CN117706278B - Fault line selection method and system for power distribution network and readable storage medium - Google Patents
Fault line selection method and system for power distribution network and readable storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of power systems, in particular to a fault line selection method, a fault line selection system and a readable storage medium of a power distribution network, wherein the method comprises the following steps: after the power distribution network fails, zero sequence current in a preset time window of each feeder line and zero sequence voltage of a bus are obtained; according to the zero sequence current and the zero sequence voltage, two-dimensionally processing a feeder line in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster through a KPCA algorithm to determine a corresponding principal component score; and performing BIRCH clustering according to the principal component score to determine whether the feeder is a fault feeder. Whether the feeder line is a line with faults or not is judged through principal component score clustering, so that the target fault feeder line in the power distribution network can be accurately and rapidly determined when the current amount of the power distribution network is small, and the detection precision is improved. The problem of how to quickly identify a faulty feeder line when a single-phase earth fault occurs in a low-current grounded power distribution network is solved.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a power distribution network fault line selection method, a system and a readable storage medium.
Background
With the continuous rapid development of the power distribution network in China, the number and the scale of the power distribution network are gradually increased, and great workload is increased for operation and maintenance of the power distribution network. How to effectively guarantee and improve the safe and reliable operation of the power distribution network relates to the safe and reliable problem of power consumption of users, so that power supply enterprises have to face a very important challenge, namely how to continuously guarantee and improve the safe and reliable power supply of the power distribution network.
The neutral point of the 10kV power distribution network in China is generally grounded in a non-grounding or resonance grounding mode, when single-phase grounding faults occur, the current flowing through the fault point is small, meanwhile, the symmetry of the system is not influenced, and the system can be operated for a period of time with faults, so that the system is called a small-current grounding system. The small-current grounding system has the main advantages that the current flowing into the ground is small, and the protection action can not be caused for some disturbing transient faults, so that the power supply reliability is effectively improved. Of the various short-circuit faults, about 70% of the power failure accidents are caused by single-phase earth faults of the distribution network lines, and among the single-phase earth faults, a majority are earth arc faults which, if left untreated for a long time, are liable to cause fire accidents. In particular, in recent years, with rapid economic development and larger urban scale, in the distribution network lines, the proportion of the input cable lines is higher and higher, when single-phase grounding faults are formed due to the influence of some factors, larger grounding current is generated due to the remarkable increase of the capacitance of the system to the ground, the electric arcs are difficult to self-extinguish, equipment and lines are easy to burn down during long-term operation, and developing faults or mountain fires are formed in serious cases. According to the current research, the single-phase earth fault of the power distribution network can directly cause an arc fault, and the arc fault is difficult to extinguish in the transient process, so that the fault is timely removed when the single-phase earth fault is needed, and the single-phase earth fault is prevented from developing into an arc fault.
In the related technical scheme of the power distribution network, a neutral point of the power distribution network is generally grounded in a non-grounding or resonant grounding mode, and when a single-phase grounding fault occurs, the current flowing through the fault point is small, meanwhile, the symmetry of the system is not influenced, and the power distribution network can be operated for a period of time with the fault, so that the power distribution network is called a small-current grounding power distribution network. The small-current grounding power distribution network has the main advantages that the current flowing into the ground is small, and the protection action can not be caused for some disturbing transient faults, so that the power supply reliability is effectively improved.
However, the inventors have found at least the following drawbacks when they conceived and realized the present solution: because the current quantity in the low-current grounding power distribution network is smaller, when a single-phase grounding fault of the power distribution network occurs, the traditional fault line selection device is easy to have the problem that the fault feeder line is difficult to identify, and the defect of insufficient detection precision exists.
The KPCA-BIRCH clustering algorithm is to complete nonlinear transformation by means of a kernel function on Principal Component Analysis (PCA), further map a data set into a feature space with higher dimensionality and capable of being linearly separated, express main information of original data with fewer dimensionalities, and then perform unsupervised clustering on the obtained data through the BIRCH algorithm to determine optimal clustering data and clustering layer numbers. Compared with the PCA algorithm, the KPCA-BIRCH algorithm is more suitable for processing a nonlinear equation similar to the fault condition of the power distribution network, and the self-adaptive high-precision and high-efficiency clustering under the characteristic of no hard clustering is effectively realized by fusing the dimension reduction of data and the double algorithms without supervision, so that the accuracy of fault line selection is obviously improved, and the problem of low line selection robustness of the previous algorithm is solved.
Disclosure of Invention
The invention mainly aims to provide a fault line selection method of a power distribution network, which aims to solve the problem of how to quickly identify a fault feeder line when a single-phase grounding fault occurs in a low-current grounding power distribution network.
