CN113254441A - Variable-scale time sequence storage method for power grid telemetry data - Google Patents

Variable-scale time sequence storage method for power grid telemetry data Download PDF

Info

Publication number
CN113254441A
CN113254441A CN202110529970.0A CN202110529970A CN113254441A CN 113254441 A CN113254441 A CN 113254441A CN 202110529970 A CN202110529970 A CN 202110529970A CN 113254441 A CN113254441 A CN 113254441A
Authority
CN
China
Prior art keywords
value
power grid
data
time
telemetry data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110529970.0A
Other languages
Chinese (zh)
Inventor
夏友斌
潘文虎
苏志朋
宋铭敏
周启扬
俞鹏
赵倩
夏颖
杭慧芳
陈彦斌
代业文
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
Wuhu Power Supply Co of State Grid Anhui Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
Wuhu Power Supply Co of State Grid Anhui Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, Wuhu Power Supply Co of State Grid Anhui Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202110529970.0A priority Critical patent/CN113254441A/en
Publication of CN113254441A publication Critical patent/CN113254441A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22Indexing; Data structures therefor; Storage structures
    • G06F16/2291User-Defined Types; Storage management thereof
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02DCLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00Energy efficient computing, e.g. low power processors, power management or thermal management

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a variable-scale time sequence storage method for power grid telemetry data. The storage method provided by the invention can realize the high-efficiency storage of the power grid telemetering data, and effectively reflects the characteristics of the original data curve while further reducing the data storage capacity.

Description

Variable-scale time sequence storage method for power grid telemetry data
Technical Field
The invention relates to the field of time sequence data sampling, in particular to the field of power grid telemetering data storage.
Background
With the continuous development of the power grid automation technology, the remote measurement data range of the dispatching automation system for collecting the active power, the reactive power, the current, the voltage and the like of the power grid is wider and wider, the remote measurement data quantity of the power grid is increased in scale, and the problem of how to simply and efficiently store the historical data of the power grid and reflect the characteristics of a power grid data curve is a difficult problem in the operation and maintenance of the power grid.
The power grid telemetering data has the characteristic of time sequence, and the telemetering data value needs to be provided with a time mark. At present, a conventional telemetering data value is stored at one storage point every 5 minutes, an important telemetering data value is stored at one storage point every 30 seconds, and the telemetering data storage amount is large and needs to be stored in a large scale in consideration of the scale of a power grid.
In the face of massive power grid telemetering data, one sampling point is set in 5 minutes, although the data storage capacity is reduced, the data in the time interval is completely lost, and the distortion of a historical curve is caused. If one storage point is stored for 30 seconds, the characteristics of the historical curve are kept, but the data storage capacity is huge, the system hard disk storage capacity is occupied, and meanwhile, the later-period data calling and searching are difficult to be facilitated.
Disclosure of Invention
The technical problem to be solved by the invention is to realize the simple and efficient storage of the power grid telemetering data, further reduce the data storage capacity under the condition of fixed time interval time sequence data sampling, and reflect the characteristics of the original data curve as far as possible.
In order to achieve the purpose, the invention adopts the technical scheme that: a variable-scale time sequence storage method for power grid telemetry data comprises the following steps:
step 1, acquiring power grid telemetering data in real time;
step 2, the interval time from the last stored data is greater than delta t, if not, the step 1 is returned, and if yes, the next step is executed;
step 3, whether the power grid telemetering data acquired within the current delta t time has an extreme value or not is judged, if yes, an end point value and an extreme value within the current delta t time are recorded, the step 1 is returned, and if not, the next step is executed;
step 4, whether the difference value between the initial end data and the terminal end data of the power grid telemetering data acquired within the current delta t time is larger than a set value or not is judged, if yes, a median value and a terminal value within the current delta t time are recorded, the step 1 is returned, and if not, the next step is executed;
and 5, recording the endpoint value in the current delta t time, and returning to the step 1.
In the step 3, the extreme value includes a maximum value and a minimum value, when the maximum value of the power grid telemetry data is obtained within the time Δ t, the maximum value is recorded as the maximum value, and when the minimum value of the power grid telemetry data is obtained within the time Δ t, the minimum value is recorded as the minimum value.
When the obtained extreme value only has a maximum value, the end point value and the maximum value are recorded in the step 3, when the obtained extreme value only has a minimum value, the end point value and the minimum value are recorded in the step 3, and when the obtained extreme value has both the maximum value and the minimum value, the end point value, the maximum value and the minimum value are recorded in the step 3.
And the endpoint value is the power grid telemetering data acquired at the end moment in the current delta t time.
In the step 4, the median is the power grid telemetry data of the average value of the data at the beginning and the end of the power grid telemetry data at the time delta t.
The storage method comprises the steps that data are stored according to three modes of conventional storage, extremum storage and slope storage, and time interval endpoint values, time interval endpoint values and extremum values and time interval endpoint values and median values are stored respectively; by the storage method, the high-efficiency storage of the power grid telemetering data can be realized, and the characteristics of the original data curve are effectively reflected while the data storage amount is further reduced.
Drawings
The following is a brief description of the contents of each figure in the description of the present invention:
FIG. 1 is a schematic diagram of a variable-scale time series data storage method;
fig. 2 is a flow chart of a variable-scale time series data storage method.
Detailed Description
The following description of the embodiments with reference to the drawings is provided to describe the embodiments of the present invention, and the embodiments of the present invention, such as the shapes and configurations of the components, the mutual positions and connection relationships of the components, the functions and working principles of the components, the manufacturing processes and the operation and use methods, etc., will be further described in detail to help those skilled in the art to more completely, accurately and deeply understand the inventive concept and technical solutions of the present invention.
The variable-scale time sequence data storage method stores acquired parameter samples in three ways, including conventional storage, extremum storage and slope storage.
Storing discrete time series data values at regular time intervals, which are conventionally stored as historical data, wherein in a time period from t1 to t2 in fig. 1, a maximum value does not exist, and a difference value between y1 and y2 is smaller than delta y, so that a y2 value is recorded;
the extremum is stored as the maximum value and the minimum value of the time sequence data in the storage time interval on the basis of the discrete time sequence data value of the fixed time interval, and the acquired data is that if the maximum value and the minimum value exist in the time sequence data in the storage time interval, the extremum and the time thereof are also stored, and if the maximum value exists in the time period from t4 to t5 in the figure 1, the ymax and y5 are recorded;
the slope is stored as a time series data median value with large change in a storage time interval on the basis of discrete time series data values of a fixed time interval, if the time series data value in the storage time interval has large change, namely the slope of the time series data is large or low, the time series data median value and the time thereof are also stored, for example, in a time period from t5 to t6 in the figure 1, a maximum value does not exist, but the difference value between y5 and y6 is larger than a set parameter Δ y, wherein the intermediate value (y5/2+ y6/2) between y5 and y6 is ymid, and the ymid and y6 values are recorded;
the specific storage method flow is shown in fig. 2, and includes the following steps:
1. recording real-time sequence telemetering data of the power grid in real time, and storing the data in a temporary database;
2. if the time interval for storing the data is larger than the set time interval, judging the data in the time interval;
3. if the data in the time interval has an extreme value, recording an end point value and an extreme value between the time and the corresponding time of the data according to an extreme value storage mode;
4. if the difference value of the two end point data in the time interval is too large, and the slope of the instant sequence data is larger or lower, recording the end point value, the median value and the corresponding time of the data in the time interval according to a slope storage mode;
5. if the two conditions do not exist, the storage is carried out according to a conventional mode, and the end point value of the time interval is recorded.
The invention has been described above with reference to the accompanying drawings, it is obvious that the invention is not limited to the specific implementation in the above-described manner, and it is within the scope of the invention to apply the inventive concept and solution to other applications without substantial modification.

