CN107607897A - A kind of voltage monitoring instrument on-line testing and prediction meanss and method - Google Patents
A kind of voltage monitoring instrument on-line testing and prediction meanss and method Download PDFInfo
- Publication number
- CN107607897A CN107607897A CN201710757110.6A CN201710757110A CN107607897A CN 107607897 A CN107607897 A CN 107607897A CN 201710757110 A CN201710757110 A CN 201710757110A CN 107607897 A CN107607897 A CN 107607897A
- Authority
- CN
- China
- Prior art keywords
- mrow
- data
- voltage
- module
- target voltage
- 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.)
- Granted
Links
Abstract
The invention provides a kind of voltage monitoring instrument on-line testing and prediction meanss and method, the device includes：Interactive display module, data acquisition module, standard signal source module, controller module, forecast analysis module, memory module, by the way that the Monitoring Data of the target voltage monitor under same markers is compared with standard voltage data, to complete the on-line testing to target voltage monitor, in addition, default forecast model is modified by the Historical Monitoring data of target voltage monitor, and the predicted value of following Monitoring Data of target voltage monitor is drawn by revised forecast model, and make respective handling.The solution of the present invention had both completed the on-line testing of voltage monitoring instrument, the predicted value of voltage monitoring instrument future Monitoring Data can be drawn again, so as to judge the following possibility for needing to be verified of the voltage monitoring instrument according to predicted value, so as to improve the efficiency of voltage monitoring instrument verification from multiple temporal range.
Description
Technical field
The present invention relates to electric instrument instrument to verify monitoring technical field, more particularly to a kind of voltage monitoring instrument on-line testing
And prediction meanss and method.
Background technology
In order to ensure the stability of power network power supply, voltage monitoring is the emphasis of power monitoring.With voltage monitoring instrument
Widely use, may occur the problem of voltage monitoring instrument is inaccurate at regular intervals, therefore, it is necessary to periodically specially voltage is supervised
Instrument is surveyed to be verified.And as the increase of voltage monitoring instrument usage quantity, cost of labor are also increasing.Existing voltage monitoring instrument
Verification mode is numerous, however, be all merely resting on simple verification aspect, not to having completed the voltage monitoring instrument of verification
The situation of verification next time that future may need is made prediction so that existing voltage monitoring instrument verifying work is less efficient.
The content of the invention
In order to overcome the shortcomings of the prior art, the present invention proposes a kind of voltage monitoring instrument on-line testing and prediction dress
Put and method, can verify complete while, make further prediction for the voltage that voltage monitoring instrument is monitored, so as to
Know the situation about verifying next time that voltage monitoring instrument future may need, can be monitored from multiple temporal range booster tension
The efficiency of instrument verification.
A kind of voltage monitoring instrument on-line testing provided by the invention and prediction meanss, including：
Interactive display module, for operating generation data acquisition instructions according to user；
Data acquisition module, for obtaining the Monitoring Data of voltage monitoring instrument, and returned and monitored according to data acquisition instructions
The currently monitored data in data and/or the Historical Monitoring data in prediction instruction return Monitoring Data；
Standard signal source module, for being produced and the currently monitored same markers of data according to the data acquisition instructions
Under standard voltage data；
Controller module, according to the trueness error of obtained target voltage monitor and default verification rule, to target
Voltage monitoring instrument carries out checking treatment；
Wherein, the trueness error is obtained by the currently monitored data and standard voltage data；
It is described it is default verification rule be：When the trueness error exceedes default standard value, target voltage is monitored
Instrument carries out replacement compensating operation；When the trueness error is no more than default standard value, to data acquisition module and prediction point
Analyse module and send prediction instruction；
Forecast analysis module, according to the prediction instruction and default prediction rule, by default forecast model, to mesh
Mark voltage monitoring instrument is predicted processing；
Wherein, parameter needed for the forecast model is the Historical Monitoring data of target voltage monitor, and prediction result is mesh
Mark the predicted voltage value of voltage monitoring instrument；
The prediction rule is：When predicted voltage value exceeds or falls below range of normal value, to the interactive display module
Send alarm signal；When the predicted voltage value in prediction result is in range of normal value, sent to the interactive display module
Normal signal；
Memory module, for storing checking treatment result and prediction result；
The interactive display module, standard signal source module, forecast analysis module and memory module are and controller module
It is connected；
The data acquisition module is connected with controller module and target voltage monitor simultaneously.
