CN110362782A - A kind of power distribution network robust state estimation method based on IGGIII - Google Patents
A kind of power distribution network robust state estimation method based on IGGIII Download PDFInfo
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Abstract
The power distribution network robust state estimation method based on IGGIII that the invention discloses a kind of, it the described method comprises the following steps: using branch current and root node voltage as state variable, and node voltage phasor and voltage magnitude being expressed as to the function of branch current and root node voltage；Electric current phasor, current amplitude and branch power are shown as corresponding branch current by measuring conversion table, and so as to directly be expressed as the function of branch current, injecting power is shown as Injection Current by measuring conversion table；It is made of corresponding to the Jacobian matrix element of node voltage phasor and node voltage amplitude branch impedance, ± 1 and 0；Available each corresponding residual error is measured using measuring value and the difference for measuring function；Measurement residuals are standardized, metric data is corresponded to by weight according to the residual error after standardization and is divided into three classes, iteratively solve correction amount.
Description
Technical field
The present invention relates to power distribution network dispatching automation fields, more particularly to one kind is based on IGGIII (Chinese Academy of Sciences's measurement
With the abbreviation of Geophysics Institute) power distribution network robust state estimation method.
Background technique
Traditional real-time measurement data are mainly derived from SCADA (data acquisition is controlled with monitoring) system in distribution system,
Three-phase voltage and power measurement are mainly disposed at high and low pressure side and the Partial key node of power distribution network transformer, in switching station, ring
Current magnitude measurement is only configured in net cabinet, cable branch box and on-pole switch, without voltage and power measurement.In addition, with
The development of active distribution network and the influence of bi-directional current, the state change of system is more frequent, the disequilibrium and bus of power grid
More stringent requirements are proposed to precision of state estimation for angle difference between voltage and line current phasor, and SCADA system is being adopted
There is error during collection, digital-to-analogue conversion etc., there is also interference sometimes, therefore certainly exist bad data, and with power distribution system
The expansion of system scale and therewith bring data acquisition information amount increase, and bad data can also increase.
It cannot be guaranteed that system can since power distribution network solves real-time measurement data by the biggish pseudo- metric data of addition error
Puppet measurement is all regarded as bad data in estimation procedure by the problem of sight, the Robust filter method in power transmission network, but in power distribution network
There is no enough redundancies to measure and substitute these bad datas, so estimation of the Robust filter method in power distribution network in power transmission network
Performance is unsatisfactory.
The state change of distribution system is more frequent, and SCADA system is due to wrong during acquisition, digital-to-analogue conversion etc.
Difference, there is also interference sometimes, therefore certainly exist bad data, expansion and bring number therewith with distribution system scale
According to increasing for acquisition information content, bad data can also increase.From the disequilibrium and busbar voltage and line current phase of power grid
More stringent requirements are proposed to precision of state estimation for angle difference between amount.
Summary of the invention
The power distribution network robust state estimation method based on IGGIII that the present invention provides a kind of, the present invention can be filtered effectively
Bad data in SCADA metric data improves precision of state estimation, described below:
A kind of power distribution network robust state estimation method based on IGGIII, the described method comprises the following steps:
Using branch current and root node voltage as state variable, and node voltage phasor and voltage magnitude are expressed as propping up
The function of road electric current and root node voltage；
Electric current phasor, current amplitude and branch power are shown as corresponding branch current by measuring conversion table, so as to
It is directly expressed as the function of branch current, injecting power is shown as Injection Current by measuring conversion table；
Corresponding to the Jacobian matrix element of node voltage phasor and node voltage amplitude by branch impedance, ± 1 and 0 group
At；
Available each corresponding residual error is measured using measuring value and the difference for measuring function；Measurement residuals are standardized,
Metric data is corresponded to weight according to the residual error after standardization to be divided into three classes, iteratively solves correction amount.
Metric data is corresponded to weight and is divided into three classes by the residual error according to after standardization specifically:
In formula, wiFor the diagonal element for measuring weight W；k0、k1For robust threshold value；di=(k1-ri)/(k1-k0) be it is smooth because
Son, 0≤di≤ 1, rs,iFor i-th of residual error in kth time iterative process.
