CN106503279A - A kind of modeling method for transient stability evaluation in power system - Google Patents
A kind of modeling method for transient stability evaluation in power system Download PDFInfo
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Abstract
The present invention provides a kind of modeling method for transient stability evaluation in power system, comprises the following steps：Collection electromotor trace information and multilayer output feedback network result；Calculate generator characteristics value；Form initial mathematical model X and Transient Stability Evaluation mathematical model F；Dimensionality reduction is carried out to Transient Stability Evaluation mathematical model F, bidimensional degree standardized mathematical model D is obtained.The present invention carries out multi-faceted statistics to electromotor geometric locus, statistic curve can reflect the main average of each geometric locus, excursion and rate of change characteristic, and statistical nature is unrelated with system scale, " dimension calamity " problem that brings with institute's analysis system increase can be prevented effectively from during data modeling, at the same also can minimum degree reduce the adverse effect that electromotor loss of learning is caused to calculating.
Description
Technical field
The present invention relates to a kind of modeling method, and in particular to a kind of modeling method for transient stability evaluation in power system.
Background technology
It is that Transient Stability Evaluation problem is processed as a two-mode classification problem based on the Transient Stability Evaluation of machine learning,
That is the running status of system is divided into stable and unstable two class；By selecting one group of suitable feature come descriptive system state, adopt
Characteristic sample of the sample power system under different operating conditions, sets up the input space of a higher-dimension；Then, using one kind
Suitable sorting technique is classified to sample, its mathematical model shape such as Fig. 1.
In Transient Stability Evaluation, assessment mathematical model how is built, choose and there is general applicability, problem can be fully described
The input feature vector variable of dynamic essence is extremely difficult.Existing research work, is limited to above-mentioned traditional two-dimentional modeling approach,
When input feature vector variable is determined, it is considered to mostly be particular moment running status (as failure occurs moment and failure removal moment),
Substantial amounts of pilot process dynamic characteristic development information is not applied, and what this have impact on final assessment result to a great extent can
Lean on property.
Content of the invention
The present invention provides a kind of modeling method for transient stability evaluation in power system, by collection electromotor trace information and temporarily
State stability Calculation result, calculates generator characteristics value, and then forms initial mathematical model X and Transient Stability Evaluation mathematical model F,
Dimensionality reduction is carried out to Transient Stability Evaluation mathematical model F finally, bidimensional degree standardized mathematical model D is obtained.
In order to realize that foregoing invention purpose, the present invention are adopted the following technical scheme that：
The present invention provides a kind of modeling method for transient stability evaluation in power system, and the modeling method is comprised the following steps：
Step 1：Collection electromotor trace information and multilayer output feedback network result；
Step 2：Calculate generator characteristics value；
Step 3：Form initial mathematical model X and Transient Stability Evaluation mathematical model F；
Step 4：Dimensionality reduction is carried out to Transient Stability Evaluation mathematical model F, bidimensional degree standardized mathematical model D is obtained.
In the step 1, electromotor trace information is usedRepresent, whereinRepresent
Rotor angles of the machine i at the j moment, i=1,2 ..., m, j=1,2 ..., n, k=1,2 ..., s, m represent generators in power systems number of units,
N represents that total number of sample points, s represent operating condition sum；
The multilayer output feedback network result C={ ck}sRepresent, wherein ckRepresent the transient stability fortune of power system under operating condition k
Row state.
In the step 2, the generator characteristics value includes electromotor relative rotor angle, relative rotor angular velocity and relative rotor
Angular acceleration；
Electromotor relative rotor angle is expressed as：
Wherein,Represent that electromotor i is at the relative rotor angle at j moment under operating condition k,Represent under operating condition k
Rotor angles of the electromotor i at the j moment；
Electromotor relative rotor angular velocimeter is shown as：
Wherein,Relative rotor angular velocity of the electromotor i at the j moment under operating condition k is represented,Represent operation
Under operating mode k, at the relative rotor angle at j+1 moment, Δ t represents the sampling period to electromotor i；
Electromotor relative rotor angular acceleration is expressed as：
Wherein,Relative rotor angular acceleration of the electromotor i at the j moment under operating condition k is represented,Represent fortune
Relative rotor angular velocity of the electromotor i at the j+1 moment under row operating mode k.
