CN112054557B - Distribution network topology change type identification method based on random matrix theory - Google Patents

Distribution network topology change type identification method based on random matrix theory Download PDF

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CN112054557B
CN112054557B CN202010927628.1A CN202010927628A CN112054557B CN 112054557 B CN112054557 B CN 112054557B CN 202010927628 A CN202010927628 A CN 202010927628A CN 112054557 B CN112054557 B CN 112054557B
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周南
罗林根
韩蓓
刘亚东
盛戈皞
宋辉
钱勇
江秀臣
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Abstract

The invention discloses a distribution network topology change type identification method based on a random matrix theory, which comprises the following steps: (1) collecting node voltage data of a power distribution network (2), calculating a voltage covariance matrix of the power distribution network (3), performing characteristic decomposition on the voltage covariance matrix, and arranging obtained characteristic values from large to small into lambda1,λ2,…,λn(4) Calculating a first detection criterion C based on the feature values1And a second detection criterion C2(5) According to a first detection criterion C1And a second detection criterion C2And identifying the topology change type of the power distribution network. In addition, the invention also discloses a power distribution network topology change type identification system which comprises a voltage data acquisition device and a processing module, wherein the power distribution network topology change type identification system executes the power distribution network topology change type identification method.

Description

Distribution network topology change type identification method based on random matrix theory
Technical Field
The invention relates to a power distribution network state identification method, in particular to a power distribution network topology change type identification method.
Background
In recent years, with the grid connection of large-scale distributed power sources including wind power and photovoltaic power, the popularization of electric vehicles, the wide application of energy storage systems and the change of power utilization modes of users, the problems of randomness, uncertainty and the like of modern power distribution networks are remarkably increased, and the traditional power distribution network state perception technology is difficult to meet the continuously improved network information perception requirement.
It should be noted that, when various events (including topology change, power change, etc.) occur in the power distribution network, classification and identification of the events can well improve the sensing capability of the network, so as to provide auxiliary information for subsequent actions and processing.
Therefore, in order to improve the state perception capability of the power distribution network, a power distribution network topology change type identification method based on a random matrix theory is expected to be obtained, the power distribution network topology change type identification method can detect and identify the topology change type in the network by measuring the voltage amplitude of the network node, and meanwhile, a quantitative classification method is given, so that the state perception capability of the power distribution network is effectively improved.
Disclosure of Invention
One of the purposes of the invention is to provide a distribution network topology change type identification method based on a random matrix theory, the distribution network topology change type identification method can detect and identify the topology change type in the network by measuring the voltage amplitude of the network node, the detection precision is high, and the state perception capability of the distribution network can be effectively improved.
By adopting the method for identifying the topology change type of the power distribution network, the topology change type in the power distribution network can be effectively detected and identified, auxiliary information can be provided for follow-up action and processing, and the method has very important practical significance.
According to the above object, the present invention provides a method for identifying topology change type of a power distribution network based on a random matrix theory, which comprises the following steps:
(1) collecting node voltage data of a power distribution network;
(2) calculating a voltage covariance matrix of the power distribution network;
(3) performing feature classification on the voltage covariance matrixSolving, and arranging the obtained characteristic values from large to small as lambda1,λ2,…,λn；
(4) Calculating a first detection criterion C based on the feature values1And a second detection criterion C2；
(5) According to a first detection criterion C1And a second detection criterion C2And identifying the topology change type of the power distribution network.
In the technical scheme, the invention provides the power distribution network topology change type identification method based on the random matrix theory, the detection precision of the method is high, the topology change type in the power distribution network can be effectively detected and identified, and therefore, auxiliary information can be provided for follow-up actions and processing, and the method has very important practical significance.
In the method for identifying the topology change type of the power distribution network based on the random matrix theory, the M-P law in the random matrix is utilized to research the characteristic value distribution of the voltage data obtained by measurement in the power distribution network, and the characteristic value distribution of the voltage data is found to be changed after the topology change occurs in the network before the topology change, and the topology changes of different types correspond to different characteristic value distributions. Therefore, by analyzing the transformation situation of the characteristic value distribution, the topology change situation can be identified and classified.
Further, in the method for identifying a topology change type of a power distribution network, in the step (1), a sensor or a power management unit of the power distribution network is used to collect the node voltage data, and the voltage data at this time is recorded as a voltage v (t) before the change.
