CN108594075B - Power distribution network power failure fault positioning method based on improved ant colony algorithm - Google Patents

Power distribution network power failure fault positioning method based on improved ant colony algorithm Download PDF

Info

Publication number
CN108594075B
CN108594075B CN201810400810.4A CN201810400810A CN108594075B CN 108594075 B CN108594075 B CN 108594075B CN 201810400810 A CN201810400810 A CN 201810400810A CN 108594075 B CN108594075 B CN 108594075B
Authority
CN
China
Prior art keywords
matrix
pheromone
negative
power failure
voltage
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810400810.4A
Other languages
Chinese (zh)
Other versions
CN108594075A (en
Inventor
江和顺
王宏刚
赵永生
秦浩
甘德志
高方景
张蔚翔
朱军伟
陈慧
张良
姜海辉
武文广
周永真
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beiming Software Co ltd
State Grid Anhui Electric Power Co Ltd
NARI Group Corp
NARI Nanjing Control System Co Ltd
Original Assignee
Beiming Software Co ltd
State Grid Anhui Electric Power Co Ltd
NARI Group Corp
NARI Nanjing Control System Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beiming Software Co ltd, State Grid Anhui Electric Power Co Ltd, NARI Group Corp, NARI Nanjing Control System Co Ltd filed Critical Beiming Software Co ltd
Priority to CN201810400810.4A priority Critical patent/CN108594075B/en
Publication of CN108594075A publication Critical patent/CN108594075A/en
Application granted granted Critical
Publication of CN108594075B publication Critical patent/CN108594075B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/088—Aspects of digital computing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Biomedical Technology (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

The invention discloses a power distribution network power failure fault positioning method based on an improved ant colony algorithm, which comprises the following steps: establishing a total path matrix according to the power distribution network topology; the power failure voltage loss user groups are differentiated into positive ant groups, and the normal power supply user groups are differentiated into negative ant groups; respectively determining a positive pheromone matrix and a negative pheromone matrix according to the differentiated ant colony, driving a positive ant colony algorithm for the positive pheromone matrix, driving a negative ant colony algorithm for the negative pheromone matrix, respectively searching a target and calculating element values in each pheromone matrix; and adding and combining the positive pheromone matrix and the negative pheromone matrix by taking all ants as termination conditions to obtain a total pheromone matrix, wherein the maximum value of elements in the total pheromone matrix is the power failure fault point. The invention improves the speed and accuracy of fault location, shortens the response time, reduces the cost and improves the customer satisfaction and the system operation index without increasing the hardware input cost.

Description

Power distribution network power failure fault positioning method based on improved ant colony algorithm
Technical Field
The invention relates to a power distribution network power failure fault positioning method based on an improved ant colony algorithm, and belongs to the technical field of power distribution networks.
Background
Since the intelligent power grid is proposed in 2009, the construction of the intelligent power distribution network is comprehensively promoted in China, the automation level of the power grid is improved, meanwhile, the reliability and the quality of power supply service of a user are improved, and the fault location of the medium-low voltage circuit also becomes a focus of research attention.
For a long time, a low-voltage side fault information power supply company mainly utilizes a power failure management system to process low-voltage horizontal network fault according to resident repair calls, pre-judges the position of a fall fuse or a switch according to calls input by a user after power failure, analyzes the power failure scale, the personnel strength and the repair plan, determines the repair priority, calculates the work strength required by a site, pre-estimates the recovery time and manages the site work.
The power failure management system is a distribution network management system subsystem, and a control room performs organization scheduling and dispatching work orders for power failure emergency repair by means of the system. The method for processing the fault by using the power failure management system has the following defects: firstly, the accuracy of judgment needs to be improved; secondly, the efficiency is low; and thirdly, the time is long, especially the existing low-voltage power distribution networks such as factories, buildings, residential quarters and the like use a large number of wires or cables to realize low-voltage power distribution, but the number of low-voltage users is large, the lines are complicated, and the finding of the lines after the faults occur is difficult.
