CN113034307B - Data acquisition method for power enterprise - Google Patents
Data acquisition method for power enterprise Download PDFInfo
- Publication number
- CN113034307B CN113034307B CN202110246961.0A CN202110246961A CN113034307B CN 113034307 B CN113034307 B CN 113034307B CN 202110246961 A CN202110246961 A CN 202110246961A CN 113034307 B CN113034307 B CN 113034307B
- Authority
- CN
- China
- Prior art keywords
- data
- calculation
- sample
- time
- point
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a data acquisition method for a power enterprise, which comprises the following steps: initializing the calculation parameters of the electricity quantity of today and defining the date and time variation of today; according to the initial parameters obtained in the first step, the validity of the sample data is checked, and the cache of the calculated electric quantity is cleared by 0 at the beginning of daily calculation; the third step: according to the obtained sample data and the time parameter, completing sample data exception processing and the current electric quantity calculation, and verifying the calculation result; yesterday power result checking, namely acquiring sample data of today and yesterday 0 sample from a database according to the obtained sample parameters and time identifications, carrying out closed calculation on the yesterday power of the unit, and checking a calculation result; and (4) counting the electric quantity of the whole plant, comparing the current snapshot value of each unit in time according to the calculation result to obtain the snapshot time of the earliest unit, and acquiring the calculation filing electric quantity of each unit based on the snapshot time to perform summation calculation and write the calculation filing electric quantity into a database. The invention can improve the accuracy of the calculation result and the reliability of the program.
Description
Technical Field
The invention belongs to the technical field of power industry, and particularly relates to a data acquisition method for a power enterprise.
Background
In recent years, the power industry is rapidly developed, and particularly, the power informatization management is more rapidly developed. How to exert information management to the maximum extent on the basis of safety production, and to master the operation conditions of each plant station in real time and clearly produce and operate, thereby improving the production management level and market competitiveness of enterprises, and becoming the most concerned problem for enterprise development. In order to effectively solve the problem, the daily generated energy, the power supply quantity, the heat supply quantity and the steam supply quantity of a certain power group plant side are counted and uploaded to monitor and manage in the year, so that the safety and economic management level of a group to a power plant is improved.
The defects and shortcomings of the prior art are as follows:
due to problems with the field acquisition devices, the acquired data often has the following 4 types of problems (as shown in fig. 4):
1) The data sampling period is slow, the data density cannot reach a measurement value every 15 minutes, and the calculation result is distorted according to the samples;
2) The data is delayed by time, and the data uploading time is always delayed by a period of time, so that the calculation result is delayed, and even the deviation between the calculation data and the actual value result is large;
3) The jump of the sampled data, because the abnormal value happens occasionally when the decoding is abnormal during the data measurement error or data transmission, if the abnormal value is not judged and removed, the calculation result is seriously wrong;
4) Data abort, a data abort caused by an unexpected failure of the acquisition device or acquisition software, which must be taken into account during the calculation, otherwise a calculation abort would result.
All the problems can cause the abnormity of data calculation, and therefore, the calculation algorithm needs to be subjected to abnormity processing to ensure that the calculation result is accurate and reasonable.
Disclosure of Invention
The invention aims to provide a data acquisition method for an electric power enterprise, which has low design difficulty, can effectively prevent calculation result deviation caused by sample data under various unexpected conditions, and improves the accuracy of calculation results and the reliability of programs.
