CN111426905B - Power distribution network common bus transformation relation abnormity diagnosis method, device and system - Google Patents
Power distribution network common bus transformation relation abnormity diagnosis method, device and system Download PDFInfo
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Abstract
The invention discloses a method, a device and a system for diagnosing abnormal relation between power distribution network and bus transformation, which comprises the steps of calculating the power ratio of each distribution transformer originally belonging to a certain feeder line, and sequencing the distribution transformers according to the power ratio sequence; according to the power distribution sequence: calculating the variance of the distribution original power data in each set time window, taking the time window with the maximum variance as a typical change time window, and taking the distribution original power data in the typical change time window as typical power characteristic data; and calculating a correlation coefficient index value of the typical power characteristic data of the distribution transformer and the power data of each feeder line in a typical change time window, correcting the distribution transformer to the most possible feeder line, and if the corrected feeder line is different from the original feeder line, judging that the different feeder lines of the distribution transformer are in hooking error under the same bus. The method is simple in calculation, can help operators to find out the line change relation error existing among different feeder lines under the same bus in time, further excavates the distribution network information, and has a good application prospect.
Description
Technical Field
The invention belongs to the field of abnormal diagnosis of line-to-bus variable relations of a power distribution network, particularly relates to a method, a device and a system for abnormal diagnosis of line-to-bus variable relations of the power distribution network, and particularly relates to a method for abnormal diagnosis of line-to-bus variable relations of the power distribution network based on power jump characteristics.
Background
The line transformation relation of the medium-voltage distribution network is formed by interconnecting a medium-voltage feeder line, a distribution transformer load, a tie switch, a Power transmission line and the like, and is manually maintained to an energy Management System (PMS). Due to the strong subjectivity of manual maintenance and the influence of lagging field analysis means, the line variation relation between an actual field and a PMS is inconsistent, and further service pain points such as large potential safety hazard of line maintenance, high calculation error of medium-voltage line loss rate, low reliability analysis accuracy and the like are caused.
In addition, a plurality of feeder lines are connected to the same bus of the transformer substation, a plurality of distribution transformer loads are connected to each feeder line, and the voltage of the feeder lines is the same, so that the traditional method for distinguishing the line transformation relation errors among the plurality of feeder lines on the same bus by relying on the voltage change similarity has difficulty. The invention converts the thinking that the traditional method depends on voltage change, and considers the similarity of the distribution transformer power with the corresponding change trend of the feeder power when the distribution transformer power has larger change for the first time, namely, considers the characteristic that the sudden increase or decrease of the distribution transformer power can be reflected in the feeder power.
The diagnosis of the variable relation of the power distribution network lines in the research is helpful for tamping the operation and maintenance foundation of the power grid, promoting the deep management reform and reducing the burden of basic personnel. Therefore, the abnormal diagnosis of the same-bus variation relation of the medium-voltage distribution network is an important research subject, and research results can help operators to find out the same-bus variation relation errors in time and further mine distribution network information.
Disclosure of Invention
The invention provides a method, a device and a system for diagnosing the abnormal relation change of a distribution network and a bus, which can be used for identifying the error of the same-bus variable relation of a medium-voltage distribution network in any scale, are simple in calculation and clear in principle and can help distribution network operators to find the error of the same-bus variable relation.
In order to achieve the technical purpose and achieve the technical effects, the invention is realized by the following technical scheme:
in a first aspect, the invention provides a method for diagnosing abnormality of a variable relation between power distribution networks and buses, which comprises the following steps:
calculating the power ratio of each distribution transformer originally belonging to the ith feeder, and sequencing the distribution transformers according to the sequence of the power ratios from large to small, wherein i is 1,2 … n, and n is the total number of the feeders belonging to a bus F;
according to the sequence of the distribution transformers, the following operations are performed on the distribution transformers in sequence:
calculating the variance of the original power data of the distribution transformer in each set time window, taking the time window with the maximum variance as a typical change time window, and selecting the corresponding original power data of the distribution transformer in the typical change time window as typical power characteristic data;
calculating correlation coefficient index values between the typical power characteristic data of the distribution transformer and the power data of each feeder line belonging to the bus F in a typical change time window, correcting the distribution transformer to the most possible feeder line based on the calculated correlation coefficient index values, and judging that the different feeder lines of the distribution transformer are in wrong connection with each other under the same bus if the corrected feeder line is different from the original feeder line.