The step 3 specifically includes following steps：
Step 3-1：Form initial mathematical model X of unified time dimension；
Step 3-2：Form Transient Stability Evaluation mathematical model F.
In step 3-1, when total number of sample points is n, on time dimension,There is corresponding n eigenvalue,
There is corresponding n-1 eigenvalue,There is corresponding n-2 eigenvalue, in order to ensure the concordance of eigenvalue length, right Carry out unifying to intercept, the time dimension number of degrees n '=n-2 after intercepting, then, the initial mathematical of unified time dimension
Model X is expressed as：
In step 3-2, Transient Stability Evaluation mathematical model F is expressed as：
Wherein,Represent that value of generator characteristics h at the j moment under operating condition k, h=1,2 ..., 19 specifically have：
1) the relative rotor angle average f of electromotor1 kJ () represents, has：
2) the relative rotor angular variance f of electromotor2 kJ () represents, has：
3) the relative rotor angle extreme value difference f of electromotor3 kJ () represents, has：
4) the relative rotor angle central value f of electromotor4 kJ () represents, has：
5) the relative rotor angle average transformation rate f of electromotor5 kJ () represents, has：
Wherein, f1 k(j+1) relative rotor angle averages of the electromotor i at the j+1 moment under operating condition k is represented；
6) the relative rotor angular variance rate of change f of electromotor6 kJ () represents, has：
Wherein, f2 k(j+1) relative rotor angular variances of the electromotor i at the j+1 moment under operating condition k is represented；
7) the relative rotor angle extreme value difference rate of change f of electromotor7 kJ () represents, has：
Wherein, f3 k(j+1) represent operating condition k under electromotor i the j+1 moment relative rotor angle extreme value poor；
8) the relative rotor angle central value rate of change f of electromotor8 kJ () represents, has：
9) the standard deviation f of the relative rotor angular velocity of electromotor9 kJ () represents, has：
10) the maximum f of the relative rotor angular velocity of electromotor10 kJ () represents, has：
11) the minima f of the relative rotor angular velocity of electromotor11 kJ () represents, has：
12) the maximum f of the relative rotor angular velocity absolute value of electromotor12 kJ () represents, has：
13) the relative rotor angle average acceleration f of electromotor13 kJ () represents, has：
Wherein, f5 k(j+1) relative rotor angle average transformation rates of the electromotor i at the j+1 moment under operating condition k is represented；
14) the relative rotor angular variance acceleration f of electromotor14 kJ () represents, has：
Wherein, f6 k(j+1) relative rotor angular variance rates of change of the electromotor i at the j+1 moment under operating condition k is represented；
15) the relative rotor angle extreme value difference acceleration f of electromotor15 kJ () represents, has：
Wherein, f7 k(j+1) relative rotor angle extreme value difference rates of change of the electromotor i at the j+1 moment under operating condition k is represented；
16) the relative rotor angle central value acceleration f of electromotor16 kJ () represents, has：
Wherein, f8 k(j+1) relative rotor angle central value rates of change of the electromotor i at the j+1 moment under operating condition k is represented；
17) the standard deviation f of the relative rotor angular acceleration of electromotor17 kJ () represents, has：
18) the maximum f of the relative rotor angular acceleration of electromotor18 kJ () represents, has：
19) the minima f of the relative rotor angular acceleration of electromotor19 kJ () represents, has：
The step 4 specifically includes following steps：
Step 4-1：Transient Stability Evaluation mathematical model F is also denoted as：
F=(f1,f2,...,fh,...,f19)T(25)
Wherein, fhThe corresponding eigenmatrix of generator characteristics h is represented, and is had：
Wherein,Represent value of generator characteristics h at the n ' moment under operating condition s；
Step 4-2：For fhWith multilayer output feedback network result C={ ck}s, optimal separating hyper plane letter is constructed using support vector machine
Number fun (fh)=wT·fh+ b, obtains wTAnd b, wherein, wTRepresent that the slope of optimal separating hyper plane function, b represent optimum
The intercept of Optimal Separating Hyperplane function；
Step 4-3：Calculate the corresponding optimal separating hyper plane functional value of generator characteristics h under operating condition kHave：
Step 4-4：According toObtain bidimensional degree standardized mathematical model
Compared with prior art, the beneficial effects of the present invention is：
1) each electromotor is contained in the Transient Stability Evaluation mathematical model for building and its statistical nature is time dependent in detail
Information, improves the degree that becomes more meticulous of model.