The beneficial effect of the technical scheme provided by the present invention is that:
(1) present invention filters out influence of the bad data to estimated result during distribution network status estimation, improves shape
State estimated accuracy；
(2) it is not required to increase real-time measurement in the present invention, and in the biggish situation of pseudo- error in measurement, guarantees estimated result
Convergence and high-precision；
(3) the adaptive robust threshold value of normalization residual sum in the present invention can be quasi- according to the distribution of practical metric data
True division normal data, using measure and it is harmful measure, improve the robustness of state estimation.
Detailed description of the invention
Fig. 1 is the flow chart of the power distribution network robust state estimation provided by the invention based on IGGIII；
Fig. 2 is the measure configuration figure of improved 33 Node power distribution system of IEEE of embodiment；
Fig. 3 is the node voltage amplitude and phase angle curve schematic diagram that distinct methods are estimated.
Wherein, (a) is voltage magnitude curve synoptic diagram；It (b) is voltage phase angle curve synoptic diagram.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, embodiment of the present invention is made below further
Ground detailed description.
Embodiment 1
The purpose of the embodiment of the present invention is that for cannot accurate capture systems there are bad data in SCADA metric data
The problem of state, proposes a kind of power distribution network robust state estimation method based on IGGIII, and referring to Fig. 1, this method includes following
Step:
Step 101: using branch current and root node voltage as state variable, and by node voltage phasor and voltage magnitude
It is expressed as the function of branch current and root node voltage, as follows:
In formula,For node voltage phasor；For node h to the path between root node；Respectively
The resistance of each route and reactance on path；The voltage phase angle of node h when for (k-1) secondary iteration；For root
The real part of node voltage, imaginary part；For the measurement voltage magnitude and phase angle of node h；φ,It is a certain in b, c for a
Phase.
102: electric current phasor, current amplitude and branch power are shown as corresponding branch current by measuring conversion table, thus
It can be directly expressed as the function of branch current, injecting power is shown as Injection Current by measuring conversion table, so as to indicate
It is as follows for the function for flowing into, flowing out branch current:
In formula,Real and imaginary parts for the branch current acquired in iterative process；Repeatedly for kth -1 time
The voltage calculated value of node h during generation；The branch h-k current amplitude and phase angle respectively measured；For the branch h-k active power and reactive power of measurement；For the injection active power and nothing of node h
Function power.
Step 103: corresponding to the Jacobian matrix element of node voltage phasor and node voltage amplitude by branch impedance, ±
1 and 0 composition, can be obtained by following formula:
Corresponding Jacobian matrix after corresponding current phasor, current amplitude, branch power and injecting power measurement conversion
Element is made of ± 1 and 0.
Step 104: using measuring value and measure function difference it is available it is each measure corresponding residual error, can indicate
For；
R=[I-H (HTWH)-1HTW]Δz (9)
In formula, Δ z is measuring value and the difference for measuring function；H is Jacobian matrix；W is the weight for measuring function, to measure
The inverse of covariance of the error after error in measurement is transmitted；I is unit matrix.
Step 105: measurement residuals being standardized, i-th of residual error in kth time iterative process can indicate are as follows:
In formula, J=H (HTWH)-1HTThe diagonal element of W；L is the scale parameter of standardization, and its calculation formula is l2=
rTR-1R/ (m-n), m are to measure number, and n is state variable number, and ε is the number of a very little, prevent 1-Jii=0.
Step 106: metric data being corresponded to by weight according to the residual error after standardization and is divided into three classes, is indicated are as follows:
In formula, wiFor the diagonal element for measuring weight W；k0、k1For robust threshold value；di=(k1-ri)/(k1-k0) be it is smooth because
Son, 0≤di≤1。
Robust threshold value changes with residual self-adaptation in iterative process, designs as follows:
In formula, kt1=α rs,iAnd kt2=kt1+(|rs,i|-kt1)/3；α is set as 1.438.
Step 107: it is iterated solution correction amount according to the following formula:
Embodiment 2
Improved 33 Node power distribution system of IEEE of the embodiment of the present invention and measure configuration are as shown in figure 3, the section that PMU is configured
Point is node 12 and node 30, in order to prove the validity of proposed method, using calculation of tidal current as true value, and
The random error in measurement that the normal distribution of certain mean value, standard deviation is obeyed in superposition on the basis of this forms measuring value.It is missed using absolute
Difference indicates the precision of each node voltage amplitude estimation, and the precision of each node voltage phase angular estimation is indicated using relative error.
