Lean Network Management Based on Power Grid Equipment Operation Efficiency Analysis Platform

Dry-coupling ultrasonic technology is applied to detecting oil-level of oil-filled equipment in power grid. Vinyl silicone rubber is used to substitute liquid coupling agent to fabricate dry-coupling ultrasonic probe, which makes the ultrasonic directly enter the oil-filled equipment through the special structure of dry-coupling probe and detect oil-level of oil-filled equipment accurately and rapidly. Omitting the step that smearing liquid coupling agent on the probe can avoid the contamination on the surface of equipment and significantly improve the detection efficiency.

As one of the most important components of power grid, the reliability of the equipment plays an important role in both safe stability of power grid and the improvement of the customer’s electricity environment, which matters the economic benefits of all sides. In this paper, the lean management system of power grid equipment based on multi-service convergence is relied on the whole process of technical supervision. Through applying the management system, it can realize the scientific upgrading of the lean management level of power grid equipment.

Given the lack of distributed PV power generation operation and management capability, this paper profoundly analyzes the current situation of the application of big data technology in the process and management of PV power generation in China and proves the necessity and importance of promoting new equipment, new technology, and new mode based on traditional power system operation and management, using the extensive data information formed in the process and management of power plants, meteorology, environment, and power grid, and launching ample data analysis research. In addition, the typical application scenarios of big data technology in the operation and management of distributed PV power generation are explored, and a new mode of operation and management of distributed PV power generation at the power plant, grid, and government levels is explored and constructed. The study shows that with the dramatic increase in the number of distributed PV power generation, the use of big data technology in scenarios such as the power generation side and grid side has excellent potential to provide adequate support for PV power generation operation, grid scheduling, market-based transaction settlement, and government decision-making.

The purpose of this paper is to improve the fault early warning effect of smart grid equipment through multi-sensor information fusion technology. Therefore, based on the analytical model of power grid fault diagnosis, this paper considers the influence of distributed generation in distribution network on fault diagnosis, as well as the misoperation or refusal of protection and switch, and the false alarm or leakage of alarm signal. At the same time, in order to display the results of fault diagnosis accurately and intuitively, an analytical model of fault diagnosis of distribution network based on multi-source information fusion is proposed. Finally, this paper verifies the effectiveness of this method through an example application. This article uses the PEDL dataset for experimental research, Through the comparison of fault data, it can be seen that compared with existing methods, the method proposed in this paper achieves the highest goodness of fit for warning, indicating the best fault warning effect.When there is enough training set, the prediction accuracy of the fault set can reach over 99%, Based on experimental analysis, it can be concluded that the proposed power grid equipment model has higher accuracy and reliability compared to traditional models. And the model in this article integrates the real-time monitoring function of power grid equipment and the equipment fault warning function, which improves the practicality of the power grid equipment monitoring system.

With the integration of power grid developing further, some demand such as power grid analyses decisions centrally has become more and more urgent. At present, the information resources for the use of Power grid equipment monitoring analysis is becoming abundant, but these information, which exists in every professional system independently, is lack of connection and meets the demand of big data analysis on power equipment monitoring hardly. In this paper, a unified information model, modeled by IEC61970 common information model and IEC61850 hierarchical model, is proposed for the analysis of big data monitoring in power grid equipment, which can establish the first and second Equipment Association within the scope of the grid equipment monitoring data, as well as realize centralized access to all measurement information in the method of extending measurement model. This is significant to promote the process of deepening application of power grid equipment monitoring.

Dynamic load model is of great value in analysis and simulation of power grid, while the composition of load has an important influence on the characteristic and accuracy of load model. There is mass of load equipment in power grid, so it is usually necessary to classify the load equipment according to their different characteristic first, then establish a suitable load model separately, and synthesize them into a composite model according to their proportion. Only the power value of load can be measured when power grid operates in a normal and steady state, and their dynamic response characteristics are unknown. Therefore, it is difficult to obtain sufficient information for load classification. However, frequency characteristics of load are distinguished and easy to obtain, so it is useful for load classification indirectly. Firstly, several elector-magnetism models of typical load are established, and their harmonic curves and frequency characteristic are simulated and analyzed separately. Then, cluster analysis is carried out to obtain the typical characteristics and the membership degree which can represent the contents of load overall. Finally, a classification method combined with Neural Network is adopted, and the computation speed is also improved.