Optionally, after the step of modifying the distribution transformer to the feeder line most likely to belong to based on the calculated correlation coefficient index values, the method further includes:
and if the corrected feeder line is the same as the original feeder line, judging that the distribution transformer is not in hooking error, and simultaneously subtracting the original power data of the distribution transformer from the power data of the feeder line to which the distribution transformer belongs.
Optionally, n feeders are connected to a certain bus F of the power distribution network, and a bus set F ═ { F is constructed1,F2,…,Fi,…,FnWherein the feed line FiM distribution transformers are hung below the feed line FiDistribution transformer set TFi＝{Ti,1,Ti,2,…,Ti,j,…,Ti,mAre therefore Ti,jThe j distribution transformer under the ith feeder is represented, and the calculation formula of the power ratio is as follows:
wherein G isi,jIndicating distribution transformer Ti,jPower ratio; pi,jIndicating distribution transformer Ti,jPower sampling data of;
indicating distribution transformer Ti,jOriginal affiliated feeder FiPower data of (2).
Optionally, the calculation method of the typical power characteristic data specifically includes the following sub-steps:
for a distribution transformer Ti,jSetting the time window length as k, each time window being in turn [ i, k + i-1 ]]Where i is 1,2, …, l-k, l is distribution transformer Ti,jLength of raw power data, Ti,jIndicating the j-th distribution transformer under the i-th feeder
Calculating the variance of the original power data of the distribution transformer in each time window in sequence, selecting the time window with the maximum corresponding variance as a typical change time window, and selecting the distribution transformer Ti,jThe corresponding original power data in the typical variation time window is typical power characteristic data Pi,′jTaking out each feeder F in the feeder set FiThe corresponding power data in the time window is
Optionally, the correlation coefficient index value is calculated by the following formula:
wherein, R represents a correlation coefficient index;
representing distribution transformer data Pi,′jAverage value of (d);
representing feeder power data
Average value of (a).
Optionally, before the step of calculating power ratios of distribution transformers belonging to feeder lines connected to a bus in the distribution network, the step of calculating power ratios of distribution transformers belonging to the feeder lines respectively further includes:
acquiring relevant power data of a distribution transformer and a feeder in a power distribution network;
and denoising the acquired relevant power data of the distribution transformer and the feeder in the power distribution network to acquire the denoised relevant power data of the distribution transformer and the feeder.
Optionally, the denoising processing is performed on the obtained relevant power data of the distribution transformer and the feeder in the power distribution network, specifically:
and denoising the acquired related power data of the distribution transformer and the feeder in the medium-voltage distribution network by adopting a two-dimensional wavelet threshold denoising method.
In a second aspect, the present invention provides a power distribution network common bus variation relation abnormality diagnosis apparatus, including:
the calculating unit is used for calculating the power ratio of each distribution transformer originally belonging to the ith feeder and sequencing the distribution transformers from large to small according to the power ratio, wherein i is 1,2 … n, and n is the total number of feeders belonging to a bus F;
the judging unit is used for sequentially carrying out the following operations on the power distributions according to the sequence of the power distributions:
calculating the variance of the original power data of the distribution transformer in each set time window, taking the time window with the maximum variance as a typical change time window, and selecting the corresponding original power data of the distribution transformer in the typical change time window as typical power characteristic data;
calculating correlation coefficient index values between the typical power characteristic data of the distribution transformer and the power data of each feeder line belonging to the bus F in a typical change time window, correcting the distribution transformer to the most possible feeder line based on the calculated correlation coefficient index values, and judging that the different feeder lines of the distribution transformer are in wrong connection with each other under the same bus if the corrected feeder line is different from the original feeder line.