2) multi-faceted statistics is carried out to electromotor geometric locus, and statistic curve can reflect the main average of each geometric locus, change
Scope and rate of change characteristic, and statistical nature is unrelated with system scale, during being prevented effectively from data modeling with analyze is
System " dimension calamity " problem for bringing of increase, at the same also can minimum degree reduce electromotor loss of learning to calculating cause unfavorable
Affect.
3) Feature Conversion of time dimension is carried out using support vector machine method, can be in the situation for ensureing sample classification Information invariability
Under, many dimension time dimensions are down to 1 dimension, and most initial three-dimensional data space is converted into suitable for traditional classification problem at last
Two-dimensional data space.
Description of the drawings
Fig. 1 is the Transient Stability Evaluation mathematical model schematic diagram in prior art based on machine learning；
Fig. 2 is collection electromotor trace information schematic diagram in the embodiment of the present invention；
Fig. 3 is initial mathematical model X schematic diagram in the embodiment of the present invention；
Fig. 4 is the Transient Stability Evaluation mathematical model schematic diagram in the embodiment of the present invention after electromotor dimension dimensionality reduction；
Fig. 5 is 19 groups of temporary Transient Stability Evaluation mathematical modulos for being distributed in operating condition and time 2-D space in the embodiment of the present invention
Type schematic diagram；
Fig. 6 is time dimension dimensionality reduction schematic diagram in Transient Stability Evaluation mathematical model in the embodiment of the present invention；
Fig. 7 is the transient state assessment mathematical model schematic diagram after time dimension is changed in the embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings the present invention is described in further detail.
Transient Stability Evaluation mathematical modeling is the key issue for restricting assessment result reliability.In temporarily steady assessment is calculated, how simultaneous
Turn round and look at the input feature vector quantity of information that is related to how many and calculate whole efficiency, the problem for always needing to solve, the present invention provide a kind of using
In the modeling method of transient stability evaluation in power system, the modeling method is comprised the following steps：
Step 1：Collection electromotor trace information and multilayer output feedback network result；
Step 2：Calculate generator characteristics value；
Step 3：Form initial mathematical model X and Transient Stability Evaluation mathematical model F；
Step 4：Dimensionality reduction is carried out to Transient Stability Evaluation mathematical model F, bidimensional degree standardized mathematical model D is obtained.
In the step 1, (such as Fig. 2) electromotor trace information is usedRepresent, whereinRepresent operation work
Rotor angles of the electromotor i at the j moment under condition k, i=1,2 ..., m, j=1,2 ..., n, k=1,2 ..., s, m represent in power system and send out
Motor number of units, n represent that total number of sample points, s represent operating condition sum；
The multilayer output feedback network result C={ ck}sRepresent, wherein ckRepresent the transient stability fortune of power system under operating condition k
Row state.
In the step 2, the generator characteristics value includes electromotor relative rotor angle, relative rotor angular velocity and relative rotor
Angular acceleration；
Electromotor relative rotor angle is expressed as：
Wherein,Represent that electromotor i is at the relative rotor angle at j moment under operating condition k,Represent under operating condition k
Rotor angles of the electromotor i at the j moment；
Electromotor relative rotor angular velocimeter is shown as：
Wherein,Relative rotor angular velocity of the electromotor i at the j moment under operating condition k is represented,Represent operation
Under operating mode k, at the relative rotor angle at j+1 moment, Δ t represents the sampling period to electromotor i；
Electromotor relative rotor angular acceleration is expressed as：
Wherein,Relative rotor angular acceleration of the electromotor i at the j moment under operating condition k is represented,Represent fortune
Relative rotor angular velocity of the electromotor i at the j+1 moment under row operating mode k.
