CN106655152B - A kind of State Estimation for Distribution Network measuring characteristic based on AMI - Google Patents
A kind of State Estimation for Distribution Network measuring characteristic based on AMI Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses a kind of State Estimation for Distribution Network measuring characteristic based on AMI, including：(1) system model, parameter are determined according to distribution net work structure, according to the characteristic of different measurements, is measured using the SCADA in typical day load curve and trend true-value simulation system and AMI is measured；(2) delay disposal is carried out to AMI metric data；(3) period that state of electric distribution network is estimated in the case that SCADA is measured and AMI metric data coordinates is determined；(4) determined by step (2) and step (3) on the basis of metric data, state estimation is carried out to power distribution network, determines the operating status of system.The present invention considers that the measurement characteristic of AMI proposes the delay of AMI metric data and the solution with the SCADA data measurement period the problems such as inconsistent in state estimation comprehensively, realizes the state of electric distribution network estimation of comprehensive utilization SCADA and AMI metric data.
Description
Technical field
The invention belongs to Power System Analysis fields more particularly to a kind of state of electric distribution network measuring characteristic based on AMI to estimate
Meter method.
Background technology
State estimation is the Core Feature of Energy Management System.According to application scenarios, state estimation can be divided into it is online and from
Line both of which.The time interval of power distribution network presence estimation depends on Utilities Electric Co., generally 10 to 60 minutes, main to use
In power distribution network scheduling and energy management, Real-time Decision and application are supported.Its main feature is that using real-time data analysis accurate as possible
The current scene of system；Off-line state estimation is according to all obtained measurements, and analysis power grid goes over the feelings of some period
Scape is mainly used for electrical network analysis, stealing analysis etc..
Estimate field in state of electric distribution network, has a large amount of scholars and carried out extensive research.According to state estimation model
Difference is broadly divided into using node voltage, branch current, branch power as state estimation algorithm of state variable etc..
Since the low-voltage network of user side lacks metric data, in state of electric distribution network estimation, usually only centering is press-fitted
Power grid carries out state estimation.Since 2009, State Grid Corporation of China for construction object, is pushed away with " all standing, full acquisition, control in full "
Dynamic intelligent electric energy meter is applied and power information acquisition system construction.In by the end of October, 2013 by, installation application intelligence electricity has been added up
1.73 hundred million, table of energy, power information acquisition system cover 1.73 hundred million families.The popularization of intelligent electric meter provides largely for low-voltage network
Redundancy measures.These abundant informations measurement type of power distribution network, can effectively solve the problem that for a long time because of measuring equipment configuration not
Foot, communication port it is not perfect and caused by the unobservable problem of a large amount of feeder lines and its branch.The data that AMI is acquired are reasonable
Be applied to state of electric distribution network estimation in can more acurrate, comprehensively estimate a variety of measurements and status information, to be higher
The application of rank provides complete, reliable, high-precision analysis data.
However, compared with SCADA is measured, AMI data have its unique measurement characteristic：
1) the measurement interval that distribution SCADA is measured is generally within 20s, most a few minutes；The interval that AMI is measured can be pre-
It first sets, generally 15min, 30min or hour.
2) there are two types of reading manners for intelligent electric meter.Foreign countries mostly use freezing method, that is, preset and freeze the moment, then read
It returns.Each table data have markers, but the time of reading back is uncertain.The reading data time of each table of synchronization postpones in 20s
Within, it is even lower.In China, only freeze the data at daily zero moment, remaining time takes trick read mode, that is, passes through meter
Amount center sends instruction and is read by turns to ammeter, runs through a table, then read another.Ammeter under one taiwan area is read one time
Time 10min-15min can reach according to the quantity of user.
3) class of accuracy that SCADA is measured is generally 2 or so；It is generally 0.5 for the AMI intelligent electric meter grades measured
Grade is even higher.
In practice, the measurement of AMI there are data markers is inconsistent or time delay, with the measurement period of SCADA data
The problems such as inconsistent.These problems are the critical issues for limiting AMI data applications and estimating in state of electric distribution network.
The present invention considers that the practical reading manner with AMI of application of country's state estimation, selective analysis read AMI numbers using trick
According to off-line state estimation.