Optionally, the determining unit is further configured to:
and if the corrected feeder line is the same as the original feeder line, judging that the distribution transformer is not in hooking error, and simultaneously subtracting the original power data of the distribution transformer from the power data of the feeder line to which the distribution transformer belongs.
Optionally, n feeders are connected to a certain bus F of the power distribution network, and a bus set F ═ { F is constructed1,F2,…,Fi,…,FnWherein the feed line FiM distribution transformers are hung below the feed line FiDistribution transformer set TFi＝{Ti,1,Ti,2,…,Ti,j,…,Ti,mAre therefore Ti,jThe j distribution transformer under the ith feeder is represented, and the calculation formula of the power ratio is as follows:
wherein G isi,jIndicating distribution transformer Ti,jPower ratio; pi,jIndicating distribution transformer Ti,jPower sampling data of;
indicating distribution transformer Ti,jOriginal affiliated feeder FiPower data of (2).
Optionally, the calculation method of the typical power characteristic data specifically includes the following sub-steps:
for a distribution transformer Ti,jSetting the time window length as k, each time window being in turn [ i, k + i-1 ]]Where i is 1,2, …, l-k, l is distribution transformer Ti,jLength of raw power data, Ti,jIndicating the j-th distribution transformer under the i-th feeder
Calculating the variance of the original power data of the distribution transformer in each time window in sequence, selecting the time window with the maximum corresponding variance as a typical change time window, and selecting the distribution transformer Ti,jThe corresponding original power data in the typical variation time window is typical power characteristic data Pi,′jTaking out each feeder F in the feeder set FiThe corresponding power data in the time window is
Optionally, the correlation coefficient index value is calculated by the following formula:
wherein, R represents a correlation coefficient index;
representing distribution transformer data Pi,′jOfMean value;
representing feeder power data
Average value of (a).
In a third aspect, the present invention provides a power distribution network common bus variation relation abnormality diagnosis system, including: a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of the first aspects.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention avoids the problem that the traditional method is difficult to distinguish the line variation relation error between different feeder lines under the same bus by using the voltage variation.
(2) The invention effectively solves the problem of wrong relation of the same bus transformation by utilizing the similarity of the typical power change of the distribution transformer and the power change of the feeder line for the first time.
(3) The method is simple in calculation and clear in principle, can help distribution network operators to find out the same bus variable relation error in time, and has a good application prospect.
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In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the present disclosure taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a power distribution network common bus variation relation abnormity diagnosis method of the invention;
FIG. 2 is a schematic diagram of two-dimensional wavelet threshold denoising;
FIG. 3 shows two distribution transformers TjAnd TkSchematic diagram of connections at the same contact point.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
Example 1
As shown in fig. 1. The embodiment of the invention provides a method for diagnosing abnormal variable relation between power distribution networks and buses, which specifically comprises the following steps:
(1) calculating the power ratio of each distribution transformer (distribution transformer for short) originally belonging to the ith feeder, and sequencing the distribution transformers according to the sequence from large to small of the power ratio, wherein i is 1,2 … n, and n is the total number of feeders belonging to a bus F; (ii) a
(2) Calculating the variance of the original power data of the distribution transformer in each set time window, taking the time window with the maximum variance as a typical change time window, and selecting the corresponding original power data of the distribution transformer in the typical change time window as typical power characteristic data;
(3) calculating correlation coefficient index values between the typical power characteristic data of the distribution transformer and the power data of each feeder line belonging to the bus F in a typical change time window, correcting the distribution transformer to the most possible feeder line based on the calculated correlation coefficient index values, and judging that the different feeder lines of the distribution transformer are in wrong connection with each other under the same bus if the corrected feeder line is different from the original feeder line.
