CN103840454B - The parameter identification method of active linear network known to network structure - Google Patents
The parameter identification method of active linear network known to network structure Download PDFInfo
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Abstract
The present invention provides a kind of parameter identification methods of active linear network known to network structure, it includes the sensitivity for analyzing each parameter of active linear network, each parameter is divided into highly sensitive parameter and muting sensitivity parameter, each parameter of the active linear network is recognized by the way of global model identification, wherein highly sensitive parameter is end value in the value that this step obtains, according to the value of the obtained highly sensitive parameter, and network constraint is increased to the active linear network, recognize the muting sensitivity parameter in the active linear network, the embodiment of the present invention, using substep Identification Strategy, it can be accurate, the parameter of effective identification active linear network, with the parameter of ambiguity especially in active linear network, and parameters obtained is capable of the truth of the reaction electric system of appropriateness, the dynamic of accurate characterization electric system Characteristic.
Description
Technical field
The present invention relates to a kind of parameters of active linear network known to technical field of electric power more particularly to network structure to distinguish
Knowledge method.
Background technique
In technical field of electric power, the operating status using the analysis electric system of power system dynamic equivalence technology is to grind at present
The hot spot direction studied carefully, key point among these are to establish accurate, simple dynamic equivalent model.
Currently, the coherency method and mode method method of dynamic equivalent are mostly used for off-line analysis, and online dynamic security
Analysis often needs to make external system online dynamic equivalent, at this moment needs using identification equivalence method: first determining system equivalent
Model, model parameter are obtained by identification.PMU of the existing power grid dynamic equivalent parameter usually according to power grid installation
The voltage and current vector and active reactive of (Precision Measurement Unit, precision measurement unit) acquisition are distinguished
Know.Specifically, classifying according to the time of running number of acquisition data, identification equivalent method mainly has following two major class.
One kind is recognized for the voltage and current vector and active reactive acquired according to multiple time of running critical points.It is such suitable
The case where equivalent Internal system parameters for multiple times of running are constant, and only equivalent exterior changes.Wherein, multiple
The time difference is shorter between the time of running, and the constant probability of equivalent Internal system parameters is bigger, but equivalent exterior variation may
Very little, solves inverse matrix morbid state, and parameter identification is unstable；The time difference is bigger between multiple times of running, equivalent internal system
The probability of Parameters variation is bigger, it is possible to be unsatisfactory for the identification basis of method.
The another kind of synchronous dynamic information to acquire according to a time of running critical point is recognized.When based on an operation
The method for carving data does not have supposed premise, is not influenced by Internal system parameters variation, and the information content of required acquisition is less, identification
Accuracy it is higher.Power grid high voltage appearance, high current background under, the switching of the maximum such as load of occurrence probability, open circuit
Device cut-offs the serial systems internal structure changes such as closure, can generate multidate information in equivalent network port, utilize Dynamic Signal
Recognizing power grid dynamic linear Equivalent Model parameter has certain feasibility.
But current power grid dynamic linear Equivalent Model is fairly simple, it is difficult to the complete shadow for considering system dynamic element
It rings, is unable to the dynamic characteristic of accurate characterization power grid, in order to guarantee equivalent adaptability and validity, it is necessary to improve Equivalent Model essence
Degree, but accurate Equivalent Model inevitably increases identified parameters, not only makes that discrimination method is sufficiently complex, identification effect
Rate is low, and influences the stability of identification process and the accuracy of result, and the parameter be easy to causeing has ambiguity, into
One step increases identification difficulty, and when existing middle processing parameter ambiguity, mainly by be set as representative value or sentence it is special
Penalty constrain it as the means such as certain representative value, but be not possible to accurate recognition and go out ambiguity parameter.
Summary of the invention
In view of this, the present invention provides a kind of parameter identification method of active linear network known to network structure, it can
With the parameter of accurate and effective identification active linear network, with the parameter of ambiguity especially in active linear network, and
Parameters obtained is capable of the truth of the reaction electric system of appropriateness, the dynamic characteristic of accurate characterization electric system.
The present invention provides a kind of parameter identification method, carries out for the parameter to active linear network known to network structure
Identification, comprising:
Each parameter is divided into highly sensitive parameter and low sensitive by the sensitivity for analyzing each parameter of active linear network
Spend parameter；
Each parameter of the active linear network is recognized by the way of global model identification, wherein highly sensitive parameter is in this step
Obtained value is end value；
Increase network constraint, identification according to the value of the obtained highly sensitive parameter, and to the active linear network
Muting sensitivity parameter in the active linear network.
