CN117236937B - Power distribution network defect positioning method and device based on security domain concave visualization - Google Patents
Power distribution network defect positioning method and device based on security domain concave visualization Download PDFInfo
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Abstract
The invention provides a method and a device for positioning defects of a power distribution network based on security domain depression visualization, which are suitable for the field of security domain of the power distribution network, and the method comprises the following steps: acquiring data; identifying a concave boundary for the first time by full-dimensional observation on the safety boundary of the safety domain, identifying the concave boundary for the second time by Monte Carlo two-dimensional observation on the safety boundary of the safety domain, and mutually verifying the concave boundary identified for the first time and the concave boundary identified for the second time to determine a target concave boundary; directly observing the local part of the power distribution network based on the target concave boundary; and determining the defect of the power distribution network based on the target concave boundary. According to the invention, for a given power distribution network, all security domain depressions can be completely found and visually displayed through the combination of full-dimensional indirect observation, monte Carlo two-dimensional observation and direct observation, and a new tool is provided for security domain analysis.
Description
Technical Field
The invention relates to the field of safety domains of power distribution networks, in particular to a method and a device for positioning defects of a power distribution network based on concave visualization of the safety domains.
Background
Distribution networks are an important infrastructure in smart grids and energy networks. The theory of security domains (Distribution System Security Region, DSSR) of distribution networks provides new perspectives for observing distribution networks. DSSR is the set of all operating points in the state space that meet a given security criterion, which is the irregular shape Gao Weiji. DSSR depressions are areas of the distribution network where security violations are likely to occur, reflecting the deficiencies of the distribution network in security performance.
At present, DSSR observation can be divided into full-dimensional indirect observation and direct observation, and conventional full-dimensional indirect observation can complete integral observation of a security domain, but cannot directly observe the security domain itself; the traditional direct observation can directly observe the security domain, but the integral observation of the security domain is difficult to realize, so that the prior art has defects in the aspect of defect positioning of the power distribution network, and the power distribution network cannot be comprehensively and intuitively analyzed, so that all the defects of the power distribution network are accurately positioned.
Disclosure of Invention
The invention aims to provide a power distribution network defect positioning method and device based on security domain concave visualization.
In order to achieve the above purpose, the invention adopts the following technical scheme:
in a first aspect, a method for locating defects of a power distribution network based on security domain recess visualization is provided, including:
acquiring data, wherein the data comprises: safety boundary data and boundary point data, and average values of boundary point safety distance, boundary shortest distance and boundary point safety distance obtained based on the boundary point data;
identifying a concave boundary for the first time by full-dimensional observation on the safety boundary of the safety domain, identifying the concave boundary for the second time by Monte Carlo two-dimensional observation on the safety boundary of the safety domain, and mutually verifying the concave boundary identified for the first time and the concave boundary identified for the second time to determine a target concave boundary;
directly observing the local part of the power distribution network based on the target concave boundary;
and determining the defect of the power distribution network based on the target concave boundary.
Preferably, the boundary point safety distance satisfies:
(1)
in the middle ofRepresenting boundary points +.>Is (are) safe distance>Representing boundary points +.>Is used to determine the relative position of the components of the component(s),i representation ofComponent of each dimension->Is provided with the number of (a),krepresenting the dimension components->Is a dimension of (c).
Preferably, the boundary shortest distance satisfies:
(2)
in the middle ofFor the shortest distance of boundary->Representation ofjIs boundary->Point on->Representing boundary pointsjIs a boundary point safety distance of (c).
Preferably, the first identification of the recessed boundary of the security domain by full-dimensional observation comprises: identifying a concave boundary of a security domain through a visual view and a quantization index of the full-dimensional observation; the visual view includes: boundary radar view, boundary concave view, boundary point concave view, quantization index includes: recess depth and recess ratio.
Further, coordinate axes of the radar view in all directions led out from the origin point represent a safety boundary, and numerical values on the coordinate axes represent the shortest boundary distance of the safety boundary;
and identifying the safe boundary meeting the depression threshold expression as the depression boundary, wherein the shortest boundary distance in the radar view is smaller than the average value of the safe distances of the boundary points.
Further, the concave boundary view takes the number of the safety boundary as an abscissa and takes the shortest boundary distance as an ordinate;
and identifying the safe boundary meeting the depression threshold expression as the depression boundary, wherein the shortest boundary distance in the depression boundary view is smaller than the average value of the safe distances of the boundary points.
Further, the depression threshold expression satisfies:
（7）
threshold of dishing in a vehicleA constant of 0 to 1, < >>For the shortest distance of boundary->Is the average value of the safe distance of the boundary points.
Further, the boundary point concave view takes the serial number of the boundary point as an abscissa and takes the Manhattan distance from the boundary point to the origin point as an ordinate;
the boundary point numbers are sequentially ordered from small to large according to the Manhattan distance from the boundary point to the origin;
the value of the Manhattan distance from the boundary point to the origin point represents the power supply capability of the boundary point;
the manhattan distance from the boundary point to the origin satisfies:
(3)
in the middle ofRepresenting boundary points +.>Manhattan distance to origin, +.>Representing boundary pointsjIs used to determine the relative position of the components of the component(s),i representsComponent of each dimension->Is provided with the number of (a),krepresenting the dimension components->Is a dimension of (c).