In a specific implementation manner of the embodiment of the present invention, it is assumed that n feeder lines are connected to a certain bus of a (medium voltage) distribution network, and a feeder line set F ═ { F ═ is constructed1,F2,…,Fi,…,FnIn which F1Feed line FiM distribution transformers are hung below the feed line FiDistribution transformer set TFi＝{Ti,1,Ti,2,…,Ti,j,…,Ti,mAre therefore Ti,jRepresenting the jth distribution transformer under the ith feeder as shown in figure 3. The method for establishing the power ratio sequence comprises the following steps:
obtaining distribution transformers Ti,jAnd its original affiliated feeder line FiThe calculation formula of the related index is as follows:
wherein G isi,jIndicating distribution transformer Ti,jPower ratio; pi,jIndicating distribution transformer Ti,jPower sampling data of;
indicating feeder FiPower data of (a);
calculating each distribution transformer T based on the related index calculation formulai,jAnd its original affiliated feeder line FiThe power ratio between the distribution transformers is considered to be larger, the power change of the distribution transformers causes more obvious power change on the feeder line, and therefore the distribution transformers are sequenced from large to small according to the power ratio.
For other feeders belonging to the bus F, the distribution transformers originally belonging to each feeder are also sorted according to the above steps.
In a specific implementation manner of the embodiment of the present invention, the method for calculating the typical power characteristic data specifically includes the following sub-steps:
by distribution transformer Ti,jFor example, a time window length of k is set, and each time window is sequentially [ i, k + i-1 ]]Where i is 1,2, …, l-k, l is distribution transformer Ti,jLength of raw power data;
sequentially calculating the variance of the original power data of the distribution transformer in the l-k time windows; selecting the time window with the maximum corresponding variance as the typical change time window, and selecting the distribution transformer Ti,jCorresponding raw power data P within a typical variation time windowi,′jFor typical power characteristic data, the corresponding feeder power data in the time window is
In a specific implementation manner of the embodiment of the present invention, the calculating a correlation coefficient index between typical power characteristic data of a distribution transformer and corresponding feeder power data in a typical variation time window modifies the distribution transformer to a most likely feeder to which the distribution transformer belongs, and if the modified feeder is different from an original feeder to which the distribution transformer belongs, it is determined that different feeders of the distribution transformer are mistakenly hooked with each other under the same bus, specifically including the following steps:
extracting typical power characteristic data of the distribution transformer and power data of each feeder in the feeder set in a typical change time window;
based on a Pearson correlation coefficient method, a correlation coefficient index calculation formula between typical power characteristic data of a distribution transformer and power data of each feeder line is obtained, wherein the correlation coefficient index calculation formula is as follows:
wherein, R represents a correlation coefficient index;
representing distribution transformer data Pi,′jAverage value of (d);
representing feeder power data
Average value of (d);
calculating each distribution transformer T based on the correlation coefficient index calculation formulai,jCorrecting the distribution transformer to the feeder line with the maximum related coefficient index value according to the related coefficient index value between the distribution transformer and each feeder line in the feeder line set;
and if the corrected feeder line is different from the feeder line corresponding to the original feeder line, judging that the distribution transformer is the distribution transformer with the wrong line transformation relation among different feeder lines under the same bus.
Example 2
The embodiment of the present invention is different from embodiment 1 in that:
the step of calculating the power ratio of the distribution transformer of each feeder line connected with the bus in the distribution network further comprises the following steps of:
acquiring relevant power data of a distribution transformer and a feeder in a power distribution network;
and denoising the acquired relevant power data of the distribution transformer and the feeder in the power distribution network to acquire the denoised relevant power data of the distribution transformer and the feeder.
In a specific implementation manner of the embodiment of the present invention, the obtaining of the relevant power data of the distribution transformer and the feeder in the distribution network specifically includes:
selecting a certain bus of a medium-voltage distribution network to be processed in an equipment management system (the system is a system existing in the prior art), taking out a stored line-variable relation of 'bus-feeder-distribution transformer load', and deriving power data of a distribution transformer and a feeder sampled once every 15min under the bus, wherein the sampling frequency sampled once every 15min can be modified according to actual conditions.
