The process of degradation of essential equipment in power distribution networks is extremely nonlinear, and standard periodic maintenance does not work. This paper combines high-frequency sensing on the Internet of Things (IoT) and variational mode decomposition-Hilbert-Huang transform feature engineering to extract sensitive indicators of multi-scale energy entropy and kurtosis and constructs a CNN-LSTM-Attention hybrid model. The model uses dual-scale convolutional kernels to capture the spatiotemporal features, bidirectional gating mechanism that can capture long-term dependencies and attention module that can focus on critical windows before breakdown. Validated on a 10 kV industrial park line with 3 transformers and 12 cable joint nodes, the hybrid model achieves a prediction accuracy ≥0.922, an F1-Score between 0.911 and 0.960, and a MAE standard deviation of only 0.0098. After deployment, the average monthly unplanned power outage rate is ≤0.061%, and the relative reduction rate reaches 75.7% under extreme partial discharge scenarios.

Currently, financial personnel in the power grid are mainly responsible for basic accounting and reimbursement, and financial data is used only for simple storage and query. Power grid fund planning and operations are at risk due to inefficient financial budget control brought on by increasing data quantities. In order to improve supervision and efficiency, this work has established a power grid financial management platform by combining the ID3 decision tree algorithm with the TFP (FP-growth with traffic optimization) algorithm for financial expense forecast and risk analysis. The suggested financial expense prediction model outperformed benchmarks with an accuracy of above 0.9. With a response time of 1.54 seconds, below the 2-second requirement, the integrated power grid platform demonstrated high efficiency, guaranteeing efficient processing. At the same time, the central processing unit and memory usage of the platform were both controlled below 50%, demonstrating excellent resource utilization efficiency and providing efficient and stable financial management services for enterprises. The proposed financial management method for the power grid can monitor the financial status promptly, enhance the efficiency and accuracy of financial processing for power grid enterprises, and offer strong support for their strategic planning and operational management.

This study aims to break through the limitations of traditional power grid construction risk management models, which rely on manual experience and static analysis, by constructing a data-driven intelligent risk management system. The research integrates multimodal sensing and artificial intelligence technologies, using deployment balls and smart helmets to capture on-site video and applying the YOLOv7 algorithm to real-time detect violations. Additionally, the BERT model is employed for deep text mining of historical accident reports and work instructions, creating a “Job Activity-Hazard Factors-Historical Accidents” triple knowledge base and forming a dynamic risk factor extraction framework. In terms of risk assessment, a dynamic evaluation model that incorporates domain rules and spatiotemporal features is proposed, with the core being the fuzzy Bayesian network (FBN). This is combined with a graph convolutional network (GCN) to encode spatial correlation risks at the worksite, facilitating the transition of risk assessment from static to dynamic. A risk knowledge graph is constructed based on the Neo4j graph database, and a traceability visualization module driven by the restart random walk (RWR) and community discovery algorithms is developed to provide decision support for accurate intervention. The verification results show that this system performs excellently in the 2220 kV Substation GIS Equipment Installation” case, with a risk identification recall rate of 0.92, an $F 1$-score of 0.89, and an average early warning time of 3.2 hours, significantly outperforming traditional LEC methods and JHA analysis. Sensitivity testing indicates that “whether the supervisor is on site” is the most sensitive factor affecting risk assessment results. This study demonstrates that the constructed risk management system enables real-time, continuous, and contextual control, providing an intelligent closed-loop solution for power grid construction safety management and promoting the shift from reactive to proactive risk prevention.

Data in power grid digital operation exhibit multisource heterogeneous characteristics, resulting in low integration efficiency and slow anomaly detection response. To address this, this paper proposes a method for power grid digital operation data integration based on K-medoids clustering. The basic service layer utilizes an Field Programmable Gate Array parallel architecture. This enables millisecond-level synchronous acquisition and dynamic preprocessing of multisource data, such as mechanical vibration, partial discharge signals, and temperature. The implementation is based on the analysis of the power grid digital operation structure. The data are then fed back to the cloud service layer, which, through business integration services, data analysis, and data access services, performs data filtering and analysis. Subsequently, the data are input to the application layer via the database server. The application layer employs a K-medoids clustering method that introduces a density-weighted Euclidean distance metric and an adaptive centroid selection strategy, significantly enhancing the clustering performance of multisource data. In particular, the proposed architecture supports real-time data processing and can be extended to cross-modal scenarios, including integration with speech-to-text systems in power grid monitoring. By aligning with low-latency neural network principles, this method facilitates timely decision-making in intelligent operation environments. Experiments confirm the method's efficacy. It acquires and integrates multisource heterogeneous power grid digital operation data effectively. The data throughput of different power grid digital operation data sources all exceed 110 MB/s. The silhouette coefficient of the integrated data sets is greater than 0.91, indicating that the integration of power grid digital operation data using this method exhibits good separability and reliability, enabling rapid detection of data anomalies within the power grid, thus laying a solid foundation for the operation and maintenance management of power grid digital operation.