Further, the Manhattan distance from the boundary point to the origin point in the boundary point concave view is smaller than the threshold value of the power supply capacity, and the boundary point is a concave boundary point;
and when the Manhattan distance from the boundary point to the original point in the boundary point concave view is equal to the power supply capacity threshold value, the corresponding abscissa boundary point numbers are the number of concave boundary points.
Further, the depression depth satisfies:
(4)
in the middle ofRepresenting boundariesβiIs>Is a safety boundaryβiBoundary shortest distance of->Is the average value of the safe distance of the boundary points;
maximum value of the recess depth, satisfying:
(5)
in the middle ofAt the maximum value of the depth of the depression,nis the number of the dent depth index.
Further, the dent ratio satisfies:
(6)
in the middle ofRepresenting the proportion of dishing->For the number of concave boundaries>Is the number of safety boundaries.
In a second aspect, there is also provided a defect positioning device for a power distribution network based on security domain recess visualization, including: the system comprises an acquisition module, a determination module and an observation module;
the acquisition module is used for acquiring data, wherein the data comprises: safety boundary data and boundary point data, and average values of boundary point safety distance, boundary shortest distance and boundary point safety distance obtained based on the boundary point data;
the determining module is used for identifying the concave boundary for the first time for the safety boundary of the safety domain through full-dimensional observation, identifying the concave boundary for the second time for the safety boundary of the safety domain through two-dimensional observation of Monte Carlo, and determining the concave boundary of the target through mutual verification of the concave boundary identified for the first time and the concave boundary identified for the second time;
the observation module is used for directly observing the local part of the power distribution network based on the target concave boundary;
and the determining module is also used for determining the defects of the power distribution network based on the target concave boundary.
Preferably, the boundary point safety distance satisfies:
(1)
in the middle ofRepresenting boundary points +.>Is a safe distance of (2)，/>Representing boundary points +.>Is used to determine the relative position of the components of the component(s),i representation ofComponent of each dimension->Is provided with the number of (a),krepresenting the dimension components->Is a dimension of (c).
Preferably, the boundary shortest distance satisfies:
(2)
in the middle ofFor the shortest distance of boundary->Representation ofjIs boundary->Point on->Representing boundary pointsjIs a boundary point safety distance of (c).
Preferably, the first identification of the recessed boundary of the security domain by full-dimensional observation comprises: identifying a concave boundary of a security domain through a visual view and a quantization index of the full-dimensional observation; the visual view includes: boundary radar view, boundary concave view, boundary point concave view, quantization index includes: recess depth and recess ratio.
Further, coordinate axes of the radar view in all directions led out from the origin point represent a safety boundary, and numerical values on the coordinate axes represent the shortest boundary distance of the safety boundary;
and identifying the safe boundary meeting the depression threshold expression as the depression boundary, wherein the shortest boundary distance in the radar view is smaller than the average value of the safe distances of the boundary points.
Further, the concave boundary view takes the number of the safety boundary as an abscissa and takes the shortest boundary distance as an ordinate;
and identifying the safe boundary meeting the depression threshold expression as the depression boundary, wherein the shortest boundary distance in the depression boundary view is smaller than the average value of the safe distances of the boundary points.
Further, the depression threshold expression satisfies:
（7）
threshold of dishing in a vehicleA constant of 0 to 1, < >>For the shortest distance of boundary->Is the average value of the safe distance of the boundary points.
Further, the boundary point concave view takes the serial number of the boundary point as an abscissa and takes the Manhattan distance from the boundary point to the origin point as an ordinate;
the boundary point numbers are sequentially ordered from small to large according to the Manhattan distance from the boundary point to the origin;
the value of the Manhattan distance from the boundary point to the origin point represents the power supply capability of the boundary point;
the manhattan distance from the boundary point to the origin satisfies:
(3)
in the middle ofRepresenting boundary points +.>Manhattan distance to origin, +.>Representing boundary pointsjIs used to determine the relative position of the components of the component(s),i representsComponent of each dimension->Is provided with the number of (a),krepresenting the dimension components->Is a dimension of (c).
Further, the Manhattan distance from the boundary point to the origin point in the boundary point concave view is smaller than the threshold value of the power supply capacity, and the boundary point is a concave boundary point;
and when the Manhattan distance from the boundary point to the original point in the boundary point concave view is equal to the power supply capacity threshold value, the corresponding abscissa boundary point numbers are the number of concave boundary points.
Further, the depression depth satisfies:
(4)
in the middle ofRepresenting boundariesβiIs>Is a safety boundaryβiBoundary shortest distance of->Is the average value of the safe distance of the boundary points;
further, the maximum value of the recess depth satisfies:
(5)
in the middle ofAt the maximum value of the depth of the depression,nis the number of the dent depth index.
Further, the dent ratio satisfies:
(6)
in the middle ofRepresenting the proportion of dishing->For the number of concave boundaries>Is the number of safety boundaries.
