CN106655152A - Power distribution network state estimation method based on AMI measurement characteristics - Google Patents
Power distribution network state estimation method based on AMI measurement characteristics 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- G06Q50/06—Energy or water supply
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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Abstract
The invention discloses a power distribution network state estimation method based on AMI measurement characteristics, comprising the following steps: (1) determining a system model and parameters thereof according to the structure of a power distribution network, and simulating SCADA measurement and AMI measurement in the system through use of a typical daily load curve and a true value of power flow according to the characteristics of different measurement; (3) determining the cycle of power distribution network state estimation under the condition that SCADA measurement data and AMI measurement data match each other; and (4) on the basis of the measurement data determined in steps (2) and (3), estimating the state of the power distribution network, and determining the running state of the system. By fully considering the measurement characteristics of AMI, the invention presents a solution to the problem that AMI measurement data delay and SCADA data measurement cycle are inconsistent in state estimation. Power distribution network state estimation through comprehensive use of SCADA and AMI measurement data is realized.
Description
Technical field
The invention belongs to Power System Analysis field, more particularly to a kind of state of electric distribution network for measuring characteristic based on AMI is estimated
Meter method.
Background technology
State estimation is the Core Feature of EMS.According to application scenarios, state estimation can be divided into it is online and from
Line both of which.The time interval that power distribution network presence is estimated 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.It is characterized in using accurately data analysis in real time as far as possible
The current scene of system；Off-line state estimates it is, according to all measurements for obtaining, to analyze the feelings that electrical network goes over some time period
Scape, is mainly used in electrical network analysis, stealing analysis etc..
Estimate field in state of electric distribution network, existing a large amount of scholars have carried out extensive research.According to state estimation model
Difference, is broadly divided into state estimation algorithm with node voltage, branch current, branch power as state variable etc..
Because the low-voltage network of user side lacks metric data, in state of electric distribution network is estimated, generally only centering press-fitting
Electrical network carries out state estimation.Since 2009, State Grid Corporation of China, as construction object, is pushed away with " all standing, full collection, control in full "
Dynamic intelligent electric energy meter application and power information acquisition system construction.By in by the end of October, 2013, add up that application intelligence electricity is installed
1.73 hundred million, table of energy, power information acquisition system covers 1.73 hundred million families.The popularization of intelligent electric meter, provides in a large number for low-voltage network
Redundancy is measured.These abundant informations measurement type of power distribution network, can effectively solve the problem that for a long time because measuring equipment is configured not
Foot, communication port imperfection and the unobservable problem of a large amount of feeder lines and its branch that cause.The data that AMI is gathered are reasonable
Be applied to during state of electric distribution network is estimated and can more accurately and comprehensively estimate various measurements and status information, so as to for higher
The application of rank provides complete, reliable, high-precision analyze data.
However, compared with SCADA is measured, AMI data have the measurement characteristic of its uniqueness：
1) measurement that distribution SCADA is measured is spaced typically within 20s, most a few minutes；The interval that AMI is measured can be pre-
First set, generally 15min, 30min or hour.
2) intelligent electric meter has two kinds of reading manners.It is external to adopt freezing method more, that is, preset and freeze the moment, then read
Return.Each table data have markers, but the time of reading back does not know.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 point moment, remaining time takes trick read mode, i.e., by meter
Amount center sends instruction and ammeter is read by turns, runs through a table, then reads another.Ammeter under one Ge Tai areas reads 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；The intelligent electric meter grade measured for AMI is generally 0.5
Level even more high.
In practice, there is the measurement cycle of inconsistent data markers or time delay and SCADA data in the measurement of AMI
Inconsistent the problems such as.These problems are to limit the key issue that AMI market demands are estimated in state of electric distribution network.
The present invention considers actual and AMI the reading manner of the application of country's state estimation, and selective analysis reads AMI numbers using trick
According to off-line state estimate.