In a specific implementation manner of the embodiment of the present invention, the denoising processing is performed on the obtained power data related to the distribution transformer and the feeder in the power distribution network, specifically:
and denoising the acquired related power data of the distribution transformer and the feeder in the medium-voltage distribution network by adopting a two-dimensional wavelet threshold denoising method.
The method comprises the following steps of performing denoising processing on original acquired data by adopting a two-dimensional wavelet threshold denoising method, performing wavelet decomposition on the original acquired data to obtain a group of wavelet coefficients, performing threshold processing, and reconstructing according to the processed wavelet coefficients, wherein the reconstructed data is denoised data, and a basic schematic diagram of the data is shown in FIG. 2, and the method utilizes a global threshold denoising method to determine a threshold T:
wherein, λ is a noise standard, and l is a length of original power data of the distribution transformer.
And selecting a semi-soft threshold function as a threshold function, wherein the threshold function is continuous and has continuous high-order derivatives in a wavelet domain larger than a threshold, and is suitable for denoising power data with certain change characteristics.
In the formula: omegaj,kIs the original wavelet coefficient;
the wavelet coefficient after denoising; mu is a weighting factor; sgn (·) is a sign function. The denoising effect of the two-dimensional wavelet threshold can be controlled by a parameter T, and the larger the T is, the smoother the denoised original data is. After the processing by the method, the de-noised distribution transformer and feeder line measurement data are obtained.
Example 3
Based on the same inventive concept as embodiment 1, the embodiment of the present invention provides a power distribution network same bus variation relation abnormality diagnosis apparatus, including:
the first calculating unit is used for calculating the power ratio of each distribution transformer originally belonging to the ith feeder and sequencing the distribution transformers from large to small according to the power ratio, wherein i is 1,2 … n, and n is the total number of feeders belonging to a bus F;
the judging unit is used for sequentially carrying out the following operations on the power distributions according to the sequence of the power distributions:
calculating the variance of the original power data of the distribution transformer in each set time window, taking the time window with the maximum variance as a typical change time window, and selecting the corresponding original power data of the distribution transformer in the typical change time window as typical power characteristic data;
calculating correlation coefficient index values between the typical power characteristic data of the distribution transformer and the power data of each feeder line belonging to the bus F in a typical change time window, correcting the distribution transformer to the most possible feeder line based on the calculated correlation coefficient index values, and judging that the different feeder lines of the distribution transformer are in wrong connection with each other under the same bus if the corrected feeder line is different from the original feeder line.
In a specific implementation manner of the embodiment of the present invention, the determining unit is further configured to:
and if the corrected feeder line is the same as the original feeder line, judging that the distribution transformer is not in hooking error, and simultaneously subtracting the original power data of the distribution transformer from the power data of the feeder line to which the distribution transformer belongs.
The rest of the process was the same as in example 1.
Example 4
Based on the same inventive concept as embodiment 1, the embodiment of the present invention provides a power distribution network same bus variation relation abnormality diagnosis system, including: a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any of embodiment 1.
In summary, the following steps:
(1) the method for diagnosing the abnormal variable relation between the medium-voltage distribution network and the bus based on the power jump characteristic avoids the problem that the line variable relation between different feeder lines under the same bus is difficult to distinguish by using voltage change in the prior art.
(2) The medium-voltage distribution network same-bus variation relation abnormity diagnosis method based on the power jump characteristics effectively solves the problem of same-bus variation relation error by utilizing the similarity of typical power variation of a distribution transformer and feeder line power variation for the first time.
