In information technology, big data means that it is impossible to use conventional software tools (such as existing database management tools or data processing applications) to capture, manage, store, search, share, analyze, and visualize content within a certain period of time. It deals with large, complex data sets that are made up of huge numbers, complex structures, and types of data. Big data has 4V characteristics, namely, High Volume, Velocity, Variety, and Value Value.
Intelligent distribution network has a wealth of data sources. Now most cities and regions have multiple distribution management systems, including distribution automation systems, dispatch automation systems, power grid meteorological information systems, power quality monitoring and management systems, production management systems, and geographic information. System, power consumption information collection system, distribution transformer load monitoring system, load control system, marketing business management system, ERP system, 95598 customer service system, economic and social data, and other data sources. The overall status of these data sources is shown in Table 1.
These data sources cover a wide range of management services such as dispatch, shipment inspection, marketing, and most of the 110kV and below multi-voltage grid monitoring and acquisition information. From the perspective of data source types, there are abundant types of data sources for intelligent deployment of large-scale data applications, covering distribution transformers, distribution substations, distribution switchgears, electricity meters, power quality and other power automation and informatization data, and user data. Social and economic data.
Scenario 1 Load Forecasting for Active Distribution Network Planning
With the rapid development of distribution network informationization and the increasing influence of power demand factors, the characteristics of big data used for power forecasting have become increasingly prominent, and traditional power consumption forecasting methods are no longer applicable. Because the intelligent prediction method has a good ability of non-linear fitting, a lot of research results have appeared in the area of ​​power consumption prediction in recent years. Intelligent prediction algorithms such as genetic algorithm, particle swarm optimization, support vector machine and artificial neural network have been widely used. Electricity forecasting. Traditional electricity load forecasting is limited by narrow data collection channels or low data integration, storage, and processing capabilities, making it difficult for researchers to mine more valuable information from them. By using large-scale and more-type electric power big data as analysis samples, it is possible to realize the forecast of the time distribution and spatial distribution of electric power load, provide basis for planning and design, operation scheduling of electric power grid, and improve the accuracy and effectiveness of decision-making.
Scenario II Assessment and Early Warning of Distribution Network Operation
The content of the evaluation and early warning of distribution network operation status based on big data technology is shown in Figure 1. It includes the following aspects:
1) Safety assessment of distribution network, such as power system frequency, node voltage level, main transformer and line load rate;
2) Evaluate the power supply capability of the distribution network, such as the capacity-to-load ratio and load transfer capability between lines. When the power supply capacity cannot meet the load demand, load shedding shall be carried out according to the importance of the load, the economic and social benefits generated, and the historical voltage load.
3) Evaluate distribution network reliability and power supply quality, such as load point failure rate, system average power outage frequency, system average outage time, voltage pass rate, voltage fluctuation and flicker, three-phase unbalance, waveform distortion rate, Voltage offset, frequency deviation, etc.
4) Evaluate the economics of distribution network, such as line loss rate and equipment utilization efficiency. Through the calculation of risk indicators, determine the type of risk being faced; forecast the risk profile faced by the distribution network for a period of time from now on; based on the risk type identification results, generate a corresponding prevention and control plan for the reference of dispatching decision makers; Sudden risk and cumulative risk accurately identify, locate, type judge, generate prevention control programs, etc.; based on multi-source heterogeneous data.
Scenario 3 Power Distribution Network Power Quality Monitoring and Evaluation
As distributed power supplies are continuously connected to the distribution network, small, medium, and large-scale active distribution networks are gradually formed. With the power fluctuations of distributed power sources, the power quality in the distribution network is subject to greater impact. By collecting data such as operational data, load data, and distributed power supply operations in the distribution network, it is possible to carry out power quality analysis and evaluation studies in the distribution network so as to obtain refined distribution network shelves and reactive power sources. Adjustment program and so on. The schematic diagram of power quality monitoring and evaluation of active distribution network is shown in Figure 2.
The monitoring and evaluation of power quality of active distribution networks based on big data includes the following aspects.
1) Power distribution network power quality analysis and monitoring.
With the continuous expansion of the power grid scale and the continuous access of distributed power sources, data such as operational data, load data, and distributed power supply operations in the distribution network are gradually increasing. The characteristics of big data in power quality analysis are increasingly evident, and traditional power quality analysis methods are used in electrical energy. It is difficult to completely solve the problems of quality denoising, feature extraction, disturbance classification, and parameter estimation. In the face of the emergence of power quality problems, many comprehensive analysis methods have emerged in recent years. However, power quality monitoring devices based on traditional power quality analysis methods face poor performance, low precision, and low intelligence. It is necessary to study high-performance power quality analysis methods and develop real-time online power quality monitoring systems.
