Power distribution network load overload visualization method and system based on big data

Abstract

The invention discloses a power distribution network load overload visualization method and system based on big data, belongs to the field of power distribution network management service and auxiliary information decision, relates to transformer overload visualization, and aims to provide the power distribution network load overload visualization system based on big data. Fault diagnosis, prediction and fault positioning of an equipment state are realized. Data of an EMS system and a GIS system are acquired by establishing data interfaces with the EMS system and the GIS system so that the working efficiency of a dispatching desk is improved, and the safe operation coefficient of a power grid is improved; an HDFS system is adopted as a platform system, and the system has high fault tolerance and can be deployed on a cheap machine; by configuring a power distribution network data processing module, power distribution regulation and control are improved and upgraded, and real-time data acquisition and monitoring in a power distribution network are realized; and through the fault positioning module, equipment with faults and a power failure area and a user caused by the faults can be accurately positioned, and on-site repair personnel can conveniently know fault details and formulate a fault solution.

Description

technical field [0001] The invention belongs to the field of distribution network management business and auxiliary signal decision-making, and relates to a visualization of transformer overload. Background technique [0002] In recent years, the construction of domestic distribution network has developed rapidly, and the power supply capacity has been greatly improved. However, the auxiliary decision-making ability of distribution network planning and operation and maintenance is still insufficient, mainly reflected in: [0003] (1) The distribution network equipment lacks effective and reliable status assessment and risk assessment, such as frequent power outages in some urban villages, distribution transformers damaged by floods, and line overloads; [0004] (2) Lack of reliability assessment, fault diagnosis and weak point location capabilities for the power grid; [0005] (3) The user evaluation is not in place, and there are problems such as long average customer powe...

AI Technical Summary

Problems solved by technology

[0003] (1) The distribution network equipment lacks effective and reliable status assessment and risk assessment, such as frequent power outages in some urban villages, distribution transformers damaged by floods, and line overloads;

[0004] (2) Lack of reliability assessment, fault diagnosis and weak point location capabilities for the power grid;

[0005] (3) The user evaluation is not in place, and there are problems such as long average customer power outage time, failure to meet the power supply needs of important customers, and insufficient communication with customers;

[0006] (4) It is impossible to evaluate the comprehensive operation status of the entire distribution network, so that it is impossible to provide comprehensive guidance for the business

[0009] In addition, real-time monitoring and real-time collection of operating data parameters are carried out for tens of thousands of lines in different geographic locations. The line operating data is collected in real time through the power consumption information collection system, and the massive data that affects the distribution transformer overload is selected to affect the urban distribution transformer load. Mining and analysis of equipment and customer data, and carrying out the scene application of city network distribution transformer overload early warning analysis is quite difficult, because it is obviously unrealistic to use conventional communication to solve the problem of line operation overload early warning and monitoring center data communication