CN111768109A - Reliability early warning method and system for power electronic medium-voltage distribution network and terminal equipment - Google Patents

Reliability early warning method and system for power electronic medium-voltage distribution network and terminal equipment Download PDF

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CN111768109A
CN111768109A CN202010626814.1A CN202010626814A CN111768109A CN 111768109 A CN111768109 A CN 111768109A CN 202010626814 A CN202010626814 A CN 202010626814A CN 111768109 A CN111768109 A CN 111768109A
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高崇
王天霖
张黎明
罗强
张俊潇
唐俊熙
吴亚雄
曹华珍
李�浩
陈沛东
何璇
黄烨
李阳
欧阳森
杨墨缘
张真
李卓环
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Guangdong Power Grid Co Ltd
Grid Planning Research Center of Guangdong Power Grid Co Ltd
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Abstract

The application provides a reliability early warning method, a system and terminal equipment for a power electronic medium-voltage distribution network, wherein the method comprises the following steps: in basic equipment attribute data and load data related to the reliability of the feeder line of the power electronic medium-voltage distribution network of the same power supply partition, an index of which the reliability contribution degree reaches a set value is determined as a static index; the annual statistical data of the feeder line power supply reliability index is determined as a dynamic index; clustering the feeders by adopting a DBSCAN algorithm based on static indexes, and summarizing year-by-year statistical data of the power supply reliability indexes of the clustered feeders of the same type; the mean value of the annual power supply reliability data of the similar feeders is used as an early warning threshold value, the feeders with potential reliability problems are screened out, and early warning judgment is carried out on the reliability of each feeder in the similar feeders, so that the current situation that the potential reliability problems existing in the power electronic medium-voltage distribution network cannot be judged in advance in the prior art is solved, the reliability of the power electronic medium-voltage distribution network is improved, and the loss of the reliability problems to power users and power supply enterprises is avoided.

Description

Reliability early warning method and system for power electronic medium-voltage distribution network and terminal equipment
Technical Field
The invention relates to the technical field of electric power, in particular to a method, a system and terminal equipment for early warning the reliability of a power electronic medium-voltage distribution network.
Background
The medium-voltage distribution network is a main component of the distribution network and is a main object for reliability improvement of the distribution network. The medium-voltage distribution network specifically refers to a medium-voltage distribution system formed by a 6kV or 10kV or 20kV bus side isolating switch of a certain transformer substation or power plant from a secondary side outgoing line bushing of a public distribution transformer or a property right boundary point of a user and a power supply enterprise, a medium-voltage feeder is called a feeder for short and is in the simplest medium-voltage distribution network form, the feeder is fed out by the 6kV or 10kV or 20kV bus of the transformer substation or the power plant and supplies power to the carried medium-voltage user, and the medium-voltage user comprises the public distribution transformer and a medium-voltage special transformer.
The power electronic medium-voltage distribution network is used as a new development form of the medium-voltage distribution network and is mainly characterized in that: the medium-voltage feeder line comprises a large number of power electronic devices such as power electronic transformers, power electronic circuit breakers, new energy grid-connected devices, flexible current converters, flexible soft switches, static var generators, active power filters and the like. Compared with the traditional medium-voltage distribution network, the power electronic medium-voltage distribution network is more complicated in power distribution equipment contained in the power electronic medium-voltage distribution network, the faced reliability problem is more diversified, the reliability of any power distribution equipment is problematic, the continuous reliability power utilization of users can be influenced, and economic losses are caused to users and power supply enterprises. Therefore, the problem that the power electronic medium-voltage distribution network with the potential reliability hazard is found in advance, reliability improving measures are taken in time, and the problem is urgently needed to be solved.
At present, the upgrading and reconstruction of a power electronic medium-voltage distribution network (hereinafter referred to as a distribution network) are mostly carried out when the power supply reliability cannot meet the power consumption demand of a user, that is, after a reliability problem occurs, reliability improving measures such as capacity increasing and expanding, line reconstruction and the like are taken according to specific problems. However, in the passive mode for improving the reliability of the power distribution network, which solves the problem after the problem occurs, although the reliability of the power distribution network can be improved aiming at the specific problem, the problem is solved after the problem occurs, which is difficult to avoid causing unnecessary loss to power consumers and power supply enterprises, and in order to further improve the reliability of the power distribution network, the problem is better avoided, which causes loss to the power consumers and the power supply enterprises, and the active mode for improving the reliability of the power distribution network, which prevents the problem before the problem occurs, should be actively promoted. However, when the distribution grid reliability level meets the customer demand for power, it does not necessarily indicate that the distribution grid is at a good reliability level. How to carry out the degree of depth excavation to distribution network reliability differentiation information based on similar attribute, give reasonable early warning to the distribution network that has the potential reliability problem to "just prevent the problem before producing the problem" is the problem that power consumer and power supply enterprise paid close attention to at present, also is the technological difficulty that urgent need solve. The existing research aiming at improving the reliability of the power distribution network mainly focuses on a passive mode, namely, the vertical comparison of the reliability of a single power distribution network based on a time sequence is mostly carried out, the horizontal comparison research of the reliability of the power distribution network based on the difference between different medium-voltage feeders of the same power supply partition is less, and the research of giving reasonable early warning to the power distribution network with the potential reliability problem is not found.
Disclosure of Invention
Based on the above, the invention aims to provide a reliability early warning method, a system and terminal equipment for a power electronic medium voltage distribution network, solve the current situation that the potential reliability problem of the power electronic medium voltage distribution network cannot be judged in advance in the prior art, improve the reliability of the power electronic medium voltage distribution network, and avoid the loss of the reliability problem to power users and power supply enterprises.
In a first aspect, the invention provides a reliability early warning method for a power electronic medium-voltage distribution network, which comprises the following steps:
s1, acquiring basic equipment attribute data and load data related to reliability of the feeder line of the power electronic medium-voltage distribution network of the same power supply partition; acquiring year-by-year statistical data of a feeder line power supply reliability index through a power failure information acquisition and statistical analysis system and a historical database;
s2, determining an index, which has a contribution degree to reliability reaching a set value, in the basic equipment attribute data and the load data as a static index; determining year-by-year statistical data of the feeder line power supply reliability index as a dynamic index;
s3, clustering the feeders by adopting a DBSCAN algorithm based on static indexes, and summarizing year-by-year statistical data of the power supply reliability indexes of the clustered feeders of the same type;
s4, taking the annual power supply reliability data mean value of all the similar feeders as an early warning threshold, combining the early warning threshold of the similar feeders, the power supply partition power supply reliability control target of all the similar feeders and the dynamic index development trend of all the similar feeders, screening out the feeders with potential reliability problems, and performing early warning judgment on the reliability of all the similar feeders.
