CN111064186A - Transient stability analysis method and device - Google Patents

Transient stability analysis method and device Download PDF

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CN111064186A
CN111064186A CN201911244909.0A CN201911244909A CN111064186A CN 111064186 A CN111064186 A CN 111064186A CN 201911244909 A CN201911244909 A CN 201911244909A CN 111064186 A CN111064186 A CN 111064186A
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ellipsoid
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杨跃
张俊峰
张毅超
吴晓宇
赵艳军
王钤
唐景星
梁晓兵
刘军
王义勇
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Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Abstract

The application discloses a transient stability analysis method and a device, wherein the method comprises the following steps: after the system is disturbed, the operation data of the generator is measured in real time through the PMU, and the operation data comprises: generator speed and generator bus voltage; mapping the operation data to a multi-dimensional space based on a characteristic ellipsoid theory to generate a characteristic ellipsoid; constructing an ellipsoid characteristic quantity according to the geometric attributes of the characteristic ellipsoid, wherein the geometric attributes comprise eccentricity, volume change rate and a central point set; and taking the ellipsoid characteristic quantity as an input parameter of the preset decision tree model to obtain an output transient stability analysis result of the preset decision tree model. The method and the device solve the technical problems that in the prior art, the system scale is large, the analysis characteristic quantity construction and selection observability are poor, the transient stability analysis and calculation is long in time consumption and not visual enough.

Description

Transient stability analysis method and device
Technical Field
The present application relates to the field of power systems, and in particular, to a transient stability analysis method and apparatus.
Background
Currently, Transient Stability Analysis (TSA) methods can be broadly divided into two categories: one is a mathematical model-based method, and some methods adopt a method of combining a transient energy function with a credible domain; some methods calculate the transient stability based on the extended equal-area rule, and the method obtains a corresponding index for judging the transient stability by solving a differential algebraic equation. The other type is a data mining method based on sample learning, and some methods take the energy margin of a system after a fault as a characteristic variable and combine two types of fuzzy C-mean and vector quantization classification analysis algorithms to screen transient stability faults; according to the method, the power angle information of each generator after the fault is used as the input characteristic quantity, the transient stability is judged by utilizing the self-organizing characteristic mapping network based on the rough set theory, a mathematical model is not required to be established, and potential problems are found and rules are searched from the data through offline learning of sample data.
Along with the increase of the power demand, the connection of power grids among areas is tighter and tighter, and when a system is greatly disturbed, the transient stability of the system is judged in time, so that the method has important significance for the safe and stable operation of the system. However, the existing transient stability analysis method has the problems of large system scale, poor observability of analyzing the characteristic quantity structure and selecting and the like, so that the transient stability analysis is long in calculation time and not visual enough.
Disclosure of Invention
The application provides a transient stability analysis method and a transient stability analysis device, which are used for solving the technical problems that in the prior art, the system scale is large, the analysis characteristic quantity construction and selection observability are poor, the transient stability analysis and calculation is long in time consumption and not visual enough.
In view of the above, a first aspect of the present application provides a transient stability analysis method, including:
after the system is disturbed, measuring the operation data of the generator in real time through a PMU, wherein the operation data comprises: generator speed and generator bus voltage;
mapping the operation data to a multi-dimensional space based on a characteristic ellipsoid theory to generate a characteristic ellipsoid;
constructing ellipsoid characteristic quantities according to geometric properties of the characteristic ellipsoids, wherein the geometric properties comprise eccentricity, volume change rate and central point set;
and taking the ellipsoid characteristic quantity as an input parameter of a preset decision tree model to obtain an output transient stability analysis result of the preset decision tree model.
Preferably, the real-time measurement of the operation data of the generator by the PMU after the system is disturbed includes:
and simulating the running data of the generator measured by the PMU in real time by adopting power system analysis software, or directly measuring the running data of the generator of the disturbed power system in real time by the PMU.
Preferably, the eccentricity ratio is calculated by a preset eccentricity ratio formula, and the preset eccentricity ratio formula is as follows:
Figure BDA0002307256640000021
wherein r ismaxAnd rminThe longest semi-axis length and the shortest semi-axis length of the characteristic ellipsoid are respectively.