Invention content
Currently, having the research of application start of the scholar around AMI metric data in state of electric distribution network estimation.But
There is research to fail to consider the measurement characteristic of AMI comprehensively.The reality of demand and AMI metric data that the present invention estimates from state of electric distribution network
Border characteristic is set out, and recruits the off-line state for reading AMI data to estimate application scenarios for application, it is proposed that AMI is measured in state estimation
Data delay and with SCADA data measure the period it is inconsistent the problems such as solution, realize comprehensive utilization SCADA and AMI
The state of electric distribution network of metric data is estimated.It is measured in conjunction with domestic AMI and reads present situation, analyze AMI delayed datas distribution before and after the processing
The error condition of net state estimation；Analyze influence of the adjustment in state estimation period to resultant error；Analysis measures noise to shape
The influence of state estimated result.
In order to solve the above-mentioned technical problem, a kind of State Estimation for Distribution Network measuring characteristic based on AMI of the present invention, packet
Include following steps：
Step 1: state of electric distribution network estimation scene is using the off-line state estimation for recruiting reading AMI data, according to power distribution network knot
Structure determines system model, parameter, is measured and AMI amounts using the SCADA in typical day load curve and trend true-value simulation system
It surveys, wherein the maximum noise that SCADA is measured is that the maximum noise that 2%, AMI is measured is 0.5%, the time that setting SCADA is measured
Between be divided into 1min, the time interval that AMI is measured is 15min；
Step 2: carrying out delay disposal to AMI metric data；
In the case where application recruits the off-line state for reading AMI data to estimate scene, system has collected the electric energy before and after each time point
Metric data, for active power, the average active power that the energy value of intelligent electric meter is calculated replaces instantaneous active work(
Rate,
In formula (1), to each intelligent electric meter, t0、t1The moment is read for its adjacent measurement；P is t0To t1The wink at each moment
When active power；W is t0、t1The difference that two moment ammeter electric energy measure；For t0To t1Average active power；
To average active powerData are modified, and are ignored network loss and are disregarded, the AMI of each user of the same taiwan area of low-pressure side
Average active power stacks up from bottom to up, obtains the superposed average active power of platform change；Meanwhile it being also equipped at platform change
Real-time SCADA is measured, and for the same point of synchronization, is obtained a real-time SCADA and is measured PSWith a superposition wattful power
Rate PA；With real-time measurement PSCorrect superposition active-power PA, i.e., a correction factor is multiplied by the superposition active power of platform changeCorrespondingly, the average active power for being assigned to each user is also multiplied by the correction factor；
The power factor value of user is 0.95-0.98, and in t0To t1The power factor of user is constant in period, root
According to user's average active powerThe average reactive power of user is calculated with power factor
Step 3: determining the period that state of electric distribution network is estimated in the case of SCADA measurements and the cooperation of AMI metric data, i.e.,
The AMI measuring values of prediction in the same time are measured with the SCADA at each moment；
The period that SCADA is measured is Ts, the period that AMI is measured is TA, TAMore than Ts；
(1) in T0Moment obtains the AMI electric energy metric data of user, each user is calculated by the method for step 2
Average active powerAnd average reactive power
(2) in T0To T0+TAIn period, moment T is measured for n-th of SCADA0+nTs, obtain the active measurements of SCADA
Value, according to T0Average active power between moment different userAccount for the active measurement P of moment SCADASPercentage, by T0+nTsWhen
It carves the active measuring values of SCADA and distributes to each user, acquire T0+nTsThe AMI active power predicted values of each user at moment；Pass through again
The power factor of user, is calculated T0+nTsThe AMI reactive power predicted values of each user at moment；
Step 4: carrying out state estimation to power distribution network, the operating status of system is determined, after step 2 and step 3 processing
SCADA metric data and AMI metric data as input, state estimation close to power distribution network determines the operating status of system.