Preferably, basic device attribute data and load data related to reliability of different feeders of the power electronic medium-voltage distribution network of the same power supply partition include: total line length, power supply radius, cabling rate, number of section switches, number of interconnection switches, number of load points, total load point capacity and load peak value.
Preferably, the yearly statistical data of the feeder line power supply reliability index includes: average system outage time SAIDI and average system outage frequency SAIFI.
Preferably, the clustering based on the static index is performed on the feeder line by using the DBSCAN algorithm to obtain the clustered similar feeder line, and the annual statistical data of the power supply reliability index of the similar feeder line is summarized, including:
s301, assuming that n medium-voltage feeders are in common in the same power supply partition power electronization medium-voltage distribution network to be subjected to reliability early warning analysis, namely, clustering object set X ═ X { (X)1,X2,...,XnEach object is characterized by m static indices, i.e. Xi＝{xi1,xi2,...,xim1,2, n, wherein X isiFor the ith clustering object, the spatial data set D ═ x of DBSCAN is obtainedij)n×m，j＝1,2...,m；
S302, inputting a spatial data set D, a clustering radius R, a minimum number of objects in a field MP and a current object set N;
s303, initializing a core object set
Figure BDA0002566824140000031
Clustering cluster k is 0, non-visited sample set is D, cluster division
Figure BDA0002566824140000032
S304, traversing all the clustering objects XiFinding X by Euclidean distanceiR Domain object set N ∈ (X)i) Then, it is judged whether or not | N ∈ (X) is satisfiedi) | ≧ MP, if satisfied, XiAs a core object, update Ω - Ω ∪ { Xi}; after the traversal is completed, all the core objects are found. If X is presentiNot belonging to any cluster, then X isiAdding a noise point set Ck＝1；
S305, if
Figure BDA0002566824140000033
Proceeding to S302, the input value of R, MP is reset; otherwise, turning to S306;
s306, if
Figure BDA0002566824140000034
If C is generated completely, the step is shifted to S309; otherwise, in omega, randomly selecting a core object o, and initializing the current cluster core object queue omegacurThe cluster sample set C comprises a current cluster sample set C, a class sequence number k and k +1kO, update o;
s307, if
Figure BDA0002566824140000035
Then C iskAfter generation, update C ═ C1,C2,...,CkAnd Ω -CkAnd then, turning to S306; otherwise only omega-C is updatedkGo to S308;
s308, from omegacurTaking out the core object o, finding out all N ∈ (o) according to R, making delta be N ∈ (o) ∩, updating Ck＝Ck∪Δ、＝-Δ、Ωcur＝Ωcur∪ (Δ ∩ Ω) -o, transition to S307;
s309 and output C ═ C1,C2,...,Ck}，C1Dividing elements of the noise point set into cluster clusters of the nearest core object as a noise point set of interest;
s310, summarizing and clustering each clustered cluster CpAnd p is 2,3, the power supply reliability of each feeder line of the same type in k is counted year by year.
The feeder lines are clustered by adopting a partial clustering algorithm DBSCAN based on density division, the same parameters are input during iteration, and the same iteration result can be kept being output, so that the DBSCAN algorithm has robustness, noise points in an analysis object can be found by utilizing the algorithm, the noise points are used as outlier points worth attention to carry out key analysis, the noise points are evaluated to be unreasonable reasonably, and the noise points are classified into the nearest core points, so that the analysis result is more accurate.
Preferably, the taking the mean value of the annual power supply reliability data of all similar feeders as an early warning threshold includes:
setting cluster CpI.e. k, there are h feeder lines of the same type, i.e. p 2,3
Figure BDA0002566824140000041
Wherein C ispH is the p-th clustering cluster and varies according to p; describing the power supply reliability characteristics of each feeder line by adopting the dynamic indexes of each feeder line to obtain
Figure BDA0002566824140000042
r 1,2, a, h, wherein
Figure BDA0002566824140000043
The r feeder line of the p cluster;
Figure BDA0002566824140000044
respectively, the system average power failure time SAIDI and the system average power failure frequency SAIFI of the feeder line are counted by year, the subscript 0 of the data in the data set represents the current year, and the subscript 1 represents the last yearDegree, y represents the history year y;
c is to bepThe mean value of the reliability index data of the annual power supply of h similar feeders serves as an early warning threshold βpFor the average system power failure time SAIDI in the reliability index of power supply in the current year, the early warning threshold value is
Figure BDA0002566824140000045
For the average system power failure frequency SAIFI in the reliability index of power supply in the year, the early warning threshold value is
Figure BDA0002566824140000046
Preferably, the method for screening the feeder line with the potential reliability problem by combining the early warning threshold of the similar feeder line, the power supply partition power supply reliability control target where each similar feeder line is located, and the dynamic index development trend of each similar feeder line, and performing early warning judgment on the reliability of each feeder line in the similar feeder lines includes:
describing the feeder power supply reliability based on the system average power failure time SAIDI, and carrying out the operation on the r-th feeder of the p-th cluster
Figure BDA0002566824140000047
Carrying out early warning judgment on the reliability of the system;
based on the description of the system average power failure frequency SAIFI on the power supply reliability of the feeder line, the r-th feeder line of the p-th cluster is subjected to
Figure BDA0002566824140000048
And carrying out early warning judgment on the reliability of the system.