Preferably, the volume is calculated by a preset volume formula, and the preset volume formula is as follows:
Figure BDA0002307256640000022
wherein Γ () is a standard gamma function, A is a positive definite matrix, n is the number of PMUs, EA，cIs the characteristic ellipsoid.
Preferably, the volume change rate is calculated by a preset volume change rate formula, and the preset volume change rate formula is as follows:
Figure BDA0002307256640000023
wherein Δ V is the volume change of the characteristic ellipsoid within Δ t time.
Preferably, the set of center points is O ═ O1,O2,…,Oi,…,On]In which O isiThe average value of the generator running data collected by the ith PMU in a preset time window is obtained through a preset average value formula, wherein the preset average value formula is as follows:
Figure BDA0002307256640000024
where m is the total number, piIs the operational data.
Preferably, the obtaining a transient stability analysis result of the output of the preset decision tree model by using the ellipsoid feature quantity as an input parameter of the preset decision tree model further includes:
adopting simulation software to simulate the fault of the power system;
constructing different power system fault scenes for simulation, and taking the obtained generator operation data as a sample data set;
obtaining the geometric attributes of the sample ellipsoid according to the sample ellipsoid generated by the generator operation sample dataset, and constructing a training feature set and a testing feature set;
and testing the preset decision tree model obtained by training the training feature set by using the test feature set.
The second aspect of the present application provides a transient stability analysis apparatus, including:
the measuring module is used for measuring the operation data of the generator in real time through the PMU after the system is disturbed, and the operation data comprises: generator speed and generator bus voltage;
the characteristic ellipsoid module is used for mapping the operation data to a multi-dimensional space based on a characteristic ellipsoid theory to generate a characteristic ellipsoid;
the constructing module is used for constructing ellipsoid characteristic quantities according to the geometric properties of the characteristic ellipsoids, wherein the geometric properties comprise eccentricity, volume change rate and central point set;
and the analysis module is used for taking the ellipsoid characteristic quantity as an input parameter of a preset decision tree model to obtain an output transient stability analysis result of the preset decision tree model.
Preferably, the construction module comprises:
the eccentricity ratio module is used for calculating the eccentricity ratio through a preset eccentricity ratio formula, wherein the preset eccentricity ratio formula is as follows:
Figure BDA0002307256640000031
wherein r ismaxAnd rminThe longest semiaxis length and the shortest semiaxis length of the characteristic ellipsoid are respectively;
a volume module for calculating the volume by a preset volume formula, the preset volume formula being:
Figure BDA0002307256640000032
wherein Γ () is a standard gamma function, A is a positive definite matrix, n is the number of PMUs, EA，cIs the characteristic ellipsoid;
a volume change rate module for calculating the volume change rate according to a preset volume change rate formula, wherein the preset volume change rate formula is as follows:
Figure BDA0002307256640000033
wherein Δ V is the volume change of the characteristic ellipsoid within Δ t time;
a center point set module, wherein the center point set is O ═ O1,O2,…,Oi,…,On]In which O isiThe average value of the generator running data collected by the ith PMU in a preset time window is obtained through a preset average value formula, wherein the preset average value formula is as follows:
Figure BDA0002307256640000041
where m is the total number, piIs the operational data.
Preferably, the method further comprises the following steps:
the preset decision module is used for performing fault simulation on the power system by adopting simulation software;
constructing different power system fault scenes for simulation, and taking the obtained generator operation data as a sample data set;
obtaining the geometric attributes of the sample ellipsoid according to the sample ellipsoid generated by the generator operation sample dataset, and constructing a training feature set and a testing feature set;
and testing the preset decision tree model obtained by training the training feature set by using the test feature set.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides a transient stability analysis method, which comprises the following steps: after the system is disturbed, the operation data of the generator is measured in real time through the PMU, and the operation data comprises: generator speed and generator bus voltage; mapping the operation data to a multi-dimensional space based on a characteristic ellipsoid theory to generate a characteristic ellipsoid; constructing an ellipsoid characteristic quantity according to the geometric attributes of the characteristic ellipsoid, wherein the geometric attributes comprise eccentricity, volume change rate and a central point set; and taking the ellipsoid characteristic quantity as an input parameter of the preset decision tree model to obtain an output transient stability analysis result of the preset decision tree model.