The invention is realized by the following technical scheme:
a data acquisition method for a power enterprise comprises the following steps:
the first step is as follows: initializing the calculation parameters of the current electric quantity and defining the current date and time variable;
the second step is that: according to the initial parameters obtained in the first step, the validity of the sample data is checked, and the cache of the calculated electric quantity is cleared by 0 at the beginning of daily calculation;
the third step: according to the first step and the second step, sample data and time parameters are obtained, sample data exception processing and today electric quantity calculation are completed, and a calculation result is verified;
the fourth step: yesterday power result checking, namely acquiring sample data of today and yesterday 0 sample from a database according to the sample parameters and the time identification obtained in the first step, carrying out closed calculation on the yesterday power of the unit, and checking a calculation result;
the fifth step: and (4) counting the electric quantity of the whole plant, comparing the current snapshot value of each unit in time according to the calculation result of the fourth step to obtain the snapshot time of the earliest unit, and obtaining the calculation filing electric quantity of each unit based on the snapshot time to sum and calculate and write the calculation filing electric quantity into the database.
The invention has the further improvement that the concrete implementation method of the first step is as follows:
step S101, defining the collected sample measuring point name, the calculated secondary measuring point name, the electric quantity multiplying power coefficient and the generated energy calculating capacity coefficient;
and step S102, acquiring the current actual time, performing variable assignment on the data of the current date, hour and second, and preparing for calling the subsequent time.
The invention has the further improvement that the concrete implementation method of the second step is as follows:
step S201, obtaining the current latest snapshot value and time of the sample label, judging whether the snapshot time is less than the time of the point 0 today, and alarming that an output device is abnormal until the snapshot time of the point 2 today is not more than the point 0 today;
step S202, judging the effectiveness of a sample value of 0 point, sampling sample data of 2 decimal places before and after 0 point of the measuring point, and judging whether the sample obtains a data sample before and after 0 point or 0 point and is effective;
step S203, judging the current time, and enforcing the rule 0 once for the current generated energy data at 0 point 30 and 1 point 30, so as to avoid data interruption and locally cache and write back the old value.
The further improvement of the invention is that the concrete realization method of the third step is as follows:
step S301, obtaining data samples 2 hours before and after the current time of the samples, and writing no-sample-data quality alarm information into a calculation label if no sample data exists;
step S302, obtaining the latest 2 historical data of the sample, and judging whether the latest sample data is greater than the last sample data; if the latest sample data is smaller than the last sample data, writing sample data bad quality alarm in the calculation label;
step S303, acquiring a current snapshot value of the computation tag and judging whether the current snapshot value is normal or not;
step S304, obtaining a 0-point interpolation and a current snapshot value of a sample label, wherein the interpolation of the 0 point can calculate y0= y (0-1) + ((t (0) -t (0-1))/(t (0+1) -t (0-1))) (y (0+1) -y (0-1)) by using samples before and after the 0 point, wherein (0-1) and y (0+1) represent samples before and after the 0 point, y represents sample data, and t represents sample time;
step S305, calculating the current electric quantity, wherein result = (y (n) -y (0)) × g _ key, y (n) represents the current sample data, y (0) represents 0 sample interpolation data, and g _ key represents table code multiplying power;
step S306, judging the current result of result, requiring result >0 and result < g _ capacity > 2, if the condition is satisfied, writing the calculation result value; g _ capacity is the maximum load output of the computer unit, and the unit is ten thousand kWh,2 represents that the data minimum density time interval is 2 hours.
The invention has the further improvement that the concrete implementation method of the fourth step is as follows:
step S401, 0 point 30 per day and yesterday power 0 point historical value and today 0 point power historical value are obtained
Step S402, calculating yesterday power generation amount result 2= (todayValue-yesterday Value) × g _ key, wherein todayValue represents interpolation of 0 sample today, yesterday Value represents interpolation of 0 sample yesterday, g _ key is table code comprehensive transformation ratio, interpolation calculation is to perform regression calculation on a sample according to samples before and after the time when the sample has no data at the time, and if the time has effective data, interpolation calculation is not needed;
step S403, judging result 2>0 and result 2< g_capacity × 24, writing yesterday electric quantity according with conditions, wherein g _ capacity is the ultimate load output of the computer unit, and the unit ten thousand kWh and 24 represent the maximum running fraction of the unit per day;
in step S404, the yesterday power generation amount final calculation result is written at the present 0 point.