The step 3 specifically includes following steps：
Step 3-1：Form initial mathematical model X of unified time dimension；
Step 3-2：Form Transient Stability Evaluation mathematical model F.
In step 3-1, when total number of sample points is n, on time dimension,There is corresponding n eigenvalue,
There is corresponding n-1 eigenvalue,There is corresponding n-2 eigenvalue, in order to ensure the concordance of eigenvalue length, right Carry out unifying to intercept, the time dimension number of degrees n '=n-2 after intercepting, then, such as Fig. 3, unified time dimension
Initial mathematical model X is expressed as：
In step 3-2, such as Fig. 4, Transient Stability Evaluation mathematical model F are expressed as：
Wherein,Represent that value of generator characteristics h at the j moment under operating condition k, h=1,2 ..., 19 specifically have：
1) the relative rotor angle average f of electromotor1 kJ () represents, has：
2) the relative rotor angular variance f of electromotor2 kJ () represents, has：
3) the relative rotor angle extreme value difference f of electromotor3 kJ () represents, has：
4) the relative rotor angle central value f of electromotor4 kJ () represents, has：
5) the relative rotor angle average transformation rate f of electromotor5 kJ () represents, has：
Wherein, f1 k(j+1) relative rotor angle averages of the electromotor i at the j+1 moment under operating condition k is represented；
6) the relative rotor angular variance rate of change f of electromotor6 kJ () represents, has：
Wherein, f2 k(j+1) relative rotor angular variances of the electromotor i at the j+1 moment under operating condition k is represented；
7) the relative rotor angle extreme value difference rate of change f of electromotor7 kJ () represents, has：
Wherein, f3 k(j+1) represent operating condition k under electromotor i the j+1 moment relative rotor angle extreme value poor；
8) the relative rotor angle central value rate of change f of electromotor8 kJ () represents, has：
9) the standard deviation f of the relative rotor angular velocity of electromotor9 kJ () represents, has：
10) the maximum f of the relative rotor angular velocity of electromotor10 kJ () represents, has：
11) the minima f of the relative rotor angular velocity of electromotor11 kJ () represents, has：
12) the maximum f of the relative rotor angular velocity absolute value of electromotor12 kJ () represents, has：
13) the relative rotor angle average acceleration f of electromotor13 kJ () represents, has：
Wherein, f5 k(j+1) relative rotor angle average transformation rates of the electromotor i at the j+1 moment under operating condition k is represented；
14) the relative rotor angular variance acceleration f of electromotor14 kJ () represents, has：
Wherein, f6 k(j+1) relative rotor angular variance rates of change of the electromotor i at the j+1 moment under operating condition k is represented；
15) the relative rotor angle extreme value difference acceleration f of electromotor15 kJ () represents, has：
Wherein, f7 k(j+1) relative rotor angle extreme value difference rates of change of the electromotor i at the j+1 moment under operating condition k is represented；
16) the relative rotor angle central value acceleration f of electromotor16 kJ () represents, has：
Wherein, f8 k(j+1) relative rotor angle central value rates of change of the electromotor i at the j+1 moment under operating condition k is represented；
17) the standard deviation f of the relative rotor angular acceleration of electromotor17 kJ () represents, has：
18) the maximum f of the relative rotor angular acceleration of electromotor18 kJ () represents, has：
19) the minima f of the relative rotor angular acceleration of electromotor19 kJ () represents, has：
The step 4 specifically includes following steps：
Step 4-1：Such as Fig. 5, Transient Stability Evaluation mathematical model F are also denoted as：
F=(f1,f2,...,fh,...,f19)T(25)
Wherein, fhThe corresponding eigenmatrix of generator characteristics h is represented, and is had：
Wherein,Represent value of generator characteristics h at the n ' moment under operating condition s；
Step 4-2：For fhWith multilayer output feedback network result C={ ck}s, optimal separating hyper plane letter is constructed using support vector machine
Number fun (fh)=wT·fh+ b, obtains wTAnd b, wherein, wTRepresent that the slope of optimal separating hyper plane function, b represent optimum
The intercept of Optimal Separating Hyperplane function；
Support vector machine (support vector machine, SVM) are built upon Statistical Learning Theory VC dimension theory and structure wind
Danger minimizes the machine learning method on basis, can successfully process the problems such as regression problem and pattern recognition, SVM
Mechanism be find an optimal separating hyper plane for meeting classificating requirement so that the hyperplane ensure nicety of grading while,
Maximize can the white space of hyperplane both sides.