(3) The medium-voltage distribution network common-bus variation relation abnormity diagnosis method based on the power jump characteristics is simple in calculation and clear in principle, can help distribution network operators to find out common-bus variation relation errors in time, and has a good application prospect.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (13)
1. A power distribution network common bus variable relation abnormity diagnosis method is characterized by comprising the following steps:
calculating the power ratio of each distribution transformer originally belonging to the ith feeder, and sequencing the distribution transformers according to the sequence of the power ratios from large to small, wherein i is 1,2 … n, and n is the total number of the feeders belonging to a bus F;
according to the sequence of the distribution transformers, the following operations are performed on the distribution transformers in sequence:
calculating the variance of the original power data of the distribution transformer in each set time window, taking the time window with the maximum variance as a typical change time window, and selecting the corresponding original power data of the distribution transformer in the typical change time window as typical power characteristic data;
calculating correlation coefficient index values between the typical power characteristic data of the distribution transformer and the power data of each feeder line belonging to the bus F in a typical change time window, correcting the distribution transformer to the most possible feeder line based on the calculated correlation coefficient index values, and judging that the different feeder lines of the distribution transformer are in wrong connection with each other under the same bus if the corrected feeder line is different from the original feeder line.
2. The method for diagnosing the abnormal variable relation between the distribution network and the bus according to claim 1, wherein the method comprises the following steps: after the step of modifying the distribution transformer to the most likely feeder based on the calculated correlation coefficient index values, the method further comprises:
and if the corrected feeder line is the same as the original feeder line, judging that the distribution transformer is not in hooking error, and simultaneously subtracting the original power data of the distribution transformer from the power data of the feeder line to which the distribution transformer belongs.
3. The method for diagnosing the abnormal variable relation between the distribution network and the bus according to claim 1, wherein the method comprises the following steps: n feeder lines are connected to a certain bus F of the power distribution network, and a bus set F ═ F is constructed1,F2,…,Fi,…,FnWherein the feed line FiM distribution transformers are hung below the feed line FiDistribution transformer set TFi＝{Ti,1,Ti,2,…,Ti,j,…,Ti,mAre therefore Ti,jThe j distribution transformer under the ith feeder is represented, and the calculation formula of the power ratio is as follows:
wherein G isi,jIndicating distribution transformer Ti,jPower ratio; pi,jIndicating distributionTransformer Ti,jPower sampling data of;
indicating distribution transformer Ti,jOriginal affiliated feeder FiPower data of (2).
4. The method for diagnosing the abnormal variable relation between the distribution network and the bus according to claim 1, wherein the method comprises the following steps: the calculation method of the typical power characteristic data specifically comprises the following sub-steps:
for a distribution transformer Ti,jSetting the time window length as k, each time window being in turn [ i, k + i-1 ]]Where i is 1,2, …, l-k, l is distribution transformer Ti,jLength of raw power data, Ti,jRepresenting that the jth distribution transformer under the ith feeder line sequentially calculates the variance of the original power data of the distribution transformer in each time window, selecting the time window with the maximum corresponding variance as a typical change time window, and selecting the distribution transformer Ti,jThe corresponding original power data in the typical variation time window is typical power characteristic data P'ijTaking out each feeder F in the feeder set FiThe corresponding power data in the time window is
5. The method for diagnosing the abnormal variable relation between the distribution network and the bus according to claim 4, wherein the method comprises the following steps: the calculation formula of the correlation coefficient index value is as follows:
wherein, R represents a correlation coefficient index;
represents distribution transformer data P'i,jAverage value of (2)；
Representing feeder power data
Average value of (a).
6. The method for diagnosing the abnormal variable relation between the distribution network and the bus according to claim 1, wherein the method comprises the following steps: the step of calculating the power ratio of the distribution transformer of each feeder line connected with the bus in the distribution network further comprises the following steps of:
acquiring relevant power data of a distribution transformer and a feeder in a power distribution network;
and denoising the acquired relevant power data of the distribution transformer and the feeder in the power distribution network to acquire the denoised relevant power data of the distribution transformer and the feeder.