Claims (5)

1. A variable-scale time sequence storage method for power grid telemetry data is characterized by comprising the following steps:
step 1, acquiring power grid telemetering data in real time;
step 2, the interval time from the last stored data is greater than delta t, if not, the step 1 is returned, and if yes, the next step is executed;
step 3, whether the power grid telemetering data acquired within the current delta t time has an extreme value or not is judged, if yes, an end point value and an extreme value within the current delta t time are recorded, the step 1 is returned, and if not, the next step is executed;
step 4, whether the difference value between the initial end data and the terminal end data of the power grid telemetering data acquired within the current delta t time is larger than a set value or not is judged, if yes, a median value and a terminal value within the current delta t time are recorded, the step 1 is returned, and if not, the next step is executed;
and 5, recording the endpoint value in the current delta t time, and returning to the step 1.
2. The variable-scale time sequence storage method of the power grid telemetry data as claimed in claim 1, wherein: in the step 3, the extreme value includes a maximum value and a minimum value, when the maximum value of the power grid telemetry data is obtained within the time Δ t, the maximum value is recorded as the maximum value, and when the minimum value of the power grid telemetry data is obtained within the time Δ t, the minimum value is recorded as the minimum value.
3. The variable-scale time sequence storage method of the power grid telemetry data as claimed in claim 2, wherein: when the obtained extreme value only has a maximum value, the end point value and the maximum value are recorded in the step 3, when the obtained extreme value only has a minimum value, the end point value and the minimum value are recorded in the step 3, and when the obtained extreme value has both the maximum value and the minimum value, the end point value, the maximum value and the minimum value are recorded in the step 3.
4. The variable-scale time sequence storage method of the power grid telemetry data as claimed in claim 1, wherein: and the endpoint value is the power grid telemetering data acquired at the end moment in the current delta t time.
5. The variable-scale time sequence storage method of the power grid telemetry data as claimed in claim 1, wherein: in the step 4, the median is the power grid telemetry data of the average value of the data at the beginning and the end of the power grid telemetry data at the time delta t.
CN202110529970.0A 2021-05-14 2021-05-14 Variable-scale time sequence storage method for power grid telemetry data Pending CN113254441A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110529970.0A CN113254441A (en) 2021-05-14 2021-05-14 Variable-scale time sequence storage method for power grid telemetry data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110529970.0A CN113254441A (en) 2021-05-14 2021-05-14 Variable-scale time sequence storage method for power grid telemetry data