In the context of deep integration of CPSS (Cyber Physical Social Systems), energy system data presents multi-source, complexity, and dynamic interactivity. To solve the problem of identifying power outage sensitive users, we propose a power outage sensitive user analysis and identification method based on CNN+LSTM. Firstly, perform preprocessing such as cleaning and structuring of power load data to ensure data quality; Next, conduct correlation analysis to explore the intrinsic relationship between the factors and characteristics affecting power load and the sensitivity to power outages; Then, the coefficient correction method is used to extract the user load curve and optimize the feature weights to enhance the adaptability of the model; The final design is a power outage sensitive user recognition model based on CNN+LSTM, which integrates time series and spatial features to achieve accurate recognition of power outage sensitive users. The experimental results show that in multiple experiments covering multidimensional data such as household electricity consumption and energy consumption, this method effectively improves the accuracy of anomaly detection, with an average power outage sensitive user recognition rate of 95.93%. It performs well in key indicators such as recall rate and F1 score, providing strong support for energy system optimization management and user service.

文章分析了异构图模校验的总体思路,细致阐述了图模校验工具的研究内容和实现要点.对图模校验规则引擎、异构图模接入适配器、图模校验方法和图模校验结果的可视化展示技术进行了研究.通过基于异构系统、多部署模式的图模校验差异性适配和校验方法的研究,开发对应的变电站图模校验工具,实现了多个厂家、多个型号调度主站系统图模的定制适配、统一接入、模型校验和问题可视化展示,有力地支撑了自动化主站变电图模与GIS平台输配电图模的交互和融合工作.

In the future, the development of energy will change from a single energy system to an integrated energy system. The recyclable comprehensive energy system has promoted the revolution of energy production and consumption, and built a clean, low-carbon, safe and efficient energy system. GIS provides network planning engineers with more intuitive, more image, more efficient and more accurate network planning scheme. The design and construction of recyclable comprehensive energy platform based on network GIS is a complex process. The main objective of the project is to create a system to effectively manage all data related to renewable energy and transport from one location to another. In addition, the platform should be able to provide information about the status and performance of these resources throughout their life cycle.

原有的230 MHz射频技术在授权用户受到认知用户干扰时,通信过程误码率较高,为此,提出一种基于电力信息可视化采集的230 MHz射频技术.设计射频技术中接收灵敏度、动态范围、接收带外抑制等接收发射指标,优化基带对有用信号的调节,消除认知用户的干扰;引入可视化信息采集技术,由230 MHz无线专网承担通信工作,设计时序逻辑控制,采用过分配方式定义GTS和CFP的分配,保留通信过程中的处理时间,减小干扰.对比实验结果表明在光谱强度相同时,设计的技术误码率较低,具有有效性.

In view of the poor recognition effect of power equipment fault features in China, a method for building power equipment fault feature model based on unified semantic expression is proposed. The power equipment fault information is identified by combining the unified semantic expression principle. And the phase space reconstruction algorithm is constructed according to the feature semantics of the identified fault information. The power equipment fault feature model is optimized based on the reconstruction results. Finally, it is verified by experiments, the power equipment fault feature model based on unified semantic expression can quickly identify the semantic features of fault information in the process of practical application, and effectively improve the recognition effect.

电力文本语料稀缺造成了训练数据的不平衡,使得主流的双向长短期记忆网络和条件随机场方法表现较差.为此引入一个分类模型,将语句分为强弱两类,分别训练优化网络模型.通过在公开语料数据上进行实验,证明了在对电力语料数据进行命名实体识别时,该方法比传统的聚类方法和原双向长短期记忆模型分别高12％和4％.