The content of the invention
At present, existing scholar surrounds the research of application start of the AMI metric data in state of electric distribution network estimation.But
There is research to fail to consider the measurement characteristic of AMI comprehensively.Demand and the reality of 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 cycle 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.Measure with reference to domestic AMI and read present situation, distribution after analysis AMI delayed data before processings
The error condition that net state is estimated；Impact of the adjustment in analysis state estimation cycle to resultant error；Analysis measurement noise is to shape
The impact of state estimated result.
In order to solve above-mentioned technical problem, a kind of State Estimation for Distribution Network that characteristic is measured based on AMI of the present invention, bag
Include following steps：
Step one, state of electric distribution network estimate that scene is to estimate using the off-line state for recruiting reading AMI data, are tied according to power distribution network
Structure determines system model, parameter, is measured using the SCADA in typical day load curve and trend true-value simulation system and AMI amounts
Survey, wherein, it is 0.5% that the maximum noise that SCADA is measured is the maximum noise that 2%, AMI is measured, and sets the time that SCADA is measured
At intervals of 1min, the time interval that AMI is measured is 15min；
Step 2, delay disposal is carried out to AMI metric data；
Recruit in application and read under the off-line state estimation scene of AMI data, system has collected the electric energy before and after each time point
Metric data, for active power, by the calculated average active power of the energy value of intelligent electric meter instantaneous active work(is replaced
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 are measured；For t0To t1Average active power；
To average active powerData are modified, and ignore network loss and disregard, the AMI of each user in same 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 is also equipped with platform change
In 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；Use real-time measurement PSAmendment superposition active-power PA, i.e., a correction factor is multiplied by the superposition active power that platform becomesAccordingly, 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 time period, root
According to user's average active powerWith the average reactive power that power factor calculates user
Step 3, the cycle for determining state of electric distribution network estimation in the case that SCADA is measured and AMI metric data coordinates, i.e.,
Prediction AMI measuring values in the same time are measured with the SCADA at each moment；
The cycle that SCADA is measured is Ts, the cycle that AMI is measured is TA, TAMore than Ts；
(1) in T0At the moment, the AMI electric energy metric data of user is obtained, each user is calculated by the method for step 2
Average active powerAnd average reactive power
(2) in T0To T0+TAIn time period, for n-th SCADA measures moment T0+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
Carve the active measuring values of SCADA and distribute to each user, try to achieve 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, state estimation is carried out to power distribution network, determine the running status of system, after being processed with step 2 and step 3
SCADA metric data and AMI metric data as input, to the nearly state estimation of power distribution network, determine the running status of system.
Compared with prior art, the invention has the beneficial effects as follows：
In practice, there is the measurement cycle of inconsistent data markers or time delay and SCADA data in the measurement of AMI
Inconsistent the problems such as.These problems are to limit the key issue that AMI market demands are estimated in state of electric distribution network.Enclose 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, the main matching problem for postponing including AMI measurements and measuring with SCADA, it is proposed that corresponding data processing and determination shape
The method of state cycle estimator.
Description of the drawings
Fig. 1 is the 13 node system wiring diagrams that the present invention is provided；
Fig. 2 is the state estimation voltage magnitude relative error at moment 1 after the AMI data delay before processings that the present invention is provided；
Fig. 3 is the state estimation voltage phase angle absolute error at moment 1 after the AMI data delay before processings that the present invention is provided；
Fig. 4 is the n1 point voltages estimate and m7 point SCADA virtual voltage measuring values that the present invention is provided.
Specific embodiment
Below in conjunction with the accompanying drawings technical solution of the present invention is described in further detail with specific embodiment, described is concrete
Embodiment is only explained to the present invention, not to limit the present invention.
A kind of State Estimation for Distribution Network that characteristic is measured based on AMI of the present invention, is comprised the following steps：
Step one, state of electric distribution network estimate that scene is to estimate using the off-line state for recruiting reading AMI data, are tied according to power distribution network
Structure determines system model, parameter, according to the different characteristics for measuring, 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 measure 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, delay disposal is carried out 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, for active power, the energy value of intelligent electric meter is calculated
The average active power for obtaining 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 are measured；For t0To t1Average active power.