Publications (1)

Publication Number Publication Date
CN113254441A true CN113254441A (en) 2021-08-13

Family

ID=77182037

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110529970.0A Pending CN113254441A (en) 2021-05-14 2021-05-14 Variable-scale time sequence storage method for power grid telemetry data

Country Status (1)

Country Link
CN (1) CN113254441A (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1525395A (en) * 2003-02-24 2004-09-01 深圳迈瑞生物医疗电子股份有限公司 Method for detecting extreme point and period of signal time domain
CN102435912A (en) * 2011-10-13 2012-05-02 华北电力大学(保定) Method for positioning fault disturbance point in power grid
CN102568018A (en) * 2010-12-09 2012-07-11 成都交大光芒科技股份有限公司 Method for drawing remote-measuring trend curve
CN102981071A (en) * 2012-09-18 2013-03-20 广东电网公司佛山供电局 Electrical power system major parameter monitoring method supplied for electric network management automation system
CN103294713A (en) * 2012-02-29 2013-09-11 鸿富锦精密工业(深圳)有限公司 Monitoring data memory system and method
CN107807271A (en) * 2017-09-29 2018-03-16 中国电力科学研究院 A kind of method and system for being compressed automatically to over-voltage monitoring data
CN108471128A (en) * 2018-05-05 2018-08-31 石家庄科林电气股份有限公司 A kind of three-phase load unbalance Automatic adjustment method realized using balanced optimizing algorithm

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1525395A (en) * 2003-02-24 2004-09-01 深圳迈瑞生物医疗电子股份有限公司 Method for detecting extreme point and period of signal time domain
CN102568018A (en) * 2010-12-09 2012-07-11 成都交大光芒科技股份有限公司 Method for drawing remote-measuring trend curve
CN102435912A (en) * 2011-10-13 2012-05-02 华北电力大学(保定) Method for positioning fault disturbance point in power grid
CN103294713A (en) * 2012-02-29 2013-09-11 鸿富锦精密工业(深圳)有限公司 Monitoring data memory system and method
CN102981071A (en) * 2012-09-18 2013-03-20 广东电网公司佛山供电局 Electrical power system major parameter monitoring method supplied for electric network management automation system
CN107807271A (en) * 2017-09-29 2018-03-16 中国电力科学研究院 A kind of method and system for being compressed automatically to over-voltage monitoring data
CN108471128A (en) * 2018-05-05 2018-08-31 石家庄科林电气股份有限公司 A kind of three-phase load unbalance Automatic adjustment method realized using balanced optimizing algorithm

Similar Documents

Publication Publication Date Title
CN107943831B (en) HBase-based power grid historical data centralized storage method
CN110289631B (en) Method and system for calculating capacity of energy storage device of wind power plant
CN102611454A (en) Dynamic lossless compressing method for real-time historical data
CN112286962B (en) Meter reading success rate statistics method and system for electricity consumption information acquisition terminal
CN106557550B (en) Method and device for realizing rapid storage, retrieval and completion of self-description fixed-point records of power distribution terminal
CN113254441A (en) Variable-scale time sequence storage method for power grid telemetry data
CN114389231B (en) Load prediction diagnosis method based on power distribution network protection and equipment real-time data
CN110995320A (en) Three-phase electricity consumption data acquisition method and device based on broadband carrier communication
CN103605759A (en) Power grid history state data sharing method
CN111625517B (en) New energy real-time data processing method and device based on change storage
CN109270360B (en) Method for calculating online line loss with high precision
CN113629871A (en) Wave recording master station optimized operation method applying source end wave recording information filtering
CN112463793A (en) EMS information display system and method based on influxdb database
CN101751389A (en) Steady state data search method for transformer
CN112688692A (en) Meter reading data compression method, data format, device and storage medium
CN113567721B (en) Power failure statistics method for intelligent ammeter
CN113960478B (en) Asynchronous electric quantity acquisition method for Internet of things equipment
CN115525654A (en) Intelligent substation data storage method based on object model
CN112330090B (en) Low-voltage distribution network data driving service method
CN220208244U (en) SMB-based embedded data storage power system dynamic recording device
CN115797102A (en) Power equipment data management method and device
CN117997353B (en) Hydraulic engineering water level data processing method
Data Analysis of Data Storage Technologies for the Management of Real-Time Process Manufacturing Data
CN114254500A (en) Node Newton line loss calculation method based on data partitioning
Zhang Application of improved revolving door algorithm in FMS real-time data compression

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20210813

RJ01 Rejection of invention patent application after publication