative wireless communication management system, which aims to enhance the automation and cooperative management capability of power communication systems. By studying key technologies such as Self-Organizing Network (SON), Quality of Service (QoS) framework and 3GPP (3rd Generation Partnership Project) security framework, and combining with IoT technology, the system achieves efficient network management and optimization, enhancing the operational efficiency and security of the power system. As the network traffic increases, the system response time grows progressively. It ranges from 100 ms at 100 packets/sec to 310 ms at 1000 packets/sec, which shows the limitation of the system processing capacity. The system can significantly improve the response speed and scheduling efficiency of the power system, reduce the operation and maintenance cost, and has important practical value in promoting the modernization and intelligent transformation of the power system.

To improve the service transmission-performance at power transmission and substation construction-site, this paper proposes a wireless self-organization hybrid routing method considering services priority for the safety monitoring scenario. Firstly, the scenarios of communication between safety monitoring devices at transmission and substation construction sites are analyzed. Then, a hybrid data routing method considering services priority is depicted and its implementation steps are described. Finally, the performance of the proposed architecture is verified by using a typical wireless self-organized network scenario. The experimental results illustrate that the proposed routing method can minimize the transmission energy consumption while meeting the delay QoS requirements for different priority services.

The security of data transmission has become more important in modern network communications in new power system. In order to guarantee the secure and reliable communication of power load management terminals under the new power system, this paper proposes a Secure Channel Service based on multi-connection communication. With the multi-connection feature, we divide the data into multiple slices and encrypt each slice before transmitting it over a plurality of connections. Meanwhile, this paper proposes a simple and efficient encryption algorithm for transmitted data that utilizes the multi-connection feature to ensure the simplicity and efficiency of the transmission computation and processing without generating a large amount of key data. Using time-slot scheduling strategy and multi-connection strategy as the main strategy and slice encryption strategy as the auxiliary strategy, the proposed Secure Channel Service can satisfy the requirements of higher level of secure data transmission of power load management terminals.

In view of the current lack of comprehensive evaluation of energy measurement system evaluation and the need for comprehensive measurement of electricity, water, gas and heat energy, a multi-table integrated energy measurement status evaluation method based on hierarchical structure is proposed. Based on the analysis of the comprehensive measurement indicators of electricity, water, gas and heat energy and the evaluation needs, a hierarchical evaluation index of the operating status of the multi-table integrated energy measurement system has been established. The comprehensive evaluation of the operation status of multimeter integration of energy measurement is realized through a hierarchical structure analysis method. Experimental analysis shows that this method can scientifically quantify the multimeter integrated energy measurement.

Power grid control services can only be carried on private networks, such as wireless private networks or 5G network slices. Wireless private networks have high construction costs and are difficult to scale. Due to the deterioration of wireless signal quality, 5G network slice may fall back to 4G network. Therefore, it is necessary to study and propose a transmission scheme for power grid control services facing any network environment to ensure the security and reliability of control services. This paper proposes a transmission method for power grid control services based on 5G wireless air interface quality monitoring. Through real-time monitoring and link detection of wireless air interface signals, it can ensure that the blocking control function is notified to the power grid control terminal when the 5G network slice does not meet the control service transmission conditions. The scheme proposed in this paper provides an effective means for the power network control terminal to decide whether to block the control function according to the network state, to sense the deterioration of wireless air interface quality in time and to select the attached cell.

5G has the advantages of large bandwidth, low delay, and wide connection, providing strong support for sensing acquisition and interactive control equipment access, data transmission, and online interaction in all aspects of the power grid. The open transmission environment of wireless communication leads to the interference of space electromagnetic waves. Timely detection and diagnosis of interference types and effective measures are the key to ensure the reliable operation of power 5G services. In this paper, an improved SNN neural network is designed to diagnose interference types in 5G bearer power applications. By integrating network topology, terminal position information and air port signal quality data collection, the diagnosis accuracy is effectively improved.

For reducing the operation cost of shared energy storage stations and ensure the operation stability of power grid, this paper proposes an operation strategy of shared energy storage station and power grid considering power flow. Firstly, the interaction model is described between the shared energy storage station and power grid. Secondly, the cost model of shared energy storage station and the stability model of distribution network is also depicted including its related implementing method. Finally, the paper carries out the simulation case by means of the IEEE33 node distribution network system based on the above optimization model. The example illustrates that the proposed method can effectively lower the cost and ensure power grid to operate stably.

随着智能电网的高速发展,窃电方式呈现多样化,窃电数据也具有难以标注且样本类不平衡的特征.针对窃电数据无标签且类不平衡的窃电检测问题,提出一种基于Bagging二次加权集成的孤立森林窃电检测算法.首先,通过分析居民和商业用户存在的窃电模式,基于孤立类间相似度最低准则,对各类窃电模式的孤立特征顺序进行优选并训练对应的孤立森林模型;其次,使用加权投票法获得二次集成孤立森林模型,实现了窃电模式不平衡分布条件下的窃电检测.对7种常用学习算法和Bagging异质集成学习算法进行了比较,仿真实验结果表明所提算法的孤立特征顺序优选策略有效提高了无标签且类不平衡条件下的窃电检测效果,二次加权集成策略提高了窃电模式不平衡分布条件下的窃电检测效果.