In order to achieve the above object, the present invention provides a fault line selection method for a power distribution network, where the method includes:
After the power distribution network fails, zero sequence current in a preset time window of each feeder line and zero sequence voltage of a bus are obtained;
According to the zero sequence current and the zero sequence voltage, two-dimensionally processing a feeder line in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster through a KPCA algorithm to determine a corresponding principal component score;
and performing BIRCH clustering according to the principal component score to determine whether the feeder is a fault feeder.
Optionally, before the step of determining the corresponding principal component score by performing two-dimensional processing on the feeder line in the power distribution network through a KPCA algorithm in a preset short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage, the method further includes:
acquiring first intercepted data, and taking the first intercepted data as the upper limit of a short time window, wherein the first intercepted data is intercepted data which is intercepted at intervals of a first preset duration before a fault;
obtaining second intercepted data, and taking the second intercepted data as the lower limit of a short time window, wherein the second intercepted data is intercepted data which is intercepted at intervals of a second preset time length after a fault;
Determining a short time window interception interval according to the short time window upper limit and the short time window lower limit;
Determining a zero sequence instantaneous power curve cluster of the power distribution network in the short time window interception section as the zero sequence instantaneous power curve cluster of the short time window;
The first preset duration is smaller than the second preset duration.
Optionally, according to the zero sequence current and the zero sequence voltage, the step of performing two-dimensional processing on the feeder line in the power distribution network in a preset short time window zero sequence instantaneous power curve cluster through a KPCA algorithm to determine a corresponding principal component score includes:
determining a target instantaneous power curve in a short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage;
Determining two-dimensional coordinates of the target instantaneous power curve based on a KPCA algorithm, wherein the two-dimensional coordinates represent fault zero-sequence power of the feeder line;
And taking the two-dimensional coordinates as the principal component scores.
The two-dimensional coordinates are obtained through a KPCA algorithm, and the formula is as follows:
；
Wherein the method comprises the steps of For the generation of a matrix of feature spaces,,...,Representing characteristic samples in a characteristic space, N being the number of samples, K being a gram matrix, elements in the matrix，Is a characteristic value.
Optionally, the step of determining whether the feeder is a faulty feeder by BIRCH clustering according to the principal component score includes:
Grouping the two-dimensional curve clusters, finding out fault modes existing in data in an unsupervised clustering mode, and mining the internal coupling relation of the data;
the unsupervised clustering mode is that a fault mode existing in data is automatically found without supervision under the condition that the number of clustering layers is set by people;
The best clustering data value k is determined through a BIRCH algorithm, the best clustering data value k is acted by combining the contour coefficient S i and the CH index, the best number of layers is iterated, and the larger the contour coefficient Si and the CH index value is, the better the clustering number is.
Simultaneously determining that the feeder associated with the principal component score is a normal feeder or a fault feeder;
the optimal clustering data value k has two layering numbers of 1 and 2:
When the k value of the optimal cluster data value is 1, indicating that the fault does not belong to the feeder line, and determining the feeder line associated with the principal component score as a normal feeder line;
When the k value of the optimal cluster data value is 2, representing that the fault belongs to the feeder, performing sensitive prompt on an abnormal point (fault point) by using a BIRCH algorithm, and determining the feeder associated with the principal component score as the fault feeder;
The BIRCH clustering algorithm has the following formula:
wherein a (i) represents the average of the distances of point i to all other points in the cluster in which it is located; b (i) represents the minimum of the average of points i to all points within a cluster that does not contain it, B is the variance between different clusters, W is the variance of data points within all clusters, n is the total number of data points, and the CH value relates to the number of clusters and the trace of the dispersion matrix between the classes.
Optionally, after obtaining the zero sequence current in the preset time window of each feeder line and the zero sequence voltage of the bus after the power distribution network fails, the method further includes:
determining whether the zero sequence voltage is greater than a preset phase voltage threshold;
And if so, carrying out two-dimensional processing on the feeder line in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster through a KPCA algorithm according to the zero sequence current and the zero sequence voltage so as to determine a corresponding principal component score.
In addition, in order to achieve the above object, the present invention further provides a fault line selection system of a power distribution network, where the fault line selection system of the power distribution network includes:
the data acquisition module is used for acquiring zero sequence current and zero sequence voltage of a bus in a preset time window of each feeder line after the power distribution network fails;
the numerical calculation module is used for determining corresponding principal component scores of feeder lines in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster according to the zero sequence current and the zero sequence voltage;
And the logic judgment module is used for determining whether the feeder is a fault feeder or not after clustering according to the principal component scores.
Optionally, the data acquisition module further includes:
the zero sequence voltage acquisition unit is used for acquiring the zero sequence voltage of the bus through a voltage transformer arranged on the bus;
the zero sequence current acquisition unit is used for acquiring the zero sequence current of each feeder line through a current transformer arranged on each feeder line.