The method for analyzing the transient stability comprises the steps of acquiring the rotating speed of a generator and bus voltage when an electric power system is interfered as characteristic indexes for researching transient stability analysis, mapping acquired characteristic data to a multidimensional space to obtain a characteristic ellipsoid with strong observability, solving the geometric attribute of the characteristic ellipsoid, then constructing a characteristic quantity by using the obtained geometric attribute, and performing transient stability classification by adopting a preset decision tree model. The problems that the time consumption for analyzing and calculating the transient stability is long and the transient stability is not intuitive enough are caused by poor construction and selection observability of the analysis characteristic quantity.
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Fig. 1 is a schematic flowchart of a transient stability analysis method according to a first embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a second embodiment of a transient stability analysis method provided in the present application;
FIG. 3 is a diagram illustrating a change in a characteristic ellipsoid shape in a transient stability scenario;
FIG. 4 is a schematic diagram of a change in a characteristic ellipsoid shape in a transient destabilization scenario;
fig. 5 is a schematic structural diagram of an embodiment of a transient stability analysis apparatus provided in the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For easy understanding, referring to fig. 1, a first embodiment of a transient stability analysis method provided in the present application includes:
step 101, measuring operation data of the generator in real time through a PMU after a system is disturbed, wherein the operation data comprises: generator speed and generator bus voltage.
It should be noted that there are two ways of measuring the generator operation data in real time by a Phasor Measurement Unit (PMU), one way is to directly obtain the generator operation data in the operation process of a disturbed power system, but the transient characteristics obtained by this way are single, or the period for obtaining abundant data is long, so the other way is to simulate the operation condition of the generator in the transient stable state and the transient unstable state of the system by system analysis software according to the requirements of different scenes, so that more representative generator operation data can be obtained in a short time. The number of PMUs is at least one, and in the case of multiple PMUs, the generator operation data measured by each PMU is used as a column to generate a generator operation data matrix.
And 102, mapping the operation data to a multi-dimensional space based on a characteristic ellipsoid theory to generate a characteristic ellipsoid.
It should be noted that the characteristic ellipsoid theory is that in a multidimensional sample space, a minimum-volume-enclosing ellipsoid (MVEE) containing a specific part of a system trajectory is found, the defined characteristic ellipsoid needs to be optimized on different levels, the characteristic ellipsoid in the multidimensional space includes a generator rotation speed and generator bus voltage data measured by a PMU, and trajectory information is basically kept unchanged under normal conditions, so that parameters of the characteristic ellipsoid are basically unchanged, and morphological changes of the characteristic ellipsoid are small; when the system is disturbed, the sudden change generated by the track information can be synchronously reflected on the dynamic change of the characteristic ellipsoid, and the characteristic ellipsoids under different stable scenes have respective change trends, so that the transient stability of the system can be qualitatively identified from the form change of the ellipsoids.
And 103, constructing ellipsoid characteristic quantities according to the geometric attributes of the characteristic ellipsoids, wherein the geometric attributes comprise eccentricity, volume change rate and central point set.
It should be noted that the feature ellipsoid is generated by multidimensional mapping of generator operation data of the disturbed system, the ellipsoid has unique geometric attributes as a geometric figure, and if the generator operation data is mapped to a multidimensional space, the generator operation data is dynamically changed along with the disturbed condition of the system, so that the purpose of enhancing observability can be achieved, and the geometric attributes of the feature ellipsoid are obtained for analysis, namely, the morphological change condition of the feature ellipsoid along with the change of the disturbed system is analyzed from a quantitative angle. The obtained geometric attributes can form ellipsoid feature quantities, and the ellipsoid feature quantities can be in the form of feature vectors for classification analysis.