Preferably, the description of the feeder power supply reliability based on the system average power failure time SAIDI is used for the r-th feeder of the p-th cluster
Figure BDA0002566824140000049
The reliability of the system is judged by early warning, comprising the following steps:
the power supply reliability control target of the power supply partition where the p-th cluster is located is βtargetTherein mainly comprising
Figure BDA0002566824140000051
The r-th feeder line of the p-th cluster
Figure BDA0002566824140000052
Has a dynamic index of
Figure BDA0002566824140000053
When in use
Figure BDA0002566824140000054
And is
Figure BDA0002566824140000055
When, to
Figure BDA0002566824140000056
Is early-warned by the reliability of the early-warning level
Figure BDA0002566824140000057
Determining the development trend of the dynamic indexes; the above-mentioned
Figure BDA0002566824140000058
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure BDA0002566824140000059
The dynamic index data set carries out linear fitting on the annual statistical data of the feeder line power supply reliability index, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400000510
Then pair
Figure BDA00025668241400000511
Carrying out I-level early warning on the reliability of the system; otherwise, it is to
Figure BDA00025668241400000512
Carrying out II-level early warning on the reliability of the system;
when in use
Figure BDA00025668241400000513
Or
Figure BDA00025668241400000514
When, to
Figure BDA00025668241400000515
Whether the reliability of (2) needs to be warned by
Figure BDA00025668241400000516
Determining a dynamic index development trend of said
Figure BDA00025668241400000517
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure BDA00025668241400000518
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400000519
Then pair
Figure BDA00025668241400000520
Carrying out III-level early warning on the reliability of the system; otherwise, it is not right
Figure BDA00025668241400000521
Carrying out early warning on the reliability of the system;
when in use
Figure BDA00025668241400000522
And is
Figure BDA00025668241400000523
When it is not right
Figure BDA00025668241400000524
The reliability of the system is early-warned.
The p-th polyThe power supply reliability control target of the power supply subarea where the cluster is located is βtargetTherein mainly comprising
Figure BDA00025668241400000525
The r-th feeder line of the p-th cluster
Figure BDA00025668241400000526
Has a dynamic index of
Figure BDA00025668241400000527
When in use
Figure BDA00025668241400000528
And is
Figure BDA00025668241400000529
When, to
Figure BDA00025668241400000530
Is early-warned by the reliability of the early-warning level
Figure BDA00025668241400000531
Determining the development trend of the dynamic indexes; the above-mentioned
Figure BDA00025668241400000532
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure BDA00025668241400000533
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400000534
Then pair
Figure BDA00025668241400000535
Carrying out I-level early warning on the reliability of the system; otherwise, it is to
Figure BDA00025668241400000536
Carrying out II-level early warning on the reliability of the system;
when in use
Figure BDA00025668241400000537
Or
Figure BDA00025668241400000538
When, to
Figure BDA00025668241400000539
Whether the reliability of (2) needs to be warned by
Figure BDA00025668241400000540
Determining a dynamic index development trend of said
Figure BDA00025668241400000541
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure BDA00025668241400000542
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400000543
Then pair
Figure BDA00025668241400000544
Carrying out III-level early warning on the reliability of the system; otherwise, it is not right
Figure BDA00025668241400000545
Carrying out early warning on the reliability of the system;
when in use
Figure BDA00025668241400000546
And is
Figure BDA00025668241400000547
When it is not right
Figure BDA00025668241400000548
The reliability of the system is early-warned.
In a second aspect, the present application provides a power electronics middling voltage distribution network reliability early warning system, includes:
the data acquisition and storage module is used for acquiring basic equipment attribute data and load data related to reliability of different feeder lines in the same power supply partition; acquiring year-by-year statistical data of the power supply reliability indexes of the feeder lines through a power failure information acquisition and statistical analysis system and a historical database;
the data screening and classifying module is used for screening out indexes with reliability contribution degrees reaching set values from the data acquisition and storage module, classifying the screened indexes, determining the attribute data and the load data of the basic equipment as static indexes, and determining year-by-year statistical data of the feeder line power supply reliability indexes as dynamic indexes;
the cluster analysis module is used for carrying out cluster analysis based on static indexes on different feeders of the power electronic medium-voltage distribution network in the same power supply partition by adopting a DBSCAN algorithm, and the cluster data adopts the static indexes obtained by screening by the data screening and classifying module;
the early warning threshold generation module is used for calculating the mean value of feeder power supply reliability index data of the similar feeders in the current year according to the clustered feeder power supply reliability index year-by-year statistical data of the similar feeders obtained from the data acquisition and storage module, and the mean value is used as a feeder reliability early warning threshold;
and the early warning judging module is used for screening the feeder lines with potential reliability problems by combining the early warning threshold values of the similar feeder lines, the power supply partition power supply reliability control targets of the similar feeder lines and the dynamic index development trends of the similar feeder lines, and performing grading early warning on the reliability of each feeder line in the similar feeder lines.
The third aspect, this application provides power electronics ization medium voltage distribution network reliability early warning terminal equipment includes:
a memory for storing computer program code corresponding to any of the above described power electronization medium voltage distribution network reliability warning methods;
a processor for executing the computer program code to implement the method of power electronization medium voltage distribution network reliability warning as defined in any one of the above.
According to the technical scheme, the scheme has the following advantages:
the invention provides a reliability early warning method for a power electronic medium-voltage distribution network, which comprises the following steps: acquiring basic equipment attribute data and load data related to reliability of different feeders of the power electronic medium-voltage distribution network of the same power supply partition; acquiring year-by-year statistical data of the power supply reliability indexes of the feeder lines through a power failure information acquisition and statistical analysis system and a historical database; determining an index of which the reliability contribution degree reaches a set value in the basic equipment attribute data and the load data as a static index; determining year-by-year statistical data of the feeder line power supply reliability index as a dynamic index; clustering the feeder lines based on static indexes by adopting a DBSCAN algorithm to obtain the clustered similar feeder lines; summarizing year-by-year statistical data of power supply reliability indexes of similar feeders; and taking the mean value of the annual power supply reliability data of the similar feeders as an early warning threshold, and screening the feeders with potential reliability problems by combining the power supply partition power supply reliability control target and the development trend of the dynamic index, so as to perform early warning judgment on the reliability of each feeder in the similar feeders.
Therefore, the reliability early warning method for the power electronic medium voltage distribution network provided by the invention combines the yearly statistical data of the feeder line power supply reliability index on the basis of the basic equipment attribute data and the load data related to the reliability of the feeder line, uses the feeder line basic equipment attribute data and the load data as the index parameters of cluster analysis, compares the power supply reliability index difference of the similar power electronic medium voltage distribution network, combines the power supply reliability control target of the power supply subareas and the development trend of the dynamic index, and performs early warning judgment on the reliability of the power electronic medium voltage distribution network, thereby being capable of distinguishing the power electronic medium voltage distribution network with potential problems in advance, giving reasonable early warning to the distribution network with potential reliability problems, solving the technical difficulty that power users and power supply enterprises pay attention to prevent problems before generating problems at present, the reliability of the power distribution network is improved, loss of reliability problems to power consumers and power supply enterprises is avoided, and a power distribution network reliability improvement active mode of preventing problems before problems are generated is actively promoted.