Compared with the prior art, the invention has the following advantages:
according to the method and the device for positioning the defects of the power distribution network based on the security domain concave visualization, through the combination of full-dimensional indirect observation, monte Carlo two-dimensional observation and direct observation, the whole observation of the security domain of the power distribution network can be completed, all the concave areas of the security domain of the power distribution network can be completely found, the security domain can be directly observed, and the visual observation is particularly carried out on the local parts of the concave areas. The invention provides a method for firstly integrating and then locally integrating; the scheme is indirect firstly and direct secondly, the form of the safety domain Gao Weiyu of the power distribution network can be completely observed, the defect of the power distribution network in safety performance can be found, a new tool is provided for safety domain analysis, meanwhile, in power distribution network planning, the safety performance of the current network and planning scheme can be more finely analyzed, and a reconstruction scheme or planning scheme with better safety performance can be obtained.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for locating defects of a power distribution network based on security domain recess visualization in an embodiment of the present invention;
FIG. 2 is a diagram of an example power grid in an embodiment of the invention;
FIG. 3 is a comparison of the boundary radar views of example 1 and example 2 in accordance with an embodiment of the present invention;
FIG. 4 is a comparison of boundary concave views of example 1 and example 2 according to an embodiment of the present invention;
FIG. 5 is a comparative view of boundary point depression of example 1 and example 2 in an embodiment of the present invention;
FIG. 6 is a summary of the number of occurrences of each boundary under two-dimensional Monte Carlo observation of example 1 in accordance with an embodiment of the present invention;
FIG. 7 is a comparison of two-dimensional views (SL 17-SL18 planes) of the security domains of example 1 and example 2 in the embodiment of the invention;
fig. 8 is a schematic structural diagram of a defect positioning device for a power distribution network based on security domain recess visualization according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The flow chart of the distribution network defect positioning method based on the security domain concave visualization is shown in fig. 1.
S101 acquires data.
Based on the distribution network structure column, writing a distribution network safety boundary expression, removing invalid boundaries, and obtaining a plurality of pieces of effective safety boundary data, wherein the safety boundaries have common origin points, points on the safety boundaries are boundary points, and points closest to the origin points on the boundaries are concave points. And obtaining a plurality of boundary points on each boundary through sampling, and further calculating the obtained boundary point safety distance (the geometric distance from the boundary point to the origin on the safety boundary), the shortest boundary distance (the minimum value of the geometric distance from the boundary point to the origin on the safety boundary) and the average value of the boundary point safety distances (the average value of the geometric distances from the boundary point to the origin on the safety boundary) based on the boundary point data.
Calculating the safe distance of the boundary point, and meeting the following conditions:
(1)
in the middle ofRepresenting boundary points +.>Is (are) safe distance>Representing boundary points +.>Is used to determine the relative position of the components of the component(s),i representation ofComponent of each dimension->Is provided with the number of (a),krepresenting the dimension components->Is a dimension of (c).
Calculating the shortest boundary distance, and meeting the following conditions:
(2)
in the middle ofFor the shortest distance of boundary->Representation ofjIs boundary->Point on->Representing boundary pointsjIs a boundary point safety distance of (c).
Calculating the average value of the safety distances of boundary points, namely the radius of the safety domain of the power distribution network。
The list writes the distribution network safety boundary expression, eliminates the invalid boundary, and is known to those skilled in the art, and the embodiment of the present invention will not be repeated.
S102, identifying a concave boundary for the first time on the safety boundary of the safety domain through full-dimensional observation, identifying the concave boundary for the second time on the safety boundary of the safety domain through Monte Carlo two-dimensional observation, and mutually verifying the concave boundary identified for the first time and the concave boundary identified for the second time to determine the concave boundary of the target.
Wherein, carry out the first identification recess boundary to the security boundary of security domain through the full dimension observation, include: identifying a concave boundary of a security domain through a visual view and a quantization index of the full-dimensional observation; the visual view comprises: boundary radar view, boundary concave view, boundary point concave view, the quantization index includes: recess depth and recess ratio.
The following separately discusses the visualization view of the full-dimensional observation:
(1) Boundary radar view.
The boundary radar view is a graph for displaying high-dimensional data in a two-dimensional form, coordinate axes of the radar view in all directions led out from an origin point represent a safety boundary, and numerical values on the coordinate axes represent the shortest boundary distance of the safety boundary. The invention adopts the radar map to compare the shortest distance of each safety boundary, can visually observe the sinking degree of the safety domain, and determines the safety boundary which is smaller than the average value of the safety distances of boundary points and meets the sinking threshold expression as the sinking boundary in the boundary radar view through observation. Depending on how many safety boundaries are, either a full-quadrant radar map or a single-quadrant radar map may be selectively employed. The specific drawing method is as follows:
drawing coordinates, namely sequentially determining the coordinate directions of the subsequent boundaries by taking the horizontal right direction of an origin as the coordinate direction of a first boundary, wherein the directions need to be separated by the same angle; then, drawing a plurality of concentric circles with proper radius by taking the origin as the circle center, and representing different numerical values;
drawing points, namely drawing corresponding points in the corresponding coordinate directions according to the shortest distance of each boundary; then, the points are sequentially connected in the boundary number order.
The rendered boundary radar pattern is shown, for example, in fig. 3.
(2) A boundary concave view.
The boundary concave view is another two-dimensional view of the shortest distance of each safety boundary, completely displays the distances from all effective safety boundaries to the original point, takes the serial numbers of the safety boundaries as the abscissa and takes the shortest distance of the boundary as the ordinate, and is obtained by sequentially tracing points according to the serial numbers of the boundaries. A drawing-completed boundary recess view is shown in fig. 4, for example.