Optionally, the numerical calculation module further includes:
the signal calculation unit is used for constructing a starting signal when the acquired instantaneous value of the zero sequence voltage is greater than a preset voltage threshold value;
The instantaneous power curve calculation unit is used for determining a target instantaneous power curve in a short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage;
And the KPCA calculation unit is used for determining the two-dimensional coordinates of the target instantaneous power curve based on a KPCA-BIRCH cluster analysis method, wherein the two-dimensional coordinates represent the fault zero sequence power of the feeder line.
Optionally, the logic determination module further includes:
the zero sequence voltage judging unit is used for determining whether the zero sequence voltage is larger than a preset phase voltage threshold value or not;
If yes, executing the step of determining corresponding principal component scores of feeder lines in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster according to the zero sequence current and the zero sequence voltage;
The fault line selection judging unit is used for determining whether the feeder is a fault feeder after BIRCH clustering is carried out on principal component scores
In addition, in order to achieve the above object, the present invention further provides a computer readable storage medium, on which a fault line selection program of a power distribution network is stored, where the fault line selection program of the power distribution network, when executed by a processor, implements the steps of the fault line selection method of the power distribution network as described above.
The invention provides a fault line selection method, a system and a readable storage medium of a power distribution network, which are characterized in that by extracting zero sequence current of a feeder line and zero sequence voltage of a bus line at intervals of a preset power frequency period after a fault occurs, then calculating principal component scores corresponding to the zero sequence current and the zero sequence voltage in a short-time window zero sequence instantaneous power curve cluster, judging whether the feeder line is a line with the fault or not according to the principal component scores, so that a target fault feeder line in the power distribution network can be accurately and rapidly determined when the current amount of the power distribution network is smaller, and the detection precision is improved.
Drawings
Fig. 1 is a schematic diagram of a fault line selection system of a power distribution network according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a simulation model of a power distribution network;
FIG. 3 is a flow chart of a first embodiment of a fault line selection method of the power distribution network of the present invention;
FIG. 4 is a schematic diagram of a single phase earth fault distribution network;
FIG. 5 is a flow chart of a second embodiment of a fault line selection method of the power distribution network of the present invention;
FIG. 6 is a flow chart of a third embodiment of a fault line selection method of the power distribution network of the present invention;
FIG. 7 is a schematic diagram of a zero sequence instantaneous power curve cluster of each feeder line of a power distribution network;
FIG. 8 is a flow chart of a fourth embodiment of a fault line selection method of the power distribution network of the present invention;
FIG. 9 is a schematic diagram of a distribution between principal component scores of a healthy line and a faulty line based on a KPCA-BIRCH cluster analysis result;
FIG. 10 is a flow chart of a fifth embodiment of a fault line selection method for a power distribution network according to the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The fault line selection method of the power distribution network can be used for protecting the power distribution networks with different voltage levels and has different view field views, and the method is flexibly configured on a 10-35kV overhead line, a cable line and an overhead-cable mixed line, can accurately identify single-phase earth faults of the power distribution network, is timely in protection action, timely isolates and eliminates the faults, and improves the stability of a power system.
In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As an implementation scheme, fig. 1 is a schematic architecture diagram of a hardware operation environment of a fault line selection system of a power distribution network according to an embodiment of the present invention.
As shown in fig. 1, the system includes a data acquisition module 101, a numerical calculation module 102, and a logic determination module 103. The data acquisition module 101 is used for acquiring zero sequence current in a preset time window of each feeder line and zero sequence voltage of a bus after the power distribution network fails; the numerical calculation module 102 is configured to determine, according to the zero-sequence current and the zero-sequence voltage, a principal component score corresponding to a feeder line in the power distribution network in a preset short-time window zero-sequence instantaneous power curve cluster; the logic judgment module 103 is configured to determine whether the feeder is a faulty feeder after clustering according to the principal component scores. Wherein:
The data acquisition module 101 may include a zero sequence voltage acquisition unit 1011 and a zero sequence current acquisition unit 1012. The zero sequence voltage acquisition unit 1011 is used for acquiring the zero sequence voltage of the bus through a voltage transformer arranged on the bus; the zero sequence current acquisition unit 1012 is used for acquiring the zero sequence current of each feeder line through a current transformer installed on each feeder line.
The numerical calculation module 102 may include a signal calculation unit 1021, an instantaneous power curve calculation unit 1022, and a kpca calculation unit 1023. The signal calculating unit 1021 is configured to construct a start signal when the acquired instantaneous value of the zero sequence voltage is greater than a preset voltage threshold, the instantaneous power curve calculating unit 1022 is configured to determine a target instantaneous power curve in a zero sequence instantaneous power curve cluster of a short time window according to the zero sequence current and the zero sequence voltage, and the KPCA calculating unit 1023 is configured to determine two-dimensional coordinates of the target instantaneous power curve based on a KPCA-BIRCH cluster analysis method, where the two-dimensional coordinates represent fault zero sequence power of the feeder line, and the two-dimensional coordinates are used as the principal component score.