The invention also provides a reliability early warning system and terminal equipment of the power electronic medium-voltage distribution network, and the system and the terminal equipment have the same beneficial effects as the control method.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flow chart of a reliability early warning method for a power electronic medium-voltage distribution network provided by the invention;
FIG. 2 is a diagram of a clustering index system and a feeder reliability index system according to the present invention;
fig. 3 is a schematic diagram illustrating the reliability warning and judgment of the feeder line provided by the present invention;
FIG. 4 is a linear fit graph of yearly statistical data of a feeder line power supply reliability index provided by the present invention;
fig. 5 is a structural diagram of a reliability early warning system of a power electronic medium voltage distribution network provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Specifically, an embodiment of the present application provides a method for early warning reliability of a power electronic medium-voltage distribution network, please refer to fig. 1, where the method includes:
s1, acquiring basic equipment attribute data and load data related to reliability of the feeder line of the power electronic medium-voltage distribution network of the same power supply partition; acquiring year-by-year statistical data of a feeder line power supply reliability index through a power failure information acquisition and statistical analysis system and a historical database;
s2, screening out an index with the reliability contribution degree reaching a set value from the attribute data and the load data of the feeder line basic equipment and determining the index as a static index; determining year-by-year statistical data of the feeder line power supply reliability index as a dynamic index;
s3, clustering feeders by adopting a DBSCAN algorithm based on static indexes, and summarizing year-by-year statistical data of power supply reliability indexes of the similar feeders after clustering;
s4, taking the annual power supply reliability data mean value of all the similar feeders as an early warning threshold, combining the early warning threshold of the similar feeders, the power supply partition power supply reliability control target of all the similar feeders and the dynamic index development trend of all the similar feeders, screening out the feeders with potential reliability problems, and performing early warning judgment on the reliability of all the similar feeders.
Specifically, referring to fig. 2, in the present embodiment, the basic device attribute data and load data related to reliability of different feeders of the power electronic medium-voltage distribution network of the same power supply partition in step S1 include: total line length in km; the power supply radius is km; the cabling rate is expressed in units of%; the number of the section switches is the unit of a table; the number of the communication switches is the unit of the station; the number of power electronic transformers is the unit of a transformer; the number of the flexible converters is the unit of a platform; the number of new energy grid-connected devices is the unit of a platform; the number of the flexible soft switches is the unit of a table; the number of load points is one; total capacity of load point, unit is MVA; load peak in MW.
Step S1, the annual statistical data of the feeder line power supply reliability index includes: average system outage time SAIDI and average system outage frequency SAIFI.
Specifically, in this embodiment, the step S3 of clustering the feeders by using the DBSCAN algorithm based on the static indicators to obtain clustered similar feeders, and summarizing year-by-year statistical data of power supply reliability indicators of the similar feeders includes:
s301, assuming that n medium-voltage feeders are in common in the same power supply partition power electronization medium-voltage distribution network to be subjected to reliability early warning analysis, namely, clustering object set X ═ X { (X)1,X2,...,XnEach object is characterized by m static indices, i.e. Xi＝{xi1,xi2,...,xim1,2, n, wherein X isiFor the ith clustering object, the spatial data set D ═ x of DBSCAN is obtainedij)n×m，j＝1,2...,m；
S302, inputting a spatial data set D, a clustering radius R, a minimum number of objects in a field MP and a current object set N;
s303, initializing a core object set
Figure BDA0002566824140000091
Clustering cluster k is 0, non-visited sample set is D, cluster division
Figure BDA0002566824140000092
S304, traversing all the clustering objects XiFinding X by Euclidean distanceiR Domain object set N ∈ (X)i) Then, it is judged whether or not | N ∈ (X) is satisfiedi) | ≧ MP, if satisfied, XiAs a core object, update Ω - Ω ∪ { Xi}; after the traversal is completed, all the core objects are found. If X is presentiNot belonging to any cluster, then X isiAdding a noise point set Ck＝1；
S305, if
Figure BDA0002566824140000093
Proceeding to S302, the input value of R, MP is reset; otherwise, turning to S306;
s306, if
Figure BDA0002566824140000094
If C is generated completely, the step is shifted to S309; otherwise, in omega, randomly selecting a core object o, and initializing the current cluster core object queue omegacurThe cluster sample set C comprises a current cluster sample set C, a class sequence number k and k +1kO, update o;
s307, if
Figure BDA0002566824140000095
Then C iskAfter generation, update C ═ C1,C2,...,CkAnd Ω -CkAnd then, turning to S306; otherwise only omega-C is updatedkGo to S308;
s308, from omegacurTaking out the core object o, finding out all N ∈ (o) according to R, making delta be N ∈ (o) ∩, updating Ck＝Ck∪Δ、＝-Δ、Ωcur＝Ωcur∪ (Δ ∩ Ω) -o, transition to S307;
s309 and output C ═ C1,C2,...,Ck}，C1Dividing elements of the noise point set into cluster clusters of the nearest core object as a noise point set of interest;
s310, summarizing and clustering each clustered cluster CpAnd p is 2,3, the power supply reliability of each feeder line of the same type in k is counted year by year.
N feeder lines are divided into k types by adopting a partial clustering algorithm DBSCAN based on density division, the classification basis is the static index of the feeder lines, and the number of each type is determined by the clustering algorithm. The DBSCAN algorithm is used for inputting the same parameters during iteration, the same iteration result can be kept being output, therefore, the DBSCAN algorithm has robustness, noise points in an analysis object can be found out by the aid of the algorithm, the noise points are used as outliers worth focusing on analysis, the noise points are evaluated to be unreasonable in reasonableness and classified into the nearest core points, and the analysis result is more accurate.