In order to understand the application performance of text mining technology in power enterprise complaint work order, this paper analyzes. Firstly, the text mining technology is summarized, and the basic principle of this technology is described. Secondly, the construction method of the work order text mining model is analyzed. Finally, the validity of the model is verified by a case. The results show that text mining technology can analyze the complaint work orders of power enterprises and then classify all work orders according to the analysis results, which is conducive to manual work and plays a role in improving work efficiency. At the same time, the application of text mining technology model in this paper is effective and has a certain application value. Because of the large number of modern power users, the number of complaint work orders increases, so the traditional labor is difficult to deal with all complaint work orders efficiently, and text mining technology can break through the limitations of labor and improve the status of complaint work orders.

构建电力企业干部资质画像时,大多忽略了电力企业干部资质信息清洗的必要性,导致画像的信息覆盖率低、F1系数低、构建时间长,由此,提出基于Agent模型的电力企业干部资质画像构建方法.引入Agent模型,建立电力企业干部资质相关信息采集系统,利用堆栈式降噪自编码器清洗电力企业干部资质信息,通过隐半马尔可夫模型提取电力企业干部的行为特征,将提取的特征输入长短期记忆网络LSTM中,构建电力企业干部资质画像.实验结果表明,所提方法的信息覆盖率高、F1系数高、画像构建时间短.

由于主数据驱动质量控制过程对于资产档案架构的适配性较差,导致数据传输能力较差,因此,设计基于IPv6流标签的企业资产档案主数据驱动质量控制方法.更新主数据IPv6流标签报头格式,将处理后的数据输入到驱动控制器中,通过设定约束条件,将控制器等价为动态线性化数据模型.通过实验结果对比可知,使用此方法后数据包的转发能力与传输能力得到明显的提升,在日后的资产档案主数据驱动管理中可使用此方法,为资产管理提供便利.

由于当前已有方法未能考虑客户画像预测建模问题,导致多维客户画像精准构建准确性以及运行效率下降,对此,提出一种考虑电力营销能力的多维客户画像精准构建方法.对电力企业的电力营销能力以及电网用户进行分析,通过双通道方法对不同客户画像进行预测建模,组建多维客户多源特征体系,组建基于机器学习的多视角融合模型,通过模型输出多视角融合结果,以达到多维客户画像精准构建的目的.仿真实验结果表明,所提方法能够有效提升多维客户画像精准构建的准确性以及运行效率.

为支撑电力物联网数据共享,发挥电网数据价值,文章针对当前电力物联网数据平台中存在的技术组件多样、应用难度大、检索数据困难、数据应用门槛高和数据模型管控机制不完善等问题,优化电力物联网数据平台整体框架,提出基于孤立森林的量测类实时数据质量异常检测及改进算法,通过抢修故障研判和停电故障研判2个场景仿真验证数据质量检测算法的可行性,仿真试验结果表明该模型能够有效提升数据质量检测准确率.

本次研究详细介绍了配电网雷害风险评估的数据处理方案,提出了导线因雷击而产生过电压的计算方法.最后基于雷电灾害停电风险计算原理引入蒙特卡罗抽样调查法对配电网各个块区域的故障状态进行抽样检测,并阐述了抽样检测的具体流程.

After three decades of development, data warehouse has been generally accepted by the industry, but the technical implementation defects and requirements such as scalability have led to the evolution of its data architecture. Data Vault provides coordinated management of multiple data areas and multiple datasets, which better meets the technical requirements of data warehouses, and makes the corresponding metadata version management an important research issue. Firstly, various entity conceptual models and metadata version management requirements in the Data Vault architecture are described, and then a meta-model for metadata version management is proposed to support expression, controlling and comparison of version evolution. Finally, based on this metamodel, a structural integrity detection algorithm is discussed to verify the ability of the metamodel to maintain metadata consistency.

Objective: In order to build a more accurate and effective power load forecasting model, this paper analyzes it. Methods: Based on the improved parallel fuzzy kernel clustering algorithm, this paper expounds the advantages of the algorithm, the calculation process, and the modeling method and verifies the application effect of the algorithm. Results: The improved parallel fuzzy kernel clustering algorithm is successfully obtained through the analysis and is built by using the algorithm. The application of the model in the verification is effective, which shows that the idea of this topic has certain reference value. Conclusion: The improved parallel fuzzy kernel clustering algorithm has more advantages than the traditional clustering algorithm and has good performance in the actual work, which is worth considering.