Further, in the method for identifying a topology change type of a power distribution network, in the step (1), the average value of the collected node voltage data is set to zero, and the voltage at this time is recorded as a changed voltage and is an N × N dimensional matrix v' (t), where N represents the number of sensors or the number of nodes collecting data, and N represents the length of the voltage data collected by each sensor.
Further, in the method for identifying topology change type of power distribution network according to the present invention, in the step (2), a voltage covariance matrix S of the power distribution network is calculated based on the following formula:
Figure BDA0002668995150000021
wherein v' (t)*Representing the conjugate transpose of v' (t).
Further, in the method for identifying topology change type of power distribution network according to the present invention, in the step (4), the first detection criterion C is set to be lower than the first detection criterion C1And a second detection criterion C2Are obtained based on the following respectively:
Figure BDA0002668995150000031
Figure BDA0002668995150000032
where c is N/N, N represents the number of sensors or the number of nodes collecting data, N represents the length of voltage data collected by each sensor, and f represents the number of nodes collecting dataKDE(λi) Representing a characteristic value λiThe estimation of the nuclear density of (a),
Figure BDA0002668995150000033
k (x) represents a Gaussian kernel,
Figure BDA0002668995150000034
fESD(λi) Representing a characteristic value λiThe distribution of (a) to (b) is,
Figure BDA0002668995150000035
c+and c-Respectively representing a maximum eigenvalue and a minimum eigenvalue,
Figure BDA0002668995150000036
and
Figure BDA0002668995150000037
wherein sigma2Representing the variance of the collected voltage data.
Further, in the method for identifying a topology change type of a power distribution network according to the present invention, in the step (5), the topology change type of the power distribution network includes: the method comprises the steps of power distribution network normality, line topology change of a power distribution network, node topology change of the power distribution network and breaker action.
Further, in the method for identifying a topology change type of a power distribution network according to the present invention, in the step (5):
when the first detection criterion C1∈[0.95,1.05]And a second detection criterion C2∈[0,0.025]Judging the normal operation of the power distribution network;
when the first detection criterion C1∈[3.75×104,4.10×104]And a second detection criterion C2∈[0.035,0.047]Judging that the circuit topology changes; when the first detection criterion C1∈[0.95×106,1.05×106]And a second detection criterion C2∈[0.065,0.080]Judging that node topology change occurs; when the first detection criterion C1∈[2.95×1010,3.35×1010]And a second detection criterion C2∈[0.055,0.067]And judging the action of the breaker.
Accordingly, another object of the present invention is to provide a power distribution network topology change type identification system, which detects and identifies topology change types in a network by measuring voltage amplitudes of network nodes, and provides a quantitative classification method, so as to provide auxiliary information for subsequent actions and processing, and the detection precision is high, so that the state sensing capability of a power distribution network can be effectively improved, and the power distribution network topology change type identification system has good popularization prospects and application values.
According to the above object, the present invention provides a power distribution network topology change type identification system, which includes a voltage data acquisition device and a processing module, and the power distribution network topology change type identification system executes the above power distribution network topology change type identification method.
Compared with the prior art, the method for identifying the topology change type of the power distribution network based on the random matrix theory has the following advantages and beneficial effects:
in conclusion, the method for identifying the topology change type of the power distribution network based on the random matrix theory can detect and identify the topology change type in the network by measuring the voltage amplitude of the network node, can provide a quantitative method for classification at the same time, has high detection precision, and can effectively improve the state perception capability of the power distribution network.
By adopting the method for identifying the topology change type of the power distribution network, the topology change type in the power distribution network can be effectively detected and identified, auxiliary information can be provided for follow-up action and processing, and the method has very important practical significance.
Accordingly, the power distribution network topology change type identification system of the invention also has the advantages and beneficial effects.
Drawings
Fig. 1 is a schematic flow chart illustrating steps of a distribution network topology change type identification method based on a random matrix theory according to an embodiment of the present invention.
FIG. 2 schematically shows a first detection criterion C for different topology changes after a number of topology change experiments1And a second detection criterion C2Distribution of (2).
FIG. 3 schematically shows a first detection criterion C before a topology change in a multiple topology change experiment1And a second detection criterion C2Distribution of (2).
Fig. 4 schematically shows a comparison of classification success rates of three topology changes, namely, a line topology change, a node topology change and a breaker action under the condition of different signal-to-noise ratios.