The further improvement of the invention is that the concrete implementation method of the fifth step is as follows:
step S501, carrying out validity judgment on yesterday 0 sample data of each unit calculation power data of the whole plant, and ensuring that yesterday 0 sample data of each unit is valid;
step S502, obtaining the latest snapshot data of the calculated electric quantity of each unit of the whole plant, and circularly judging the latest snapshot time of each unit;
step S503, summing the calculated electric quantity of each unit of the whole plant to obtain a calculation result of the electric quantity of the whole plant, and writing the calculation result into a database according to the latest snapshot time;
step S504, starting to correct the data of the 0 point of each unit at this day from the 0 point at this day until the power generation amount of all the units at yesterday is calculated, and finally finishing the calculation of the power statistics of the whole plant.
The invention has at least the following beneficial technical effects:
1. in order to clarify the production and operation conditions of each plant station of a certain electric power group, each plant needs to summarize and count the operation data of each plant before 1 hour every day and report the operation data to a group production duty room, the system always obtains the statistical operation data by a manual input method or a later-stage calculation method before being put into use in the electric power industry, therefore, a large amount of manpower and time are consumed, and the electric quantity acquisition system basically finishes the automatic acquisition and uploading work of the electric quantity.
2. The invention fully considers various errors in the data acquisition of the station side, and provides abnormal data correction during program statistical calculation after deep analysis is carried out on the reasons which can be generated by the errors, so that the calculation result is more real and reliable.
3. Considering that the table data of 0 point per day is the reference for the calculation of the daily capacity, the 0 point sample capacity data must be acquired accurately. And because various reasons of the station collectors can cause the time lag of the sample or the lack of the sample density and no sample data exists at the time of 0 point, the validity of the data of 0 point must be judged before calculating the electric quantity, if the data acquisition of the unit is lagged, the program will carry out delayed processing until the system is found to have no valid data before 2 hours after waiting for 2 hours, an alarm is sent to the information system and the reason of the alarm is explained, the sample data of 0 point caused by the lack of the acquisition density is lost, the program carries out regression calculation on the data of 0 point sample according to a sample interpolation regression method, and therefore the error processing caused by the non-uniform acquisition cycle and the non-synchronous time of each station in the electric quantity calculation can be solved.
4. When the calculation result is abnormal due to sample data abnormality caused by meter maintenance, program decoding, accidental interference and the like during data acquisition, the program also performs verification processing on the calculation result, filters the abnormal result of the calculated electric quantity, and ensures that the result of the calculated electric quantity is reasonably and effectively subjected to data statistics within the range of the generating capacity of the unit.
5. Before the daily electric quantity calculation statistics, the program also carries out closed check processing on yesterday calculated electric quantity, perfect statistics of the daily electric quantity is guaranteed, the statistical calculation theoretically obtains time samples in minutes and seconds, and the obtained sample data has the effect of making millicentis.
6. The program carries out time synchronization processing when calculating the electric quantity data of the whole plant, so that finally, the electric quantity statistics of the whole plant also achieves closed-loop processing of time and data on a data model, and the accuracy of the statistical result of the whole plant is ensured.
In a word, after various data abnormal conditions which can occur in each acquisition system in a factory station are deeply considered, the electric quantity calculation part of the electric quantity uploading system is processed and corrected in two aspects of sample time delay and sample data abnormity, so that the work of performing statistical calculation and summarizing uploading by spending a large amount of labor cost originally is realized, the real-time automatic uploading of a network is realized, the calculation result is verified, and the accuracy and reliability of the statistical result are ensured. At present, the system realizes automatic uploading of electric quantity in more than one hundred plant stations within a certain group range, has good effect and ensures that the group masters the operation condition of the whole group every day in real time.
Drawings
FIG. 1 is a diagram of a typical SIS system network configuration;
FIG. 2 is a group side information management system
FIG. 3 is a system data flow;
FIG. 4 is a sample data problem;
fig. 5 is a flowchart of the procedure.