Specifically, in this embodiment, in step S4, taking the average value of the annual power supply reliability data of each similar feeder line as an early warning threshold, the calculation method includes:
setting cluster CpI.e. k, there are h feeder lines of the same type, i.e. p 2,3
Figure BDA0002566824140000101
Wherein C ispH is the p-th clustering cluster and varies according to p; describing the power supply reliability characteristics of each feeder line by adopting the dynamic indexes of each feeder line to obtain
Figure BDA0002566824140000102
r 1,2, a, h, wherein
Figure BDA0002566824140000103
The r feeder line of the p cluster;
Figure BDA0002566824140000104
respectively are a year-by-year statistical data set of the system average power failure time SAIDI and the system average power failure frequency SAIFI of the feeder line, wherein a subscript 0 of data in the data set represents the current year, a subscript 1 represents the previous year, and a subscript y represents the historical y year;
c is to bepThe mean value of the reliability index data of the annual power supply of h similar feeders serves as an early warning threshold βpFor the average system power failure time SAIDI in the reliability index of power supply in the current year, the early warning threshold value is
Figure BDA0002566824140000105
For the average system power failure frequency SAIFI in the reliability index of power supply in the year, the early warning threshold value is
Figure BDA0002566824140000106
Because the similar feeder lines have similar static attributes and load attributes and have similar power supply reliability index values, the annual power supply reliability data mean value of the similar feeder lines is used as an early warning threshold value, so that the analysis structure is more accurate.
Specifically, in this embodiment, the grades of the hierarchical warning are classified into "i-grade warning", "ii-grade warning", "iii-grade warning", and "no warning".
Referring to fig. 3 and 4, in step S4, the screening out a feeder with a potential reliability problem by combining the early warning threshold of the similar feeders, the power supply partition power supply reliability control target where each similar feeder is located, and the dynamic index development trend of each similar feeder, and performing early warning judgment on the reliability of each feeder in the similar feeders includes:
describing the feeder power supply reliability based on the system average power failure time SAIDI, and carrying out the operation on the r-th feeder of the p-th cluster
Figure BDA0002566824140000107
The reliability of the system is judged by early warning, and the early warning judgment mode is as follows:
the power supply reliability control target of the power supply partition where the p-th cluster is located is βtargetTherein mainly comprising
Figure BDA0002566824140000111
The r-th feeder line of the p-th cluster
Figure BDA0002566824140000112
Has a dynamic index of
Figure BDA0002566824140000113
When in use
Figure BDA0002566824140000114
And is
Figure BDA0002566824140000115
When, to
Figure BDA0002566824140000116
Is early-warned by the reliability of the early-warning level
Figure BDA0002566824140000117
Determining the development trend of the dynamic indexes; the above-mentioned
Figure BDA0002566824140000118
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure BDA0002566824140000119
The dynamic index data set carries out linear fitting on the annual statistical data of the feeder line power supply reliability index, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400001110
Then pair
Figure BDA00025668241400001111
Carrying out I-level early warning on the reliability of the system; otherwise, it is to
Figure BDA00025668241400001112
Carrying out II-level early warning on the reliability of the system;
when in use
Figure BDA00025668241400001113
Or
Figure BDA00025668241400001114
When, to
Figure BDA00025668241400001115
Whether the reliability of (2) needs to be warned by
Figure BDA00025668241400001116
Determining a dynamic index development trend of said
Figure BDA00025668241400001117
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure BDA00025668241400001118
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400001119
Then pair
Figure BDA00025668241400001120
Carrying out III-level early warning on the reliability of the system; otherwise, it is not right
Figure BDA00025668241400001121
Carrying out early warning on the reliability of the system;
when in use
Figure BDA00025668241400001122
And is
Figure BDA00025668241400001123
When it is not right
Figure BDA00025668241400001124
The reliability of the system is early-warned.
Based on the description of the system average power failure frequency SAIFI on the power supply reliability of the feeder line, the r-th feeder line of the p-th cluster is subjected to
Figure BDA00025668241400001125
The reliability of the system is judged by early warning, and the early warning judgment mode is as follows:
the power supply reliability control target of the power supply partition where the p-th cluster is located is βtargetTherein mainly comprising
Figure BDA00025668241400001126
The r-th feeder line of the p-th cluster
Figure BDA00025668241400001127
Has a dynamic index of
Figure BDA00025668241400001128
When in use
Figure BDA00025668241400001129
And is
Figure BDA00025668241400001130
When, to
Figure BDA00025668241400001131
Is early-warned by the reliability of the early-warning level
Figure BDA00025668241400001132
Determining the development trend of the dynamic indexes; the above-mentioned
Figure BDA00025668241400001133
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure BDA00025668241400001134
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400001135
Then pair
Figure BDA00025668241400001136
Carrying out I-level early warning on the reliability of the system; otherwise, it is to
Figure BDA00025668241400001137
Carrying out II-level early warning on the reliability of the system;
when in use
Figure BDA00025668241400001138
Or
Figure BDA00025668241400001139
When, to
Figure BDA00025668241400001140
Whether the reliability of (2) needs to be warned by
Figure BDA00025668241400001141
Determining a dynamic index development trend of said
Figure BDA00025668241400001142
Calculation of dynamic index development tendencyThe method comprises the following steps: to pair
Figure BDA00025668241400001143
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400001144
Then pair
Figure BDA00025668241400001145
Carrying out III-level early warning on the reliability of the system; otherwise, it is not right
Figure BDA00025668241400001146
Carrying out early warning on the reliability of the system;
when in use
Figure BDA00025668241400001147
And is
Figure BDA00025668241400001148
When it is not right
Figure BDA00025668241400001149
The reliability of the system is early-warned.
In order to further explain the effect principle of the reliability early warning method for the power electronic medium-voltage distribution network provided by the embodiment, a specific application scenario example is combined for further detailed description.
In this embodiment, 227 pieces of feeder data of a class B power supply partition in a certain city in the south are selected for analysis, and the analysis is limited to data collection and space limitation, and the static evaluation indexes collected in this embodiment are as follows: the power supply radius, the cabling rate, the number of power electronic transformers, the number of flexible soft switches and the number of medium-voltage load points; the dynamic evaluation indexes are as follows: average power off time of the system.