Device provided by the invention has two functions：On-line testing function to voltage monitoring instrument and to power supply monitoring instrument
The forecast function of following Monitoring Data.The present apparatus is built-in with standard signal source and forecast model, and standard signal source provides and voltage
Standard voltage data of the Monitoring Data of monitor under same markers, is compared using Monitoring Data and standard voltage data
Compared with then according to the verification operation of comparative result progress voltage monitoring instrument；, can be with according to the Historical Monitoring data of voltage monitoring instrument
Built-in forecast model is modified, some the following time of voltage monitoring instrument is then obtained according to revised forecast model
Predicted voltage value, when the predicted voltage value of voltage monitoring instrument multiple times is above fluctuating error scope, then illustrate that the voltage is supervised
It is larger to survey the monitoring voltage fluctuation range of instrument, future needs the possibility that is further verified higher, so as to remind work people
Member is paid special attention to.
Further, the workflow of the forecast analysis module is：
The prediction sent according to controller module instructs, and obtains the Historical Monitoring data of target voltage monitor；
Linear fit amendment is carried out using the Historical Monitoring data as sample data set pair forecast model；
The predicted voltage value of target voltage monitor is drawn by revised forecast model；
According to the prediction rule and predicted voltage value, processing is predicted to target voltage monitor；
Wherein, the Historical Monitoring data include voltage harmonic, frequency, electric current, load and magnitude of voltage.
The forecast analysis module is built-in with forecast model, and in this programme, inventor is used based on the polynary of time series
Time point in Historical Monitoring data is established sample data set as forecast model by regression parameter model as partitioning standards,
Using the voltage harmonic at same time point, frequency, electric current, load and magnitude of voltage as a sample data, wherein, voltage harmonic,
Frequency, electric current, load are as the input parameter in sample data, and magnitude of voltage is as the output parameter in sample data, according to
The prediction error of some historical data assessment prediction models, is modified to forecast model.
When the prediction error of forecast model is in allowed band, then operation is predicted using the forecast model；Otherwise,
The voltage monitoring instrument historical data of other times section is obtained, forecast model is modified again.
Further, the forecast model in the forecast analysis module is specially multiple regression parameter model, pattern function
For：
F (t)=g (t) β (m)+ξ
Wherein, f (t) represents the output variable of forecast model, i.e. predicted voltage value, and g (t) represents target voltage monitor institute
The Monitoring Data function of monitoring, β (m) represent auto-correlation regression coefficient, and ξ is recurrence harmonic coefficient；
Wherein, auto-correlation regression coefficient β (m) calculation formula is：
In formula, r (m) is auto-correlation function, and m is calculates intermediate quantity, and x (n) is sample set, and n is number of samples, and t is sampling
Time span, sampling time length t and sampling interval determine number of samples n.
The present invention program using the multiple regression parameter model based on time series, wherein, returning harmonic coefficient ξ can
To be determined based on experience value and with reference to actual conditions by those skilled in the art, number of samples n by sampling time length t with adopt
Sample time interval determines that the sampling time interval can determine according to actual conditions, and it is 1s to commonly use value.
Further, the device also includes：
Communication module, for establishing communication connection by 3G or 4G networks and remote monitoring service platform；
The communication module is connected with controller module.
Device provided by the invention also includes communication module, and the module can pass through 3G the or 4G real-time performance present apparatus
Telecommunication and control function, further improve the convenience of device operation.
Present invention also offers a kind of voltage monitoring instrument on-line testing and Forecasting Methodology, including：
Step S1：Obtain verification data；
The verification data includes the Monitoring Data of target voltage monitor and the mark of standard signal source under same markers
Quasi- voltage data；
Step S2：On-line testing is carried out to target voltage monitor according to the verification data；
Using the step S1 Monitoring Datas obtained and standard voltage data, the trueness error of target voltage monitor is determined,
And checking treatment is carried out according to default verification rule；
Wherein, the default verification rule is：When the trueness error exceedes default standard value, to voltage monitoring
Instrument carries out replacement compensating operation；When the trueness error is no more than default standard value, step S3 is performed；
Step S3：According to the default forecast model of Historical Monitoring data correction of target voltage monitor；
The Historical Monitoring data of target voltage monitor are obtained, sample data set, line are used as using the Historical Monitoring data
Property fitting correct default forecast model；
Wherein, the Historical Monitoring data include voltage harmonic, frequency, electric current, load and magnitude of voltage；
Step S4：According to the revised forecast models of step S3, the predicted voltage value of target voltage monitor, and root are obtained
Processing is predicted to target voltage monitor according to default prediction rule；
Wherein, the default prediction rule is：When predicted voltage value exceeds or falls below range of normal value, warning is sent
Signal；When predicted voltage value is in range of normal value, normal signal is sent.