Detailed Description
The invention is further described below with reference to the following figures and examples.
1. Network foundation
Referring to fig. 1, for most power plants at present, a Plant-Level real-time monitoring Information SIS System (Supervisory Information System in Plant Level) of a thermal power Plant is deployed, the existing SIS System is used to complete the collection, storage, calculation and uploading of group work of power generation/power supply quantity, heat supply quantity and steam supply quantity of each Plant side. The conventional SIS system network at present is a three-layer network framework, namely a safety I area, and is provided with a data acquisition interface machine for realizing the data acquisition function of each system; a convergence switch or an SIS storage database is deployed for the security II area after the fire wall protects the boundary; the security III area is formed by taking the network one-way isolation device after the protection boundary, and an MIS access switch, an SIS mirror database, a computing server and a supervision server interface machine are deployed. The SIS network is positioned in the middle layer of the power plant network and plays a role in starting and stopping. At present, a gateway electric quantity system and a heat supply system in a safety I area are connected through an interface machine, electric quantity data and heat supply/steam primary data of relevant equipment of the system are collected in real time and sent to an SIS network III area mirror database server, and meanwhile, a set of computing agent tools independently developed by our company are deployed on a computing server to complete daily calculation and statistics of generated energy, on-line electric quantity, heat supply quantity and steam supply quantity of each unit and the whole plant. And data uploading software developed by our company is deployed in a supervision server interface machine in the area III in parallel to realize the synchronous uploading work of the calculated data.
The data which is calculated is stored in a factory-level database, and is transmitted to a group monitoring system by using a remote special line from each company to the group through data transmission software developed by the company; the supervision system is characterized in that a super-fusion all-in-one machine system divides a plurality of virtual machines, a data receiving server, a database server, a website publishing server and a relational database are deployed, and finally, data gathered by all stations are displayed in an intranet website in a report form or graph mode to realize benchmarking management. In order to more effectively improve the office efficiency, after the intranet is protected by a gateway, a set of WeChat publishing server is deployed, so that a set of group application management software can be deployed on a user mobile phone, and a decision is provided for realizing the mobile office and production management of a group.
2. System components
At present, most stations are provided with electric quantity metering equipment and heat metering equipment, an electric quantity metering system generally obtains the meter code value of each unit from an electric power telecontrol device through an electric power 102 protocol, and a heat metering device generally obtains the current heat meter code through an MODBUS protocol. The collected surface code data is sent into an SIS database of a power plant information management area through a network protection device, and calculation agent software is deployed on a calculation server in the area to realize the summary calculation of plant-level daily generated energy and daily heat supply; and uploading the calculation data to a group database after the summary is finished, and finally realizing the benchmarking management of the group.
3. Computing program
3.1 calculation requirements, see FIG. 3
1) According to the technical requirements of 'implementation scheme of substation side of automatic electric quantity acquisition project of a certain electric power group', the design idea of automatic electric quantity acquisition and uploading is based on the reliability and accuracy of the following two aspects, wherein the first aspect is the reliability and accuracy of real-time online data, which is derived from primary data of a field acquisition system plant and is the basis of the system; and the second is group side uploading data counted after the unit statistical calculation model is correctly established. Both support each other, but none.
2) Each station finishes data base code rate conversion, data summarization and data return-to-0 processing (uploading data every day starts from 0 point to be newly accumulated), and finally uploads the processed data to a group at the frequency of every 15 minutes (according to the field sampling frequency);
3) The group performs electricity quantity summarizing and confirming at 0 point 45 every day, and each substation needs time synchronization service on a data interface, so that the timeliness of data is guaranteed;
4) The group company finishes data verification and data distribution work in about 30 minutes at 01 days, and once the data is distributed on the day, each unit cannot be modified. If the data is still found to be wrong, the group production duty room needs to be contacted in time, and the data is uniformly modified and redistributed by the group production duty room.