Step 4-3：Calculate the optimal separating hyper plane functional value of generator characteristics h under operating condition k Contain each
The Annual distribution characteristic of motor characteristic, while further comprises the corresponding stable classification information of sample, can be used to substitute originally
Many time dimension information.So, corresponding time dimension is just converted to 1 dimension, such as Fig. 6 by n ' dimensions,It is expressed as：
Step 4-4：According toObtain bidimensional degree standardized mathematical modelSuch as Fig. 7.
Embodiment
The carried electric power system transient stability rapid evaluation modeling method of the present invention is described by taking 10 machine of New England, 39 node system as an example
Effectiveness.Generators in power systems number of units m=10, reference frequency are 60Hz, sampling period Δ t=0.0167s, emulate soft
Part adopts PSASP.
1) single-phase short circuit, line to line fault, two-phase grounding fault and three phase short circuit fault are set gradually on the line, failure is chosen
Track data after excision in 9 cycles, i.e. n=9, altogether emulation generate all generator amature angles failure under s=4104 kind operating modes
Track after excision, obtains initial acquisition data model.
2) corresponding electromotor relative rotor angle is calculatedRelative rotor angular velocityWith relative rotor angular accelerationWherein i=1,2 ..., 10；J=1,2 ..., 9；K=1,2 ..., 4104.
3) unified intercepting time dimension n '=9-2=7, obtains temporarily steady assessment initial mathematical model
4) dimensionality reduction is carried out to the electromotor dimension of initial mathematical model X, calculates 19 system trajectory features described in second section,
The generator characteristics dimension values of initial mathematical model X are converted to 19 dimensions by 30 initial dimensions, after obtaining electromotor dimension dimensionality reduction
Transient state assesses mathematical model
5) to electromotor dimension dimensionality reduction after the time dimension of temporary steady assessment mathematical model F carry out dimensionality reduction.First, as shown in figure 5,
To each featureCan be entered using constructing 19 based on the support vector machine of linear kernel function
The classification of row data and the optimal separating hyper plane function fun (f with maximum generalization abilityh)=wT·fh+ b, wherein b ∈ R,
w∈Rn′.Secondly, each operating condition sample f in each group of 2-D datah k(k=1,2 ..., 4104), bring which into right
The optimal hyperlane function that answers, obtains corresponding4) 7 dimension sampling time data in are down to 1 dimension.
6) temporarily steady assessment bidimensional degree standardized mathematical model is formedFrom decision tree C4.5 algorithm to obtaining
Standardized mathematical model is trained.C4.5 algorithms determine branch's criterion of tree with information gain-ratio as standard, and using " minimizing is missed
Difference " method judges whether to prune.
Decision tree (Decision Tree, DT) is a series of tree shape model being made up of nodes and branch.DT induced learning algorithms
It is one of most representational algorithm in machine learning field, comprising two steps of achievement and prediction.Contribute is exactly to conclude from training set
Go out a DT, generally comprise growth and prune two processes.Prediction be using study to DT to the example (test set) that has no
Carry out kind judging.Current DT algorithms mainly have ID3, CART and C4.5 algorithm.