And clustering analysis is carried out on the feeder lines by using a DBSCAN algorithm, and data used for clustering comprise the power supply radius, the cabling rate, the number of interconnection switches and the number of medium-voltage load points corresponding to each feeder line. 227 feeder data constitute a spatial data set D, dimension: 227 × 4, setting algorithm clustering radius: r ═ 2.0; minimum number of objects in the domain: MP 15; the sample measurement mode is as follows: the euclidean distance. The obtained feeder cluster analysis results are shown in table 1:
TABLE 1 clustering analysis results
Clustering clusters Number of feeder lines
C1 19
C2 34
C3 27
C4 41
C5 13
C6 54
C7 31
Noise point 8
And planning the noise point elements to a cluster closest to the core point category, and dividing the early warning category into 'I-level early warning'. Dividing 8 noise points according to the nearest clustering core points, wherein the noise point division result is shown in table 2:
TABLE 2 noise Point partition results
Clustering clusters Number of feeder lines
C1 2
C3 5
C 6 1
Taking the cluster C2 as an example, the cluster C2 has 34 feeders, and the annual statistical data of the system average power failure time SAIDI of the 34 feeders obtained in step S1 is defined as
Figure BDA0002566824140000121
r is 1,2, …, 34. Calculating SAIDI-based early warning threshold value of feeder line of the type
Figure BDA0002566824140000122
The obtained early warning threshold of the cluster C2 based on SAIDI is 5.37h, and the other 6 cluster early warning thresholds based on SAIDI are calculated in the same way, and the result is shown in Table 3:
TABLE 3 SAIDI-based clustering early warning threshold
Clustering clusters Early warning threshold
C1 6.32
C2 5.37
C3 4.41
C4 8.33
C5 3.56
C6 4.89
C7 7.35
According to the above
Figure BDA0002566824140000131
And combines the power supply partition power supply reliability control target of the same type of feeder line
Figure BDA0002566824140000132
3h, carry out early warning judgement to each feeder reliability in the feeder of the same kind, give hierarchical early warning, include: 'level I early warning'The method comprises four levels of II-level early warning, III-level early warning and no early warning so as to judge the power electronic medium-voltage distribution network with potential problems in advance. The early warning judgment mode is as follows:
the power supply reliability control target of the power supply partition where the p-th cluster is located is βtargetTherein mainly comprising
Figure BDA0002566824140000133
The r-th feeder line of the p-th cluster
Figure BDA0002566824140000134
Has a dynamic index of
Figure BDA0002566824140000135
Describing the feeder power supply reliability based on the system average power failure time SAIDI, and carrying out the operation on the r-th feeder of the p-th cluster
Figure BDA0002566824140000136
The reliability is early-warning and judged:
when in use
Figure BDA0002566824140000137
And is
Figure BDA0002566824140000138
When, to
Figure BDA0002566824140000139
Is early-warned by the reliability of the early-warning level
Figure BDA00025668241400001310
Determining the development trend of the dynamic indexes; the above-mentioned
Figure BDA00025668241400001311
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure BDA00025668241400001312
MoveCarrying out linear fitting on the annual statistical data of the feeder line power supply reliability index in the state index data set, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400001313
Then pair
Figure BDA00025668241400001314
Carrying out I-level early warning on the reliability of the system; otherwise, it is to
Figure BDA00025668241400001315
Carrying out II-level early warning on the reliability of the system;
when in use
Figure BDA00025668241400001316
Or
Figure BDA00025668241400001317
When, to
Figure BDA00025668241400001318
Whether the reliability of (2) needs to be warned by
Figure BDA00025668241400001319
Determining a dynamic index development trend of said
Figure BDA00025668241400001320
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure BDA00025668241400001321
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure BDA00025668241400001322
Then pair
Figure BDA00025668241400001323
Carrying out III-level early warning on the reliability of the system; otherwise, it is not right
Figure BDA00025668241400001324
Carrying out early warning on the reliability of the system;
when in use
Figure BDA00025668241400001325
And is
Figure BDA00025668241400001326
When it is not right
Figure BDA00025668241400001327
The reliability of the system is early-warned.
The obtained early warning judgment results are shown in table 4:
TABLE 4 early warning judgment results
Clustering clusters Class I warning Level II warning Level III warning Without warning Summary of the invention
C
1 3 3 4 11 19
C 2 3 2 8 21 34
C3 5 1 10 16 27
C 4 1 4 6 30 41
C 5 2 0 1 10 13
C 6 4 0 11 40 54
C 7 0 5 7 19 31
Summary of the invention 18 15 47 147 227
As can be seen from the above embodiments, the reliability early warning method for the power electronic medium voltage distribution network provided in this embodiment uses the attribute data of the feeder line basic device and the load data as the index parameters of cluster analysis by combining the year-by-year statistical data of the power supply reliability index of the feeder line on the basis of the attribute data and the load data of the basic device related to the reliability of the feeder line, and performs early warning judgment on the reliability of the power electronic medium voltage distribution network by comparing the power supply reliability index differences of the similar power electronic medium voltage distribution networks and combining the power supply reliability control target of the power supply partition and the development trend of the dynamic index, so as to be able to previously identify the power electronic medium voltage distribution network with potential problems and give reasonable early warning to the distribution network with potential reliability problems, thereby solving the technical problem that power users and power supply enterprises are concerned at present that "problems are prevented before they occur", the reliability of the power distribution network is improved, loss of reliability problems to power consumers and power supply enterprises is avoided, and a power distribution network reliability improvement active mode of preventing problems before problems are generated is actively promoted.
The embodiment of the present application further provides a reliability early warning system for a power electronic medium voltage distribution network, please refer to fig. 5, which specifically includes:
the data acquisition and storage module 11 is used for acquiring basic equipment attribute data and load data related to reliability of different feeder lines in the same power supply partition; acquiring year-by-year statistical data of the power supply reliability indexes of the feeder lines through a power failure information acquisition and statistical analysis system and a historical database;
the data screening and classifying module 12 is configured to screen out an index, whose reliability contribution degree reaches a set value, from the data acquisition and storage module, classify the screened index, determine the basic device attribute data and the load data as static indexes, and determine year-by-year statistical data of the feeder line power supply reliability index as dynamic indexes;
the cluster analysis module 13 is used for carrying out cluster analysis based on static indexes on different feeders of the power electronic medium-voltage distribution network in the same power supply partition by adopting a DBSCAN algorithm, and cluster data adopts the static indexes obtained by screening by the data screening and classifying module;
the early warning threshold generation module 14 is configured to calculate an average value of feeder power supply reliability index data of the feeder of the same type in the same year according to the clustered feeder power supply reliability index year by year statistical data of the feeder of the same type obtained from the data acquisition and storage module, and use the average value as a feeder reliability early warning threshold;
and the early warning judging module 15 is configured to screen out a feeder with a potential reliability problem by combining the early warning threshold of the similar feeders, the power supply partition power supply reliability control target where each similar feeder is located, and the dynamic index development trend of each similar feeder, and perform a hierarchical early warning on the reliability of each feeder in the similar feeders.
