Briefly analyze the application of big data in smart distribution network
Due to the digitization and intelligence of power distribution system equipment, the application of big data to the traditional power industry is undergoing profound changes. Based on smart grid big data, it provides users with services such as optimal scheduling and demand response. In this context, the conceptual characteristics of big data in the field of modern smart distribution network are discussed, and several typical scenarios of smart distribution network big data analysis are expounded. Finally, the new opportunities and challenges brought by the application of big data technology in smart distribution network are prospected.
With the continuous development of modern computer information technology, the application of big data has received more and more attention in the trend of intelligent and informatization of power grid operation, and has gradually become an important technological innovation field in the development of the power industry. Big data has four characteristics, namely high speed (Velocity), large capacity (volume), value density (Value) and diversity (Variety). The big data system is real-time processing, and the data processing method is from structured data to unstructured data. In the general way, the data structure of relational database is very difficult to deal with the big data system.
The scale of the power system is getting bigger and bigger, and the application environment of big data is also increasing. It is necessary to further explore the application of big data in the distribution network, thus providing a guarantee for the application of big data technology in the smart grid.
1. Analysis of the source of big data characteristics of smart distribution network
The operation of smart grid has obtained abundant data sources, and the adoption of big data systems has a wide range of applications. At present, most cities have a variety of management systems based on computer databases. The data sources come from the power transmission scheduling automation system, substation and distribution automation system, power quality monitoring system, power grid environment system, load control system, distribution Variable load monitoring system, geographic information GIS system, electricity information collection system, enterprise resource ERP system, marketing business management system, customer service system 95598, and data from economic and social categories.
2. Big data scenario analysis of smart distribution network
Load forecasting based on active distribution network planning. The prediction of the time distribution and spatial distribution of power load has not been realized, which can provide a basis for planning and design, power grid operation and scheduling, improve the accuracy and effectiveness of decision-making, and use larger and more types of power big data as analysis samples.
Distribution network operation status assessment and early warning. The evaluation and early warning of distribution network operation status based on big data technology mainly includes the following four aspects.
(1) Evaluate the power supply capacity of the distribution network, such as load capacity ratio, load transfer capacity between lines, etc. When the power supply capacity cannot meet the load demand, load shedding is carried out according to the importance of the load, the economic and social benefits produced, and the historical voltage load situation.
(2) Evaluate the reliability and power supply quality of the distribution network, such as load point failure rate, system average outage frequency, system average outage time, voltage qualification rate, voltage fluctuation and flicker, three-phase unbalance and other parameters.
(3) Evaluate the safety of the distribution network, such as the node voltage level of the power system, the main transformer and the line load rate, etc.
(4) Evaluate the economics of the distribution network, such as line loss rate and equipment utilization efficiency.
Power quality monitoring and evaluation based on active distribution network. The power quality monitoring and evaluation of active distribution network based on big data includes the following two aspects.
(1) Power quality analysis and monitoring of active distribution network. In the face of the emerging power quality problems, many comprehensive analysis methods have been produced in recent years. However, power quality monitoring devices based on traditional power quality analysis methods face problems such as 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.
(2) Power quality assessment of active distribution network. Add large-scale structured and unstructured data to evaluate the quality of power operation; add quality evaluation indicators of active distribution network power, and by mining the data collected by the power quality monitoring system, it is revealed that the previous analysis cost was too high The neglected information provides power companies and grid users with high value-added services such as grid structure analysis, rationality analysis of reactive power source configuration plans, sensitive load installation location analysis, and monitoring point configuration plans. These value-added services are conducive to strengthening Power grid security and power grid stability enable the power grid to operate economically.
3. Big data analysis method of distribution network
(1) Rapid analysis of distribution network big data. Distributed parallel computing technology provides a large number of high value-added services for power supply enterprises and users, and provides strong support for large-scale and complex distribution network analysis and calculation. This is beneficial to power grid security monitoring and control, including decision support for power supply and power dispatch, fault early warning and processing, and more accurate electricity consumption forecasting. There are also refined operation management of power companies, customer power behavior analysis and customer segmentation, and more scientific demand management.
(2) Feature clustering of distribution network data. Clustering can be used to split data into multiple classes or subsets, the number of classes is unknown in cluster analysis. Common clustering methods include hierarchical clustering, partition clustering, grid clustering, intelligent clustering, and model-based clustering. According to the characteristics of distribution network big data and the scope of application of different clustering methods, by studying the spatiotemporal characteristics and data clustering methods of distribution network, corresponding solutions are proposed.
Epilogue
Power informatization construction uses big data technology to obtain effective data such as power enterprise production data, management data, topography data, water resources data under the platform of enterprise data sharing, which can extract accurate and valuable data, and can provide management benefits. , to improve decision-making ability to provide effective help.
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