In the present invention program, step S1 and step S2 complete the on-line testing operation to voltage monitoring instrument, step S3 and
Step S4 completes the predicted operation to voltage monitoring instrument future Monitoring Data.The future that this method passes through predicted voltage monitor
Monitoring Data, it can be determined that the following possibility for needing to be verified of voltage monitoring instrument, so as to remind staff to give especially
Pay attention to, from the efficiency of multiple temporal range booster tension monitor verification.The forecast model used in this method, art technology
Personnel can flexibly select according to actual conditions.
Wherein, step S2 checking treatment is in order to correct the measurement error present in voltage monitoring instrument in time, so as to obtain
Obtain more accurate voltage monitoring data；Step S4 prediction processing is to by the predicted voltage value of voltage monitoring instrument, sentence
The disconnected following possibility for needing to verify of voltage monitoring instrument, improve the efficiency of voltage monitoring instrument verification.
Further, the default forecast model is specially multiple regression parameter model, and pattern function is：
F (t)=g (t) β (m)+ξ
Wherein, f (t) represents the output variable of forecast model, i.e. predicted voltage value, and g (t) represents target voltage monitor institute
The voltage parameter function of monitoring, β (m) represent auto-correlation regression coefficient, and ξ is recurrence harmonic coefficient；
Wherein, auto-correlation regression coefficient β (m) calculation formula is：
In formula, r (m) is auto-correlation function, and m is calculates intermediate quantity, and x (n) is sample set, and n is number of samples, and t is sampling
Time span, sampling time length t and sampling interval determine number of samples n.
This method using the multiple regression parameter model based on time series, wherein, returning harmonic coefficient ξ can be by
Those skilled in the art are based on experience value and combination actual conditions determine, when number of samples n is by sampling time length t and sampling
Between be spaced and determine, the sampling time interval can determine according to actual conditions, and it is 1s to commonly use value.
Beneficial effect
A kind of voltage monitoring instrument on-line testing provided by the invention and prediction meanss and method, had both been realized to voltage monitoring
The on-line testing of instrument, Monitoring Data that again can be following to voltage monitoring instrument are predicted, sentenced by the fluctuation situation of data
The disconnected following possibility for needing to be verified of voltage monitoring instrument, and then the effect verified from multiple temporal range booster tension monitor
Rate.
Brief description of the drawings
Fig. 1 shows that a kind of voltage monitoring instrument on-line testing of the offer of the embodiment of the present invention one and the structure of prediction meanss are shown
It is intended to；
Fig. 2 shows another voltage monitoring instrument on-line testing of the offer of the embodiment of the present invention one and the combination of prediction meanss
Schematic diagram；
Fig. 3 shows a kind of voltage monitoring instrument on-line testing of the offer of the embodiment of the present invention two and the flow of Forecasting Methodology
Figure；
Fig. 4 shows a kind of voltage monitoring instrument on-line testing of the offer of the embodiment of the present invention two and the principle of Forecasting Methodology
Figure.
Embodiment
In order to which technical scheme is expanded on further, it is described in detail with reference to specific embodiment.
Embodiment one
Fig. 1 shows that a kind of voltage monitoring instrument on-line testing of the offer of the embodiment of the present invention one and the structure of prediction meanss are shown
It is intended to.The present apparatus includes：Interactive display module 100, data acquisition module 200, standard signal source module 300, controller module
400th, forecast analysis module 500 and memory module 600, interactive display module 100, data acquisition module 200, standard signal source mould
Block 300, forecast analysis module 500 and memory module 600 are connected with controller module 400, meanwhile, data acquisition module
200 are connected with voltage monitoring instrument 700.
The verifying work flow of the device is as follows：Interactive display module 100 operates generation data acquisition instructions according to user,
And it is sent to controller module 400；Data acquisition instructions are transmitted to the He of data acquisition module 200 by controller module 400 respectively
Standard signal source module 300；Data acquisition module 200 and standard signal source module 300 upload respectively according to data acquisition instructions
Voltage monitoring instrument Monitoring Data and standard voltage data under same markers is in controller module 400；Controller module 400 passes through
Compare and be calculated the trueness error of voltage monitoring instrument, if the trueness error has exceeded standard value, to the voltage monitoring instrument
Replacement compensation is carried out, and generates verification report and is transferred to interactive display module 100, is tied for being shown to user, while by verification
Fruit is transferred to memory module 600, for achieving；If the trueness error, within standard value, controller module 400 then generates prediction
Instruction, and it is sent to forecast analysis module 500 and data acquisition module 200.