In order to deal with diversity of massive data structures and the variety of information formats, a novel mechanism is designed for unified management of power grid planning data. By integrating many business systems including production management system (PMS), geographic information system (GIS), energy management system (EMS), distribution network information acquisition system in the power supply company's system and adopting various technical means such as data warehouse technology (e.g. ETL, Extract-Transform-Load) and incremental capture, data structure that supports the whole process management of power grid business is designed, data correlation analysis and integration & migration are carried out, and efficient access and deep fusion of massive relational data, file-type data, distributed data and spatial data are realized. Besides, through computing modes such as diagnostic analysis, load analysis and spatial analysis, the integrated database for power grid planning that integrates data fusion, storage, mining, modeling, computing, analysis and intelligent perception is finally constructed based on the designed data management mechanism, which could provide the comprehensive model and data support for power grid development. Field application shows the engineering benefit of the designed data management mechanism.

In the modern new power grid, it is no longer a single voltage level, but a complex large power grid composed of AC and DC and multiple voltage levels, in which 800kV DC transmission project is the main artery of the large power grid, in the operation and management of the new large power grid, green safety vertical is its core pillar, If there is a forest fire in the transmission line corridor, it is likely to cause a tripping fault, especially for the ultra-high voltage DC transmission lines. The conductor insulation on the line will be damaged by the fire at a height of tens of meters above the ground, causing the power grid to trip. What causes this? The research group has concluded through big data analysis and multi-dimensional analysis that the “hot air column” of the forest fire is the main reason for destroying the external insulation of the ultra- high voltage DC transmission lines. The theory of the hot air column (i.e. the column theory) is extremely easy to destroy the external insulation characteristics of the power grid.

With the application and popularization of distributed power supply and power electronic equipment in power grid, AC distribution is faced with problems such as low efficiency and low power quality. At the same time, due to the advantages of large power supply capacity, long power supply radius, high efficiency, good controllability and adaptability to renewable energy access, DC power distribution has become a crucial development direction of the future distribution system. Reasonable and systematic analysis of power quality problems of DC distribution network plays an important role in its construction and application. Firstly, this paper introduces the background, significance and status of the research on DC distribution network and its power quality. Then introduces the three main topology of DC distribution network structure and connection mode. After selecting pseudo bipolar structure to build the simulation circuit, the power quality phenomena such as voltage sag are simulated and analyzed by PSCAD. The results obtained are consistent with theoretical analysis.

With the increasing number of power electronic devices and renewable energy units in the power network, wide frequency oscillation accidents occur frequently in China and abroad. The oscillation frequency covers several hertz to several thousand hertz, which causes great harm to the safe and stable operation of the units. As the most important equipment in power grid, the safety and stability of synchronous generator under wide frequency is very important. In this paper, the response of synchronous generator as load to wide frequency oscillation is studied, and the formula for calculating the impedance of synchronous generator is deduced and verified. Then the reason of shaft torsional vibration caused by synchronous generator as oscillation source is introduced, and the equivalent impedance response characteristic of synchronous generator at this time is given.

Aiming at the problem that the traditional fault monitoring method of mine power supply equipment is single and can not effectively reflect the real-time working state of equipment and cables, this project studies a comprehensive intelligent fault diagnosis and monitoring early warning technology method. Through the research of two key technologies, power quality analysis technology based on harmonic method and insulation condition monitoring technology of electrical equipment, an intelligent diagnosis system for the health condition of mine power supply equipment is designed and formed. Through real-time monitoring of power grid harmonic parameters, power grid operation parameters, insulation parameters, and temperature parameters, the system can comprehensively analyze and judge the working status of various equipment and connecting cables in the underground power grid to comprehensively judge the faults of high explosive switches, transformers, and connecting cables, and achieve the purpose of fault location and early warning.