The logic determination module 103 may include a zero sequence voltage determination unit 1031 and a fault line selection determination unit 1032. The zero sequence voltage judging unit 1031 is configured to determine whether the zero sequence voltage is greater than a preset phase voltage threshold, where if yes, the step of determining a principal component score corresponding to a feeder line in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster according to the zero sequence current and the zero sequence voltage is performed; the fault line selection judging unit 1032 is configured to determine whether the feeder is a fault feeder after determining that the principal component scores are BIRCH clustered
In addition, the fault line selection system of the power distribution network shown in fig. 1 further includes a memory 104 and a processor 105, where the memory 104 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 103 is used for storing a fault line selection program of the distribution network as a computer readable storage medium, and the processor 105 may be used for calling the fault line selection program of the distribution network stored in the memory 104 and performing the following operations:
After the power distribution network fails, zero sequence current in a preset time window of each feeder line and zero sequence voltage of a bus are obtained;
determining corresponding principal component scores of feeder lines in the power distribution network in a preset short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage;
and determining whether the feeder line is a fault feeder line according to the principal component score.
In one embodiment, the processor 105 may be configured to call a fault line selection program of the power distribution network stored in the memory 104, and perform the following operations:
acquiring first intercepted data, and taking the first intercepted data as the upper limit of a short time window, wherein the first intercepted data is intercepted data which is intercepted at intervals of a first preset duration before a fault;
obtaining second intercepted data, and taking the second intercepted data as the lower limit of a short time window, wherein the second intercepted data is intercepted data which is intercepted at intervals of a second preset time length after a fault;
Determining a short time window interception interval according to the short time window upper limit and the short time window lower limit;
Determining a zero sequence instantaneous power curve cluster of the power distribution network in the short time window interception section as the zero sequence instantaneous power curve cluster of the short time window;
The first preset duration is smaller than the second preset duration.
In one embodiment, the processor 105 may be configured to call a fault line selection program of the power distribution network stored in the memory 104, and perform the following operations:
determining a target instantaneous power curve in a short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage;
Determining two-dimensional coordinates of the target instantaneous power curve based on a KPCA algorithm, wherein the two-dimensional coordinates represent fault zero-sequence power of the feeder line;
And taking the two-dimensional coordinates as the principal component scores.
In one embodiment, the processor 105 may be configured to call a fault line selection program of the power distribution network stored in the memory 104, and perform the following operations:
and performing BIRCH clustering according to the principal component score to determine whether the feeder is a fault feeder.
In one embodiment, the processor 105 may be configured to call a fault line selection program of the power distribution network stored in the memory 104, and perform the following operations:
determining whether the zero sequence voltage is greater than a preset phase voltage threshold;
and if so, executing the step of determining the corresponding principal component score of the feeder line in the power distribution network in a preset short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage.
Based on the hardware architecture of the fault line selection system of the power distribution network based on the power system technology, the embodiment of the fault line selection method of the power distribution network is provided.
Referring to fig. 2, fig. 2 is a schematic diagram of a power distribution network simulation model, wherein the power distribution network simulation model is constructed according to actual operation of a power distribution network, a power distribution network arc simulation model shown in fig. 2 is constructed by using PSCAD/EMTDC, six lines are taken out of a 110kV/10kV substation, 4 overhead lines are respectively l1=20 km, l2=24 km, l4=16 km and l6=12 km, and 2 pure electric lines are respectively l3=16 km and l5=15 km. The positive sequence impedance of the overhead line is as follows: r1=0.45 Ω/km, l1=1.172 mH/km, c1=6.1 nF/km, zero sequence impedance is: r0=0.7Ω/km, l0=3.91 mH/km, c0=3.8 nF/km; the positive sequence impedance of the cable feeder is: r1=0.075 Ω/km, l1=0.254 mH/km, c1=318 nF/km, zero sequence impedance is: r0=0.102 Ω/km, l0=0.892 mH/km, c0=212 nF/km. The neutral point of the power distribution system is not grounded, single-phase earth faults are arranged in a simulation model, the fault points are respectively arranged at the positions of the feeder line L1, which are 10km, 11km, 12km, 13km, 14km and 15km away from the first-section bus, the initial angles of the faults are 90 degrees, and the transition resistance is 0 omega.
Referring to fig. 3, in a first embodiment, the fault line selection method of the power distribution network includes the following steps:
Step S10, acquiring zero sequence current and zero sequence voltage of a bus in a preset time window of each feeder after a power distribution network fails;
In this embodiment, referring to fig. 4, fig. 4 is a schematic diagram of a single-phase ground fault power distribution network, when the power distribution network fails, first, the zero sequence current of a feeder line and the zero sequence voltage of a bus in the power distribution network with a preset period interval after the power distribution network fails are collected.
Illustratively, the power distribution network fault is a single-phase earth fault, the initial fault angle is 90 °, and the transition resistance is 0Ω. And obtaining the zero sequence current of the corresponding feeder line and the zero sequence voltage of the bus generated by the distribution network line under the single-phase ground fault.