Further, described that network constraint is increased to the active linear network, and according to the obtained highly sensitive ginseng
Several values recognizes the muting sensitivity parameter in the active linear network, comprising:
When obtaining the active linear network in the presence of disturbance, the voltage disturbance signal of the port of the active linear network
△ v' and current disturbing signal △ i'；
The value for the highly sensitive parameter that identification obtains is brought intoA is calculated first0,
a1,…,aKAnd b0,b1,…,bK, then according to the parameter a being calculated0,a1,…,aKAnd b0,b1,…,bK, A is calculated0、
A1、B0And B1, finally A is calculated in basis0、A1、B0And B1, equivalent parameters R is calculatedeq1And Leq1；
It is calculated using least square methodParameter a0,a1,…,aKAnd b0,b1,…,
bK, then according to the parameter a being calculated0,a1,…,aKAnd b0,b1,…,bK, A is calculated0、A1、B0And B1, finally according to meter
Calculation obtains A0、A1、B0And B1, equivalent parameters R is calculatedeq2And Leq2；
According to Req1、Leq1、Req2And Leq2, obtain the value of muting sensitivity parameter；
Wherein, when differential equation order K is even number, then
When K is odd number, then
Wherein,
Further, the mode of the global model identification is Chemistry.
Further, before the acquisition voltage disturbance signal △ v' and current disturbing signal △ i', further includes:
Sampling step: the voltage and current of the port of the active linear network is sampled, corresponding voltage is obtained
Digital signal v' and current digital signal i'；
Detecting step: the voltage digital signal v' or current digital signal i' is detected with the presence or absence of disturbance；
It disturbs if it does not exist, then continues to execute sampling step and detecting step；
It disturbs if it exists, then extracts corresponding voltage disturbance signal △ v' and current disturbing signal △ i'.
Beneficial effects of the present invention:
The embodiment of the present invention is realized the identification of each parameter of active linear network using substep Identification Strategy, i.e., passed through first
The trace sensitivity for analyzing equivalent network parameter, determines the easy identification degree of each parameter, then carries out global model identification, and acquisition is easily distinguished
The exact value for knowing parameter (i.e. highly sensitive parameter), then accurately obtains active linear network by increasing network global restriction
In more difficult identification i.e. with the parameter of ambiguity, so that each parameter of accurate and effective identification active linear network, can paste
It reacts the real conditions of electric system with cutting, carries out effective monitoring to change to power grid, guarantee its safe and reliable, economic fortune
Row.
Detailed description of the invention
The invention will be further described with reference to the accompanying drawings and examples:
Fig. 1 is the knot of the embodiment of the parameter identification device of active linear network known to network structure provided by the invention
Structure schematic diagram.
Fig. 2 is the structural schematic diagram of the embodiment of the disturbing signal detecting module in Fig. 1.
Fig. 3 is the realization principle figure of the sef-adapting filter in Fig. 2.
Fig. 4 is the structural schematic diagram of the embodiment of the disturbing signal extraction module in Fig. 1.
Fig. 5 is the structural schematic diagram of the embodiment of the active linear network parameter identification module in Fig. 1.
Fig. 6 is the structural schematic diagram of single-phase active Linear Network to be identified.
Fig. 7 is the waveform diagram for motivating △ u.
Fig. 8 is the trace sensitivity schematic diagram of each parameter.
Specific embodiment
Referring to FIG. 1, being the reality of the parameter identification device of active linear network known to network structure provided by the invention
Apply the structural schematic diagram of example comprising: frequency locking lock-in sample module 1, disturbing signal detecting module 2, disturbing signal extraction module 3
With active linear network parameter identification module 4.
Wherein, frequency locking lock-in sample module 1, for acquire the port of active linear network known to structure electric current i and
Voltage v, and corresponding current digital signal i' is exported to disturbing signal detecting module 2, the corresponding current digital signal i' of output
With voltage digital signal v' to disturbing signal extraction module 3.
In a kind of implementation, current analog that the Current Transformer Secondary coil of the port of active linear network is come
The signal i and voltage analog signal v come from secondary coil of voltage transformer on the bus of port, is respectively fed to frequency locking lock-in sample
It is handled in module 1, exports synchronous current digital signal i' and voltage digital from frequency locking lock-in sample module 1 after processing
Signal v'.