And determining the safe boundary which is smaller than the average value of the safe distances of the boundary points and meets the depression threshold value expression as the depression boundary in the depression boundary view by observing the depression boundary view.
(3) And a boundary point depression view.
The boundary point depression view shows the distance from each safety boundary point to the origin point, and can be used for finding out depression boundary points and determining the number of depression points. And (3) obtaining a plurality of boundary points on each boundary through sampling, calculating Manhattan distances from the boundary points to an original point, sorting according to the Manhattan distances from small to large, numbering the boundary points, for example, the shortest Manhattan distance is numbered 1, the second shortest boundary point is numbered 2, the third shortest boundary point is numbered 3, and the nth shortest boundary point is numbered n, wherein the boundary point concave view is obtained by drawing the points sequentially from small to large by taking the boundary point number as an abscissa and taking the Manhattan distance from the boundary point to the original point as an ordinate. A drawing-completed boundary recess view is shown in fig. 5, for example.
The Manhattan distance is adopted for the ordinate of the concave view of the boundary point instead of the geometric distance used for the first two types of views, because the Manhattan distance from the boundary point to the original point has practical physical significance, the power supply capacity of the boundary point is represented, the total load carried by the boundary point is equal in value, the Manhattan distance from the boundary point to the original point in the concave view of the boundary point is smaller than the power supply capacity threshold, the boundary point is the concave boundary point, and when the Manhattan distance from the boundary point to the original point in the concave view of the boundary point is equal to the power supply capacity threshold, the number of the boundary points of the corresponding abscissa is the number of the concave boundary points. The calculation formula of the Manhattan distance satisfies the following conditions:
(3)
in the middle ofRepresenting boundary points +.>Manhattan distance to origin, +.>Representing boundary pointsjIs used to determine the relative position of the components of the component(s),i representsComponent of each dimension->Is provided with the number of (a),krepresenting the dimension components->Is a dimension of (c).
Quantization indices for full-dimensional indirect observation include: recess depth, recess ratio. Discussed separately below:
(1) Depth of the recess.
The dent depth index DDI (DDI) may reflect the degree of dent of the security boundary. The calculation formula of the concave depth meets the following conditions:
(4)
in the middle ofRepresenting boundariesβiIs>Is a safety boundaryβiBoundary shortest distance of->Is the average value of the safe distance of the boundary points;
further, the maximum value of the recess depth satisfies:
(5)
in the middle ofAt the maximum value of the depth of the depression,nis the number of the dent depth index.
(2) Dishing ratio.
Dent ratio indexDRI(dent-ratio index, DRI) reflects the number of dents.
The calculation formula of the sinking proportion meets the following conditions:
(6)
in the middle ofRepresenting the proportion of dishing->For the number of concave boundaries>Is the number of safety boundaries.
Further, in the calculation process of the quantization index according to the requirement, determining the safe boundary meeting the depression threshold expression as the depression boundary, wherein the shortest boundary distance is smaller than the average value of the safe distances of the boundary points.
The depression threshold expression described in the present invention satisfies:
（7）
threshold of dishing in a vehicleA constant of 0 to 1, < >>For the shortest distance of boundary->Is the average value of the safe distance of the boundary points.
The method comprises the specific steps of carrying out second identification on the concave boundary of the safety domain through two-dimensional observation of Monte Carlo, wherein the specific steps comprise:
step 1, performing Monte Carlo sampling on DSSR to obtainkEach sampling point corresponds to a set of non-observation variable value scheme;
step 2, for sampling pointsiTwo-dimensional observation of DSSR under all possible combinations of observation variables, distribution network dimensions arenThen there is C2nSeed combinationObtaining C2nA two-dimensional image;
step 2-1, writing a security boundary expression in a column, and simplifying;
step 2-2, selecting 2 variables as observation variables, fixing non-observation variables as constants, and further simplifying a boundary expression;
step 2-3, drawing a boundary line corresponding to the simplified equation in a coordinate system formed by the observation variables;
step 2-4, the enclosed graph surrounded by the boundary line and the coordinate axis is a two-dimensional image of DSSR;
step 3, traversing allkThe Monte Carlo sampling points can be finally obtainedk×C2n) A two-dimensional image;
and 4, counting the occurrence times of each boundary in all the two-dimensional images. The more occurrences, the more stringent the boundary relative to the other boundaries, which is indicative of a concave boundary.
The method exhibits relatively stable performance in processing data of different sample amounts.
S103, based on the target concave boundary, direct observation is carried out on the local part of the power distribution network.
The target concave boundary corresponds to a concave region of a power distribution network security domain, and a corresponding directly observed two-dimensional projection view is drawn based on the plurality of target concave boundaries, and the specific steps are as follows:
step one, listing a safety boundary equation of the concave boundary;
selecting two variables representing feeder load with the largest occurrence times in the safety boundary equation for observation, wherein variables except the two selected variables in the safety boundary equation are fixed as constants;
thirdly, constructing a coordinate system by taking the selected two variables as an abscissa and an ordinate;
drawing a corresponding safety boundary of the concave boundary in a coordinate system, wherein the safety boundary and the horizontal and vertical coordinate axes enclose a closed graph;
and fifthly, enclosing a safety boundary and an abscissa axis in the two-dimensional projection view into a closed graph, and determining the closed graph as a corresponding safety domain of the target concave boundary.