3.2 data problem
Referring to fig. 4, due to the problems of the field acquisition device, the acquired data often has the following 4 types of problems:
1) The data sampling period is slow and the data density cannot reach one measurement value every 15 minutes.
2) The data adopts time lag, and the data uploading time always lags the current time by a period of time.
3) The sampling data jumps, and abnormal values occasionally occur due to decoding abnormality in data measurement errors or data transmission.
4) Data abort, data abort caused by an unexpected failure of the acquisition device or acquisition software.
All the problems can cause the abnormity of data calculation, and therefore, the calculation result can be ensured to be accurate and reasonable only by carrying out abnormity processing on a calculation algorithm.
3.3 program flow chart, see fig. 5.
According to the technical requirements of 'substation side implementation scheme of automatic electric quantity acquisition project of certain electric power group', the invention furthest exerts information management on the basis of safe production, and can master the operation conditions of each plant station in real time and clearly operate production, thereby improving the production management level and market competitiveness of enterprises, and the electric power industry develops rapidly in recent years, especially the electric power informatization management develops more rapidly. How to exert information management to the maximum extent on the basis of safety production, and to master the operation conditions of each plant station in real time and clearly produce and operate, thereby improving the production management level and market competitiveness of enterprises, and becoming the most concerned problem for enterprise development. In order to effectively solve the problem, statistics of daily generated energy, power supply quantity, heat supply quantity and steam supply quantity of the Huaneng group plant side are uploaded to group work in the year, and therefore the safety and economic management level of the group on the power plant is improved.
The automatic electric quantity acquisition and uploading design idea is based on the reliability and accuracy of the following two aspects, the first is the reliability and accuracy of a network system framework, the reliability and accuracy of network transmission are required, and the safety and reliability of protection are required to be achieved, so that the basis of the system is provided; the second is the accuracy of the statistical calculation model of the unit and the stability and reliability of the calculation program for processing abnormal data, and the two are mutually supported and can not be realized.
The invention completes the work of group collection, storage, calculation and uploading of power generation/power supply quantity, heat supply quantity and steam supply quantity at each Plant side by means of the conventional thermal power Plant Level real-time monitoring Information SIS (Supervisory Information System in Plant Level). The data which is calculated is stored in a factory-level database, and is transmitted to a group monitoring system by using a remote special line from each company to the group through data transmission software developed by the company; the supervision system is characterized in that a super-fusion all-in-one machine system divides a plurality of virtual machines, a data receiving server, a database server, a website publishing server and a relational database are deployed, and finally, data gathered by all stations are displayed on an intranet website in a report form or a graph mode to realize the management of the targets.
In fact, the electric quantity and heat metering devices on each plant side are numerous, and the communication mode and the acquisition time of each device are not uniform, so that a lot of difficulties are brought to data statistics and uploading. The invention takes the existing SIS network platform as the basis, carries out synchronous processing on the time processing of the sample data on the basis of the statistical algorithm, carries out error correction judgment on the sample data value according to the actual working condition on site, and effectively solves the problems of data calculation deviation and network investment cost. The method has the following advantages:
firstly, the method comprises the following steps: by means of the existing SIS network platform, the computing power and mass storage advantages of a server of the platform are fully exerted, and the hardware investment cost is effectively reduced;
secondly, the method comprises the following steps: the protection is carried out step by step depending on the existing network protection capability, so that the safety protection capability of sensitive equipment of the power system is guaranteed;
thirdly, the method comprises the following steps: by means of the filing processing capacity of the existing database on data and time scales, sample data is synchronously processed in calculation, and the difference between the calculation result and the time of counting samples in minutes and seconds is guaranteed;
fourthly: according to a sample statistical regression algorithm, performing regression verification on key moment data on the calculation model, eliminating unreliable sample data to participate in calculation, and ensuring that the reliability of the data sample value is improved;
fifth, the method comprises the following steps: the reliable acquisition sample and the actual operation condition of the unit are combined, the future condition of the unit is scientifically and reasonably budgeted, and the accurate calculation of the calculation output result in a reasonable range is ensured;
sixth: and error correction processing is carried out on various types of abnormalities caused by the adoption of time, communication analysis and other reasons of sample data in the program, so that the reliability of the data calculation program is ensured.
