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power grid maximum load and heavy overload big data analysis method based on MapReduce aggregation calculation

An analysis method and maximum load technology, applied in the field of data processing, can solve problems such as weak aggregation support, incompressible storage, and inability to distribute deployment

Pending Publication Date: 2019-06-21
广州锐敏信息科技有限公司
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Foreign commercial large databases (such as PI, Wonderware) have been basically monopolized by foreign manufacturers for a period of time in the past, which are expensive, costly to expand functions, and cannot provide personalized services
[0005] Large-scale relational databases (such as Oracle and SQL Server) can also store massive amounts of big data after certain configurations, but they have to face various problems such as inability to distribute deployment, storage compression, slow query, weak aggregation support, and unfriendly display. question

Method used

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  • power grid maximum load and heavy overload big data analysis method based on MapReduce aggregation calculation
  • power grid maximum load and heavy overload big data analysis method based on MapReduce aggregation calculation
  • power grid maximum load and heavy overload big data analysis method based on MapReduce aggregation calculation

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Embodiment 1

[0087] This embodiment discloses the operation data analysis process of 10kV medium voltage feeder, see figure 1 , including the following four modules:

[0088] 1. Medium voltage 10kV feeder operation data analysis module

[0089] Obtain the real-time data DT files provided by the source system every 5 minutes, and analyze the collected measurement data of each feeder according to the configuration rules: A-phase current, B-phase current, C-phase current and occurrence time data (or obtain each feeder by connecting to the real-time database measurement data), according to the feeder ledger data, obtain the safety current of the corresponding feeder, feeder ID, feeder name and substation ID and other data, according to the feeder snapshot model (including fields: primary key ID, equipment ID, equipment name, Time, safety current, apparent power, measured value) are stored in a massive database (MongoDB). Among them, the calculation algorithm of apparent power and measurement...

Embodiment 2

[0118] This embodiment discloses a 10kV medium-voltage distribution transformer operation data analysis process, see image 3 , including the following four modules:

[0119] 1. 10kV medium voltage distribution transformer operation data analysis module

[0120] Obtain the real-time data CSV file provided by the source system every 15 minutes, and analyze the collected measurement data of each distribution transformer according to the configuration rules: active power, reactive power, power factor, A-phase current, B-phase current, and C-phase current and the time of occurrence data (or obtain various measurement data by connecting to the real-time database), and obtain the rated capacity, distribution transformer ID, distribution transformer name and substation ID of the corresponding distribution transformer according to the distribution transformer ledger data Transformer snapshot model (including fields: primary key ID, device ID, device name, substation ID, time of occur...

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Abstract

The invention relates to the technical field of data processing. in particular to a power grid maximum load and heavy overload big data analysis method based on MapReduce aggregation calculation. Current (I) data collected every 5 minutes by a medium voltage 10kV feeder, the active power (P), reactive power (Q), power factor (Pf) and other data collected by the 10kV distribution transformer every15 minutes are used to make load analyses of data. Then, a result is acquired through heavy overload times and time accumulation analysis by real-time load such that staff in charge of planning gainsa better understanding of running condition of accounting equipment. The big data platform is used to calculate the number of 10kV feeders in the distribution network, the number of overloads or overloads of 10kV distribution transformers, and each overload or heavyload duration. Therefore, overload or heavyload data is accurately calculated such that staff can have a better understanding of running condition of accounting equipment. The power grid maximum load and heavy overload big data analysis method is highly relevant to solution of current prolems and investments.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a large data analysis method for power grid maximum load and heavy overload based on MapReduce aggregation calculation. Background technique [0002] Currently, each collection point of power grid equipment can generate multiple pieces of data per minute, and each piece of data has a time attribute. If there are 10,000 collection devices, about 0.7M of data will be generated per minute, and the amount of data in a day will be nearly 10G. The storage pressure brought about by this "data expansion" has become one of the pain points in the development of power informatization and digitalization . [0003] At present, the way people deal with the rapid growth of massive big data is either to use foreign commercial large databases or large relational databases, but there are various problems. [0004] Large foreign commercial databases (such as PI and Wonderware) have been b...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06G06F16/25
CPCY04S10/50
Inventor 张辉
Owner 广州锐敏信息科技有限公司
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