The two-dimensional projection view comprises projection of all concave boundaries as much as possible, so that corresponding security domains of the concave boundaries are obtained, and local direct observation of the concave regions is facilitated.
And listing a safe expression of the concave boundary of the target, and further observing and analyzing the safe expression in combination with a two-dimensional projection view.
S104, determining the defects of the power distribution network based on the target concave boundary.
After observation analysis is carried out according to the relevant data of the target concave boundary and the safe expression of the two-dimensional projection view combined concave boundary, the bottleneck element is positioned based on the target concave boundary, the bottleneck element is further analyzed, a transformation scheme of the power distribution network is proposed, after targeted transformation is carried out based on the proposed transformation scheme, the method is carried out again, a two-dimensional view and an amount index of full-dimensional observation are obtained, a new two-dimensional projection view based on the target concave boundary are obtained, defects of the power distribution network are further determined according to the corresponding safe domain range change of the concave boundary in the observation view, and the transformation effect of the transformation scheme is evaluated.
In summary, the method and the device for positioning the defects of the power distribution network based on the security domain concave visualization provided by the invention can complete the overall observation of the security domain of the power distribution network through the combination of full-dimensional indirect observation, monte Carlo two-dimensional observation and direct observation, completely find all the concave areas of the security domain of the power distribution network, directly observe the security domain, and especially observe the local parts of the concave areas in a visualized manner. The invention provides a method for firstly integrating and then locally integrating; the scheme is indirect firstly and direct secondly, the form of the safety domain Gao Weiyu of the power distribution network can be completely observed, the defect of the power distribution network in safety performance can be found, a new tool is provided for safety domain analysis, the safety performance of the current network and the planning scheme can be more finely analyzed in power distribution network planning, and a reconstruction scheme or a planning scheme with better safety performance can be obtained.
In order to further explain the technical scheme of the invention, specific examples are used for illustration. The calculation example comprises a substation 3 seat, the power transformation level of which is 33/11kV,20 feedback lines and 104 load nodes. The structure of the example power distribution network is shown in fig. 2, and the main transformer and feeder parameters of example 1 are shown in table 1. In order to distinguish between the cases before and after the modification, the original case is called case 1, and the case after the security domain defect is found and the modification is called case 2.
And (3) writing the safe boundary expression of the power grid of the example 1, and removing the invalid boundary to obtain the example 1 which totally comprises 30 valid boundaries. Obtaining a plurality of boundary points on each boundary through sampling, finding a concave point closest to an origin, calculating the distance from the point to the origin to obtain the shortest boundary distance, and calculating the average value of the safety distances of all the boundary points in the calculation example 1R DSSR 2.99 MVA. The effective safety boundary equation of the safety domain of the power distribution network in the calculation example 1 is shown in table 2.
Based on the data, a visual view and a quantization index are determined, the sinking condition of the security domain is indirectly observed in a full-dimension mode through the visual view and the quantization index, a sinking boundary is found, the sinking degree of the security domain can be observed in a visual mode through the visual view, and the defects of the security domain can be quantized through the index.
Based on the above data, the obtained boundary radar view is shown in fig. 3, the boundary depression view is shown in fig. 4, and the boundary depression point view is shown in fig. 5.
To distinguish between the severity of different depressions, 2 different depression thresholds were taken:is a common depression, is->For severe depression->Is a safety boundaryβiIs the shortest boundary of (2)Distance, average value of all boundary point safety distancesR DSSR 。
I.e.When it is, satisfy->Is a normal concave boundary;
i.e.When it is, satisfy->Is a severe pit boundary.
By observing the boundary radar view fig. 3 and the boundary dip view fig. 4, it can be found that example 1 has 5 severe dip boundaries, respectivelyβ12，β13，β14，β15 andβ26。
the DSSR of example 1 was comprehensively observed in two dimensions based on the monte carlo method. The number k of sampling points is 100, the dimension of example 1 is 20, and finally 100×c2=19000 two-dimensional images are obtained. The number of occurrences of each security boundary is summarized in fig. 6. The DSSR boundaries of example 1 are divided into 3 groups by the number of variables contained in the expression, namely, circular line segments, triangular line segments, rectangular line segments in fig. 6.
As can be seen from fig. 6, in the triangle-shaped wiring portion and the rectangle-shaped wiring portion, the number of occurrences of the partial boundaries is significantly higher than that of the other boundaries of the same group:
1) The triangular wire connection part is displayed,β12~β151625 to 2860 timesβ16~β21Only 311 to 625 occurrences. This indicatesβ12~β15Is a concave boundary.
2) The rectangular wire portions are shown as being,β264162 times occurβ22~β25、β27~β30Only 392 to 1278 times. This indicatesβ26Is a concave boundary.
To sum up, a Monte Carlo-based DSSR two-dimensional observation method is used for identifying boundariesβ12~β15、β26Is a concave boundary, and is based on a full-dimensional observation methodAnd the identification results are consistent, the identification results are mutually verified, and the concave boundary is further determined.
The expression of the severe pit boundary of example 1 is shown in table 3.
As can be seen from table 3, the reason why the power distribution network security domain is depressed is that the capacities of the main transformers T5 and T6 are too small, i.e., T5 and T6 are bottleneck elements causing the power distribution network security domain to be depressed. Accordingly, a planning scheme for improving the defect of the example 1 is provided, and the capacities of the bottleneck elements T5 and T6 are increased.