Detailed Description
The method for identifying topology change type of power distribution network based on random matrix theory according to the present invention will be further explained and illustrated with reference to the drawings and specific embodiments of the specification, however, the explanation and illustration should not be construed as an undue limitation on the technical solution of the present invention.
In the method for identifying the topology change type of the power distribution network based on the random matrix theory, the M-P law in the random matrix is utilized to research the characteristic value distribution of the voltage data obtained by measurement in the power distribution network, and the characteristic value distribution of the voltage data is found to be changed after the topology change occurs in the network before the topology change, and the topology changes of different types correspond to different characteristic value distributions. Based on this, the topology change situation can be identified and classified by analyzing the transformation situation of the above-mentioned feature value distribution.
Before a topology change occurs in the distribution network, the admittance of the line (i, j) connecting node i and node j of the distribution network can be recorded as yijAccording to kirchhoff's law, the relationship of electrical quantities in the power distribution network can be expressed as:
Figure BDA0002668995150000051
wherein,
Figure BDA0002668995150000052
representing the floating value of the network node voltage vector minus its mean value,
Figure BDA0002668995150000053
representing the floating value of the network node current and Y representing the admittance matrix.
Accordingly, the number of the first and second electrodes,
Figure BDA0002668995150000054
represents a NxN dimensional network admittance matrix, and when i ≠ j, Yij＝-yijWhen i is j, Yjj＝∑i≠jyij。
The admittance matrix Y is a symmetric matrix, which is characterized by:
Figure BDA0002668995150000055
wherein, UYRepresenting unitary matrices containing Y eigenvectors, ΛYThen a diagonal matrix containing Y eigenvalues is represented,
Figure BDA0002668995150000056
represents UYThe conjugate transpose of (c).
Multiplication of equation (1) on the left of equal sign
Figure BDA0002668995150000058
And substituting into equation (2), one can obtain:
Figure BDA0002668995150000059
thus, the following formula (4) can be obtained:
Figure BDA00026689951500000510
wherein, E [. C]Representing a mathematical expectation, INRepresenting an identity matrix.
By substituting equation (1) into equation (4) again, the following can be obtained:
E[v′(t)·v′(t)*]＝IN (5)
wherein, in the formula (5),
Figure BDA0002668995150000061
v′(t)*denotes the conjugate transpose of v ' (t), E [ v ' (t) · v ' (t)*]The covariance matrix of v' (t) is represented.
It should be noted that, when a topology change occurs in the power distribution network, the topology change is reflected in the model as a change in the admittance matrix Y, and let us note that the admittance matrix B after the topology change is B ═ Y + Δ Y. Only three types of topology changes, including line topology changes, node topology changes, and breaker actions, are discussed herein.
For a line topology change, it refers to the addition or removal of a certain line in the power distribution network, and assuming that the line is line (i, j), for the line topology change, Δ Y ═ Δ YL. Accordingly, Δ YLCan be obtained by the following formula (6):
ΔYL＝(ei-ej)yij(ei-ej)*＝ω·ω* (6)
wherein,
Figure BDA0002668995150000062
ω*denotes the conjugate transpose of ω, eiRepresenting a vector of length N and in which the elements are 0 except the ith element which is 1, ejRepresents a vector of length N and in which the elements are 0 except for the jth element which is 1. Vector quantity
Figure BDA0002668995150000063
Is defined as: e.g. of the typei(i)＝1，ei(j) 0 when j ≠ i; knowing Δ YLIs 1.
For node topology change, the node in the power distribution network is removed in the network due to problems such as faults, and if the ith node in the network is removed, for the node topology change, Δ Y ═ Δ YN. Accordingly, Δ YNThis can be obtained from the following equation (7):
Figure BDA0002668995150000064
wherein, Yr(i) And Yc(i) Each representing respectively the ith row and the ith column of the admittance matrix Y, ei *Denotes eiThe conjugate transpose of (c), diag (·) represents the diagonal matrix. In the diagonal matrix, yi1Representing the element in row i, column 1 of the admittance matrix Y, Y accordinglyiNAn element representing the ith row and the nth column in the admittance matrix Y; knowing Δ YNIs 2.