The power utilization information acquisition system (AMI) and the power grid Geographic Information System (GIS) which are built and fully covered by the current power distribution network are utilized to help the power failure fault of the power distribution network to be quickly researched and judged.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a power distribution network power failure fault positioning method based on an improved ant colony algorithm, and solves the technical problems that power distribution network power failure fault points are difficult to find, low in efficiency, long in time and the like in the prior art.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the power distribution network power failure fault positioning method based on the improved ant colony algorithm comprises the following steps:
triggering a power failure fault positioning function, determining a calling target range, screening a power failure and voltage loss user group and a normal power supply user group, and acquiring a power distribution network topology in the calling target range;
establishing an ant colony aiming at a power failure and voltage loss user colony and a normal power supply user colony, and establishing a total path matrix according to the power distribution network topology;
and (3) differentiating the ant colony: wherein, the power failure decompression user group is differentiated into a positive ant group, and the normal power supply user group is differentiated into a negative ant group;
respectively determining a positive pheromone matrix and a negative pheromone matrix according to the differentiated ant colony, driving a positive ant colony algorithm for the positive pheromone matrix, driving a negative ant colony algorithm for the negative pheromone matrix, respectively searching a target and calculating element values in each pheromone matrix;
taking all ants as termination conditions, adding and combining the positive pheromone matrix and the negative pheromone matrix to obtain a total pheromone matrix, wherein the maximum value of elements in the total pheromone matrix is a power failure fault point;
the method for calculating the element values in the forward pheromone matrix is as follows:
step 101) according to the voltage grade of the power distribution network, a forward pheromone matrix P is formedPIs divided into
Figure GDA0002480487030000021
And
Figure GDA0002480487030000022
two forward pheromone sub-matrixes are set
Figure GDA0002480487030000023
And
Figure GDA0002480487030000024
the initial values are all 0;
step 102) according to the serial number of the positive ants, starting to search targets, wherein the walking path of each ant is the reverse direction of the trend, walking from the initial position of the ant to the root node of the research and judgment range, and leaving pheromones on the walking paths, namely a positive pheromone matrix
Figure GDA0002480487030000025
And
Figure GDA0002480487030000026
all the corresponding elements of (1);
step 103) taking all the forward ants finish walking as a judgment basis of a termination condition, and performing forward pheromone matrix forward iteration for i times if i forward ant users finish walking;
the method for calculating the element values in the negative pheromone matrix is as follows:
step 201) according to the voltage grade of the power distribution network, a negative direction pheromone matrix P is obtainedNIs divided into
Figure GDA0002480487030000031
And
Figure GDA0002480487030000032
two negative direction pheromone sub-matrixes are set
Figure GDA0002480487030000033
And
Figure GDA0002480487030000034
the initial values are all 0;
step 202) starting to search targets according to the number of negative ants, wherein the walking path of each ant is the reverse direction of trend, walking from the initial position of the ant to the root node of the judging range, and eliminating once pheromone from the walking path, namely negative pheromone submatrix
Figure GDA0002480487030000035
And
Figure GDA0002480487030000036
all the corresponding elements of (1);
step 203) taking all negative ants finish walking as a judgment basis of a termination condition, and performing negative iteration on the negative pheromone matrix j times if j negative ants are used by j negative ant users;
the specific method for determining the power failure fault point is as follows:
combining the positive pheromone submatrix and the negative pheromone submatrix:
Figure GDA0002480487030000037
Figure GDA0002480487030000038
statistical pheromone matrix PHAnd PLThe maximum value of the numerical values of all the elements is the position of the fault point;
and if the element values corresponding to the paths in the result are equal, judging that the downstream path is the position of the fault point.
The method for triggering the power failure fault positioning function comprises the following steps: automatic triggering of voltage monitoring abnormity and active triggering of power failure alarm.
The automatic triggering condition of the voltage monitoring abnormity is as follows: the voltage value of a single electric meter is 0V acquired twice continuously, or the voltage value of more than 2 users at the same time is 0V.
The active triggering conditions of the power failure alarm are as follows: the power failure management system receives a power failure repair call of a user.
The method for determining the range of the summoning target comprises the following steps:
step 101) judging a 10kV/400V low-voltage transformer where a trigger user is located, and directly calling and testing a coverage area of the low-voltage transformer;
step 102) calling at least two electric meters at each branch box;
step 103), in the fault positioning process, continuously judging, and if the user fault outside the area is continuously called, repeating the step 101) and the step 102) in a new area.