The bottleneck elements T5 and T6 are expanded to 16MVA, the data of the example 2 are prepared again after improvement, and the average value of the safety distances of all boundary points of the example 2 is calculatedR DSSR Is 3.14 MVA. The same visual observation method as that of the above-described example 1 was performed based on the example 2.
And carrying out comprehensive observation of the safety domain of the distribution network on the distribution network before and after improvement, and measuring the improvement effect.
The comparison results of the full-dimensional indirect observation visual views of the safety domains of the power distribution network in the calculation example 1 and the calculation example 2 are shown in fig. 3, fig. 4 and fig. 5.
As can be seen from fig. 3 and 4, the concave boundary of example 2 is reduced from 5 to 1 in example 1. As can be seen from fig. 5, the concave boundary points are reduced from 60 to 31.
In the calculation example 1, the quantization index result in the full-dimensional indirect observation of the safety domain of the power distribution network in the calculation example 2 is shown in table 4.
As can be seen from table 4, example 1 has 5 severe recess boundaries with a recess depth of 35.8%, while example 2 reduces to 1 normal recess boundary with a recess depth of 35.8% to 14.3%, indicating that the distribution network security domain of example 2 has a smaller and lighter recess scale than example 1. This is consistent with the direct observation of fig. 7.
Based on the concave boundary obtained in example 1 and example 2, the safe boundary expression is selectedS L17 ，S L18 To observe the variables, draw to project toS L17 -S L18 A comparison of two-dimensional projection views of a portion of a recess for example is shown in fig. 7.
As can be seen from fig. 7, the recess boundaries of example 2 were all shifted in the direction from the origin, indicating that the recess degree was reduced relative to example 1, which is consistent with the result shown in fig. 5, which is a visual view of the full-dimensional indirect observation. Two-dimensional view [ (]S L17 ,S L18 ) The area of the safety boundary of the following example 2 is obviously enlarged compared with that of example 1, the safety domain range is enlarged, and the concave part is effectively improved.
In practical application, the visual observation is carried out on the power distribution network security domain, the concave position of the security domain can be found, the concave position corresponds to the bottleneck element of the system, the scope of the power distribution network security domain can be enlarged through improvement of the bottleneck element, and the power supply reliability is improved.
Exemplary, fig. 8 is a schematic structural diagram of a defect positioning device for a power distribution network based on security domain recess visualization according to an embodiment of the present invention.
As shown in fig. 8, the apparatus 800 includes: an acquisition module 801, a determination module 802, and an observation module 803;
the acquiring module 801 is configured to acquire data, where the data includes: safety boundary data and boundary point data, and average values of boundary point safety distance, boundary shortest distance and boundary point safety distance obtained based on the boundary point data;
the determining module 802 is configured to identify a concave boundary for the first time for the security boundary of the security domain through full-dimensional observation, identify a concave boundary for the second time for the security boundary of the security domain through two-dimensional observation of monte carlo, and mutually verify the concave boundary identified for the first time and the concave boundary identified for the second time to determine a target concave boundary;
the observation module 803 is configured to directly observe a local part of the power distribution network based on the target concave boundary;
the determining module 802 is further configured to determine a power distribution network defect based on the target pit boundary;
it should be noted that, for convenience of description, fig. 8 only shows main components of the defect positioning device for the power distribution network based on the security domain recess visualization. In practical applications, the defect positioning device for the power distribution network based on the security domain recess visualization may further comprise a part or assembly not shown in the figure.
Although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (12)
1. A power distribution network defect positioning method based on security domain recess visualization is characterized by comprising the following steps:
acquiring data, the data comprising: safety boundary data and boundary point data, and average values of boundary point safety distance, boundary shortest distance and boundary point safety distance obtained based on the boundary point data;
identifying a concave boundary for the first time by full-dimensional observation on the safety boundary of the safety domain, identifying the concave boundary for the second time by Monte Carlo two-dimensional observation on the safety boundary of the safety domain, and mutually verifying the concave boundary identified for the first time and the concave boundary identified for the second time to determine a target concave boundary;
directly observing the local part of the power distribution network based on the target concave boundary;
determining a power distribution network defect based on the target concave boundary;
the first identifying of the concave boundary by the full-dimensional observation of the security boundary of the security domain includes: identifying a concave boundary of a security domain through a visual view and a quantization index of the full-dimensional observation; the visual view comprises: at least one view of a boundary radar view, a boundary concave view and a boundary point concave view, wherein the quantization index comprises: recess depth and recess ratio;
when the visual view is a boundary radar view, coordinate axes of all directions led out from an origin of the radar view represent a safety boundary, and numerical values on the coordinate axes represent the shortest boundary distance of the safety boundary; the shortest boundary distance in the radar view is smaller than the average value of the safe distances of boundary points, and the safe boundary meeting the depression threshold expression is identified as a depression boundary;
when the visual view is a concave boundary view, the concave boundary view takes the serial number of the safety boundary as an abscissa and takes the shortest boundary distance as an ordinate; the shortest boundary distance in the concave boundary view is smaller than the average value of the safe distances of boundary points, and the safe boundary meeting the concave threshold expression is identified as a concave boundary;
when the visual view is a boundary point concave view, the boundary point concave view takes the serial number of the boundary point as an abscissa and takes the Manhattan distance from the boundary point to the origin as an ordinate;
the boundary point numbers are sequentially ordered from small to large according to the Manhattan distance from the boundary point to the origin;
the value of the Manhattan distance from the boundary point to the origin point represents the power supply capability of the boundary point;
the Manhattan distance from the boundary point to the origin satisfies:
(3)
in the middle ofRepresenting boundary points +.>Manhattan distance to origin, +.>Representing boundary pointsjIs used to determine the relative position of the components of the component(s),irepresenting the dimension components->Is provided with the number of (a),krepresenting the dimension components->Dimension of (2);
the Manhattan distance from the boundary point to the original point in the boundary point concave view is smaller than the threshold value of the power supply capacity;
and when the Manhattan distance from the boundary point to the original point in the boundary point concave view is equal to the power supply capacity threshold value, the number of the boundary points of the corresponding abscissa is the number of concave boundary points.