Claims (1)
1. A data acquisition method for a power enterprise is characterized by comprising the following steps:
the first step is as follows: initializing the calculation parameters of the current electric quantity and defining the current date and time variable; the specific implementation method is as follows:
step S101, defining the collected sample measuring point name, the calculated secondary measuring point name, the electric quantity multiplying power coefficient and the generated energy calculating capacity coefficient;
step S102, acquiring the current actual time, performing variable assignment on the data of the current date in time, minutes and seconds and preparing for calling the subsequent time;
the second step is that: according to the initial parameters obtained in the first step, the validity of the sample data is checked, and the cache of the calculated electric quantity is cleared by 0 at the beginning of daily calculation; the specific implementation method comprises the following steps:
step S201, obtaining the current latest snapshot value and time of the sample label, judging whether the snapshot time is less than the time of the point 0 today, and if the snapshot time of the point 2 today is not more than the point 0 today, the alarm output device is abnormal;
step S202, judging the effectiveness of a sample value of 0 point, sampling sample data of 2 decimal places before and after 0 point of the measuring point, and judging whether the sample obtains a data sample before and after 0 point or 0 point and is effective;
step S203, judging the current time, and forcing the generated energy data of this day to go to 0 once at point 0 and 30 and point 1 and 30, so as to avoid data interruption and locally cache and write back an old value;
the third step: according to the first step and the second step, sample data and time parameters are obtained, sample data exception processing and today electric quantity calculation are completed, and a calculation result is verified; the specific implementation method comprises the following steps:
step S301, obtaining data samples 2 hours before and after the current time of the samples, and writing no-sample-data quality alarm information into a calculation label if no sample data exists;
step S302, obtaining the latest 2 historical data of the sample, and judging whether the latest sample data is greater than the last sample data; if the latest sample data is smaller than the last sample data, writing sample data bad quality alarm in the calculation label;
step S303, acquiring a current snapshot value of the computation tag and judging whether the current snapshot value is normal or not;
step S304, obtaining a 0-point interpolation and a current snapshot value of a sample label, wherein the interpolation of the 0 point can calculate y0= y (0-1) + ((t (0) -t (0-1))/(t (0+1) -t (0-1))) (y (0+1) -y (0-1)) by using samples before and after the 0 point, wherein (0-1) and y (0+1) represent samples before and after the 0 point, y represents sample data, and t represents sample time;
step S305, calculating the current electric quantity, wherein result = (y (n) -y (0)) × g _ key, y (n) represents the current sample data, y (0) represents 0 sample interpolation data, and g _ key represents table code multiplying power;
step S306, judging the current result of result, requiring result >0 and result < g _ capacity > 2, if the condition is satisfied, writing the calculation result value; g _ capacity is the maximum load output of the computer unit, and the unit is ten thousand kWh,2 represents that the data minimum density time interval is 2 hours;
the fourth step: yesterday power result checking, acquiring today and yesterday 0 sample application data from a database according to the sample parameters and the time identification acquired in the first step, performing closed calculation on the yesterday power of the unit, and checking a calculation result; the specific implementation method comprises the following steps:
step S401, 0 point 30 per day and yesterday power 0 point historical value and today 0 point power historical value are obtained