Optionally, the zero sequence voltage of the bus can be obtained through a voltage transformer installed on the bus, and the zero sequence current of each feeder can be obtained through a current transformer installed on each feeder.
Alternatively, the preset power frequency period may be one quarter of the power frequency period after the fault.
In this embodiment, the zero sequence current refers to the average value of the three-phase currents of the feeder, and the zero sequence voltage refers to the average value of the three-phase voltages of the bus. These data can be used as input for subsequent steps.
Step S20, determining corresponding principal component scores of feeder lines in the power distribution network in a preset short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage;
In this embodiment, after the zero sequence current of the feeder and the zero sequence voltage of the bus are obtained, a short-time window zero sequence instantaneous power curve cluster technology is adopted to calculate the principal component score of each feeder in a preset time window. Specifically, the short-time window zero-sequence instantaneous power curve cluster technology can take the zero-sequence current and the zero-sequence voltage of the feeder line as input, and calculate the zero-sequence instantaneous power curve of the feeder line in a time window. These power curves may be used to determine a score for each feeder line over a preset time window.
In this embodiment, principal component score (PRINCIPAL COMPONENT SCORE) is a data analysis technique that is typically used for dimension reduction and feature extraction of multidimensional data. In this embodiment, the principal component score is used to determine faulty feeders. Principal component scores are characterized by a new set of variable values obtained by linearly transforming the original data, the variables being ordered according to the variance size of the data, wherein the first variable is called the first principal component, the second variable is called the second principal component, and so on. Each principal represents a specific combination of the original data that maximizes the degree to which the principal interprets the changes in the data among all possible combinations.
And step S30, determining whether the feeder is a fault feeder according to the principal component score.
In this embodiment, after determining the principal component score, it is determined whether the feeder is a faulty feeder according to the principal component score after clustering.
Optionally, in some embodiments, the principal component score of each feeder line within a preset time window may be compared to a predefined threshold. If the score exceeds the threshold, the feeder may be determined to be a faulty feeder. Otherwise, the feeder is not a faulty feeder. Wherein different thresholds may be set to accommodate different environments and scenarios.
Optionally, in other embodiments, BIRCH clustering is performed according to the principal component score to determine whether the feeder is a faulty feeder, and determining that the feeder associated with the principal component score is a faulty feeder.
In the technical scheme provided by the embodiment, when a fault occurs in the power distribution network, the zero sequence current of the feeder line and the zero sequence voltage of the bus line at intervals of a preset power frequency period after the fault occurs are extracted, then the principal component scores corresponding to the zero sequence current and the zero sequence voltage in the short-time window zero sequence instantaneous power curve cluster are calculated, and whether the feeder line is a line with the fault or not is judged after clustering according to the principal component scores, so that the target fault feeder line in the power distribution network can be accurately and rapidly determined when the current amount of the power distribution network is smaller, and the detection precision is improved.
Referring to fig. 5, in the second embodiment, based on any one of the embodiments, the step S20 includes:
s21, determining a target instantaneous power curve in a zero-sequence instantaneous power curve cluster of a short-time window according to the zero-sequence current and the zero-sequence voltage;
Step S22, determining two-dimensional coordinates of the target instantaneous power curve based on a KPCA algorithm, wherein the two-dimensional coordinates represent fault zero-sequence power of the feeder line;
and step S23, taking the two-dimensional coordinates as the principal component scores.
Optionally, in this embodiment, after the power distribution network fails, a zero sequence current of a feeder line and a zero sequence voltage of a bus line of a preset power frequency period are obtained. And determining a target instantaneous power curve in a short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage.
Specifically, the method can be carried out according to the following steps:
First, the zero sequence current and zero sequence voltage are separately discrete fourier transformed (Discrete Fourier Transform, DFT) to obtain their frequency spectra, where only the fundamental component (i.e., the power frequency component) needs to be considered.
And next, calculating zero sequence instantaneous power, and dividing the zero sequence instantaneous power into a plurality of fragments according to a time window, wherein each fragment comprises data of a plurality of power frequency periods. For each time segment, combining the zero sequence instantaneous power curves into an average curve to obtain a short-time window zero sequence instantaneous power curve.
And next, processing the target instantaneous power curve by using a KPCA algorithm, and determining that the two-dimensional coordinates represent the fault zero-sequence power of the feeder line. Specifically, the method can be carried out according to the following steps:
(1) For each target instantaneous power curve, the first n principal component scores are calculated to obtain an n-dimensional vector.
(2) And (3) placing n-dimensional vectors of all target instantaneous power curves in a matrix, and performing KPCA dimension reduction to obtain a two-dimensional coordinate system.
(3) The coordinates of each target instantaneous power curve in the two-dimensional coordinate system are the two-dimensional coordinates, and the abscissa is the first principal component score of the target instantaneous power curve.