2. The distribution network defect positioning method based on security domain recess visualization according to claim 1, wherein the boundary point security distance satisfies:
(1)
in the middle ofRepresenting boundary points +.>Is (are) safe distance>Representing boundary points +.>Is used to determine the relative position of the components of the component(s),irepresenting the dimension components->Is provided with the number of (a),krepresenting the dimension components->Is a dimension of (c).
3. The method for positioning defects of a power distribution network based on security domain depression visualization according to claim 1, wherein the shortest boundary distance satisfies:
(2)
in the middle ofFor the shortest distance of boundary->Representation ofjIs boundary->Point on->Representing boundary pointsjIs a boundary point safety distance of (c).
4. The method for locating defects in a power distribution network based on security domain recess visualization of claim 1, wherein the recess threshold expression satisfies:
（7）
threshold of dishing in a vehicleA constant of 0 to 1, < >>For the shortest distance of boundary->Is the average value of the safe distance of the boundary points.
5. The method for positioning defects of a power distribution network based on security domain recess visualization according to claim 1, wherein the recess depth satisfies:
(4)
in the middle ofRepresenting boundariesβiIs>Is a safety boundaryβiBoundary shortest distance of->Is the average value of the safe distance of the boundary points;
the maximum value of the concave depth satisfies the following conditions:
(5)
in the middle ofAt the maximum value of the depth of the depression,nis the number of the dent depth index.
6. The distribution network defect positioning method based on security domain recess visualization according to claim 1, wherein the recess ratio satisfies:
(6)
in the middle ofRepresenting the proportion of dishing->For the number of concave boundaries>Is the number of safety boundaries.
7. A distribution network defect positioning device based on security domain recess visualization, the device comprising: the system comprises an acquisition module, a determination module and an observation module;
the acquisition module is configured to acquire data, where the data includes: safety boundary data and boundary point data, and average values of boundary point safety distance, boundary shortest distance and boundary point safety distance obtained based on the boundary point data;
the determining module is used for identifying the concave boundary for the first time for the safety boundary of the safety domain through full-dimensional observation, identifying the concave boundary for the second time for the safety boundary of the safety domain through two-dimensional observation of Monte Carlo, and determining the concave boundary of the target through mutual verification of the concave boundary identified for the first time and the concave boundary identified for the second time;
the observation module is used for directly observing the local part of the power distribution network based on the target concave boundary;
the determining module is further used for determining the defects of the power distribution network based on the target concave boundary;
the first identifying of the concave boundary by the full-dimensional observation of the security boundary of the security domain includes: identifying a concave boundary of a security domain through a visual view and a quantization index of the full-dimensional observation; the visual view comprises: at least one view of a boundary radar view, a boundary concave view and a boundary point concave view, wherein the quantization index comprises: recess depth and recess ratio;
when the visual view is a boundary radar view, coordinate axes of all directions led out from an origin of the radar view represent a safety boundary, and numerical values on the coordinate axes represent the shortest boundary distance of the safety boundary; the shortest boundary distance in the radar view is smaller than the average value of the safe distances of boundary points, and the safe boundary meeting the depression threshold expression is identified as a depression boundary;
when the visual view is a concave boundary view, the concave boundary view takes the serial number of the safety boundary as an abscissa and takes the shortest boundary distance as an ordinate; the shortest boundary distance in the concave boundary view is smaller than the average value of the safe distances of boundary points, and the safe boundary meeting the concave threshold expression is identified as a concave boundary;
when the visual view is a boundary point concave view, the boundary point concave view takes the serial number of the boundary point as an abscissa and takes the Manhattan distance from the boundary point to the origin as an ordinate;
the boundary point numbers are sequentially ordered from small to large according to the Manhattan distance from the boundary point to the origin;
the value of the Manhattan distance from the boundary point to the origin point represents the power supply capability of the boundary point;
the Manhattan distance from the boundary point to the origin satisfies:
(3)
in the middle ofRepresenting boundary points +.>Manhattan distance to origin, +.>Representing boundary pointsjIs used to determine the relative position of the components of the component(s),i representsComponent of each dimension->Is provided with the number of (a),krepresenting the dimension components->Dimension of (2);
the Manhattan distance from the boundary point to the original point in the boundary point concave view is smaller than the threshold value of the power supply capacity;
and when the Manhattan distance from the boundary point to the original point in the boundary point concave view is equal to the power supply capacity threshold value, the number of the boundary points of the corresponding abscissa is the number of concave boundary points.