To average active powerData are modified, and ignore network loss and disregard, the AMI of each user in same 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 is also equipped with platform change
In 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；Use real-time measurement PSAmendment superposition active-power PA, i.e., a correction factor is multiplied by the superposition active power that platform becomesAccordingly, 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 t1Use in time period
The power factor at family is constant, the present invention user's average active power tried to achieveThe average idle of user is calculated with power factor
Power
Step 3, the cycle for determining state of electric distribution network estimation in the case that SCADA is measured and AMI metric data coordinates, i.e.,
Prediction AMI measuring values in the same time are measured with the SCADA at each moment；The cycle that SCADA is measured is Ts, the cycle that AMI is measured
For TA, T in practiceAMore than Ts。
(1) in T0At the moment, the AMI electric energy metric data of user is obtained, each user is calculated by the method for step 2
Average active powerAnd average reactive power
(2) in T0To T0+TAIn time period, for n-th SCADA measures moment T0+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
Carve the active measuring values of SCADA and distribute to each user, try to achieve 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, state estimation is carried out to power distribution network, determine the running status of system, including：With step 2 and step 3
SCADA metric data and AMI metric data after process, to the nearly state estimation of power distribution network, determines the operation of system as input
State.
Research material：
Using IEEE13 node systems as example is analyzed, as shown in Figure 1.M2 to m7 is medium voltage distribution network, and each node is arranged
SCADA is measured, and n1 to n6 is user side, and each node arranges AMI and measures.
Each point accurate measurement data and trend true value of the example comprising 1～15min totally 15 moment.Addition clothes are measured to each
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.For during simulation
Between the impact that postpones, order to recruit the AMI data of different nodes for reading to obtain under different markers at the moment 1.Each node AMI
The due in of data differs 1 minute.
The AMI delayed datas at moment 1 are processed 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.As a result show, due to AMI
Measurement is surviveed late, and directly carrying out state estimation using delayed data greatly increases can the error of state estimation；Use institute of the present invention
After the delay disposal method of proposition is processed data, the error of state estimation is reduced in the range of acceptable.
Determine the calculating cycle of state estimation with the method in step 3, i.e., measured with the SCADA at 15 moment and estimated
The AMI at 15 moment is measured.By taking n1 nodes as an example, not in the same time under, the SCADA virtual voltage amounts of its voltage estimate and m7 points
The contrast 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, not the relative error percentage of the voltage magnitude of lower node and trend true value in the same time, with
And the absolute error situation of voltage phase angle and trend true value is as shown in table 1, table 2.
The not each node state estimated voltage amplitude relative error percentage in the same time of table 1
The not each node state estimated voltage phase angle absolute error percentage in the same time of table 2
As can be seen that estimating AMI measuring values using said method from table 1, table 2, and then state estimation is carried out, can be obtained
To the state outcome at multiple moment, can the more meticulously situation of change of descriptive system state.Because n1～n6 nodes are adopted
AMI is measured, and its performance number is estimate, so error ratio m2～m7 nodes are big, but still within the acceptable range.Need
It is noted that invention describes it is a kind of it is easy carry out the pseudo- method for measuring modeling by AMI data, if improving load prediction
Precision, the result of calculation of the inventive method will be more accurate, and the running status of power system will obtain more fully accurately retouching
State.
In order to further verify effectiveness of the invention, 100 groups of stochastic simulation meets the data of above-mentioned accuracy in measurement.Per 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
Of the invention by taking the larger n1 nodes of voltage magnitude relative error as an example according to more, description noise condition is to precision of state estimation
Affect.Table 3 reflects the change feelings of the percentage of the n1 node voltage amplitude relative errors under 100 kinds of measurement noise scenes
Condition.As a whole, the fluctuation range of the voltage magnitude relative error that noise change brings is between 0.10% to 0.20%.