(4) And taking the abscissa of the two-dimensional coordinates as a principal component score of the feeder line. Specifically, for each feeder line, the abscissa of the corresponding target instantaneous power curve is taken as the principal component score of the feeder line.
Illustratively, the principal component score is two, KP1 and KP2, respectively, and an instantaneous power matrix Δp 0 of 2-dimensional vectors is formed according to KP1 and KP 2:
In the matrix, 5 sampling points before the initial transient of the fault and 20 sampling points after the initial transient of the fault are selected from each sample, 36 pieces of historical sample data form a 36×25 matrix, and KPCA-BIRCH clustering is carried out. The cumulative contribution rate of the fault information amount contained in KP1 and KP2 is more than 97%. Representing fault zero-sequence power by KP1 and KP2, obtaining two-dimensional coordinates X (KP 1, KP 2) representing fault zero-sequence power of the feeder line
Finally, two-dimensional coordinates X, i.e., (KP 1, KP 2), are used as principal component scores to determine a faulty feeder from the values clustered using BIRCH.
It should be noted that, the zero sequence currents corresponding to different feeders are different, so that the corresponding target instantaneous power curves in the short-time window zero sequence instantaneous power curve clusters are also different.
In the technical scheme provided by the embodiment, the target instantaneous power curve is determined through the zero sequence current and the zero sequence voltage, so that the target fault feeder line in the power distribution network can be accurately and rapidly determined when the current amount of the power distribution network is smaller, and the detection precision is improved.
Referring to fig. 6, in the third embodiment, before step S10, based on any embodiment, the method further includes:
Step S40, acquiring first intercepted data, and taking the first intercepted data as the upper limit of a short time window; acquiring second intercepted data, and taking the second intercepted data as the lower limit of a short time window;
s50, determining a short time window interception interval according to the short time window upper limit and the short time window lower limit;
And step S60, determining a zero sequence instantaneous power curve cluster of the power distribution network in the short time window interception section as the zero sequence instantaneous power curve cluster of the short time window.
Optionally, a method for how to construct a short time window zero sequence instantaneous power curve cluster is provided in this embodiment. In this embodiment, the size of the interception section of the short time window is first determined, the first intercepted data intercepted at a first preset time interval before the fault is used as the upper limit of the short time window, and the second intercepted data intercepted at a second preset time interval after the fault is used as the lower limit of the short time window. And determining a zero sequence instantaneous power curve cluster of the power distribution network in a short time window interception section as the zero sequence instantaneous power curve cluster of the short time window. Wherein the first preset time period is smaller than the second preset time period
Alternatively, the first preset time period may be data of 0.2ms apart before failure.
Alternatively, the second preset time period may be 1ms of data that is separated by a post-fault period.
Referring to fig. 7, fig. 7 is a schematic diagram of a short-time window zero-sequence instantaneous power curve cluster of each feeder line of a power distribution network. It can be seen that there is a significant difference between a faulty line and a healthy line (normal line).
In the technical scheme provided by the embodiment, the short-time window zero-sequence instantaneous power curve clusters are formed by intercepting the zero-sequence instantaneous power curve clusters of the power distribution network in the short-time window interval, a precondition is provided for the subsequent determination of corresponding principal component scores of the feeder lines in the power distribution network in the preset short-time window zero-sequence instantaneous power curve clusters according to the zero-sequence current and the zero-sequence voltage, and further after BIRCH clustering is carried out on data, the target fault feeder lines in the power distribution network can be accurately and rapidly determined when the current amount of the power distribution network is small, and the detection precision is improved.
Referring to fig. 8, in a fourth embodiment, based on any one of the embodiments, the step S30 includes:
s31, BIRCH clustering is carried out according to the principal component score to determine whether the feeder is a fault feeder.
Optionally, in this embodiment, whether the feeder is a faulty feeder is determined according to the BIRCH cluster condition that scores the principal element.
Illustratively, referring to fig. 9, fig. 9 is a schematic diagram of a distribution between principal component scores of a healthy line and a faulty line based on KPCA-BIRCH cluster analysis results. Grouping the two-dimensional curve clusters, and automatically and unsupervised finding out fault modes existing in data and mining the internal coupling relation of the data under the condition that the clustering layering number is not set by people;
And the BIRCH algorithm uses a tree model to determine the best cluster data value k. By combining the contour coefficients S i and the CH indexes, k values are acted together, the optimal number of layers is iterated, and the larger the index values of the two indexes are, the more optimal the number of clusters is. Simultaneously determining that the feeder associated with the principal component score is a normal feeder or a fault feeder;
The value of k is 1 and 2 layering numbers:
When the k value is 1, indicating that the fault does not belong to the feeder line, and determining the feeder line associated with the principal component score as a normal feeder line;
When the k value is 2, representing that the fault belongs to a feeder, performing sensitive prompt on an abnormal point (fault point) by using a BIRCH algorithm, and determining the feeder associated with the principal component score as the fault feeder;
The BIRCH clustering algorithm has the following formula:
wherein a (i) means the average of the distances of point i to all other points in the cluster in which it is located; b (i) is the minimum of the average of points i to all points within a cluster that does not contain it, B is the variance between different clusters, W is the variance of data points within all clusters, n is the total number of data points, and the CH value relates to the number of clusters and the trace of the dispersion matrix between the classes. Finally, the two-dimensional coordinate points are effectively divided into two characteristic clusters, principal component scores with fewer occupied classes are selected, and then the corresponding fault feeder lines are selected.