The effectiveness of model constructed by checking, selects three kinds of models 3), 4) He 5) obtaining as the input of C4.5 algorithms
Model, wherein, to the initial mathematical model for 3) obtainingInput feature vector number is tieed up for 30 × 7=210；Right
Transient Stability Evaluation mathematical model after the dimensionality reduction for 4) obtainingInput feature vector number is tieed up for 19 × 7=133；
For the bidimensional degree standardized mathematical model for 5) obtainingInput feature vector number is 19 dimensions.Take wherein 1755 samples
Composing training collection, remaining 2349 samples as test set, the final mask obtained after training to the prediction accuracy of test set such as
Shown in table 1.It can be seen that the temporary steady assessment modeling method designed by this patent has certain effectiveness.
Table 1
Finally it should be noted that：Above example only in order to technical scheme to be described rather than a limitation, art
Those of ordinary skill with reference to above-described embodiment still can to the present invention specific embodiment modify or equivalent,
These any modification or equivalents without departing from spirit and scope of the invention, in the claim for applying for the pending present invention
Within protection domain.
Claims (7)
1. a kind of modeling method for transient stability evaluation in power system, it is characterised in that：The modeling method is comprised the following steps：
Step 1：Collection electromotor trace information and multilayer output feedback network result；
Step 2：Calculate generator characteristics value；
Step 3：Form initial mathematical model X and Transient Stability Evaluation mathematical model F；
Step 4：Dimensionality reduction is carried out to Transient Stability Evaluation mathematical model F, bidimensional degree standardized mathematical model D is obtained.
2. the modeling method for transient stability evaluation in power system according to claim 1, it is characterised in that：In the step 1, electromotor trace information is usedRepresent, whereinRepresent rotor angles of the electromotor i at the j moment under operating condition k, i=1,2 ..., m, j=1,2 ..., n, k=1,2 ..., s, m represent that generators in power systems number of units, n represent that total number of sample points, s represent operating condition sum；
The multilayer output feedback network result C={ ck}sRepresent, wherein ckRepresent the transient stability running status of power system under operating condition k.
3. the modeling method for transient stability evaluation in power system according to claim 2, it is characterised in that：In the step 2, the generator characteristics value includes electromotor relative rotor angle, relative rotor angular velocity and relative rotor angular acceleration；
Electromotor relative rotor angle is expressed as：
Wherein,Represent that electromotor i is at the relative rotor angle at j moment under operating condition k,Represent rotor angles of the electromotor i at the j moment under operating condition k；
Electromotor relative rotor angular velocimeter is shown as：
Wherein,Relative rotor angular velocity of the electromotor i at the j moment under operating condition k is represented,Under expression operating condition k, at the relative rotor angle at j+1 moment, Δ t represents the sampling period to electromotor i；
Electromotor relative rotor angular acceleration is expressed as：
Wherein,Relative rotor angular acceleration of the electromotor i at the j moment under operating condition k is represented,Represent relative rotor angular velocity of the electromotor i at the j+1 moment under operating condition k.
4. the modeling method for transient stability evaluation in power system according to claim 3, it is characterised in that：The step 3 specifically includes following steps：
Step 3-1：Form initial mathematical model X of unified time dimension；
Step 3-2：Form Transient Stability Evaluation mathematical model F.
5. the modeling method for transient stability evaluation in power system according to claim 4, it is characterised in that：In step 3-1, when total number of sample points is n, on time dimension,There is corresponding n eigenvalue,There is corresponding n-1 eigenvalue,There is corresponding n-2 eigenvalue, in order to ensure the concordance of eigenvalue length, right Carry out unifying to intercept, the time dimension number of degrees n '=n-2 after intercepting, then, initial mathematical model X of unified time dimension is expressed as：
.