Step S402, calculating yesterday power generation amount result 2= (todayValue-yesterday Value) × g _ key, wherein todayValue represents interpolation of 0 sample today, yesterday Value represents interpolation of 0 sample yesterday, g _ key is table code comprehensive transformation ratio, interpolation calculation is to perform regression calculation on a sample according to samples before and after the time when the sample has no data at the time, and if the time has effective data, interpolation calculation is not needed;
step S403, judging whether result 2>0 and result 2 </g _capacity24 meet the conditions, writing yesterday power, wherein g _ capacity is the ultimate load output of the computer unit, and the unit of ten thousand kWh and 24 represents the maximum operation fraction of the unit per day;
step S404, writing a final calculation result of yesterday power generation at the 0 point of this day;
the fifth step: counting the electric quantity of the whole plant, comparing the current snapshot value of each unit in time according to the fourth step of calculation result to obtain the snapshot time of the earliest unit, and acquiring the calculation filing electric quantity of each unit based on the snapshot time to sum and calculate and write the calculation filing electric quantity into a database; the specific implementation method comprises the following steps:
step S501, carrying out validity judgment on yesterday 0 sample data of each unit calculation power data of the whole plant, and ensuring that yesterday 0 sample data of each unit is valid;
step S502, obtaining the latest snapshot data of the calculated electric quantity of each unit of the whole plant, and circularly judging the latest snapshot time of each unit;
step S503, summing the calculated electric quantity of each unit of the whole plant to obtain a calculation result of the electric quantity of the whole plant, and writing the calculation result into a database according to the latest snapshot time;
step S504, starting to correct the data of the 0 point of each unit at this day from the 0 point at this day until the power generation amount of all the units at yesterday is calculated, and finally finishing the calculation of the power statistics of the whole plant.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110246961.0A CN113034307B (en) | 2021-03-05 | 2021-03-05 | Data acquisition method for power enterprise |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110246961.0A CN113034307B (en) | 2021-03-05 | 2021-03-05 | Data acquisition method for power enterprise |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113034307A CN113034307A (en) | 2021-06-25 |
| CN113034307B true CN113034307B (en) | 2023-02-24 |
Family
ID=76468225
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110246961.0A Active CN113034307B (en) | 2021-03-05 | 2021-03-05 | Data acquisition method for power enterprise |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113034307B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114113778A (en) * | 2021-10-19 | 2022-03-01 | 黄兵 | Metering big data calculation method of big data system of electric energy data management platform |
| CN114167944A (en) * | 2021-11-23 | 2022-03-11 | 北京华能新锐控制技术有限公司 | Power station daily generated energy calculation method and device and electronic equipment |
Family Cites Families (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2468129A1 (en) * | 1979-10-24 | 1981-04-30 | Enertec | METHOD AND DEVICE FOR MEASURING ELECTRIC POWER |
| WO2002095382A1 (en) * | 2001-05-21 | 2002-11-28 | Pressco Technology, Inc. | An apparatus and method for providing snapshot action thermal infrared imaging within automated process control article inspection applications |
| JP3791503B2 (en) * | 2003-03-12 | 2006-06-28 | 株式会社日立製作所 | Power generation plan / power trading plan creation method, apparatus and program |
| US6945125B2 (en) * | 2003-12-03 | 2005-09-20 | Daniel Industries, Inc. | High resolution pulse count interface |
| CN101718814A (en) * | 2009-11-13 | 2010-06-02 | 国网电力科学研究院 | Method for measuring and calculating electrical quantities of safety and stability control device based on FPGA |
| JP2011135651A (en) * | 2009-12-22 | 2011-07-07 | Panasonic Electric Works Co Ltd | Power supply system |
| CN103426080A (en) * | 2013-08-09 | 2013-12-04 | 国家电网公司 | Daily production report system of pumped storage power station |
| CN104215856B (en) * | 2014-09-10 | 2016-08-24 | 国家电网公司 | A kind of bulk power grid dynamic check method of electric energy value |