For circuit breaker action, circuit breaker refers toWhen a switching device in a certain range is connected to a distribution network, the action of a circuit breaker will affect the change of the network topology in the distribution network in a large range, and the action of the circuit breaker is delta Y-delta YSA. Accordingly, Δ YSAThis can be obtained from the following equation (8):
ΔYSA＝ΔYL(i,j)+ΔYN(m)+...+ΔYN(n) (8)
wherein, Delta YSAIs dependent on the size of the network range of the breaker connection, Δ YL(i, j) represents the change of admittance matrix when the topology of the ith to j-th lines changes, and the form is shown in formula (6), Δ YN(m) represents the change of the admittance matrix when the topology of the mth node changes, and the form is shown in formula (7), Δ YNAnd (n) represents the admittance matrix change when the topology change occurs to the nth node, and the form of the admittance matrix change is shown in formula (7).
It can be seen that different types of topology changes have different influence ranges, and the quantitative index of the influence ranges is the rank of the Δ Y.
Correspondingly, the characteristic decomposition can be carried out on the admittance matrix B after the topological change, and the admittance matrix B is projected to the subspace U of the original admittance matrixYFrom this, equation (9) can be derived:
Figure BDA0002668995150000071
wherein, ΛBIs a diagonal matrix containing B eigenvalues.
By deriving with reference to the analogous procedure of the above equation (3) -equation (5), it is possible to obtain:
E[v″(t)·v″(t)*]＝IN+ΔC (10)
wherein v "(t) represents a node voltage vector after the topology change, v" (t)*Denotes the conjugate transpose of v "(t), E [ v '(t). v' (t)*]Covariance matrix, I, representing v' (t)NDenotes an identity matrix, and Δ C denotes a variation in the voltage covariance matrix.
Comparing the above formula (5) and formula (10), it can be known that the topology change in the power distribution network can be judged by the change in the covariance matrix of the node voltage vector. According to the above description, different types of topology changes have different types of admittance matrix changes, and therefore the rank of the corresponding subspace perturbation DC is also different. Based on this, the topology change situation can be identified and classified by analyzing the transformation situation of the above-mentioned feature value distribution.
Fig. 1 is a schematic flow chart illustrating steps of a distribution network topology change type identification method based on a random matrix theory according to an embodiment of the present invention.
As shown in fig. 1, in this embodiment, the method for identifying a topology change type of a power distribution network based on a random matrix theory according to the present invention may include the following steps:
(a) collecting node voltage data of a power distribution network;
(b) calculating a voltage covariance matrix of the power distribution network;
(c) performing characteristic decomposition on the voltage covariance matrix, and arranging the obtained characteristic values from large to small as lambda1,λ2,…,λn；
(d) Calculating a first detection criterion C based on the feature values1And a second detection criterion C2；
(e) According to a first detection criterion C1And a second detection criterion C2And identifying the topology change type of the power distribution network.
In step (a), the node voltage data of the power distribution network collected by a sensor or a power management unit of the power distribution network may be recorded as the voltage v (t) before the change.
In some embodiments, the mean value of the collected node voltage data of the power distribution network may be set to zero, and the voltage at this time is recorded as a changed voltage and is an N × N dimensional matrix v' (t), where N represents the number of sensors or nodes collecting data, and N represents the length of the voltage data collected by each sensor.
Accordingly, in step (b) of the present invention, a voltage covariance matrix S of the power distribution network may be calculated based on the following formula (11) according to the node voltage data collected in step (a):
Figure BDA0002668995150000081
wherein, v' (t)*Representing the conjugate transpose of v' (t).
In step (d), it should be noted that the first detection criterion C1And a second detection criterion C2Can be obtained based on the following formula (12) and formula (13), respectively:
Figure BDA0002668995150000082
Figure BDA0002668995150000083
in the above formula (12), c is N/N, N represents the number of sensors or the number of nodes that collect data, and N represents the length of voltage data collected by each sensor.
In the above formula (13), fKDE(λi) Representing a characteristic value λiThe nuclear density of (2) can be estimated by the following equation (14)KDE(λi)：
Figure BDA0002668995150000084
Wherein the kernel function is selected from Gaussian kernel K (x), and
Figure BDA0002668995150000085
accordingly, in the above formula (13), fESD(λi) Representing a characteristic value λiThe distribution of (A) is known according to the M-P law in the random matrix theory, and the characteristic value lambda of the independent and identically distributed N x N-dimensional random variable matrixiShould satisfy the following distribution:
Figure BDA0002668995150000091
in the above formula, c+And c-Respectively representing a maximum eigenvalue and a minimum eigenvalue,
Figure BDA0002668995150000092
and
Figure BDA0002668995150000093
wherein sigma2Representing the variance of the collected voltage data.