To fully understand the energy consumption characteristics of 5G base-station, a DBSCAN-based energy consumption pattern clustering identification method is proposed for 5G base-station. Firstly, this paper analyzes the daily-curve characteristics of power consumption behavior in typical application scenarios of 5G base-station for further pattern clustering identification. Then, the proposed pattern clustering identification method is depicted based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering decision, which is composed of the feature extraction for power consumption daily-curve of 5G base-station. Finally, the experiment is implemented using actual operation data of 5G base-station as data source. The experiment results illustrate that the proposed method can effectively identify the clustering characteristics of the energy consumption behavior for 5G base-station.

智慧能源站是以变电站为基础,实现变电站、储能站、光伏站、充电站与数据中心站功能为一体的新一代能源枢纽基础设施.为了提高智慧能源站差异化业务处理能力及各站数据通信高效协同,文章提出一种多站融合智慧能源站5G通信组网应用方案设计.首先分析智慧能源站的多站融合业务的通信需求,然后构建含终端感知层、边缘计算层、通信网络层、企业中台层和应用平台层的智慧能源站5G通信组网模型,最后基于模型提出智慧能源站5G通信组网应用方案,并结合用户面功能(user plane function,UPF)部署、切片部署及切片配置阐述关键组网技术.多站融合5G组网应用方案可为智慧能源站通信组网提供应用实例参考.

针对系统保护通信专网局部节点与链路业务负载过重的问题,提出一种考虑负载均衡的系统保护通信专网路由规划方法.首先,阐述了系统保护通信专网的概念,分析了局部节点和链路业务负载过重的问题;然后,构建了综合考虑业务特性和备份路径的负载均衡路由规划优化模型,实现了模型的负载均衡路由规划求解;最后,以某省系统保护通信专网光传送网(0TN)拓扑为仿真网络,采用PSCPlanner电力通信网规划平台与K条最短路径(KSP)算法进行了对比实验.仿真结果表明:该方法均衡了链路容量,降低了业务请求拒绝率,有效地分配了网络资源.

针对当前双路由规划算法不适应当前电力通信网业务需求的问题,文章提出一种基于改进K条最短路径(K Shortest Paths,KSP)方法的电力通信网双路由均衡算法.首先介绍了电力通信网业务,然后提出一种以业务传输时延、业务可靠度、共享风险组和不相交双路由为约束条件,以链路占用率标准差最小为优化目标的基于改进KSP的电力通信网双路由均衡算法;以某区域电力骨干通信网为仿真拓扑,实验验证了算法的有效性.仿真结果表明,提出的算法运行时间短,可以提高业务负载的均衡程度,降低业务拒绝率.

WSN (Wireless Sensor Network) is an important way to transmit intelligent electricity business data, but it is more and more difficult to meet different performance requirements of different businesses. In the view of WSN power control problem, an adaptive power control algorithm based on intelligent electricity business classification was proposed in this paper. Firstly, the theory of node power control was analyzed. Secondly, the business classification and interference power adjustment factors were increased, to combine intelligent electricity business with network topology of WSN and realize the modeling of business classification based adaptive power control algorithm. Finally, this algorithm was compared with optimal power algorithm and LINT algorithm by simulation experiment in the performance of packet loss rate, delay and throughput. Simulation results showed that the algorithm proposed in this paper is better than others.

Smart energy station is a new generation of energy hub infrastructure based on substation, which realizes the multi-station functions of substation, energy storage station, photovoltaic station, charging station and data center station. To improve the processing capacity of the differentiated services of the smart energy station and the efficient coordination of data communication between each station, this paper proposed a multi-station integrated 5G communication scheme for smart energy station. Firstly, the related communication QoS requirements of the multi-site converged services is analyzed for smart energy stations. Secondly, a layered 5G-based communication networking model for smart energy stations is depicted including terminal-perception layer, edge-computing layer, communication-network layer, middle-platform layer and service-platform layer. Finally, a multi-station integrated 5G communication scheme is specified based on the proposed model by an application case, which is combined with UPF deployment strategy for Edge computing and 3-layered 5G-slicing strategy for services’ security. The case illustrates that the proposed scheme performs effectively for the multi-station Integrated services of smart energy stations.

文章提出一种基于安全度的电力通信网双路由配置方法.首先,通过分析电力通信网的需求,综合考虑可靠度、链路均衡度和业务传输时延,定义安全度的概念,以给业务配置最安全路径为优化目标;然后,基于传统的Bhandri算法,结合电力通信网实际,提出一种基于安全度的最大不相交双路由配置方法;最后,通过仿真实验验证配置方法的有效性.仿真结果表明,与传统的删除发现(Remove-Find,RF)算法相比,所提配置方法能在配置最大不相交双路由的基础上提高全网链路资源的均衡度.

In view of the current high-speed power line carrier technology (HPLC) lack of an effective monitoring system, this paper proposed a design scheme of an HPLC operation monitoring system based on noise decomposition. The system adopts modular design, including signal acquisition, information transmission and noise decomposition. The signal acquisition module adopts capacitive coupling technology, which considers the convenience and accuracy of the system monitoring performance. The information transmission module adopts wireless transmission technology to meet the needs of HPLC channel monitoring. The noise decomposition module completes the decomposition and identification of noise by constructing a feature library, and realizes the quantitative evaluation of on-site noise. The proposed scheme is of technical reference for improving the portability and accuracy of the monitoring system.