And step 104, taking the ellipsoid characteristic quantity as an input parameter of the preset decision tree model to obtain an output transient stability analysis result of the preset decision tree model.
It should be noted that the geometric attributes can quantitatively describe the morphological change condition of the feature ellipsoid, and the feature ellipsoid can reflect the transient characteristics of the disturbed system, so that the classification of the transient characteristics can be performed through the feature quantity constructed by the geometric attributes; the preset decision tree model is a classification model which is trained offline, can be used for online classification tasks and can be regarded as a decision tree classifier; the transient stability analysis is judged according to a transient stability classification rule, and can be mainly classified as transient stability or transient instability.
The transient stability analysis method provided by this embodiment is to obtain the rotation speed of the generator and the bus voltage when the power system is interfered as the characteristic indexes for studying the transient stability analysis, map the collected characteristic data to a multidimensional space, obtain a characteristic ellipsoid with strong observability, solve the geometric attributes of the characteristic ellipsoid, then construct the characteristic quantity by using the obtained geometric attributes, and perform the classification of transient stability by using a trained decision tree model, and the method provided by this embodiment has the advantages of small calculated quantity and simple processing procedure, so the analysis time consumption is short, and the generator operation data is collected in real time, and the data is generated into the characteristic ellipsoid, so that the ellipsoid evolvement is continuous in shape although the time change, and the transient stability process of the interfered system is directly reflected, and the transient stability characteristic change when the system is interfered is easier to grasp and analyze, therefore, the method and the device can solve the technical problems that in the prior art, the system scale is large, the analysis characteristic quantity construction and selection observability are poor, the transient stability analysis and calculation is long in time consumption and not visual enough.
For easy understanding, please refer to fig. 2, an embodiment two of the transient stability analysis method provided in the present application includes:
step 201, simulating the operation data of the generator measured by the PMU in real time by using power system analysis software, or directly measuring the operation data of the generator of the disturbed power system in real time by the PMU.
Wherein the generator operating data comprises: generator speed and generator bus voltage.
It should be noted that, in the two methods for obtaining the generator operation data, it is difficult to acquire complete operation data in a short time by directly measuring the generator operation data of the disturbed power system in real time by using the PMU, and the operation states of the generator in different scenes can be simulated according to needs by using the method for simulating the power system analysis software, which is more beneficial to analysis and research.
Step 202, based on the characteristic ellipsoid theory, mapping the operation data to a multidimensional space to generate a characteristic ellipsoid.
It should be noted that the theory of characteristic ellipsoids refers to finding a minimum-volume-enclosing ellipsoid (MVEE) containing a specific part of the system trajectory in the multidimensional sample space; if the generator operation data is obtained through simulation of analysis software, the set power frequency period can be used as a unit, updating compensation is performed, a characteristic ellipsoid changing along with time is generated by using a characteristic ellipsoid theory, and an ellipsoid in a transient stable state and an ellipsoid in a transient destabilization state are mainly obtained, wherein the deviation between the ellipsoid and the ellipsoid is the largest, please refer to fig. 3 and 4, fig. 3 is the change of the form of the characteristic ellipsoid in the transient stable scene, and fig. 4 is the change of the form of the characteristic ellipsoid in the transient destabilization scene. If the characteristic ellipsoid is composed of m generator operation data measured in real time, i.e. p1,p2,……,pm∈RnWhere n is the number of PMUs, RnRepresenting an n-dimensional sample space, m being the number of measured generator operating data in a particular order, and P representing an n x m matrix with a column vector of P1,p2,……,pm∈Rn：
P＝[p1|p2|......|pm|]
Then a defined characteristic ellipsoid can be obtained:
EA,c＝{pi∈Rn|(pi-c)TA(pi-c)≤1}
wherein E isA,cMapping generator operation data to a feature ellipsoid generated in a multi-dimensional sample space, wherein a vector c represents the center of the feature ellipsoid, a positive definite matrix A determines the direction and the shape of the ellipsoid, an optimization model with the minimized feature ellipsoid volume is constructed, and a vector c belonging to R which meets the feature ellipsoid is solvednAnd a positive definite matrix A, the minimum value of the characteristic ellipsoid volume is proportional to the square root of the determinant of the matrix A, and the following mathematical model is obtained:
Figure BDA0002307256640000081
s.t(pi-c)TA(pi-c)≤1 i＝1,......,m
defining the dataset P' { ± q { -q) according to ky (kumar and yildirim) algorithm1,......,±qnWherein q isi＝[pi,1]TAnd if the P 'is centrosymmetric, the MVEE (P') can be used for replacing MVEE (P) for solving, and the following optimization model is obtained:
min(-log(detM))
s.t qi TMqi≤1 i＝1,2,......,m
wherein, the positive definite symmetric matrix M belongs to R(n+1)×(n+1)For the optimization goal of the model, the obtained optimization model is further subjected to dual operation, so that a final optimization model can be obtained:
min(-log(detV(s)))
s.t eTs＝1,s≥0
wherein s ∈ RnFor the purpose of the optimization of the model,
Figure BDA0002307256640000082
e is the total 1 column vector, and the final optimization model is solved to obtain a central vector c and a positive definite matrix A.