The method for screening the power failure and voltage loss user group and the normal power supply user group comprises the following steps:
acquiring user data in a summoning target range by using an AMI system, wherein the user data comprises the following steps: user voltage data, asset numbers, node numbers and the position information of the power distribution network where the nodes are located; the summoning users are all ant users;
and (3) judging the voltage data of the ant user: if the user voltage data are normal, classifying the user voltage data as a negative ant colony; if the user voltage data is 0, it is classified as a forward ant colony.
And acquiring the topology of the power distribution network within the summoning target range by utilizing the GIS system which is in butt joint with the user, wherein the topology includes acquiring the node number of the power distribution network and the user node number.
The specific method for establishing the overall path matrix is as follows:
let the total path matrix be P, split into medium-voltage network path matrix PHAnd a low voltage network path matrix PLRespectively establishing path matrixes as follows:
constructing a medium voltage network path matrix PH: the total number of nodes is n, all the nodes are arranged in sequence in the horizontal direction and the vertical direction, elements of the matrix respectively represent a path between the two nodes, and subscripts of the elements are numbers of the nodes at two ends of the path:
Figure GDA0002480487030000041
for medium voltage network path matrix P in performing pheromone iteration by ant colony algorithmHIf the partial elements correspond to paths which do not exist in the actual power grid, the element values are set to be 0 all the time; medium voltage network path matrix PHWith respect to diagonal pair matrices, symmetric elements represent the same path;
constructing a low voltage network path matrix PL: the total number of nodes is m, all the nodes are arranged in sequence in the horizontal direction and the vertical direction, elements of the matrix respectively represent a path between two nodes, and subscripts of the elements are numbers of the nodes at two ends of the path:
Figure GDA0002480487030000051
for low voltage electrical network path matrix P in performing pheromone iteration using ant colony algorithmLIf the partial elements correspond to paths which do not exist in the actual power grid, the element values are set to be 0 all the time; low-voltage power network path matrix PLWith respect to the diagonal pair matrix, symmetric elements represent the same path.
Compared with the prior art, the invention has the following beneficial effects: based on an electricity utilization information acquisition system (AMI) and a power grid Geographic Information System (GIS) which are built and fully covered by the current power distribution and utilization grid, the ant colony algorithm is utilized to carry out the rapid positioning method research of the medium-low voltage power failure fault, the speed and the accuracy of fault positioning are improved, the response time is shortened, the cost is reduced, and the customer satisfaction and the system operation index are improved under the condition that the hardware input cost is not increased.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a network frame topology diagram of a medium and low voltage distribution network to which the present invention is applied.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, the flow chart of the present invention is a flow chart of the present invention, and the present invention uses ant colony algorithm to perform path search and target study and judgment based on measurement data and user information of the AMI system and medium and low voltage distribution network topology data provided by the GIS system, and includes four links of power failure fault location function triggering, initial data collection processing, ant colony fault location analysis process improvement and fault information notification forwarding. The following description is made for four links:
the method comprises the following steps: and triggering a power failure fault positioning function: the function trigger comprises automatic trigger of voltage monitoring abnormity and active trigger of power failure alarm. The automatic triggering of the abnormal voltage monitoring is that a single ammeter collects the voltage value of 0V for two times continuously, or the voltage values of more than 2 users at the same time are 0V, and a fault positioning module is triggered immediately; the active triggering of the power failure alarm is that the power failure management system receives a power failure repair call of a user, and then actively triggers the fault positioning module to search a fault source.
Step two: initial data collection processing: the method comprises the steps of entering an initial data collection and processing link after the power failure fault positioning module is triggered, determining a calling target range, calling and testing user voltage data by using an AMI system, screening out a power failure voltage loss user group and a normal power supply user group, and butting a GIS system to acquire a power distribution network topology in the calling target range, including acquiring node numbers and user node numbers of the power distribution network.
The method for determining the range of the summoning target comprises the following steps:
step 101) judging a 10kV/400V low-voltage transformer where a trigger user is located, and directly calling and testing a coverage area of the low-voltage transformer;
step 102) calling at least two electric meters at each branch box;
103) and in the fault positioning process, continuously judging, and if the user fault outside the area is continuously called, repeating the step 101) and the step 102) in a new area.