Compared with prior art, the beneficial effects of the invention are as follows：
In practice, the measurement of AMI there are data markers is inconsistent or time delay, with the measurement period of SCADA data
The problems such as inconsistent.These problems are the critical issues for limiting AMI data applications and estimating in state of electric distribution network.It is enclosed with other scholars
Estimate that the research in field is compared in state of electric distribution network around AMI data, the present invention has following advantage：The measurement of AMI is considered comprehensively
Characteristic, main matching problem measurement delay including AMI and measured with SCADA, it is proposed that corresponding data processing and determining shape
The method of state cycle estimator.
Description of the drawings
Fig. 1 is 13 node system wiring diagram provided by the invention；
The state estimation voltage magnitude relative error at Fig. 2 is AMI data delays provided by the invention moment 1 before and after the processing；
The state estimation voltage phase angle absolute error at Fig. 3 is AMI data delays provided by the invention moment 1 before and after the processing；
Fig. 4 is n1 point voltage estimated values provided by the invention and m7 point SCADA virtual voltage measuring values.
Specific implementation mode
Technical solution of the present invention is described in further detail in the following with reference to the drawings and specific embodiments, it is described specific
Embodiment is only explained the present invention, is not intended to limit the invention.
A kind of State Estimation for Distribution Network being measured characteristic based on AMI of the present invention, is included the following steps：
Step 1: state of electric distribution network estimation scene is using the off-line state estimation for recruiting reading AMI data, according to power distribution network knot
Structure determines system model, parameter, according to the characteristic of different measurements, using in typical day load curve and trend true-value simulation system
SCADA measure and AMI measure, wherein SCADA measure maximum noise be 2%, AMI measurement maximum noise be 0.5%,
The time interval of SCADA measurements is set as 1min, the time interval that AMI is measured is 15min.
Step 2: carrying out delay disposal to AMI metric data；The off-line state for reading AMI data is recruited to estimate scene in application
Under, system has collected the electric energy metric data before and after each time point, and for active power, the energy value of intelligent electric meter is calculated
Obtained average active power replaces instantaneous active power,
In formula (1), to each intelligent electric meter, t0、t1The moment is read for its adjacent measurement；P is t0To t1The wink at each moment
When active power；W is t0、t1The difference that two moment ammeter electric energy measure；For t0To t1Average active power.
To average active powerData are modified, and are ignored network loss and are disregarded, the AMI of each user of the same taiwan area of low-pressure side
Average active power stacks up from bottom to up, obtains the superposed average active power of platform change；Meanwhile it being also equipped at platform change
Real-time SCADA is measured, and for the same point of synchronization, is obtained a real-time SCADA and is measured PSWith a superposition wattful power
Rate PA；With real-time measurement PSCorrect superposition active-power PA, i.e., a correction factor is multiplied by the superposition active power of platform changeCorrespondingly, the average active power for being assigned to each user is also multiplied by the correction factor.
According to historical statistical data, the power factor value of user is 0.95-0.98, it is believed that in t0To t1It is used in period
The power factor at family is constant, the present invention user's average active power acquiredThe average idle of user is calculated with power factor
Power
Step 3: determining the period that state of electric distribution network is estimated in the case of SCADA measurements and the cooperation of AMI metric data, i.e.,
The AMI measuring values of prediction in the same time are measured with the SCADA at each moment；The period that SCADA is measured is Ts, the period of AMI measurements
For TA, T in practiceAMore than Ts。
(1) in T0Moment obtains the AMI electric energy metric data of user, each user is calculated by the method for step 2
Average active powerAnd average reactive power
(2) in T0To T0+TAIn period, moment T is measured for n-th of SCADA0+nTs, obtain the active measurements of SCADA
Value, according to T0Average active power between moment different userAccount for the active measurement P of moment SCADASPercentage, by T0+nTsWhen
It carves the active measuring values of SCADA and distributes to each user, acquire T0+nTsThe AMI active power predicted values of each user at moment；Pass through again
The power factor of user, is calculated T0+nTsThe AMI reactive power predicted values of each user at moment；
Step 4: carrying out state estimation to power distribution network, the operating status of system is determined, including：With step 2 and step 3
As input, state estimation close to power distribution network determines the operation of system for treated SCADA metric data and AMI metric data
State.
Research material：
Using IEEE13 node systems as analysis example, as shown in Figure 1.M2 to m7 is medium voltage distribution network, each node setting
SCADA is measured, and n1 to n6 is user side, and each node setting AMI is measured.
