Power distribution network mass data fusion method and device

Abstract

The invention provides a power distribution network mass data fusion method and device. The method includes the steps of establishing a unified data channel for multi-source data, extracting data information through the data channel, determining a data belonging individual according to the extracted data information, and establishing an analysis dataset for the data information by means of a corresponding preset fusion mode on the basis of the data belonging individual. By means of the technical scheme, the data availability of a power distribution network is improved, the fusion of static data and dynamic data is realized, the cross-system in-data service collaboration is realized, the precision of aid decision making and computational analysis of the power distribution network is improved, and the requirements of unified decision making and analysis are met.

Description

technical field [0001] The invention relates to a data fusion method, in particular to a massive data fusion method and device for a distribution network. Background technique [0002] With the continuous deepening of smart grid construction, computer-aided decision-making has been widely used, such as switch status evaluation, fault handling, maintenance based on reliability, network weak link and bottleneck analysis, operation mode optimization, power supply quality statistics, etc., for each Business systems have a set of independent databases and data logics that meet their own standards, and carry out various independent business decisions and calculation analysis. It is extremely difficult to make unified decisions and work across departments. The bottleneck of the intelligent auxiliary decision-making process of the distribution network lies in data fusion. , currently in the field of information research, a number of big data tools and systems have implemented a seri...

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Problems solved by technology

[0002] With the continuous deepening of smart grid construction, computer-aided decision-making has been widely used, such as switch status evaluation, fault handling, maintenance based on reliability, network weak link and bottleneck analysis, operation mode optimization, power supply quality statistics, etc., for each Business systems have a set of independent databases and data logics that meet their own standards, and carry out various independent business decisions and calculation analysis. It is extremely difficult to make unified decisions and work across departments. The bottleneck of the intelligent auxiliary decision-making process of the distribution network lies in data fusion. , currently in the field of information research, a number of big data tools and systems have implemented a series of data fusion and data splicing technologies. However, for complex logic and high real-time requirements, massive static ledger data and high-speed dynamic measurement data For the distribution network that coexists, it is one of the challenges in the current distribution network research field to effectively and fully integrate multi-source heterogeneous data to meet the requirements of distribution network intelligent auxiliary decision-making and unified data services.

[0003] The distribution network data has the following characteristics: (1) High concurrency, the number of remote monitoring terminal objects is huge and widely distributed, and the distribution network data is submitted simultaneously based on the acyclic telecontrol protocol, and the flow rate is astonishing

(2) The timing is strong, and the data continuously appears in the form of data flow during the process of collection, transmission, analysis and storage, and the data needs to be processed in an efficient manner as much as possible

(3) The cost of data processing is high, and the measurement data are generally high-precision floating-point numbers with strong numerical sensitivity. At the same time, the distribution network analysis algorithm is complex and requires a lot of hardware resources to realize data transmission and processing.

[0004] The data structure in various data systems also has the following characteristics: (1) Each business system operates independently, with the goal of meeting the minimum business requirements for system operation, and there are not enough sharing mechanisms and channels between systems

(2) Due to the limitations of communication and processing capabilities, the amount of data is seriously missing, sometimes unable to meet the application needs of the interactive object system

To sum up, for the massive data of the distribution network, the data sources are diverse and naturally formed, and the massive data is often semi-structured or unstructured. If it is to be fully and effectively integrated, its technical difficulty and management system difficulty are huge

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