Taking the fault a in fig. 2 as an example, assuming that the initially triggered power failure and voltage loss users are the low-voltage users 6 and 18 and the medium-voltage large user 18, the above rule is used for determining, and the summoning target range is directly determined as the root node of the 110kV/10V transformer in the framework of fig. 2, where the summoning users are all users shown in the legend.
The method for screening the power failure and voltage loss user group and the normal power supply user group comprises the following steps:
acquiring user data in a summoning target range by using an AMI system, wherein the user data comprises the following steps: user voltage data, asset numbers, node numbers and the position information of the power distribution network where the nodes are located; the summoning users are all ant users.
And (3) judging the voltage data of the ant user: if the user voltage data are normal, classifying the user voltage data as a negative ant colony; if the user voltage data is 0, it is classified as a forward ant colony. To reduce the sparsity of the matrix, 10kV medium-voltage and 400V low-voltage nodes are separately coded. Also taking fault a in fig. 2 as an example, the forward ant colony includes 2 medium voltage users 5, 7, 12 low voltage users 5, 6, 8, 9, 11, 12, 14, 15, 18, 19, 21, 22. The negative ant colony includes 2 medium voltage users 14, 16, 20 low voltage users 27, 28, 30, 31, 33, 34, 36, 37, 43, 44, 45, 46, 48, 49, 51, 52, 55, 56, 58, 59.
Step three: improving the ant colony fault positioning analysis process:
the first step is ant colony construction: aiming at all the calling users and the regional power grid, an ant colony and a total path matrix are established, and the method comprises the following steps:
let P be the total path matrix, and split P into the medium-voltage network path matrix PHAnd a low voltage network path matrix PLRespectively establishing pheromone matrixes;
constructing medium-voltage network path matrices, i.e. pheromone matrices PH: the total number of nodes is n, all the nodes are arranged in sequence in the horizontal and vertical directions, elements of the matrix respectively represent paths between two nodes, and subscripts of the elements are both two end nodes of the pathsAnd point numbering:
Figure GDA0002480487030000071
part of elements in the matrix correspond to an actual power grid, the path does not exist, for convenience in processing, the elements are still listed in the matrix construction, when an ant colony algorithm is utilized for pheromone iteration, the initial value of the elements is set to be 0, and the pheromone value does not change because the path does not exist all the time;
the matrix being paired with respect to a diagonal, the symmetrical elements representing the same path, e.g. ph1-2And ph2-1Representing the same path. Pheromone matrix P established for medium-voltage power grid, taking fault A as exampleHThe total number of the nodes is 16, all the nodes are arranged in the sequence from 1 to 16 in the horizontal and vertical directions to build a 16 x 16 pheromone matrix, and elements of the matrix respectively represent paths between the two nodes.
Constructing low-voltage electrical network path matrices, i.e. pheromone matrices PL: the total number of the nodes is m, all the nodes are arranged in sequence in the horizontal direction and the vertical direction, elements of the matrix respectively represent a path between two nodes, and subscripts of the elements are numbers of the nodes at two ends of the path:
Figure GDA0002480487030000081
part of elements in the matrix correspond to an actual power grid, the path does not exist, for convenience in processing, the elements are still listed in the matrix construction, when an ant colony algorithm is utilized for pheromone iteration, the initial value of the elements is set to be 0, and the pheromone value does not change because the path does not exist all the time;
the matrix being paired with respect to a diagonal, the symmetrical elements representing the same path, e.g. pl1-2And pl2-1Representing the same path. Pheromone matrix P established for low-voltage power grid by taking fault A as exampleLThe total number of the nodes is 59, all the nodes are arranged in the horizontal direction and the vertical direction according to the sequence from 1 to 59, a 59 x 59 pheromone matrix is built, and elements of the matrix respectively represent paths between the two nodes.