The characteristic ellipsoid in the multidimensional sample space contains the generator rotation speed or generator bus voltage data measured by the PMU, and under normal conditions, track information basically keeps unchanged, and the ellipsoid parameters cannot be changed too much when the track information is mapped into the characteristic ellipsoid; when the system is disturbed, the sudden change generated by the track information can be synchronously reflected on the dynamic change of the characteristic ellipsoid, and the characteristic ellipsoids under different stable scenes have respective change trends, so that the transient stability of the system can be qualitatively identified from the form change of the ellipsoids.
And 203, constructing ellipsoid characteristic quantities according to the geometric attributes of the characteristic ellipsoids, wherein the geometric attributes comprise eccentricity, volume change rate and central point set.
It should be noted that the geometric attributes represent the change of the shape of the characteristic ellipsoid, and are mainly obtained by mathematical calculation, and the eccentricity of the characteristic ellipsoid is calculated by a preset eccentricity formula, where the preset eccentricity formula is:
Figure BDA0002307256640000091
wherein r ismaxAnd rminRespectively the longest semiaxis length and the shortest semiaxis length of the characteristic ellipsoid;
calculating the volume of the characteristic ellipsoid through a preset volume formula, wherein the preset volume formula is as follows:
Figure BDA0002307256640000092
wherein gamma is standard gamma function, A is positive definite matrix, n is PMU number, EA，cIs a characteristic ellipsoid;
calculating the volume change rate of the characteristic ellipsoid through a preset volume change rate formula, wherein the preset volume change rate formula is as follows:
Figure BDA0002307256640000093
wherein, Δ V is the volume change of the characteristic ellipsoid within Δ t time;
the central point is set as O ═ O1,O2,…,Oi,…,On]In which O isiThe average value of the generator running data collected by the ith PMU in a preset time window is obtained through a preset average value formula, and the preset average value formula is as follows:
Figure BDA0002307256640000094
where m is the total number, piIs the operational data.
And 204, performing power system fault simulation by adopting simulation software, and constructing generator operation sample data sets of different scenes according to simulation.
And step 205, obtaining the geometric attributes of the sample ellipsoid according to the sample ellipsoid generated by the generator operation sample dataset, and constructing a training feature set and a testing feature set.
And step 206, testing the preset decision tree model obtained by training the training feature set by using the test feature set.
It should be noted that, steps 204 to 206 are performed to train a preset decision tree model, the decision tree model is obtained based on offline training of a large number of simulation samples, fault simulation is performed on the test system by using simulation software, the load level is randomly fluctuated within a certain range to simulate different operation conditions of the system, and fault points and fault clearing time are randomly set to construct sample sets under different disturbed scenes. And extracting disturbed trajectory information in the sample set, mapping by using a characteristic ellipsoid theory, calculating the geometric attributes of the obtained characteristic ellipsoid, and dividing the input characteristic quantity set into a training set and a testing set. The training set is used for training the decision tree classification model, and the testing set is used for evaluating the classification performance of the classification model. The finally obtained decision tree model can be directly used as a classifier for online classification, and the speed of transient stability classification is improved.