Wherein, disturbing signal detecting module 2, the current digital signal i' transported to for detecting frequency locking lock-in sample module 1
Or voltage digital signal v', with the presence or absence of disturbance, if there are disturbance situations through detection, the existing instruction of output disturbance signal is extremely
Disturbing signal extraction module 3；If disturbance situation, the instruction that output disturbance signal is not present to disturbing signal is not present through detection
Extraction module 3.
As shown in Fig. 2, disturbing signal detecting module 2 includes: adaptive sine filter 21, in a kind of implementation
One comparator 22 and the second comparator 23.When work, the current digital signal i' exported from frequency locking lock-in sample module 1 is accessed
In adaptive sine filter 21, by the adaptive sine filter 21 output current digital signal i' and filter sinusoidal signal
Between error e, then in first comparator 22 by error e and first error definite value eset1It is compared, if e > eset1, and
Relative error and ∑ in second comparator 23 | e(j) | with the second error definite value eset2If ∑ | e(j) | > eset2, then believe to disturbance
The existing instruction of number 3 output disturbance signal of extraction module；If any be judged as is less than in first comparator or the second comparator
It is equal to, then the instruction being not present to 3 output disturbance signal of disturbing signal extraction module.The above are real based on current digital signal i'
Existing Disturbance Detection, wherein to those skilled in the art, realizing that Disturbance Detection can use based on voltage digital signal v'
Similar mode.
In Fig. 2, adaptive sine filter 21 a kind of implementation principle can with as shown in figure 3, its principle be by
The sinusoidal signal of one and current digital signal i' identical frequency approaches current digital signal, obtain current signal the frequency at
Point amplitude and phase, wherein the mathematical expression of adaptive sine filter can be with are as follows:
y(t)=Acos(ωt)+Bsin(ωt)
e(t)=y(t)-i(t)
In order to adjust corrected parameter A and B, following correction algorithm is carried out in adaptive sine filter:
A'=A-μe(t)cos(ωt)
B'=B-μe(t)sin(ωt)
A', B' are the correction values of adaptive sine filter parameter in formula, and μ (μ > 0) is the algorithmic statement factor and empirically
Value.Error definite value eset1And eset2It is according to disturbing signal detecting precision set.
Among the above, if what disturbing signal detecting module 2 exported is instruction that disturbing signal is not present, frequency locking lock-in sample
Module 1 continues the operation of repeated acquisition i and v, and disturbing signal detecting module 2 continues the operation of Repeated Disturbances detection.
If what disturbing signal detecting module 2 exported is the existing instruction of disturbing signal, disturbing signal extraction module 3 is extracted
Disturbing signal △ v', the △ i' of voltage, electric current, and △ v', △ i' are transported into active linear network parameter identification module 4.
In one implementation, disturbing signal extraction module 3 includes: that voltage disturbance signal extraction unit 31 and electric current are disturbed
Dynamic signal extraction unit 32, wherein lock is respectively adopted in voltage disturbance signal extraction unit 31 and current disturbing signal extraction unit 32
Frequency lock-in sample module 1 acquire there are voltage, the current digital signal v' when disturbing signalt、i'tSubtract frequency locking lock-in sample
Module 1 acquire be not present disturbing signal when voltage, current digital signal v'p、i'pBelieve to get to the disturbance of voltage, electric current
Number △ v', △ i', that is, △ v'=v' is executed respectivelyt(t)-v'p(t-kT) and △ i'=i't(t)-i'p(t-kT), wherein T is
The period of Current Voltage digital signal, k are integers.
Wherein, active linear network parameter identification module 4 is carried out for disturbing signal △ v', the △ i' to voltage, electric current
It recognizes and exports the active linear network parameter values under this kind of disturbance situation.The one of the active linear network parameter identification module 4
Kind of implementation is as shown in figure 5, comprising: Calculation of Sensitivity unit 41, global model identification unit 42 and processing unit 43, wherein locating
Managing unit 43 includes: the first computation subunit 431, the second computation subunit 432 and third computation subunit 433.