In the technical scheme provided by the embodiment, normal feeder lines and fault feeder lines are distinguished through BIRCH clustering conditions of principal component score, so that the target fault feeder line in the power distribution network can be accurately and rapidly determined when the current amount of the power distribution network is small, and the detection precision is improved.
Referring to fig. 10, in the fifth embodiment, after step S10, based on any embodiment, the method further includes:
step S70, determining whether the zero sequence voltage is greater than a preset phase voltage threshold;
And S80, if so, executing the step of determining the principal component score corresponding to the feeder line in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster according to the zero sequence current and the zero sequence voltage.
Optionally, in this embodiment, a bus zero sequence voltage sensor is provided in the power distribution network, and an instantaneous value of the bus zero sequence voltage is obtained by the sensor. Meanwhile, a voltage threshold detection module is arranged, and when the instantaneous value of the zero sequence voltage of the bus is larger than a preset phase voltage threshold, the module can send a power-on signal to the fault wave-recording line selection device.
The fault wave recording line selecting device is started after receiving the power-on signal, and starts to record the zero sequence voltage of each feeder line in a preset period after the fault occurs. During this period, the fault recording line selection device will transmit the recorded data to the upper computer for subsequent data analysis and processing.
It should be noted that, in order to ensure accuracy and stability of data, a high-precision voltage sensor should be selected, and the data should be reasonably filtered and corrected. In addition, the selection of the preset period should be adjusted according to specific conditions so as to fully ensure the integrity and accuracy of the data.
Illustratively, let the zero sequence voltage of the bus beA voltage threshold ofWherein, the method comprises the steps of, wherein,Generally, the amount of the catalyst is 0.15,Indicating the bus voltage rating.
If it isGreater thanAnd the fault line selection device is started immediately, and the zero sequence current of a cycle after the fault occurs is recorded.
In this embodiment, when the instantaneous value of the zero sequence voltage of the bus is greater than the preset phase voltage threshold, and the power distribution network is primarily judged that a fault occurs, the fault wave recording line selection device is started to execute step S20, and when the power distribution network fails, judgment and accurate line selection can be timely made, so that the detection precision is improved, and meanwhile, the safety and stability of the system are ensured.
Furthermore, it will be appreciated by those of ordinary skill in the art that implementing all or part of the processes in the methods of the above embodiments may be accomplished by computer programs to instruct related hardware. The computer program comprises program instructions, and the computer program may be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in a fault line selection system of the power distribution network to implement the flow steps of the embodiments of the method described above.
The present invention therefore also provides a computer readable storage medium storing a fault line selection program for a power distribution network, which when executed by a processor implements the steps of the fault line selection method for a power distribution network according to the above embodiment.
The computer readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, etc. which may store the program code.
It should be noted that, because the storage medium provided in the embodiments of the present application is a storage medium used for implementing the method in the embodiments of the present application, based on the method described in the embodiments of the present application, a person skilled in the art can understand the specific structure and the modification of the storage medium, and therefore, the description thereof is omitted herein. All storage media adopted by the method of the embodiment of the application belong to the scope of protection of the application.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. The fault line selection method for the power distribution network is characterized by comprising the following steps of:
After the power distribution network fails, zero sequence current in a preset time window of each feeder line and zero sequence voltage of a bus are obtained;
According to the zero sequence current and the zero sequence voltage, two-dimensionally processing a feeder line in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster through a KPCA algorithm to determine a corresponding principal component score;
performing BIRCH clustering according to the principal component score to determine whether the feeder is a fault feeder;
The short-time window zero-sequence instantaneous power curve cluster is a two-dimensional curve cluster, and the step of determining principal component scores of the zero-sequence current and the zero-sequence voltage in the short-time window zero-sequence instantaneous power curve cluster comprises the following steps:
determining a target instantaneous power curve in a short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage;
Determining two-dimensional coordinates of the target instantaneous power curve based on a KPCA algorithm, wherein the two-dimensional coordinates represent fault zero-sequence power of the feeder line;
taking the two-dimensional coordinates as the principal component scores;
the two-dimensional coordinates are obtained through a KPCA algorithm, and the formula is as follows:
；
；
Wherein the method comprises the steps of Generating a matrix for a feature space,/>Representing feature samples in a feature space, N being the number of samples, K being a gram matrix, elements/>，/>Is a characteristic value;
the step of determining the fault feeder line in the power distribution network after using BIRCH clustering according to the principal component score comprises the following steps:
Grouping the two-dimensional curve clusters, finding out fault modes existing in data in an unsupervised clustering mode, and mining the internal coupling relation of the data;