The second step is ant colony differentiation: the ant colony is differentiated into a positive ant colony (power failure and voltage loss user) and a negative ant colony (normal power supply user), and a positive pheromone matrix P is determinedPAnd negative pheromone matrix PN。
For the forward pheromone matrix PPThe node number, the matrix dimension, the element meaning and the initial value of each element are all consistent with the overall path, and the actual judgment is divided into
Figure GDA0002480487030000082
And
Figure GDA0002480487030000083
for negative pheromone matrix PNThe node number, the matrix dimension, the element meaning and the initial value of each element are all consistent with the overall path, and the actual judgment is divided into
Figure GDA0002480487030000091
And
Figure GDA0002480487030000092
in the example of the failure a, the differentiated positive and negative pheromone matrices are the same in form as the matrices established in the first step, and the initial values are 0.
The third step is ant colony search: the method comprises the following steps of starting a positive ant colony algorithm and a negative ant colony algorithm respectively aiming at a positive ant colony (a power failure and voltage loss user) and a negative ant colony (a normal power supply user), searching a target and calculating element values in each pheromone matrix respectively, wherein the method comprises the following steps:
the forward ant colony algorithm, namely the pheromone forward accumulation iterative process, comprises the following steps:
1) forward pheromone matrix
Figure GDA0002480487030000093
And
Figure GDA0002480487030000094
the initial values are all 0;
2) according to the serial number of positive ant, starting to search target, the walking path of each ant is the reverse direction of trend, and walking from the initial position of ant to the root node of the judging range, and the walking path has retained pheromone, i.e. positive pheromone matrix
Figure GDA0002480487030000095
And
Figure GDA0002480487030000096
all the corresponding elements of (1);
3) and taking all the forward ants finish walking as a judgment basis of the termination condition, and performing forward pheromone matrix forward iteration for i times if i forward ant users finish walking.
Taking the fault A as an example, considering the universality of the method, taking a medium-voltage distribution network with relatively small number of nodes as an example, listing a forward pheromone matrix
Figure GDA0002480487030000097
The matrix is not marked with element values which are all 0:
Figure GDA0002480487030000101
the negative ant colony algorithm refers to the idea of pheromone disappearance in the existing ant colony algorithm to perform negative iteration process of pheromone disappearance accumulation, and comprises the following steps:
1) negative pheromone matrix
Figure GDA0002480487030000102
Or
Figure GDA0002480487030000103
The initial values are all 0;
2) according to the number of negative ants, starting to search for targets, wherein the walking path of each ant is the reverse direction of the trend, and walking from the initial position of the ant to the root node of the judging rangeClearing a pheromone once, i.e. negative pheromone matrix
Figure GDA0002480487030000104
Or
Figure GDA0002480487030000105
All the corresponding elements of (1);
3) and taking all negative ants which finish walking as a judgment basis of a termination condition, and performing j times of negative pheromone matrix negative iteration if j negative ant users finish walking.
In the example of the fault A, considering the universality of the method, taking a medium-voltage distribution network with relatively small number of nodes as an example, a negative pheromone matrix is listed
Figure GDA0002480487030000106
The matrix is not marked with element values which are all 0:
Figure GDA0002480487030000111
the fourth step is target determination, comprising the steps of:
1) merging the positive pheromone matrix and the negative pheromone matrix:
Figure GDA0002480487030000112
2) statistical pheromone matrix PHAnd PLThe maximum value of the numerical values of the elements is the position of the fault point.
3) In the example of failure A, the pheromone matrix PHThe final values are:
Figure GDA0002480487030000113
the path 1-2 section can be judged as the fault source from the element value of the matrix.
4) And if the element values corresponding to the paths in the result are equal, judging that the downstream path is a fault position.
Step four: and (3) fault information notification forwarding: and after the fault position is determined, the forwarding power failure management system is informed to carry out next inspection and power restoration.