The embodiment of the application also provides a power electronics middling voltage distribution network reliability early warning terminal equipment, include:
a memory for storing computer program code corresponding to any of the above described power electronics medium voltage distribution network reliability warning methods;
a processor for executing the computer program code to implement any of the above described power electronization medium voltage distribution network reliability early warning methods.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. The reliability early warning method for the power electronic medium-voltage distribution network is characterized by comprising the following steps of:
s1, acquiring basic equipment attribute data and load data related to reliability of the feeder line of the power electronic medium-voltage distribution network of the same power supply partition; acquiring year-by-year statistical data of a feeder line power supply reliability index through a power failure information acquisition and statistical analysis system and a historical database;
s2, screening out an index with the reliability contribution degree reaching a set value from the attribute data and the load data of the feeder line basic equipment and determining the index as a static index; determining year-by-year statistical data of the feeder line power supply reliability index as a dynamic index;
s3, clustering feeders by adopting a DBSCAN algorithm based on static indexes, and summarizing year-by-year statistical data of power supply reliability indexes of the similar feeders after clustering;
s4, taking the annual power supply reliability data mean value of all the similar feeders as an early warning threshold, combining the early warning threshold of the similar feeders, the power supply partition power supply reliability control target of all the similar feeders and the dynamic index development trend of all the similar feeders, screening out the feeders with potential reliability problems, and performing early warning judgment on the reliability of all the similar feeders.
2. The power electronics medium voltage distribution network reliability early warning method according to claim 1, wherein basic device attribute data and load data related to reliability of different feeders of the power electronics medium voltage distribution network of the same power supply partition comprise: the system comprises a circuit, a power supply radius, a cabling rate, the number of section switches, the number of interconnection switches, the number of power electronic transformers, the number of flexible converters, the number of new energy grid-connected devices, the number of flexible soft switches, the number of load points, the total capacity of the load points and a load peak value.
3. The power electronics medium voltage distribution network reliability early warning method according to claim 1, wherein the feeder line power supply reliability index year by year statistics data include: average system outage time SAIDI and average system outage frequency SAIFI.
4. The power electronics medium voltage distribution network reliability early warning method according to claim 1, wherein the clustering based on the static index is performed on the feeders by using the DBSCAN algorithm to obtain clustered similar feeders, and year-by-year statistical data of the power supply reliability indexes of the similar feeders are summarized, and the method comprises the following steps:
s301, assuming that n medium-voltage feeders are in common in the same power supply partition power electronization medium-voltage distribution network to be subjected to reliability early warning analysis, namely, clustering object set X ═ X { (X)1,X2,...,XnEach object is characterized by m static indices, i.e. Xi＝{xi1,xi2,...,xim1,2, n, wherein X isiFor the ith clustering object, the spatial data set D ═ x of DBSCAN is obtainedij)n×m，j＝1,2...,m；
S302, inputting a spatial data set D, a clustering radius R, a minimum number of objects in a field MP and a current object set N;
s303, initializing a core object set
Figure FDA0002566824130000021
Clustering cluster k is 0, non-visited sample set is D, cluster division
Figure FDA0002566824130000022
S304, traversing all the clustering objects XiFinding X by Euclidean distanceiR Domain object set N ∈ (X)i) Then, it is judged whether or not | N ∈ (X) is satisfiedi) | ≧ MP, if satisfied, XiAs a core object, update Ω - Ω ∪ { Xi}; after the traversal is completed, all the core objects are found. If X is presentiDo not belong toAny cluster, then X will beiAdding a noise point set Ck＝1；
S305, if
Figure FDA0002566824130000023
Proceeding to S302, the input value of R, MP is reset; otherwise, turning to S306;
s306, if
Figure FDA0002566824130000024
If C is generated completely, the step is shifted to S309; otherwise, in omega, randomly selecting a core object o, and initializing the current cluster core object queue omegacurThe cluster sample set C comprises a current cluster sample set C, a class sequence number k and k +1kO, update o;
s307, if
Figure FDA0002566824130000025
Then C iskAfter generation, update C ═ C1,C2,...,CkAnd Ω -CkAnd then, turning to S306; otherwise only omega-C is updatedkGo to S308;
s308, from omegacurTaking out the core object o, finding out all N ∈ (o) according to R, making delta be N ∈ (o) ∩, updating Ck＝Ck∪Δ、＝-Δ、Ωcur＝Ωcur∪ (Δ ∩ Ω) -o, transition to S307;
s309 and output C ═ C1,C2,...,Ck}，C1Dividing elements of the noise point set into cluster clusters of the nearest core object as a noise point set of interest;
s310, summarizing and clustering each clustered cluster CpAnd p is 2,3, the power supply reliability of each feeder line of the same type in k is counted year by year.
5. The power electronic medium voltage distribution network reliability early warning method according to claim 1, wherein the using the average value of the annual power supply reliability data of all similar feeders as an early warning threshold value comprises:
polymer settingClass CpI.e. k, there are h feeder lines of the same type, i.e. p 2,3
Figure FDA0002566824130000026
Wherein C ispH is the p-th clustering cluster and varies according to p; describing the power supply reliability characteristics of each feeder line in the same kind of feeder lines by adopting the dynamic indexes of each feeder line to obtain
Figure FDA0002566824130000027
r 1,2, a, h, wherein
Figure FDA0002566824130000028
The r feeder line of the p cluster;
Figure FDA0002566824130000029
Figure FDA00025668241300000210
respectively taking dynamic indexes of the feeder line, namely a system average power failure time SAIDI and a system average power failure frequency SAIFI, as a year-by-year statistical data set, wherein a subscript 0 of data in the data set represents the current year, 1 represents the previous year, and y represents the historical y year;
c is to bepThe mean value of the reliability index data of the annual power supply of h similar feeders serves as an early warning threshold βpFor the average system power failure time SAIDI in the reliability index of power supply in the current year, the early warning threshold value is
Figure FDA0002566824130000031
For the average system power failure frequency SAIFI in the reliability index of power supply in the year, the early warning threshold value is
Figure FDA0002566824130000032
6. The power electronics medium voltage distribution network reliability early warning method according to claim 5, wherein the method of screening the feeder line with the potential reliability problem by combining the early warning threshold value of the same type of feeder line, the power supply partition power supply reliability control target where each same type of feeder line is located and the dynamic index development trend of each same type of feeder line, and performing early warning judgment on the reliability of each feeder line in the same type of feeder line comprises the following steps:
describing the feeder power supply reliability based on the system average power failure time SAIDI, and carrying out the operation on the r-th feeder of the p-th cluster
Figure FDA0002566824130000033
Carrying out early warning judgment on the reliability of the system;
based on the description of the system average power failure frequency SAIFI on the power supply reliability of the feeder line, the r-th feeder line of the p-th cluster is subjected to
Figure FDA0002566824130000034
And carrying out early warning judgment on the reliability of the system.