N1 point voltage amplitude 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 universal less.During this period of time node load change
Slowly, measure the error that Forecasting Methodology brings with the AMI of step 3 kind less, also less is affected on the precision of state estimation.
Although above in conjunction with accompanying drawing, invention has been described, the invention is not limited in above-mentioned being embodied as
Mode, above-mentioned specific embodiment is only schematic, rather than restricted, and one of ordinary skill in the art is 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. it is a kind of based on AMI measure characteristic State Estimation for Distribution Network, it is characterised in that：Comprise the following steps：
Step one, state of electric distribution network estimate that scene is to estimate using the off-line state for recruiting reading AMI data, 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, its
In, it is 0.5% that the maximum noise that SCADA is measured is the maximum noise that 2%, AMI is measured, and sets the time interval that SCADA is measured
For 1min, the time interval that AMI is measured is 15min；
Step 2, delay disposal is carried out to AMI metric data；
Recruit in application and read under the off-line state estimation scene of AMI data, system has collected the electric energy before and after each time point and measured
Data, for active power, by the calculated average active power of the energy value of intelligent electric meter instantaneous active power are replaced,
In formula (1), to each intelligent electric meter, t0、t1The moment is read for its adjacent measurement；P is t0To t1Each moment instantaneously has
Work(power；W is t0、t1The difference that two moment ammeter electric energy are measured；For t0To t1Average active power；
To average active powerData are modified, and ignore network loss and disregard, and the AMI of each user in same area of low-pressure side is average
Active power stacks up from bottom to up, obtains the superposed average active power of platform change；Meanwhile, it is also equipped with platform change in real time
SCADA measure, for the same point of synchronization, obtain a real-time SCADA measurement PSWith a superposition active-power PA；
Use real-time measurement PSAmendment superposition active-power PA, i.e., a correction factor is multiplied by the superposition active power that platform becomesPhase
Answer, 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 time period, according to
Family average active powerWith the average reactive power that power factor calculates user
Step 3, determine the cycle that state of electric distribution network is estimated in the case that SCADA is measured and AMI metric data coordinates, i.e., with every
The SCADA at individual moment measures prediction AMI measuring values in the same time；
The cycle that SCADA is measured is Ts, the cycle that AMI is measured is TA, TAMore than Ts；
(1) in T0At the moment, the AMI electric energy metric data of user is obtained, the average of each user is calculated by the method for step 2
Active powerAnd average reactive power
(2) in T0To T0+TAIn time period, for n-th SCADA measures moment T0+nTs, obtain the active measuring values of SCADA, root
According to T0Average active power between moment different userAccount for the active measurement P of moment SCADASPercentage, by T0+nTsMoment
The active measuring values of SCADA distribute to each user, try to achieve T0+nTsThe AMI active power predicted values of each user at moment；Again by using
The power factor at family, is calculated T0+nTsThe AMI reactive power predicted values of each user at moment；
Step 4, state estimation is carried out to power distribution network, determine the running status of system, after being processed with step 2 and step 3
SCADA metric data and AMI metric data, to the nearly state estimation of power distribution network, determine the running status of system as input.
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| CN107453357A (en) * | 2017-08-24 | 2017-12-08 | 天津大学 | A kind of State Estimation for Distribution Network based on hierarchical solving |
| CN107453357B (en) * | 2017-08-24 | 2020-08-14 | 天津大学 | Power distribution network state estimation method based on layered solution |
| CN108020716A (en) * | 2017-11-17 | 2018-05-11 | 杭州海兴电力科技股份有限公司 | Method during based on distributed clock source to accurate pair of terminal |
| CN108255951A (en) * | 2017-12-18 | 2018-07-06 | 国网上海市电力公司 | Method is determined based on the low and medium voltage distribution network state estimation puppet measurement of 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 |
| CN111064180A (en) * | 2019-10-23 | 2020-04-24 | 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 power flow matching |
| 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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