Wherein, Calculation of Sensitivity unit 41 carries out parameter trajectory Calculation of Sensitivity first, analyzes active linear network parameter
The complexity of identification.The parameter that the parameter of its medium sensitivity big (choosing according to given threshold) as easily recognizes, it is easy to just
It can recognize to obtain the value being closer to true value, and identification result is stablized, the small parameter of sensitivity is not easy to recognize, and identification result is deposited
It can be realized according to the threshold of sensitivity of setting in size (height) judgement of larger discreteness, medium sensitivity, that is, be greater than spirit
Sensitivity threshold value is high sensitivity, is muting sensitivity less than the threshold of sensitivity.
In one implementation, using the difficulty or ease of the parameter identification of trace sensitivity quantitative analysis active linear network
Degree.Under disturbance situation, electric system active linear network model can be unified to be described with subordination principle are as follows:
X(t0)=x0
0=g(X,△i,θ,△u) △i(t0)=△i0
X is the vector of state variable composition in above formula, and △ i is the vector of algebraic variable composition, and θ is model parameter, and △ u is
Input vector.
Local derviation is sought into parameter θ simultaneously in above formula both sides, trace sensitivity model can be obtained:
The sensitivity model of parameter k is as follows:
Model can be written as this form:
Derive after the trace sensitivity model of each parameter, the track for solving each parameter using 4 rank runge kutta methods is sensitive
Degree.Wherein, the biggish parameter of sensitivity easily recognizes, and identification result is stablized, and the lesser parameter of sensitivity is not easy to recognize, identification knot
Easily there is multivalue phenomenon in fruit.
Wherein, global model identification unit 42 is using nonlinear Identification method (such as: Chemistry) the active line of global model identification
Property network parameter, wherein obtain with the parameter energy accurate recognition of high trace sensitivity and (can be used as end value), there is low spirit
The parameter of sensitivity will appear parameter multivalue phenomenon, identification result inaccuracy.
Wherein, the first computation subunit 431 is used to recognize the resulting parameter with high sensitivity and substitutes into equationAnd according to the parameter a being calculated0,a1,…,aKAnd b0,b1,…,bK, it is even number in K
When:
It enablesIt calculates
When K is odd number, enableIt calculates
The equivalent parameters expression formula of active linear network is obtained as a result:
Wherein equivalent parameters Req1And Leq1Expression formula in contain only the lesser unknown parameter of sensitivity.
Wherein, the second computation subunit 432 uses least square method accounting equation
Parameter a0,a1,…,aKAnd b0,b1,…,bK, according to the parameter a being calculated0,a1,…,aKAnd b0,b1,…,bK, it is even in K
When number:
It enablesIt calculates
When K is odd number, enableIt calculates
The equivalent parameters value of active linear network is obtained as a result:
Wherein, the sub- computing unit 433 of third for the first computation subunit of simultaneous 431 export equivalent parameters expression formula and
The equivalent parameters value of second computation subunit 432 output acquires the lesser active linear network parameter of sensitivity.
The above process, both the parameter identification device of active linear network known to the network structure to the embodiment of the present invention
Structure and based on the device realize network structure known to active linear network parameter identification method main flow into
Explanation is gone.Below with reference to embodiment, the invention will be further described.
In following examples, with single-phase active Linear Network verify the parameter of above-mentioned active linear network accuracy and
Validity, to those skilled in the art, the parameter for being equally applicable to three-phase or positive and negative zero sequence active linear network are distinguished
Know.
The active electric network of this example as shown in fig. 6, now using step identification method identification active linear network parameter R1,
L1、L2、R3、L3。
Apply in active linear network-external to be identified and disturbs, disturbing signal △ i, the △ of the electric current, voltage that extract
V, as network structure it is known that can derive active linear network shown in Fig. 6 disturbing signal Differential Equation Model it is as follows:
Differential Equation Model is turned into differential algebraic equations, seeks the trace sensitivity model of each parameter.It derives respectively to join
After several trace sensitivity models, the present invention uses 4 rank runge kutta method simultaneous ordinary differential equations and algebraic equation solving five
The trace sensitivity of parameter.
Specifically, input stimulus takes △ u, as shown in Figure 7.Acquire trace sensitivity such as Fig. 8 of each parameter.As shown in Figure 8,
The trace sensitivity of parameter R1 and R3 are far smaller than the trace sensitivity of parameter L1, L2 and L3.I.e. parameter R1 and R3 is not easy to recognize,
It will appear multivalue phenomenon.Parameter L1, L2 and L3 are easier to identification and obtain exact value.