7. The power electronic medium voltage distribution network reliability early warning method according to claim 6, characterized in that the feeder line of the r-th cluster of the p-th cluster is described based on the description of the system average outage time SAIDI on the feeder line power supply reliability
Figure FDA00025668241300000323
The reliability of the system is judged by early warning, comprising the following steps:
the power supply reliability control target of the power supply partition where the p-th cluster is located is βtargetTherein mainly comprising
Figure FDA0002566824130000035
The r-th feeder line of the p-th cluster
Figure FDA0002566824130000036
Has a dynamic index of
Figure FDA0002566824130000037
When in use
Figure FDA0002566824130000038
And is
Figure FDA0002566824130000039
When, to
Figure FDA00025668241300000310
Is early-warned by the reliability of the early-warning level
Figure FDA00025668241300000311
Determining the development trend of the dynamic indexes; the above-mentioned
Figure FDA00025668241300000312
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure FDA00025668241300000313
The dynamic index data set carries out linear fitting on the annual statistical data of the feeder line power supply reliability index, and if the linear fitting slope is the linear fitting slope
Figure FDA00025668241300000314
Then pair
Figure FDA00025668241300000315
Carrying out I-level early warning on the reliability of the system; otherwise, it is to
Figure FDA00025668241300000316
Carrying out II-level early warning on the reliability of the system;
when in use
Figure FDA00025668241300000317
Or
Figure FDA00025668241300000318
When, to
Figure FDA00025668241300000319
Whether the reliability of (2) needs to be warned by
Figure FDA00025668241300000320
Determining a dynamic index development trend of said
Figure FDA00025668241300000321
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure FDA00025668241300000322
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure FDA00025668241300000431
Then pair
Figure FDA0002566824130000041
Carrying out III-level early warning on the reliability of the system; otherwise, it is not right
Figure FDA0002566824130000042
Carrying out early warning on the reliability of the system;
when in use
Figure FDA0002566824130000043
And is
Figure FDA0002566824130000044
When it is not right
Figure FDA0002566824130000045
The reliability of the system is early-warned.
8. The power electronics medium voltage distribution network reliability warning method according to claim 6, characterized in that the feeder power supply reliability is based on the system average outage frequency SAIFIDescribing, for the p cluster r feeder
Figure FDA0002566824130000046
The reliability of the system is judged by early warning, comprising the following steps:
the power supply reliability control target of the power supply partition where the p-th cluster is located is βtargetTherein mainly comprising
Figure FDA0002566824130000047
The r-th feeder line of the p-th cluster
Figure FDA0002566824130000048
Has a dynamic index of
Figure FDA0002566824130000049
When in use
Figure FDA00025668241300000410
And is
Figure FDA00025668241300000411
When, to
Figure FDA00025668241300000412
Is early-warned by the reliability of the early-warning level
Figure FDA00025668241300000413
Determining the development trend of the dynamic indexes; the above-mentioned
Figure FDA00025668241300000414
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure FDA00025668241300000415
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure FDA00025668241300000416
Then pair
Figure FDA00025668241300000417
Carrying out I-level early warning on the reliability of the system; otherwise, it is to
Figure FDA00025668241300000418
Carrying out II-level early warning on the reliability of the system;
when in use
Figure FDA00025668241300000419
Or
Figure FDA00025668241300000420
When, to
Figure FDA00025668241300000421
Whether the reliability of (2) needs to be warned by
Figure FDA00025668241300000422
Determining a dynamic index development trend of said
Figure FDA00025668241300000423
The calculation method of the development trend of the dynamic indexes comprises the following steps: to pair
Figure FDA00025668241300000424
The dynamic index data set carries out linear fitting on the annual statistical data of the power supply reliability of the feeder line, and if the linear fitting slope is the linear fitting slope
Figure FDA00025668241300000425
Then pair
Figure FDA00025668241300000426
Carrying out III-level early warning on the reliability of the system; otherwise, it is not right
Figure FDA00025668241300000427
Carrying out early warning on the reliability of the system;
when in use
Figure FDA00025668241300000428
And is
Figure FDA00025668241300000429
When it is not right
Figure FDA00025668241300000430
The reliability of the system is early-warned.
9. Electric power ization medium voltage distribution network reliability early warning system, its characterized in that includes:
the data acquisition and storage module is used for acquiring basic equipment attribute data and load data related to reliability of different feeder lines in the same power supply partition; acquiring year-by-year statistical data of the power supply reliability indexes of the feeder lines through a power failure information acquisition and statistical analysis system and a historical database;
the data screening and classifying module is used for screening out indexes with reliability contribution degrees reaching set values from the data acquisition and storage module, classifying the screened indexes, determining the attribute data and the load data of the basic equipment as static indexes, and determining year-by-year statistical data of the feeder line power supply reliability indexes as dynamic indexes;
the cluster analysis module is used for carrying out cluster analysis based on static indexes on different feeders of the power electronic medium-voltage distribution network in the same power supply partition by adopting a DBSCAN algorithm, and the cluster data adopts the static indexes obtained by screening by the data screening and classifying module;
the early warning threshold generation module is used for calculating the mean value of feeder power supply reliability index data of the similar feeders in the current year according to the clustered feeder power supply reliability index year-by-year statistical data of the similar feeders obtained from the data acquisition and storage module, and the mean value is used as a feeder reliability early warning threshold;
and the early warning judging module is used for screening the feeder lines with potential reliability problems by combining the early warning threshold values of the similar feeder lines, the power supply partition power supply reliability control targets of the similar feeder lines and the dynamic index development trends of the similar feeder lines, and performing grading early warning on the reliability of each feeder line in the similar feeder lines.
10. Electric power ization medium voltage distribution network reliability early warning terminal equipment, its characterized in that includes:
a memory for storing computer program code corresponding to a power electronics medium voltage distribution network reliability warning method according to any one of claims 1 to 8;
a processor for executing said computer program code to implement a power electronics medium voltage distribution network reliability warning method according to any one of claims 1 to 8.
CN202010626814.1A 2020-07-02 2020-07-02 Reliability early warning method and system for power electronic medium-voltage distribution network and terminal equipment Pending CN111768109A (en)

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