In the step (d) of the present invention, the first detection criterion C can be calculated and obtained from the characteristic value obtained in the step (C)1And a second detection criterion C2. Accordingly, in a subsequent step (e), the first detection criterion C may be determined1And a second detection criterion C2And identifying the topology change type of the power distribution network.
Therefore, in the method for identifying the topology change type of the power distribution network based on the random matrix theory, the distribution of the characteristic values of the voltage data obtained by measurement in the power distribution network can be researched by utilizing the M-P law in the random matrix, and the topology change condition can be identified and classified by analyzing the transformation condition of the distribution of the characteristic values.
In order to better illustrate the application of the distribution network topology change type identification method based on the random matrix theory, simulation tests are performed by using an IEEE 123 node distribution network system and an 8500 node system for further explanation.
In the simulation test, a distribution network topology change type identification system is adopted, and the distribution network topology change type identification system can comprise: voltage data acquisition device and processing module. In the present invention, the power distribution network topology change type identification system according to the present invention may be used to execute the above-mentioned power distribution network topology change type identification method according to the present invention.
The topology change problem in the power distribution network is detected, wherein simulation software is used for constructing an IEEE 123 node power distribution network model and an IEEE 8500 node power distribution network model according to the IEEE standard, and experimental verification is carried out in the network.
Table 1 shows the first detection criterion C for different topology changes in different distribution networks1And a second detection criterion C2Wherein the topology change can include three types: network line topology changes, network node topology changes, circuit breaker action.
Table 1.
Figure BDA0002668995150000101
As can be seen from Table 1, the first detection criterion C is applied before the topology of the distribution network changes1And a second detection criterion C2Tends to settle in a range closer to 1, and when the topology changes, the first detection criterion C1Will be outside the above range.
Repeating the topology change experiment for multiple times, and respectively recording the first detection standard C under different topology change conditions1And a second detection criterion C2And both are respectively taken as coordinate axes of the data, which are drawn in a two-dimensional plane, as shown in fig. 2 and 3, the data points in fig. 2 and 3 may be referred to as "event points".
FIG. 2 schematically shows a first detection criterion C for different topology changes after a number of topology change experiments1And a second detection criterion C2Distribution of (2).
FIG. 3 schematically shows a first detection criterion C before a topology change situation in a multiple topology change experiment1And a second detection criterion C2Distribution of (2).
As can be seen in conjunction with fig. 2 and 3, fig. 3 can be used to compare the data of fig. 2. In this embodiment, each "event point" corresponds to a state of the distribution network when the first detection criteria C1∈[0.95,1.05]And a second detection criterion C2∈[0,0.025]Correspondingly judging the normal operation of the power distribution network; for the three topology changes described above, it can be seen from the figure that the distribution characteristics of the "event points" of the different kinds of topology changes are different, and the clustering analysis of the "event points" can be known, when the first detection criterion C is used1∈[3.75×104,4.10×104]And a second detection criterion C2∈[0.035,0.047]Judging that the circuit topology changes; when the first detection criterion C1∈[0.95×106,1.05×106]And a second detection criterion C2∈[0.065,0.080]Judging that node topology change occurs; when the first detection criterion C1∈[2.95×1010,3.35×1010]And a second detection criterion C2∈[0.055,0.067]And judging the action of the breaker.
Fig. 4 schematically shows a comparison of classification success rates of three topology changes, namely, a line topology change, a node topology change and a breaker action under the condition of different signal-to-noise ratios.
As shown in fig. 4, fig. 4 shows a comparison of success rates of classification in the case of different signal-to-noise ratios (SNRs) of three topological changes, namely, a line topological change of the distribution network, a node topological change of the distribution network, and a breaker action.
It should be noted that, the signal-to-noise ratio calculation method is as follows:
SNR＝20·log(AS/AN) (16)
wherein A isSRepresenting the signal amplitude; a. theNRepresenting the noise amplitude.
As can be seen from fig. 4, although the classification of the topology change is affected to some extent by the noise, the success rate of classification can reach 100% when the signal-to-noise ratio is greater than 5 dB. Therefore, the method for identifying the topology change type of the distribution network based on the random matrix theory can effectively detect three types of topology changes, namely line topology change, node topology change and breaker action, and is high in precision.
In conclusion, the method for identifying the topology change type of the power distribution network based on the random matrix theory can detect and identify the topology change type in the network by measuring the voltage amplitude of the network node, can provide a quantitative method for classification at the same time, has high detection precision, and can effectively improve the state perception capability of the power distribution network.