Using Chemistry global model identification active linear network parameter, identification result is as shown in table 1:
1 global model identification parametric results of table
| Identified parameters | True value | Identification result |
| R1 | 3 | 5.1059 |
| L1 | 0.8 | 0.7895 |
| L2 | 0.5 | 0.5003 |
| R3 | 10 | 16.1868 |
| L3 | 1 | 1 |
By identification result it is found that parameter L1, L2, L3 can be recognized more accurately is worth, the identification knot of parameter R1 and R3
Fruit inaccuracy.Table 1 sufficiently demonstrates parametric sensitivity and parameter it is found that parameter identification result is completely the same with sensitivity analysis
Relationship between easy identification.
The accurate value of L1, L2 and L3 has been obtained by global model identification, has brought the identifier of L1, L2 and L3 into the differential equation
Model acquires the nonlinear function R about unknown parameter R1 and R3eqAnd Leq；Differential equation of higher order is recognized simultaneouslySolve equivalent parameters ReqAnd Leq；Simultaneous solution Solving Nonlinear Equation parameter R1 and R3.
2 substep identified parameters result of table
| Identified parameters | True value | Identification result |
| R1 | 3 | 3.0565 |
| L1 | 0.8 | 0.7895 |
| L2 | 0.5 | 0.5003 |
| R3 | 10 | 9.7267 |
| L3 | 1 | 1 |
By 2 identification result of table it is found that after increasing additional conditions, the identifier and true value of parameter R1 and R3 relatively, are distinguished
It is ideal to know result, it was demonstrated that the step identification method mentioned herein is reasonable, effective.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of technical solution of the present invention, should all be covered at this
In the scope of the claims of invention.
Claims (3)
1. a kind of parameter identification method recognizes, feature for the parameter to active linear network known to network structure
It is: includes:
Each parameter is divided into highly sensitive parameter and muting sensitivity is joined by the sensitivity for analyzing each parameter of active linear network
Number；
Each parameter of the active linear network is recognized by the way of global model identification, wherein highly sensitive parameter is obtained in this step
Value be end value；
Increase network constraint according to the value of the obtained highly sensitive parameter, and to the active linear network, described in identification
Muting sensitivity parameter in active linear network；
The value of the highly sensitive parameter obtained according to, to active linear network increase network constraint, and according to
The value of the obtained highly sensitive parameter recognizes the muting sensitivity parameter in the active linear network, comprising:
When obtaining the active linear network and there is disturbance, the voltage disturbance signal △ v' of the port of the active linear network and
Current disturbing signal △ i'；
The value for the highly sensitive parameter that identification obtains is brought intoA is calculated first0,a1,L,
aKAnd b0,b1,L,bK, then according to the parameter a being calculated0,a1,L,aKAnd b0,b1,L,bK, A is calculated0、A1、B0And B1,
A is calculated in last basis0、A1、B0And B1, equivalent parameters R is calculatedeq1And Leq1；
It is calculated using least square methodParameter a0,a1,L,aKAnd b0,b1,L,bK, then root
According to the parameter a being calculated0,a1,L,aKAnd b0,b1,L,bK, A is calculated0、A1、B0And B1, finally A is calculated in basis0、
A1、B0And B1, equivalent parameters R is calculatedeq2And Leq2；
According to Req1、Leq1、Req2And Leq2, obtain the value of muting sensitivity parameter；
Wherein, when differential equation order K is even number, then
When K is odd number, then
Wherein,
2. parameter identification method as described in claim 1, it is characterised in that: the mode of the global model identification is step-length acceleration
Method.
3. parameter identification method as described in claim 1, it is characterised in that: it is described obtain the voltage disturbance signal △ v' and
Before current disturbing signal △ i', further includes:
Sampling step: the voltage and current of the port of the active linear network is sampled, corresponding voltage digital is obtained
Signal v' and current digital signal i'；
Detecting step: the voltage digital signal v' or current digital signal i' is detected with the presence or absence of disturbance；
It disturbs if it does not exist, then continues to execute sampling step and detecting step；
It disturbs if it exists, then extracts corresponding voltage disturbance signal △ v' and current disturbing signal △ i'.
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| CN101789598A (en) * | 2010-03-05 | 2010-07-28 | 湖北省电力试验研究院 | Power system load modelling method |
| CN102801162A (en) * | 2012-08-23 | 2012-11-28 | 清华大学 | Two-stage linear weighted least-square power system state estimation method |
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