And step 207, taking the ellipsoid characteristic quantity as an input parameter of the preset decision tree model to obtain an output transient stability analysis result of the preset decision tree model.
For ease of understanding, referring to fig. 5, an embodiment of a transient stability analysis device is also provided herein, including:
the measurement module 301 is configured to measure, in real time, operation data of the generator through the PMU after the system is disturbed, where the operation data includes: generator speed and generator bus voltage;
a feature ellipsoid module 302, configured to map the operation data to a multidimensional space based on a feature ellipsoid theory to generate a feature ellipsoid;
a constructing module 303, configured to construct an ellipsoid feature quantity according to geometric attributes of the feature ellipsoid, where the geometric attributes include eccentricity, volume change rate, and center point set;
the analysis module 304 is configured to use the ellipsoid feature quantity as an input parameter of the preset decision tree model to obtain a transient stability analysis result output by the preset decision tree model.
Further, still include: a preset decision module 305, configured to perform power system fault simulation by using simulation software; constructing different power system fault scenes for simulation, and taking the obtained generator operation data as a sample data set; obtaining the geometric attributes of a sample ellipsoid according to the sample ellipsoid generated by the generator operation sample dataset, and constructing a training feature set and a testing feature set; and testing the preset decision tree model obtained by training the training feature set by using the test feature set.
Further, the configuration module 303 includes an eccentricity module 3031, a volume module 3032, a volume change rate module 3033, and a center point collection module 3034.
The eccentricity ratio module is used for calculating the eccentricity ratio through a preset eccentricity ratio formula, and the preset eccentricity ratio formula is as follows:
Figure BDA0002307256640000101
wherein r ismaxAnd rminRespectively the longest semiaxis length and the shortest semiaxis length of the characteristic ellipsoid;
the volume module is used for calculating the volume through a preset volume formula, and the preset volume formula is as follows:
Figure BDA0002307256640000102
wherein gamma is standard gamma function, A is positive definite matrix, n is PMU number, EA，cIs a characteristic ellipsoid;
the volume change rate module is used for calculating the volume change rate according to a preset volume change rate formula, wherein the preset volume change rate formula is as follows:
Figure BDA0002307256640000111
wherein, Δ V is the volume change of the characteristic ellipsoid within Δ t time;
the central point is a central point set module, and the central point set is O ═ O1,O2,…,Oi,…,On]Which isMiddle OiThe average value of the generator running data collected by the ith PMU in a preset time window is obtained through a preset average value formula, and the preset average value formula is as follows:
Figure BDA0002307256640000112
where m is the total number, piIs the operational data.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for executing all or part of the steps of the method described in the embodiments of the present application through a computer device (which may be a personal computer, a server, or a network device). And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. A method of transient stability analysis, comprising:
after the system is disturbed, measuring the operation data of the generator in real time through a PMU, wherein the operation data comprises: generator speed and generator bus voltage;
mapping the operation data to a multi-dimensional space based on a characteristic ellipsoid theory to generate a characteristic ellipsoid;
constructing ellipsoid characteristic quantities according to geometric properties of the characteristic ellipsoids, wherein the geometric properties comprise eccentricity, volume change rate and central point set;
and taking the ellipsoid characteristic quantity as an input parameter of a preset decision tree model to obtain an output transient stability analysis result of the preset decision tree model.
2. The transient stability analysis method of claim 1, wherein the real-time measurement of the operation data of the generator by the PMU after the system disturbance comprises:
and simulating the running data of the generator measured by the PMU in real time by adopting power system analysis software, or directly measuring the running data of the generator of the disturbed power system in real time by the PMU.
3. The transient stability analysis method of claim 1, wherein the eccentricity is calculated by a preset eccentricity formula, and the preset eccentricity formula is:
Figure FDA0002307256630000011
wherein r ismaxAnd rminThe longest semi-axis length and the shortest semi-axis length of the characteristic ellipsoid are respectively.