7. The method for diagnosing the abnormal variable relation between the distribution network and the bus according to claim 6, wherein the method comprises the following steps: the denoising processing is carried out on the obtained relevant power data of the distribution transformer and the feeder in the power distribution network, and the denoising processing specifically comprises the following steps:
and denoising the acquired related power data of the distribution transformer and the feeder in the medium-voltage distribution network by adopting a two-dimensional wavelet threshold denoising method.
8. The utility model provides a distribution network is with generating line variable relation anomaly diagnostic device which characterized in that includes:
the calculating unit is used for calculating the power ratio of each distribution transformer originally belonging to the ith feeder and sequencing the distribution transformers from large to small according to the power ratio, wherein i is 1,2 … n, and n is the total number of feeders belonging to a bus F;
the judging unit is used for sequentially carrying out the following operations on the power distributions according to the sequence of the power distributions:
calculating the variance of the original power data of the distribution transformer in each set time window, taking the time window with the maximum variance as a typical change time window, and selecting the corresponding original power data of the distribution transformer in the typical change time window as typical power characteristic data;
calculating correlation coefficient index values between the typical power characteristic data of the distribution transformer and the power data of each feeder line belonging to the bus F in a typical change time window, correcting the distribution transformer to the most possible feeder line based on the calculated correlation coefficient index values, and judging that the different feeder lines of the distribution transformer are in wrong connection with each other under the same bus if the corrected feeder line is different from the original feeder line.
9. The power distribution network common bus variation relation abnormality diagnosis device according to claim 8, wherein the judgment unit is further configured to:
and if the corrected feeder line is the same as the original feeder line, judging that the distribution transformer is not in hooking error, and simultaneously subtracting the original power data of the distribution transformer from the power data of the feeder line to which the distribution transformer belongs.
10. The distribution network common-bus variation relation abnormity diagnosis device according to claim 8, wherein n feeders are connected under a certain bus F of the distribution network, and a bus set F ═ { F ═ is constructed1,F2,…,Fi,…,FnWherein the feed line FiM distribution transformers are hung below the feed line FiDistribution transformer set TFi＝{Ti,1,Ti,2,…,Ti,j,…,Ti,mAre therefore Ti,jThe j distribution transformer under the ith feeder is represented, and the calculation formula of the power ratio is as follows:
wherein G isi,jIndicating distribution transformer Ti,jPower ratio; pi,jIndicating distribution transformerPressure device Ti,jPower sampling data of;
indicating distribution transformer Ti,jOriginal affiliated feeder FiPower data of (2).
11. The distribution network common bus variation relation abnormality diagnosis device according to claim 8, wherein the typical power characteristic data calculation method specifically includes the following substeps:
for a distribution transformer Ti,jSetting the time window length as k, each time window being in turn [ i, k + i-1 ]]Where i is 1,2, …, l-k, l is distribution transformer Ti,jLength of raw power data, Ti,jRepresenting that the jth distribution transformer under the ith feeder line sequentially calculates the variance of the original power data of the distribution transformer in each time window, selecting the time window with the maximum corresponding variance as a typical change time window, and selecting the distribution transformer Ti,jThe corresponding original power data in the typical variation time window is typical power characteristic data P'ijTaking out each feeder F in the feeder set FiThe corresponding power data in the time window is
12. The power distribution network and bus variation relation abnormality diagnosis device according to claim 11, wherein the calculation formula of the correlation coefficient index value is:
wherein, R represents a correlation coefficient index;
represents distribution transformer data P'i,jAverage value of (d);
representing feeder power data
Average value of (a).
13. The utility model provides a distribution network is with generating line variable relation abnormal diagnosis system which characterized in that includes: a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.
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| CN113049919A (en) * | 2021-03-23 | 2021-06-29 | 绍兴大明电力设计院有限公司 | Method and system for identifying diversity of medium-voltage feeder line power supply load characteristics |
| CN116316586B (en) * | 2023-03-15 | 2023-10-10 | 国网湖北省电力有限公司随州供电公司 | Method for tracing power jump in power system by adopting jump analysis method |
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