6. the modeling method for transient stability evaluation in power system according to claim 5, it is characterised in that：In step 3-2, Transient Stability Evaluation mathematical model F is expressed as：
Wherein,Value of generator characteristics h at the j moment under expression operating condition k, h=1,2 ..., 19, specifically have：
1) the relative rotor angle average f of electromotor1 kJ () represents, has：
2) the relative rotor angular variance f of electromotor2 kJ () represents, has：
3) the relative rotor angle extreme value difference f of electromotor3 kJ () represents, has：
4) the relative rotor angle central value f of electromotor4 kJ () represents, has：
5) the relative rotor angle average transformation rate f of electromotor5 kJ () represents, has：
Wherein, f1 k(j+1) relative rotor angle averages of the electromotor i at the j+1 moment under operating condition k is represented；
6) the relative rotor angular variance rate of change f of electromotor6 kJ () represents, has：
Wherein, f2 k(j+1) relative rotor angular variances of the electromotor i at the j+1 moment under operating condition k is represented；
7) the relative rotor angle extreme value difference rate of change f of electromotor7 kJ () represents, has：
Wherein, f3 k(j+1) represent operating condition k under electromotor i the j+1 moment relative rotor angle extreme value poor；
8) the relative rotor angle central value rate of change f of electromotor8 kJ () represents, has：
9) the standard deviation f of the relative rotor angular velocity of electromotor9 kJ () represents, has：
10) the maximum f of the relative rotor angular velocity of electromotor10 kJ () represents, has：
11) the minima f of the relative rotor angular velocity of electromotor11 kJ () represents, has：
12) the maximum f of the relative rotor angular velocity absolute value of electromotor12 kJ () represents, has：
13) the relative rotor angle average acceleration f of electromotor13 kJ () represents, has：
Wherein, f5 k(j+1) relative rotor angle average transformation rates of the electromotor i at the j+1 moment under operating condition k is represented；
14) the relative rotor angular variance acceleration f of electromotor14 kJ () represents, has：
Wherein, f6 k(j+1) relative rotor angular variance rates of change of the electromotor i at the j+1 moment under operating condition k is represented；
15) the relative rotor angle extreme value difference acceleration f of electromotor15 kJ () represents, has：
Wherein, f7 k(j+1) relative rotor angle extreme value difference rates of change of the electromotor i at the j+1 moment under operating condition k is represented；
16) the relative rotor angle central value acceleration f of electromotor16 kJ () represents, has：
Wherein, f8 k(j+1) relative rotor angle central value rates of change of the electromotor i at the j+1 moment under operating condition k is represented；
17) the standard deviation f of the relative rotor angular acceleration of electromotor17 kJ () represents, has：
18) the maximum f of the relative rotor angular acceleration of electromotor18 kJ () represents, has：
19) the minima f of the relative rotor angular acceleration of electromotor19 kJ () represents, has：
.
7. the modeling method for transient stability evaluation in power system according to claim 6, it is characterised in that：The step 4 specifically includes following steps：
Step 4-1：Transient Stability Evaluation mathematical model F is also denoted as：
F=(f1,f2,…,fh,…,f19)T(25)
Wherein, fhThe corresponding eigenmatrix of generator characteristics h is represented, and is had：
Wherein,Represent value of generator characteristics h at the n ' moment under operating condition s；
Step 4-2：For fhWith multilayer output feedback network result C={ ck}s, optimal separating hyper plane function fun (f are constructed using support vector machineh)=wT·fh+ b, obtains wTAnd b, wherein, wTRepresent that the slope of optimal separating hyper plane function, b represent the intercept of optimal separating hyper plane function；
Step 4-3：Calculate the corresponding optimal separating hyper plane functional value of generator characteristics h under operating condition kHave：
Step 4-4：According toObtain bidimensional degree standardized mathematical model
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| CN108595884A (en) * | 2018-05-09 | 2018-09-28 | 清华大学 | Power system transient stability appraisal procedure and device |
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| CN110718910A (en) * | 2019-10-29 | 2020-01-21 | 国网四川省电力公司经济技术研究院 | Transient stability evaluation method for Bayesian optimization LightGBM |
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| CN112564107A (en) * | 2020-12-15 | 2021-03-26 | 深圳供电局有限公司 | Transient stability assessment method for power system |
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