The power quality monitoring system integrates communication, measurement, analysis, and management into one, providing power companies and users with basic information on power supply quality, and enabling comprehensive, accurate, and effective monitoring of the power quality of active distribution networks. Considering economy at the same time, the optimal distribution point of the monitoring terminal in the active distribution network is also an urgent problem to be solved.
2) Power distribution network power quality assessment.
The assessment of the power quality of active distribution networks is a comprehensive evaluation of the operating level and power supply capacity of active distribution networks. It is the basis for restraining and urging power companies and power users to jointly maintain the power quality environment of the public power grid, and it is also implementing quality control. The basis of control, tools for testing governance and control effects. With more and more distributed power supplies connected to the distribution network, users have increasingly higher power quality requirements. Traditional power quality assessment methods are faced with problems such as reduced computing performance, long time-consuming, and low accuracy. How to make power quality The assessment of reasonableness, objectivity and accuracy is a severe test for power companies. Moreover, the addition of large-scale structured data and unstructured data will provide new research approaches for power quality assessment, develop reasonable power distribution network power quality assessment indicators, improve the accuracy of power quality assessment, and make deeper The data information collected by the power quality monitoring system reveals information that was overlooked due to the high cost of previous analysis, providing information such as grid structure analysis, rationality analysis of reactive power configuration schemes, analysis of sensitive load installation locations, and monitoring for power companies and users. Point configuration programs and other high value-added services, these services will be conducive to the safety, stability and economic operation of the power grid.
3) Power distribution network power quality diagnosis and treatment.
In order to meet the requirements of higher power quality, timely and correctly diagnose various abnormal operating conditions affecting power quality, identify power quality interference sources, and prevent or eliminate them, so as to avoid the expansion of faults. It is an active distribution network. Another difficult problem. Given that distributed power supply can be regarded as a non-linear load that injects harmonics into distribution feeders, the switching of distributed power supply will also cause voltage fluctuations. Access to distributed power supply will undoubtedly aggravate power quality to a certain extent. Disturbances. The traditional power quality disturbance localization methods all have certain applicable environment and limit conditions, and the reliability of location results obtained by considering only one location method is often not high. By using larger-scale and more-typed power big data as analysis samples, detailed research ideas for the location of power quality disturbances are provided, the accuracy of positioning of power quality disturbances is improved, the weak links in the structure of the grid are searched out, and fine tuning is made. The distribution network grid and reactive power regulation scheme to improve power quality are of great significance to the economic operation of the power grid.
Scenario 4 Power Outage Optimization Based on Data Fusion of Distribution Network
Distribution network power outage optimization is based on distribution network dispatching automation system, distribution automation system, power consumption information collection system, distribution network equipment management system, power distribution equipment maintenance management system, power grid graphics and geographical graphic information and marketing management system, etc. Based on the above, comprehensive analysis of distribution network operation of real-time information, equipment maintenance information, etc., in order to identify the final optimal power outage program. Planned power outage management According to the requirements of scheduled power outages (including maintenance and power cuts, etc.), system simulation is performed to determine the power outage device with the minimum power outage range, the shortest power outage time, the minimum outage loss, and the minimum power outage user, and the power failure is displayed through the screen. The area lists the list of users who have lost power, prints notifications of power outages, etc. The use of traditional techniques has the disadvantages of slow calculation speed, long calculation period, and poor scalability. In order to more accurately calculate the power outage loss of the distribution network and reduce the impact of power outage, it is necessary to use the massive data of multiple business systems for joint analysis and data mining. Power-supply network outage optimization based on big data technology includes:
Power outage information classification, sorting and classification of outage information data based on massive data of scheduling, marketing, and distribution network integration;
Power outage warning, warning of equipment failure that may lead to power outages;
Distribution network blackout planning, the use of big data technology to develop a reasonable power outage plan, improve power distribution network optimization analysis system.
With the advent of the big data era and the development of big data applications technology, it is possible to fully tap the value of existing distribution network operational data. By integrating the information of various decentralized systems, standardizing data types, and forming rich, homogenous big data samples, it can provide targeted support measures for power grid transportation inspections and provide guarantees for the safe operation of power grids and self-healing of smart grids. With great economic and social benefits.
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