Example includes each point accurate measurement data and trend true value at 1~15min totally 15 moment.Each measurement is added and is taken
From the random noise of Gaussian Profile to simulate the error of measurement, the maximum noise that wherein SCADA is measured is that 2%, AMI is measured most
Big noise is 0.5%.The time interval of SCADA measurements is set as 1min, the time interval that AMI is measured is 15min.When to simulate
Between the influence that postpones, order to recruit the AMI data for the different nodes for reading to obtain under different markers at the moment 1.Each node AMI
The arrival time of data differs 1 minute.
The AMI delayed datas at moment 1 are handled with the method in step 2, then shape is carried out to mesolow hybrid network
State is estimated.Fig. 2, Fig. 3 show the error condition of state estimation and trend true value before and after delay disposal.The result shows that due to AMI
Measurement is surviveed late, and directly carrying out state estimation using delayed data can be such that the error of state estimation greatly increases；With institute of the present invention
After the delay disposal method of proposition handles data, the error of state estimation is reduced within the scope of acceptable.
The calculating cycle of state estimation is determined with the method in step 3, i.e., is estimated with the SCADA measurements at 15 moment
The AMI at 15 moment is measured.By taking n1 nodes as an example, under different moments, the SCADA virtual voltage amounts of voltage estimated value and m7 points
The comparison of survey is as shown in Figure 4.Wherein, dotted portion is the voltage change profile after 15min.It is right respectively within 15 moment
Example carries out state of electric distribution network estimation, the voltage magnitude of different moments lower node and the relative error percentage of trend true value, with
And the absolute error situation of voltage phase angle and trend true value is as shown in table 1, table 2.
1 different moments of table each node state estimated voltage amplitude relative error percentage
2 different moments of table each node state estimated voltage phase angle absolute error percentage
As can be seen that estimating AMI measuring values using the above method from table 1, table 2, and then state estimation is carried out, can obtained
To the state outcome at multiple moment, the situation of change of system mode more can meticulously be described.Since n1~n6 nodes use
AMI is measured, and performance number is estimated value, so error ratio m2~m7 nodes are big, but still within the acceptable range.It needs
It is noted that invention describes a kind of easy methods for carrying out pseudo- measurement modeling by AMI data, if improving load prediction
Precision, the result of calculation of the method for the present invention will be more accurate, and the operating status of electric system is retouched what is be more fully accurate
It states.
In order to further verify effectiveness of the invention, 100 groups of data for meeting above-mentioned accuracy in measurement of stochastic simulation.Every group
Data include all types of measurements of 13 nodes in 15 minutes in example, and the measurement noise of different nodes is different.Due to number
According to more, the present invention is by taking the larger n1 nodes of voltage magnitude relative error as an example, and description noise condition is to precision of state estimation
It influences.Table 3 reflects the variation feelings for the percentage that the n1 node voltage amplitude relative errors under noise scenarios are measured at 100 kinds
Condition.As a whole, the fluctuation range for the voltage magnitude relative error that noise variation is brought is between 0.10% to 0.20%.
N1 point voltage magnitude relative errors under 3 100 groups of measurement noise combinations of table
As shown in Table 3, the voltage magnitude error at moment 3 to moment 6 is generally smaller.During this period of time node load changes
Slowly, the error brought with the AMI of step 3 kind measurements prediction technique is smaller, is influenced on the precision of state estimation also smaller.
Although above in conjunction with attached drawing, invention has been described, and the invention is not limited in above-mentioned specific implementations
Mode, the above mentioned embodiment is only schematical, rather than restrictive, and those skilled in the art are at this
Under the enlightenment of invention, without deviating from the spirit of the invention, many variations can also be made, these belong to the present invention's
Within protection.