By adopting the method for identifying the topology change type of the power distribution network, the topology change type in the power distribution network can be effectively detected and identified, auxiliary information can be provided for follow-up action and processing, and the method has very important practical significance.
Accordingly, the power distribution network topology change type identification system of the invention also has the advantages and beneficial effects.
It should be noted that the prior art in the protection scope of the present invention is not limited to the examples given in the present application, and all the prior art which is not inconsistent with the technical scheme of the present invention, including but not limited to the prior patent documents, the prior publications and the like, can be included in the protection scope of the present invention.
In addition, the combination of the features in the present application is not limited to the combination described in the claims of the present application or the combination described in the embodiments, and all the features described in the present application may be freely combined or combined in any manner unless contradictory to each other.
It should also be noted that the above-mentioned embodiments are only specific embodiments of the present invention. It is apparent that the present invention is not limited to the above embodiments and similar changes or modifications can be easily made by those skilled in the art from the disclosure of the present invention and shall fall within the scope of the present invention.

Claims (7)

1. A distribution network topology change type identification method based on a random matrix theory is characterized by comprising the following steps:
(1) collecting node voltage data of a power distribution network;
(2) calculating a voltage covariance matrix of the power distribution network;
(3) performing characteristic decomposition on the voltage covariance matrix, and arranging the obtained characteristic values from large to small as lambda1,λ2,…,λn；
(4) Calculating a first detection criterion C based on the feature values1And a second detection criterion C2(ii) a Wherein the first detection criterion C1And a second detection criterion C2Are obtained based on the following respectively:
Figure FDA0003298661140000011
Figure FDA0003298661140000012
where c is N/N, N represents the number of sensors or the number of nodes collecting data, N represents the length of voltage data collected by each sensor, and f represents the number of nodes collecting dataKDE(λi) Representing a characteristic value λiThe estimation of the nuclear density of (a),
Figure FDA0003298661140000013
k (x) represents a Gaussian kernel,
Figure FDA0003298661140000017
fESD(λi) Representing a characteristic value λiThe distribution of (a) to (b) is,
Figure FDA0003298661140000014
c+and c-Respectively representing a maximum eigenvalue and a minimum eigenvalue,
Figure FDA0003298661140000015
and
Figure FDA0003298661140000016
wherein sigma2Representing a variance of the collected voltage data;
(5) according to a first detection criterion C1And a second detection criterion C2And identifying the topology change type of the power distribution network.
2. The method for identifying topology change type of power distribution network according to claim 1, wherein in step (1), the node voltage data is collected by using a sensor or a power management unit of the power distribution network, and the voltage data at this time is recorded as voltage v (t) before change.
3. The method for identifying topology change type of power distribution network according to claim 1, wherein in step (1), the mean value of the collected node voltage data is set to zero, and the voltage at this time is recorded as the changed voltage and is an N × N dimensional matrix v' (t), where N represents the number of sensors or the number of nodes collecting data, and N represents the length of the voltage data collected by each sensor.
4. The distribution network topology change type identification method according to claim 3, characterized in that in said step (2), a voltage covariance matrix S of the distribution network is calculated based on the following formula:
Figure FDA0003298661140000021
wherein v' (t)*Representing the conjugate transpose of v' (t).
5. The method for identifying a type of topological change in an electricity distribution network according to claim 1, wherein in said step (5), the type of topological change in an electricity distribution network comprises: the method comprises the steps of power distribution network normality, line topology change of a power distribution network, node topology change of the power distribution network and breaker action.
6. The method for identifying the type of topological change of the power distribution network according to claim 5, characterized in that in said step (5):
when the first detection criterion C1∈[0.95,1.05]And a second detection criterion C2∈[0,0.025]Judging the normal operation of the power distribution network;
when the first detection criterion C1∈[3.75×104,4.10×104]And a second detection criterion C2∈[0.035,0.047]Judging that the circuit topology changes; when the first detection criterion C1∈[0.95×106,1.05×106]And a second detection criterion C2∈[0.065,0.080]Judging that node topology change occurs; when the first detection criterion C1∈[2.95×1010,3.35×1010]And a second detection criterion C2∈[0.055,0.067]And judging the action of the breaker.
7. A distribution network topology change type identification system, characterized in that it comprises voltage data acquisition means and a processing module, said distribution network topology change type identification system performing a distribution network topology change type identification method according to any of claims 1-6.
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