8. The distribution network defect localization apparatus based on security domain recess visualization of claim 7, wherein the boundary point security distance satisfies:
(1)
in the middle ofRepresenting boundary points +.>Is (are) safe distance>Representing boundary points +.>Is used to determine the relative position of the components of the component(s),i representsComponent of each dimension->Is provided with the number of (a),krepresenting the dimension components->Is a dimension of (c).
9. The defect localization apparatus for power distribution networks based on security domain recess visualization of claim 7, wherein the boundary shortest distance satisfies:
(2)
in the middle ofFor the shortest distance of boundary->Representation ofjIs boundary->Point on->Representing boundary pointsjIs a boundary point safety distance of (c).
10. The power distribution network defect localization apparatus based on security domain recess visualization of claim 7, wherein the recess threshold expression satisfies:
（7）
threshold of dishing in a vehicleA constant of 0 to 1, < >>For the shortest distance of boundary->Is the average value of the safe distance of the boundary points.
11. The distribution network defect localization apparatus based on security domain recess visualization of claim 7, wherein the recess depth satisfies:
(4)
in the middle ofRepresenting boundariesβiIs>Is a safety boundaryβiBoundary shortest distance of->Is the average value of the safe distance of the boundary points;
the maximum value of the concave depth satisfies the following conditions:
(5)
in the middle ofAt the maximum value of the depth of the depression,nis the number of the dent depth index.
12. The distribution network defect localization apparatus based on security domain recess visualization of claim 7, wherein the recess ratio satisfies:
(6)
in the middle ofRepresenting the proportion of dishing->For the number of concave boundaries>For the number of safety boundaries。
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103607375A (en) * | 2013-10-28 | 2014-02-26 | 天津大学 | Network N-1 security-region-boundary calculation and security evaluation method |
| DE102013103515A1 (en) * | 2013-04-09 | 2014-10-09 | Keba Ag | Charging connection device for electric vehicles |
| CN105305481A (en) * | 2014-07-29 | 2016-02-03 | 通用电气公司 | System and method for controlling a power distribution network |
| CN107330576A (en) * | 2017-05-17 | 2017-11-07 | 天津大学 | A kind of power distribution network efficiency rating method based on border power supply capacity |
| CN107590748A (en) * | 2017-08-17 | 2018-01-16 | 天津大学 | A kind of full dimension observation procedure of power distribution network security domain |
| CN108226713A (en) * | 2018-02-05 | 2018-06-29 | 山东大学 | A kind of concentric relaxation sag exposed areas analysis method of voltage dip |
| CN110441654A (en) * | 2019-07-29 | 2019-11-12 | 三峡大学 | Based on the distribution network electric energy quality disturbance detecting method for improving EWT and CMPE |
| CN110829411A (en) * | 2019-10-25 | 2020-02-21 | 国网天津市电力公司电力科学研究院 | Flexible power distribution network operation domain observation method considering alternating current power flow |
| CN112993980A (en) * | 2021-02-23 | 2021-06-18 | 国网浙江省电力有限公司电力科学研究院 | Electric vehicle charging load space-time probability distribution model calculation method |
| CN113963114A (en) * | 2021-10-25 | 2022-01-21 | 季华实验室 | Discrete boundary point tracking method based on polygon growth |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7404067B2 (en) * | 2016-07-22 | 2023-12-25 | ドルビー ラボラトリーズ ライセンシング コーポレイション | Network-based processing and delivery of multimedia content for live music performances |
-
2023
- 2023-11-13 CN CN202311498333.7A patent/CN117236937B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102013103515A1 (en) * | 2013-04-09 | 2014-10-09 | Keba Ag | Charging connection device for electric vehicles |
| CN103607375A (en) * | 2013-10-28 | 2014-02-26 | 天津大学 | Network N-1 security-region-boundary calculation and security evaluation method |
| CN105305481A (en) * | 2014-07-29 | 2016-02-03 | 通用电气公司 | System and method for controlling a power distribution network |
| CN107330576A (en) * | 2017-05-17 | 2017-11-07 | 天津大学 | A kind of power distribution network efficiency rating method based on border power supply capacity |
| CN107590748A (en) * | 2017-08-17 | 2018-01-16 | 天津大学 | A kind of full dimension observation procedure of power distribution network security domain |
| CN108226713A (en) * | 2018-02-05 | 2018-06-29 | 山东大学 | A kind of concentric relaxation sag exposed areas analysis method of voltage dip |
| CN110441654A (en) * | 2019-07-29 | 2019-11-12 | 三峡大学 | Based on the distribution network electric energy quality disturbance detecting method for improving EWT and CMPE |
| CN110829411A (en) * | 2019-10-25 | 2020-02-21 | 国网天津市电力公司电力科学研究院 | Flexible power distribution network operation domain observation method considering alternating current power flow |
| CN112993980A (en) * | 2021-02-23 | 2021-06-18 | 国网浙江省电力有限公司电力科学研究院 | Electric vehicle charging load space-time probability distribution model calculation method |
| CN113963114A (en) * | 2021-10-25 | 2022-01-21 | 季华实验室 | Discrete boundary point tracking method based on polygon growth |
Non-Patent Citations (1)
| Title |
|---|
| 肖峻等." 配电网安全域的全维直接观测".《电工技术学报》.2019,第35卷(第19期),全文. * |
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