The prediction work flow of the device is as follows：Data acquisition module 200 instructs according to prediction, obtains voltage monitoring instrument
Historical Monitoring data, and Historical Monitoring data are sent to controller module 400, the Historical Monitoring data include some period
Interior voltage harmonic, frequency, electric current, load and magnitude of voltage；Controller module 400 is by Historical Monitoring data forwarding to forecast analysis
Module 500；Forecast analysis module 500 is according to prediction instruction and Historical Monitoring data, by Historical Monitoring data to monitor time point
Sample data set is established for partitioning standards, using the voltage harmonic at same time point, frequency, electric current, load and magnitude of voltage as one
Individual sample data, wherein, voltage harmonic, frequency, electric current, load are as the input parameter in sample data, and magnitude of voltage is as sample
Output parameter in notebook data, according to the prediction error of existing historical data assessment prediction model, forecast model is repaiied
Just；If error is predicted operation in allowed band, using the forecast model；Otherwise, the voltage of other times section is obtained
Monitor historical data, is modified to forecast model again；Forecast analysis module 500 is obtained by revised forecast model
The predicted voltage value of voltage monitoring instrument in required time, and processing is predicted to target voltage monitor according to prediction rule：
When the predicted voltage value in prediction result exceeds or falls below range of normal value, alarm signal is sent to interactive display module 100；
When the predicted voltage value in prediction result is in range of normal value, interactive display module 100 sends normal signal, rower of going forward side by side
Note.
Specifically, the forecast model that the present embodiment uses is the multiple regression parameter model based on time series, model
Function is：
F (t)=g (t) β (m)+ξ
Wherein, f (t) represents the output variable of forecast model, i.e. predicted voltage value；G (t) represents target voltage monitor institute
The Monitoring Data function of monitoring；β (m) represents auto-correlation regression coefficient；ξ, can be by people in the art to return harmonic coefficient
Member is based on experience value and combination actual conditions determine；
Wherein, auto-correlation regression coefficient β (m) calculation formula is：
In formula, r (m) is auto-correlation function, and x (n) is sample set, and n is number of samples, and for m to calculate intermediate quantity, t is sampling
Time span；Number of samples n determines that the sampling time interval can basis by sampling time length t and sampling time interval
Actual conditions determine that it is 1s to commonly use value.
Fig. 2 also show another voltage monitoring instrument on-line testing and prediction meanss of the offer of the embodiment of the present invention one, Fig. 2
Device and Fig. 1 devices only difference is that：Fig. 2 devices add communication module 800 on the basis of Fig. 1 devices, the communication
Module 800 establishes communication connection by 3G or 4G networks and remote monitoring service platform, realizes the present apparatus and is taken with remote monitoring
The docking of business platform so that the present apparatus realizes telecommunication and control function, further improves the convenience of device operation.
The voltage monitoring instrument on-line testing and prediction meanss that the embodiment of the present invention one provides, both by by under same markers
Monitoring Data and standard voltage data are compared, and realize the on-line testing to voltage monitoring instrument, further through Historical Monitoring number
The prediction to following Monitoring Data is realized according to amendment forecast model, and using forecast model, passes through the fluctuation situation of prediction data
To judge the following possibility for needing to be verified of voltage monitoring instrument, and then verified from multiple temporal range booster tension monitor
Efficiency.
Embodiment two
Fig. 3 shows a kind of voltage monitoring instrument on-line testing of the offer of the embodiment of the present invention two and the flow of Forecasting Methodology
Figure, Fig. 4 show the schematic diagram for the method that the embodiment of the present invention two provides.This method includes：
Step S1：Obtain verification data.
The verification data includes the Monitoring Data of target voltage monitor and the mark of standard signal source under same markers
Quasi- voltage data.
Step S2：On-line testing is carried out to target voltage monitor according to the verification data.
Specifically, using the step S1 Monitoring Datas obtained and standard voltage data, target voltage monitor is determined
Trueness error, and checking treatment is carried out according to default verification rule；Wherein, the default verification rule is：When the essence
When degree error exceedes default standard value, replacement compensating operation is carried out to voltage monitoring instrument；When the trueness error is no more than in advance
If standard value when, perform step S3.