Claims (1)
1. a kind of State Estimation for Distribution Network measuring characteristic based on AMI, it is characterised in that：Include the following steps：
Step 1: state of electric distribution network estimation scene is using the off-line state estimation for recruiting reading AMI data, it is true according to distribution net work structure
Determine system model, parameter, measured using the SCADA in typical day load curve and trend true-value simulation system and AMI is measured,
In, it is 0.5% that the maximum noise of SCADA measurements, which is the maximum noise that 2%, AMI is measured, the time interval that setting SCADA is measured
For 1min, the time interval that AMI is measured is 15min；
Step 2: carrying out delay disposal to AMI metric data；
In the case where application recruits the off-line state for reading AMI data to estimate scene, system has collected the electric energy measurement before and after each time point
Data, for active power, the average active power that the energy value of intelligent electric meter is calculated replaces instantaneous active power,
In formula (1), to each intelligent electric meter, t0、t1The moment is read for its adjacent measurement；P is t0To t1The instantaneous of each moment has
Work(power；W is t0、t1The difference that two moment ammeter electric energy measure；For t0To t1Average active power；
To average active powerData are modified, and are ignored network loss and are disregarded, and the AMI of each user of the same taiwan area of low-pressure side is averaged
Active power stacks up from bottom to up, obtains the superposed average active power of platform change；Meanwhile it being also equipped at platform change in real time
SCADA measure, for the same point of synchronization, obtain a real-time SCADA measurements PSWith a superposition active-power PA；
P is measured with real-time SCADASCorrect superposition active-power PA, i.e., a correction factor is multiplied by the superposition active power of platform changeCorrespondingly, the average active power for being assigned to each user is also multiplied by the correction factor；
The power factor value of user is 0.95-0.98, and in t0To t1The power factor of user is constant in period, according to
Family average active powerThe average reactive power of user is calculated with power factor
Step 3: determining the period that state of electric distribution network is estimated in the case of SCADA measurements and the cooperation of AMI metric data, that is, use often
The SCADA at a moment measures the AMI measuring values of prediction in the same time；
The period that SCADA is measured is Ts, the period that AMI is measured is TA, TAMore than Ts；
(1) in T0Moment obtains the AMI electric energy metric data of user, being averaged for each user is calculated by the method for step 2
Active powerAnd average reactive power
(2) in T0To T0+TAIn period, moment T is measured for n-th of SCADA0+nTs, obtain the active measuring values of SCADA, root
According to T0Average active power between moment different userIt accounts for the moment real-time SCADA and measures PSPercentage, by T0+nTsMoment
The active measuring values of SCADA distribute to each user, acquire T0+nTsThe AMI active power predicted values of each user at moment；Pass through use again
The power factor at family, is calculated T0+nTsThe AMI reactive power predicted values of each user at moment；
Step 4: carrying out state estimation to power distribution network, the operating status of system is determined, treated with step 2 and step 3
As input, state estimation close to power distribution network determines the operating status of system for SCADA metric data and AMI metric data.
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| CN107453357B (en) * | 2017-08-24 | 2020-08-14 | 天津大学 | Power distribution network state estimation method based on layered solution |
| CN108020716B (en) * | 2017-11-17 | 2021-04-13 | 杭州海兴电力科技股份有限公司 | Method for accurately timing time of terminal based on distributed clock source |
| CN108255951B (en) * | 2017-12-18 | 2021-10-08 | 国网上海市电力公司 | Medium and low voltage distribution network state estimation pseudo quantity measurement determination method based on data mining |
| CN108649574A (en) * | 2018-06-15 | 2018-10-12 | 华北电力大学 | A kind of power distribution network fast state method of estimation based on three kinds of metric data |
| CN111064180B (en) * | 2019-10-23 | 2024-01-26 | 国网天津市电力公司电力科学研究院 | Medium-voltage distribution network topology detection and identification method based on AMI (advanced mechanical arm) power flow matching |
| CN113962053A (en) * | 2021-11-08 | 2022-01-21 | 国网江苏省电力有限公司无锡供电分公司 | Power distribution network state evaluation method based on multi-section intelligent instrument data |
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| US8527653B2 (en) * | 2010-11-08 | 2013-09-03 | At&T Mobility Ii Llc | GGSN front end processor (GFEP) system for SCADA inter-domain communications |
| CN105071388B (en) * | 2015-08-14 | 2017-06-13 | 贵州电网公司信息通信分公司 | A kind of State Estimation for Distribution Network based on Maximum-likelihood estimation |
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