4. The transient stability analysis method of claim 1, wherein the volume is calculated by a preset volume formula, wherein the preset volume formula is as follows:
Figure FDA0002307256630000012
wherein Γ () is a standard gamma function, A is a positive definite matrix, n is the number of PMUs, EA，cIs the characteristic ellipsoid.
5. The transient stability analysis method of claim 1, wherein the volume change rate is calculated by a preset volume change rate formula, wherein the preset volume change rate formula is:
Figure FDA0002307256630000013
wherein Δ V is the volume change of the characteristic ellipsoid within Δ t time.
6. The transient stability analysis method of claim 1, wherein the set of center points is O ═ O [ O ═ O1,O2,…,Oi,…,On]In which O isiThe average value of the generator running data collected by the ith PMU in a preset time window is obtained through a preset average value formula, wherein the preset average value formula is as follows:
Figure FDA0002307256630000021
where m is the total number, piIs the operational data.
7. The transient stability analysis method of claim 1, wherein the obtaining of the output transient stability analysis result of the preset decision tree model by using the ellipsoid feature quantity as an input parameter of the preset decision tree model further comprises:
adopting simulation software to simulate the fault of the power system;
constructing different power system fault scenes for simulation, and taking the obtained generator operation data as a sample data set;
obtaining the geometric attributes of the sample ellipsoid according to the sample ellipsoid generated by the operation sample dataset of the generator, and constructing a training feature set and a testing feature set;
and testing the preset decision tree model obtained by training the training feature set by using the test feature set.
8. A transient stability analysis device, comprising:
the measuring module is used for measuring the operation data of the generator in real time through the PMU after the system is disturbed, and the operation data comprises: generator speed and generator bus voltage;
the characteristic ellipsoid module is used for mapping the operation data to a multi-dimensional space based on a characteristic ellipsoid theory to generate a characteristic ellipsoid;
the constructing module is used for constructing ellipsoid characteristic quantities according to the geometric properties of the characteristic ellipsoids, wherein the geometric properties comprise eccentricity, volume change rate and central point set;
and the analysis module is used for taking the ellipsoid characteristic quantity as an input parameter of a preset decision tree model to obtain an output transient stability analysis result of the preset decision tree model.
9. The transient stability analysis device of claim 8, wherein the construction module comprises:
the eccentricity ratio module is used for calculating the eccentricity ratio through a preset eccentricity ratio formula, wherein the preset eccentricity ratio formula is as follows:
Figure FDA0002307256630000022
wherein r ismaxAnd rminThe longest semiaxis length and the shortest semiaxis length of the characteristic ellipsoid are respectively;
a volume module for calculating the volume by a preset volume formula, the preset volume formula being:
Figure FDA0002307256630000031
wherein Γ () is a standard gamma function, A is a positive definite matrix, n is the number of PMUs, EA，cIs the characteristic ellipsoid;
a volume change rate module for calculating the volume change rate according to a preset volume change rate formula, wherein the preset volume change rate formula is as follows:
Figure FDA0002307256630000032
wherein Δ V is the volume change of the characteristic ellipsoid within Δ t time;
a center point set module, wherein the center point set is O ═ O1,O2,…,Oi,…,On]In which O isiThe average value of the generator running data collected by the ith PMU in a preset time window is obtained through a preset average value formula, wherein the preset average value formula is as follows:
Figure FDA0002307256630000033
where m is the total number, piIs the operational data.
10. The transient stability analysis device of claim 8, further comprising:
the preset decision module is used for performing fault simulation on the power system by adopting simulation software;
constructing different power system fault scenes for simulation, and taking the obtained generator operation data as a sample data set;
obtaining the geometric attributes of the sample ellipsoid according to the sample ellipsoid generated by the operation sample dataset of the generator, and constructing a training feature set and a testing feature set;
and testing the preset decision tree model obtained by training the training feature set by using the test feature set.
CN201911244909.0A 2019-12-06 2019-12-06 Transient stability analysis method and device Pending CN111064186A (en)

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