Determining an optimal clustering data value k through a BIRCH algorithm, and iterating out an optimal layering number through combining the contour coefficient S i with the CH index to jointly act on the optimal clustering data value k;
simultaneously determining that the feeder associated with the principal component score is a normal feeder or a fault feeder;
the optimal clustering data value k has two layering numbers of 1 and 2:
When the k value of the optimal cluster data value is 1, indicating that the fault does not belong to the feeder line, and determining the feeder line associated with the principal component score as a normal feeder line;
when the k value of the optimal cluster data value is 2, representing that the fault belongs to the feeder line, and determining the feeder line associated with the principal component score as the fault feeder line;
The BIRCH clustering algorithm has the following formula:
；
；
Wherein a (i) represents the average of the distances of point i to all other points in the cluster in which it is located; b (i) represents the minimum of the average of points i to all points within a cluster that does not contain it, B is the variance between different clusters, W is the variance of data points within all clusters, n is the total number of data points, and the CH value relates to the number of clusters and the trace of the dispersion matrix between the classes;
after the step of obtaining the zero sequence current in the preset time window of each feeder line and the zero sequence voltage of the bus after the power distribution network fails, the method further comprises the following steps:
determining whether the zero sequence voltage is greater than a preset phase voltage threshold;
And if so, carrying out two-dimensional processing on the feeder line in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster through a KPCA algorithm according to the zero sequence current and the zero sequence voltage so as to determine a corresponding principal component score.
2. The fault line selection method of a power distribution network according to claim 1, wherein before the step of determining the corresponding principal component score by performing two-dimensional processing on a feeder line in the power distribution network through a KPCA algorithm on a preset short-time window zero sequence instantaneous power curve cluster according to the zero sequence current and the zero sequence voltage, the fault line selection method further comprises:
acquiring first intercepted data, and taking the first intercepted data as the upper limit of a short time window, wherein the first intercepted data is intercepted data which is intercepted at intervals of a first preset duration before a fault;
obtaining second intercepted data, and taking the second intercepted data as the lower limit of a short time window, wherein the second intercepted data is intercepted data which is intercepted at intervals of a second preset time length after a fault;
Determining a short time window interception interval according to the short time window upper limit and the short time window lower limit;
Determining a zero sequence instantaneous power curve cluster of the power distribution network in the short time window interception section as the zero sequence instantaneous power curve cluster of the short time window;
The first preset duration is smaller than the second preset duration.
3. A system for implementing a fault line selection method for a power distribution network according to claim 1, the system comprising:
the data acquisition module is used for acquiring zero sequence current and zero sequence voltage of a bus in a preset time window of each feeder line after the power distribution network fails;
the numerical calculation module is used for determining corresponding principal component scores of feeder lines in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster according to the zero sequence current and the zero sequence voltage;
and the logic judgment module is used for determining whether the feeder line is a fault feeder line according to the principal component score.
4. The system of claim 3, wherein the data acquisition module further comprises:
the zero sequence voltage acquisition unit is used for acquiring the zero sequence voltage of the bus through a voltage transformer arranged on the bus;
the zero sequence current acquisition unit is used for acquiring the zero sequence current of each feeder line through a current transformer arranged on each feeder line.
5. The system of claim 3, wherein the numerical computation module further comprises:
the signal calculation unit is used for constructing a starting signal when the acquired instantaneous value of the zero sequence voltage is greater than a preset voltage threshold value;
The instantaneous power curve calculation unit is used for determining a target instantaneous power curve in a short-time window zero-sequence instantaneous power curve cluster according to the zero-sequence current and the zero-sequence voltage;
and the KPCA calculation unit is used for determining the two-dimensional coordinates of the target instantaneous power curve based on a KPCA algorithm, wherein the two-dimensional coordinates represent the fault zero sequence power of the feeder line.
6. The system of claim 3, wherein the logic determination module further comprises:
the zero sequence voltage judging unit is used for determining whether the zero sequence voltage is larger than a preset phase voltage threshold value or not;
If yes, executing the step of determining corresponding principal component scores of feeder lines in the power distribution network in a preset short-time window zero sequence instantaneous power curve cluster according to the zero sequence current and the zero sequence voltage;
and the fault line selection judging unit is used for determining whether the feeder is a fault feeder after BIRCH clustering is carried out on the principal component scores.
7. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a fault line selection program of an electrical distribution network, which when executed by a processor, implements the steps of the fault line selection method of an electrical distribution network according to any one of claims 1 to 2.
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