When the load puppet accuracy in measurement used in example is 36%, 6 voltage magnitude error in measurement of node is 50%, branch 5-
6 active power error in measurement is 75%, and the performance of the robust state estimation method proposed is under the conditions of there are bad data
All verified.
The advantages of in order to protrude proposed admixture estimation method, exponential type equivalence weight robust method is used
(EXP) it compares with robust state estimation (IIGGIII) proposed by the present invention.Table 1 is that two kinds of robust methods are estimated in first time
The weight of different metric data afterwards, Fig. 3 are the node voltage amplitude and phase angle curve that distinct methods are estimated.
The weight of the different metric data after estimating for the first time of table 1
As can be seen that residual distribution can just be divided into three parts, normal number for robust method proposed by the invention
According in save area from damage, load puppet metric data be in the area Jiang Quan and bad data is in superseded area, so estimated result is more
Close to trend true value.For exponential type equivalence weight robust method, the weight of bad data is measured higher than load puppet, but normal amount
The weight of measured data is big compared to more, so the voltage curve that estimation obtains compares load puppet metric data around normal data
For the higher situation of precision more close to trend true value, and in bad data region, it is more that estimated result deviates trend true value.It improves
The performance of Robust filter further demonstrate that the normalization adaptive robust threshold value of residual sum can be effectively reduced bad data to estimating
Count the influence of result.
Bibliography
[1] huge clouds, Wu Wenchuan, Cheng Xueqi wait power distribution network currents match state estimation [J] electricity of the based on multi-source information
Force system automation, 2012,36 (16): 53-57.
[2] a kind of bilinearity robust state estimation method [J] Automation of Electric Systems of Chen Yanbo, Ma Jin, 2015 (6):
41-47.
[3] Fu Yanlan, Chen Yanbo, Yao Rui wait based on the robust state estimation method of maximal index absolute value objective function
[J] electric power network technique, 2013 (11): 3166-3171.
[4] a kind of power distribution network Robust filter algorithm [J] electric system for handling leverage measurement of Li Hui, Yang Minghao is automatic
Change, 2005,29 (3): 31-35.
[5]Singh R,Pal B C,Jabr R A.Choice of estimator for distribution
system state estimation[J].IET Generation Transmission&Distribution,2009,3
(7):666-678.
[6]Wu J,He Y,Jenkins N.A robust state estimator for medium voltage
distribution networks[J].IEEE Trans.on Power Systems,2013,28(2):1008-1016.
[7] Yan Wei, Duan Lei, Yang Huanyan, Zhao Xia, Yu Juan are estimated based on the three-phase four-wire system distribution robust that intelligent electric meter measures
Count [J] Proceedings of the CSEE, 2015,35 (01): 60-67.
The embodiment of the present invention to the model of each device in addition to doing specified otherwise, the model of other devices with no restrictions,
As long as the device of above-mentioned function can be completed.
It will be appreciated by those skilled in the art that attached drawing is the schematic diagram of a preferred embodiment, the embodiments of the present invention
Serial number is for illustration only, does not represent the advantages or disadvantages of the embodiments.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (2)
1. a kind of power distribution network robust state estimation method based on IGGIII, which is characterized in that the described method comprises the following steps:
Using branch current and root node voltage as state variable, and node voltage phasor and voltage magnitude are expressed as branch electricity
The function of stream and root node voltage；
Electric current phasor, current amplitude and branch power are shown as corresponding branch current by measuring conversion table, so as to direct
It is expressed as the function of branch current, injecting power is shown as Injection Current by measuring conversion table；
It is made of corresponding to the Jacobian matrix element of node voltage phasor and node voltage amplitude branch impedance, ± 1 and 0；
Available each corresponding residual error is measured using measuring value and the difference for measuring function；Measurement residuals are standardized, according to
Metric data is corresponded to weight and is divided into three classes by the residual error after standardization, iteratively solves correction amount.
2. a kind of power distribution network robust state estimation method based on IGGIII according to claim 1, which is characterized in that institute
It states and metric data is corresponded to by weight according to the residual error after standardization is divided into three classes specifically:
In formula, wiFor the diagonal element for measuring weight W；k0、k1For robust threshold value；di=(k1-ri)/(k1-k0) be smoothing factor, 0
≤di≤ 1, rs,iFor i-th of residual error in kth time iterative process.
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