Step S3：According to the default forecast model of Historical Monitoring data correction of target voltage monitor；
Specifically, the Historical Monitoring data of target voltage monitor are obtained, sample is used as using the Historical Monitoring data
Data set, correct default forecast model；Wherein, the Historical Monitoring data include voltage harmonic, frequency, electric current, load and
Magnitude of voltage.In the present embodiment, the time point in Historical Monitoring data is established into sample data set as partitioning standards, will be same
Voltage harmonic, frequency, electric current, load and the magnitude of voltage at time point as a sample data, wherein, voltage harmonic, frequency, electricity
Stream, load as the input parameter in sample data, magnitude of voltage is as the output parameter in sample data, according to existing history
The prediction error of data assessment forecast model, is modified to forecast model.When the prediction error of forecast model is in allowed band
When interior, then operation is predicted using the forecast model；Otherwise, the voltage monitoring instrument historical data of other times section, weight are obtained
Newly forecast model is modified.
The forecast model used in the present embodiment is for the multiple regression parameter model based on time series, pattern function：
F (t)=g (t) β (m)+ξ
Wherein, f (t) represents the output variable of forecast model, i.e. predicted voltage value；G (t) represents target voltage monitor institute
The Monitoring Data function of monitoring；β (m) represents auto-correlation regression coefficient；ξ, can be by people in the art to return harmonic coefficient
Member is based on experience value and combination actual conditions determine；
Wherein, auto-correlation regression coefficient β (m) calculation formula is：
In formula, r (m) is auto-correlation function, and x (n) is sample set, and n is number of samples, and for m to calculate intermediate quantity, t is sampling
Time span；Number of samples n determines that the sampling time interval can basis by sampling time length t and sampling time interval
Actual conditions determine that it is 1s to commonly use value.
Step S4：According to the revised forecast models of step S3, the predicted voltage value of target voltage monitor, and root are obtained
Processing is predicted to target voltage monitor according to default prediction rule.
Wherein, the default prediction rule is：When predicted voltage value exceeds or falls below range of normal value, warning is sent
Signal；When predicted voltage value is in range of normal value, normal signal is sent.
A kind of voltage monitoring instrument on-line testing and Forecasting Methodology that the embodiment of the present invention two provides, both by by same markers
Under Monitoring Data and standard voltage data be compared, realize the on-line testing to voltage monitoring instrument, supervised further through history
Data correction forecast model is surveyed, and the prediction to following Monitoring Data is realized using forecast model, passes through the fluctuation of prediction data
Situation judges the following possibility for needing to be verified of voltage monitoring instrument, and then from multiple temporal range booster tension monitor
The efficiency of verification.
Embodiments of the invention are the foregoing is only, are not intended to limit the invention, it is all in spirit of the invention and former
Within then, change, equivalent substitution, improvement etc., should be included in the scope of the protection.
Claims (6)
1. a kind of voltage monitoring instrument on-line testing and prediction meanss, it is characterised in that including：
Interactive display module, for operating generation data acquisition instructions according to user；
Data acquisition module, Monitoring Data is returned to for obtaining the Monitoring Data of voltage monitoring instrument, and according to data acquisition instructions
In the currently monitored data and/or according to prediction instruct return Monitoring Data in Historical Monitoring data；
Standard signal source module, for according under data acquisition instructions generation and the currently monitored same markers of data
Standard voltage data；
Controller module, according to the trueness error of obtained target voltage monitor and default verification rule, to target voltage
Monitor carries out checking treatment；
Wherein, the trueness error is obtained by the currently monitored data and standard voltage data；
It is described it is default verification rule be：When the trueness error exceedes default standard value, target voltage monitor is entered
Row resets compensating operation；When the trueness error is no more than default standard value, to data acquisition module and forecast analysis mould
Block sends prediction instruction；
Forecast analysis module, according to the prediction instruction and default prediction rule, by default forecast model, to target electricity
Pressure monitor is predicted processing；
Wherein, parameter needed for the forecast model is the Historical Monitoring data of target voltage monitor, and prediction result is target electricity
Press the predicted voltage value of monitor；
The prediction rule is：When predicted voltage value exceeds or falls below range of normal value, sent to the interactive display module
Alarm signal；When the predicted voltage value in prediction result is in range of normal value, sent normally to the interactive display module
Signal；
Memory module, for storing checking treatment result and prediction result；
The interactive display module, standard signal source module, forecast analysis module and memory module are connected with controller module
Connect；
The data acquisition module is connected with controller module and target voltage monitor simultaneously.
2. device according to claim 1, it is characterised in that the workflow of the forecast analysis module is：
The prediction sent according to controller module instructs, and obtains the Historical Monitoring data of target voltage monitor；
Linear fit amendment is carried out using the Historical Monitoring data as sample data set pair forecast model；
The predicted voltage value of target voltage monitor is drawn by revised forecast model；
According to the prediction rule and predicted voltage value, processing is predicted to target voltage monitor；
Wherein, the Historical Monitoring data include voltage harmonic, frequency, electric current, load and magnitude of voltage.