| CN106291030B (en) * | 2016-09-27 | 2019-04-23 | 国网山东省电力公司济南供电公司 | A kind of distribution network metering guard system |
| CN206876743U (en) * | 2017-06-20 | 2018-01-12 | 中国电力科学研究院 | A kind of electric energy meter current transformer secondary circuit anti-electricity-theft device based on Transient Method |
| US20190072920A1 (en) * | 2017-09-06 | 2019-03-07 | Ali Ridha Ali | Optimization/Modeling-Free Economic Load Dispatcher for Energy Generating Units |
| CN109461093A (en) * | 2018-10-08 | 2019-03-12 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Electric quantity measuring system Accuracy Evaluation and station service energy consumption analysis system |
| CN109634944B (en) * | 2018-12-05 | 2022-11-04 | 贵州电网有限责任公司 | Network loss data cleaning method based on multi-dimensional space-time analysis |
| CN109828229A (en) * | 2019-01-23 | 2019-05-31 | 国网安徽省电力有限公司 | Electric energy data transmits consistency desired result method between a kind of multiservice system |
| CN109977535B (en) * | 2019-03-22 | 2023-05-02 | 南方电网科学研究院有限责任公司 | Line loss abnormality diagnosis method, device, equipment and readable storage medium |
| CN110070272A (en) * | 2019-04-02 | 2019-07-30 | 国网安徽省电力有限公司 | The generated energy acquisition quality of data based on self study technology manages multidimensional evaluation method |
| CN110780261B (en) * | 2019-10-31 | 2021-12-24 | 南京国电南自电网自动化有限公司 | Method and system for checking electric quantity acquisition data |
| CN111752935B (en) * | 2020-06-03 | 2024-03-26 | 深圳供电局有限公司 | Method and device for checking power transaction user data |
-
2021
- 2021-03-05 CN CN202110246961.0A patent/CN113034307B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113034307A (en) | 2021-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3076346A1 (en) | Systems and methods for improved demand response management system (drms) | |
| CN113034307B (en) | Data acquisition method for power enterprise | |
| CN111600296B (en) | Power load prediction system based on edge calculation and prediction method thereof | |
| CN109683059B (en) | Method and device for determining line loss abnormity, storage medium and processor | |
| CN102254256A (en) | Transmission and distribution network integration comprehensive line loss management analysis system and processing flow thereof | |
| CN112701790B (en) | Intelligent joint debugging platform and joint debugging method for transformer substation and master station | |
| CN104487991A (en) | Energy management with correspondence based data auditing signoff | |
| CN107563740B (en) | Electric power transaction electricity purchasing management platform system and meter data acquisition method | |
| CN102419834B (en) | Enterprise energy settlement system | |
| US20200117151A1 (en) | Outage and switch management for a power grid system | |
| CN113422820A (en) | Automatic joint debugging device and method for main station telecontrol information | |
| CN104394020A (en) | Automatic fault processing and safety control system and method for power information collection device | |
| CN108919172A (en) | A kind of automatic time synchronization method of electric energy meter, device and terminal device | |
| CN102692558A (en) | Monitoring and analyzing system for electricity data and realization method thereof | |
| CN103631245A (en) | Power distribution terminal defect diagnostic system and method | |
| Wang et al. | A load modeling algorithm for distribution system state estimation | |
| CN102426623A (en) | Automatic failure fitting modeling method for power load modeling | |
| CN113570075A (en) | Remote monitoring software system for water meter | |
| CN109298698B (en) | Full-automatic monitoring system for energy consumption of public building | |
| CN115995880A (en) | Comprehensive monitoring and analyzing method and system for multidimensional state of power distribution automation terminal | |
| CN115526738A (en) | Electric power data acquisition and processing system, method, electronic equipment and storage medium | |
| CN112446691A (en) | Power generation side electric quantity settlement auditing system and method | |
| CN112365087A (en) | Method for predicting annual uninterrupted power operation demand | |
| Rubini et al. | Smart grids investigation and analysis in terms of cost benefits | |
| CN111639876A (en) | Main station connection and calculation system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |