The power system generates huge data information during operation, and the data grows fast and has many types, which is consistent with the characteristics of big data. With the continuous development and advancement of the power grid, the data sources in the system will continue to increase, and even a strong growth trend will occur.
The power system generates huge data information during operation, and the data grows fast and has many types, which is consistent with the characteristics of big data. With the continuous development and advancement of the power grid, the data sources in the system will continue to increase, and even a strong growth trend will occur. The past data processing technology has been unable to meet the current development needs.
Smart grid big data features
In the process of running the smart grid, huge data will be generated continuously. According to the source, the data can be divided into internal and external data of the power enterprise. The internal data includes data acquisition and monitoring system, production management system, and power distribution management system. Customer service systems, etc., most of the data comes from key application systems; external data generally comes from the Internet, weather information systems, geographic information systems, etc., external data is more dispersed, and data management units are also different. It can be seen that smart grid data has diverse and diverse sources, and the number of semi-structured and unstructured data is increasing, such as voice data in customer service systems, video in online monitoring systems, and image data. All belong to unstructured data, and the value density of these data is not high. Each data has different sampling, life cycle and frequency.
The link between smart grid, cloud computing and big data technology
The infrastructure in the current power grid is still not perfect, and the corresponding information resources cannot be collected, analyzed and stored effectively. To solve the current situation, smart grid applications need to be added to the power system.
To make rational use of data information resources in the power grid and provide an effective basis for decision-making, it is necessary to build a big data platform to achieve scientific decision-making. In the process of construction, it is also necessary to join the cloud computing technology to organically combine cloud computing technology and big data technology to realize effective analysis, calculation and storage of various types of data information under the smart grid for better control of the smart grid. Provide technical support.
There is a close relationship between smart grid, big data technology and cloud computing. Cloud computing technology has a large information storage function. When building a big data platform, it is added to calculate the data information under the smart grid. And analysis, and the grid operation will continue to generate huge data, so when building the platform, you can use this function to meet the actual needs of smart grid operation, and provide technical support for the online analysis of smart grid data. In addition, the application of this technology can also strengthen the real-time monitoring and management of the smart grid to support its economic and rational operation.
Application of Key Technologies of Power Big Data in Smart Grid
1 ETL key technology
The smart grid in the power sector has the characteristics of decentralized data distribution, a large number of data, and a large number of data types, which have brought certain difficulties to the data processing work. In this case, the data processing work should be carried out in accordance with the standard process, namely "data integration - extraction - conversion - culling - repair". Power companies usually apply data warehousing technology to data integration. ETL is an abbreviation of “Extract-Transform-Loadâ€. It can be seen that it consists of three parts, namely Extract, Transform and Load. The first is Extract, which is called data extraction. That is, the relevant data required by the destination data source system is extracted from the data source system; Transform is called data conversion technology, and the data extracted by the data extraction technique is converted according to relevant requirements, and the data is changed into another form. In this process, the deviation and error data appearing in the data source should be processed, and the data should be cleaned or processed; Load is the data loading technology, which is to process the converted data after the previous link is loaded, and then save to the destination data. Within the source system.
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