3. device according to claim 2, it is characterised in that the forecast model in the forecast analysis module is specially more
First regression parameter model, pattern function are：
F (t)=g (t) β (m)+ξ
Wherein, f (t) represents the output variable of forecast model, i.e. predicted voltage value, and g (t) represents that target voltage monitor is monitored
Monitoring Data function, β (m) represent auto-correlation regression coefficient, ξ for return harmonic coefficient；
Wherein, auto-correlation regression coefficient β (m) calculation formula is：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>r</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>t</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>n</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
<mo>-</mo>
<mi>m</mi>
</mrow>
</munderover>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>n</mi>
<mo>)</mo>
</mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>n</mi>
<mo>+</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>m</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>...</mo>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>&beta;</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>r</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>/</mo>
<mi>r</mi>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
In formula, r (m) is auto-correlation function, and m is calculates intermediate quantity, and x (n) is sample set, and n is number of samples, and t is the sampling time
Length, sampling time length t and sampling interval determine number of samples n.
4. device according to claim 3, it is characterised in that including：
Communication module, for establishing communication connection by 3G or 4G networks and remote monitoring service platform；
The communication module is connected with controller module.
5. a kind of voltage monitoring instrument on-line testing and Forecasting Methodology, it is characterised in that including：
Step S1：Obtain verification data；
The verification data includes the Monitoring Data of target voltage monitor and the standard electric of standard signal source under same markers
Press data；
Step S2：On-line testing is carried out to target voltage monitor according to the verification data；
Using the step S1 Monitoring Datas obtained and standard voltage data, the trueness error of target voltage monitor, and root are determined
Checking treatment is carried out according to default verification rule；
Wherein, the default verification rule is：When the trueness error exceedes default standard value, voltage monitoring instrument is entered
Row resets compensating operation；When the trueness error is no more than default standard value, step S3 is performed；
Step S3：According to the default forecast model of Historical Monitoring data correction of target voltage monitor；
The Historical Monitoring data of target voltage monitor are obtained, sample data set, Linear Quasi are used as using the Historical Monitoring data
Close and correct default forecast model；
Wherein, the Historical Monitoring data include voltage harmonic, frequency, electric current, load and magnitude of voltage；
Step S4：According to the revised forecast models of step S3, the predicted voltage value of target voltage monitor is obtained, and according to pre-
If prediction rule processing is predicted to target voltage monitor；
Wherein, the default prediction rule is：When predicted voltage value exceeds or falls below range of normal value, warning letter is sent
Number；When predicted voltage value is in range of normal value, normal signal is sent.
6. according to the method for claim 5, it is characterised in that the default forecast model is specially multiple regression parameter
Model, pattern function are：
F (t)=g (t) β (m)+ξ
Wherein, f (t) represents the output variable of forecast model, i.e. predicted voltage value, and g (t) represents that target voltage monitor is monitored
Monitoring Data function, β (m) represent auto-correlation regression coefficient, ξ for return harmonic coefficient；
Wherein, auto-correlation regression coefficient β (m) calculation formula is：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>r</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>t</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>n</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
<mo>-</mo>
<mi>m</mi>
</mrow>
</munderover>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>n</mi>
<mo>)</mo>
</mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>n</mi>
<mo>+</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>m</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>...</mo>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>&beta;</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>r</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>/</mo>
<mi>r</mi>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