With the problem of global energy shortage and people's awareness of energy saving, electric vehicles receive world-wide attention from government to business. Then the load of the power grid will rapidly increase in a short term, and a series of effects will bring to the power grid operation, management, production and planning. With the large-scale penetration of electric vehicles and distributed energy gradually increased, if they can be effectively controlled and regulated, they can play the roles of load shifting, stabling intermittent renewable energy sources, providing emergency power supply and so on. Otherwise they may have a negative impact, which calls for a good interaction of electric vehicles and power grid. Analyzed the status of the current study on the interaction between the electric vehicles and the power grid, this paper builds the material basis, information architecture and the corresponding control method for the interaction from the aspect of the energy and information exchanging, and then discusses the key issues, which makes a useful exploration for the further research.

This chapter introduces a novel spatiotemporal data modeling approach for power grids to enhance operational analysis, improve risk identification, and enable timely system adjustments. Leveraging the node-breaker model, it addresses the limitations of the traditional bus-branch model by providing finer granularity and dynamically reflecting equipment status changes, thereby advancing grid dispatch and operational efficiency. A spatiotemporal graph database framework is proposed, detailing the construction of graph nodes, ontology models, and entity models derived from grid dispatch data. The methodology integrates a spatiotemporal node-breaker graph model with a bus-branch transformation mechanism, embedding spatiotemporal attributes into a static grid graph to establish a dynamic, evolving graph representation. This approach improves the accuracy and speed of topological analysis and state estimation, addressing critical challenges in ensuring power system reliability and efficiency. Use of the proposed “Power Grid One Graph” model is demonstrated through applications in an operational provincial grid, highlighting its effectiveness in fault diagnosis, contingency analysis, and adaptive planning, with potential for broader applications in power grid management.

Combined with the analysis of the current management status of the production technology transformation and overhaul project in power grid enterprises, a lean management strategy of power grid production projects based on the fusion data of multiple platforms and project big data is proposed. In order to achieve accurate management and control of the process of production projects such as the technology transformation, overhaul and special cost items, the strategy include 1) analyzing the deviation of the image progress, actual progress, filing progress of the project procedure electronic documents, budget implementation process and the schedule progress respectively based on the fusion data of PMS, ERP and Economic&Legal system; 2) reporting the construction progress, budget implementation and the early warning of milestone node in the next period of time automatically. After the settlement of all projects, the big data, such as investment for different electric power equipment, procurement and implementation of the power materials and services, deviation analysis of the budget and milestone implementation, will be used to guide the investment planning of different specialty, requirement planning for the electric power materials and services, project cost control, and scientific preparation of milestone plan in the following year. Finally, by compare the analysis results of the next year’s projects progress with the milestone plan, it will form a closed-loop feedback to the project management, which strives to improve the level of lean management of the project.

Maintaining the stability of the modern power grid is becoming increasingly difficult due to fluctuating power consumption, unstable power supply coming from renewable energies, and unpredictable accidents such as man-made and natural disasters. As the operation on the power grid must consider its impact on future stability, reinforcement learning (RL) has been employed to provide sequential decision-making in power grid management. However, existing methods have not considered the environmental constraints. As a result, the learned policy has risk of selecting actions that violate the constraints in emergencies, which will escalate the issue of overloaded power lines and lead to large-scale blackouts. In this work, we propose a novel method for this problem, which builds on top of the search-based planning algorithm. At the planning stage, the search space is limited to the action set produced by the policy. The selected action strictly follows the constraints by testing its outcome with the simulation function provided by the system. At the learning stage, to address the problem that gradients cannot be propagated to the policy, we introduce Evolutionary Strategies (ES) with black-box policy optimization to improve the policy directly, maximizing the returns of the long run. In NeurIPS 2020 Learning to Run Power Network (L2RPN) competition, our solution safely managed the power grid and ranked first in both tracks.