In formula, r (m) is auto-correlation function, and m is calculates intermediate quantity, and x (n) is sample set, and n is number of samples, and t is the sampling time
Length, sampling time length t and sampling interval determine number of samples n.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710757110.6A CN107607897B (en) | 2017-08-29 | 2017-08-29 | A kind of voltage monitoring instrument on-line testing and prediction meanss and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710757110.6A CN107607897B (en) | 2017-08-29 | 2017-08-29 | A kind of voltage monitoring instrument on-line testing and prediction meanss and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107607897A true CN107607897A (en) | 2018-01-19 |
| CN107607897B CN107607897B (en) | 2019-09-03 |
Family
ID=61056319
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710757110.6A Active CN107607897B (en) | 2017-08-29 | 2017-08-29 | A kind of voltage monitoring instrument on-line testing and prediction meanss and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107607897B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108663634A (en) * | 2018-07-10 | 2018-10-16 | 深圳市科列技术股份有限公司 | A kind of determination method and apparatus of power battery internal resistance |
| CN109407031A (en) * | 2018-10-09 | 2019-03-01 | 国网四川省电力公司电力科学研究院 | Voltage transformer fault recognition method based on time series hierarchical cluster |
| CN111307199A (en) * | 2019-12-05 | 2020-06-19 | 北京普源精电科技有限公司 | Electronic measuring instrument with prediction device and prediction method of electronic measuring instrument |
| CN111505377A (en) * | 2020-05-16 | 2020-08-07 | 国网甘肃省电力公司兰州供电公司 | Voltage flicker real-time monitoring and early warning system and method |
| CN112033575A (en) * | 2020-06-29 | 2020-12-04 | 国网四川省电力公司电力科学研究院 | On-site calibration method and device for on-line monitoring system of power valve control storage battery pack |
| CN112505386A (en) * | 2020-08-25 | 2021-03-16 | 中国电力科学研究院有限公司 | Method and system for detecting current value of direct current charging pile |
| CN112924915A (en) * | 2021-01-27 | 2021-06-08 | 云南电网有限责任公司电力科学研究院 | Mutual calibration system and method for voltage monitor |
| CN117518061A (en) * | 2024-01-04 | 2024-02-06 | 山东大学 | Electric measuring instrument detection data inspection system and method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102854486A (en) * | 2012-09-26 | 2013-01-02 | 湖北省电力公司电力科学研究院 | On-site initiative error-verification system for voltage transformer and method thereof |
| CN102981137A (en) * | 2012-11-27 | 2013-03-20 | 辽宁省电力有限公司电力科学研究院 | Remote intelligent verifying device and method of voltage monitor based on general packet radio service (GPRS)/global system for mobile communication (GSM) network |
| CN203054208U (en) * | 2013-01-15 | 2013-07-10 | 湖南省电力公司科学研究院 | Automatic inspection system of voltage monitor |
| CN103558570A (en) * | 2013-11-06 | 2014-02-05 | 国家电网公司 | Portable voltage monitor field calibration tester |
| CN203519811U (en) * | 2013-11-01 | 2014-04-02 | 南京丹迪克科技开发有限公司 | Multiple-position electric energy quality analytical test calibrating device |
| CN104376383A (en) * | 2014-11-27 | 2015-02-25 | 东北大学 | Grid voltage monitoring and prediction system and method based on geographic information system |
| CN104700321A (en) * | 2015-03-16 | 2015-06-10 | 国家电网公司 | Analytical method of state running tendency of transmission and distribution equipment |
| CN105488592A (en) * | 2015-12-02 | 2016-04-13 | 国家电网公司 | Method for predicting generated energy of photovoltaic power station |
| JP2016138776A (en) * | 2015-01-27 | 2016-08-04 | 国立研究開発法人産業技術総合研究所 | Reference source module, electrical and electric apparatus, remote calibration method |
-
2017
- 2017-08-29 CN CN201710757110.6A patent/CN107607897B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102854486A (en) * | 2012-09-26 | 2013-01-02 | 湖北省电力公司电力科学研究院 | On-site initiative error-verification system for voltage transformer and method thereof |
| CN102981137A (en) * | 2012-11-27 | 2013-03-20 | 辽宁省电力有限公司电力科学研究院 | Remote intelligent verifying device and method of voltage monitor based on general packet radio service (GPRS)/global system for mobile communication (GSM) network |
| CN203054208U (en) * | 2013-01-15 | 2013-07-10 | 湖南省电力公司科学研究院 | Automatic inspection system of voltage monitor |
| CN203519811U (en) * | 2013-11-01 | 2014-04-02 | 南京丹迪克科技开发有限公司 | Multiple-position electric energy quality analytical test calibrating device |
| CN103558570A (en) * | 2013-11-06 | 2014-02-05 | 国家电网公司 | Portable voltage monitor field calibration tester |
| CN104376383A (en) * | 2014-11-27 | 2015-02-25 | 东北大学 | Grid voltage monitoring and prediction system and method based on geographic information system |
| JP2016138776A (en) * | 2015-01-27 | 2016-08-04 | 国立研究開発法人産業技術総合研究所 | Reference source module, electrical and electric apparatus, remote calibration method |
| CN104700321A (en) * | 2015-03-16 | 2015-06-10 | 国家电网公司 | Analytical method of state running tendency of transmission and distribution equipment |
| CN105488592A (en) * | 2015-12-02 | 2016-04-13 | 国家电网公司 | Method for predicting generated energy of photovoltaic power station |
Non-Patent Citations (2)
| Title |
|---|
| 刘文娟: ""基于GIS的电压质量监控软件的设计与开发"", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 王灿 等: ""湖南电网电能质量智能化监测分析系统"", 《湖南电力》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108663634A (en) * | 2018-07-10 | 2018-10-16 | 深圳市科列技术股份有限公司 | A kind of determination method and apparatus of power battery internal resistance |
| CN109407031A (en) * | 2018-10-09 | 2019-03-01 | 国网四川省电力公司电力科学研究院 | Voltage transformer fault recognition method based on time series hierarchical cluster |
| CN109407031B (en) * | 2018-10-09 | 2020-01-31 | 国网四川省电力公司电力科学研究院 | Voltage transformer fault identification method based on time sequence hierarchical clustering |
| CN111307199A (en) * | 2019-12-05 | 2020-06-19 | 北京普源精电科技有限公司 | Electronic measuring instrument with prediction device and prediction method of electronic measuring instrument |
| CN111505377A (en) * | 2020-05-16 | 2020-08-07 | 国网甘肃省电力公司兰州供电公司 | Voltage flicker real-time monitoring and early warning system and method |
| CN112033575A (en) * | 2020-06-29 | 2020-12-04 | 国网四川省电力公司电力科学研究院 | On-site calibration method and device for on-line monitoring system of power valve control storage battery pack |
| CN112505386A (en) * | 2020-08-25 | 2021-03-16 | 中国电力科学研究院有限公司 | Method and system for detecting current value of direct current charging pile |
| CN112505386B (en) * | 2020-08-25 | 2022-09-02 | 中国电力科学研究院有限公司 | Method and system for detecting current value of direct current charging pile |
| CN112924915A (en) * | 2021-01-27 | 2021-06-08 | 云南电网有限责任公司电力科学研究院 | Mutual calibration system and method for voltage monitor |
| CN112924915B (en) * | 2021-01-27 | 2023-11-21 | 云南电网有限责任公司电力科学研究院 | Mutual calibration system and method for voltage monitors |
| CN117518061A (en) * | 2024-01-04 | 2024-02-06 | 山东大学 | Electric measuring instrument detection data inspection system and method |
| CN117518061B (en) * | 2024-01-04 | 2024-03-29 | 山东大学 | Electric measuring instrument detection data inspection system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107607897B (en) | 2019-09-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107607897A (en) | A kind of voltage monitoring instrument on-line testing and prediction meanss and method | |
| Dong et al. | Frequency prediction of power systems in FNET based on state-space approach and uncertain basis functions | |
| CN104268367B (en) | Transformer state evaluation weight modification method and system based on multiple linear regression | |
| CN114640173A (en) | Early warning model of transformer and generator based on many characteristic quantities | |
| CN103200232B (en) | Belt conveyer scale remote support system and remote supporting method | |
| CN103197138A (en) | Intelligent electric meter with function of detecting power supply reliability and voltage qualified rate and detecting method thereof | |
| JP5556334B2 (en) | Power system reliability evaluation system | |
| KR101387061B1 (en) | Apparatus and method for operating facts(flexible ac transmission system) using pmu(phasor measurement unit) | |
| CN104197984A (en) | Fuel gas energy metering method | |
| CN112803592A (en) | Intelligent fault early warning method and system suitable for distributed power station | |
| CN102768029A (en) | Method and device for industrial control by aid of sag monitoring | |
| CN105137215B (en) | Medical equipment cost-benefit wireless monitoring analysis system and medical equipment cost-benefit wireless monitoring analysis method | |
| CN101788622B (en) | Debugging test method for automatic power-generating control system | |
| CN102901868A (en) | Method for electric energy acquisition system data checking | |
| CN115640915A (en) | Intelligent gas pipe network compressor safety management method and Internet of things system | |
| JP4037065B2 (en) | Water treatment management center and network system | |
| KR20150116966A (en) | Apparatus for water demand forecasting | |
| US11917010B2 (en) | Methods and internet of things (IoT) systems for gas purification management in storage and distribution station for smart gas | |
| CN113687609A (en) | Intelligent monitoring system and monitoring method for Internet of things applied to abnormal environment | |
| CN113326585B (en) | Energy efficiency abnormality early warning method and device for gas boiler and computer equipment | |
| EP3200306B1 (en) | Control device for hvdc system and method of operating the same | |
| CN106849792B (en) | The energy consumption calculation and energy conservation measure appraisal procedure of motor device and group system | |
| US10951059B2 (en) | Harmonic detection system | |
| CN111678246B (en) | Air conditioning equipment, control method, diagnosis method, control device and storage medium | |
| CN109190947A (en) | A kind of traffic control evaluation 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 |