The method is mainly applied to medium and low voltage distribution networks, the distribution network structure is in a radial shape powered by a single power supply, the tide direction is in a single direction, the medium voltage is 10kV, and the low voltage is 400V. Based on an electricity utilization information acquisition system (AMI) and a power grid Geographic Information System (GIS) which are built and fully covered by the current power distribution and utilization grid, the ant colony algorithm is utilized to carry out the rapid positioning method research of the medium-low voltage power failure fault, the speed and the accuracy of fault positioning are improved, the response time is shortened, the cost is reduced, and the customer satisfaction and the system operation index are improved under the condition that the hardware input cost is not increased.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (8)

1. The power distribution network power failure fault positioning method based on the improved ant colony algorithm is characterized by comprising the following steps of:
triggering a power failure fault positioning function, determining a calling target range, screening a power failure and voltage loss user group and a normal power supply user group, and acquiring a power distribution network topology in the calling target range;
establishing an ant colony aiming at a power failure and voltage loss user colony and a normal power supply user colony, and establishing a total path matrix according to the power distribution network topology;
and (3) differentiating the ant colony: wherein, the power failure decompression user group is differentiated into a positive ant group, and the normal power supply user group is differentiated into a negative ant group;
respectively determining a positive pheromone matrix and a negative pheromone matrix according to the differentiated ant colony, driving a positive ant colony algorithm for the positive pheromone matrix, driving a negative ant colony algorithm for the negative pheromone matrix, respectively searching a target and calculating element values in each pheromone matrix;
taking all ants as termination conditions, adding and combining the positive pheromone matrix and the negative pheromone matrix to obtain a total pheromone matrix, wherein the maximum value of elements in the total pheromone matrix is a power failure fault point;
the method for calculating the element values in the forward pheromone matrix is as follows:
step 101) according to the voltage grade of the power distribution network, a forward pheromone matrix P is formedPIs divided into
Figure FDA0002480487020000011
And
Figure FDA0002480487020000012
two forward pheromone sub-matrixes are set
Figure FDA0002480487020000013
And
Figure FDA0002480487020000014
the initial values are all 0;
step 102) according to the serial number of the positive ants, starting to search targets, wherein the walking path of each ant is the reverse direction of the trend, walking from the initial position of the ant to the root node of the research and judgment range, and leaving pheromones on the walking paths, namely a positive pheromone matrix
Figure FDA0002480487020000015
And
Figure FDA0002480487020000016
all the corresponding elements of (1);
step 103) taking all the forward ants finish walking as a judgment basis of a termination condition, and performing forward pheromone matrix forward iteration for i times if i forward ant users finish walking;
the method for calculating the element values in the negative pheromone matrix is as follows:
step 201) according to the voltage grade of the power distribution network, a negative direction pheromone matrix P is obtainedNIs divided into
Figure FDA0002480487020000021
And
Figure FDA0002480487020000022
two negative direction pheromone sub-matrixes are set
Figure FDA0002480487020000023
And
Figure FDA0002480487020000024
the initial values are all 0;
step 202) starting to search targets according to the number of negative ants, wherein the walking path of each ant is the reverse direction of trend, walking from the initial position of the ant to the root node of the judging range, and eliminating once pheromone from the walking path, namely negative pheromone submatrix
Figure FDA0002480487020000025
And
Figure FDA0002480487020000026
all the corresponding elements of (1);
step 203) taking all negative ants finish walking as a judgment basis of a termination condition, and performing negative iteration on the negative pheromone matrix j times if j negative ants are used by j negative ant users;
the specific method for determining the power failure fault point is as follows:
combining the positive pheromone submatrix and the negative pheromone submatrix:
Figure FDA0002480487020000027
Figure FDA0002480487020000028
statistical pheromone matrix PHAnd PLThe maximum value of the numerical values of all the elements is the position of the fault point;
and if the element values corresponding to the paths in the result are equal, judging that the downstream path is the position of the fault point.
2. The power distribution network power failure fault location method based on the improved ant colony algorithm as claimed in claim 1, wherein the method for triggering the power failure fault location function comprises: automatic triggering of voltage monitoring abnormity and active triggering of power failure alarm.
3. The power distribution network power failure fault location method based on the improved ant colony algorithm as claimed in claim 2, wherein the automatic trigger condition of the voltage monitoring abnormity is that: the voltage value of a single electric meter is 0V acquired twice continuously, or the voltage value of more than 2 users at the same time is 0V.
4. The power distribution network power failure fault location method based on the improved ant colony algorithm as claimed in claim 2, wherein the active triggering condition of the power failure alarm is as follows: the power failure management system receives a power failure repair call of a user.
5. The power distribution network power failure fault location method based on the improved ant colony algorithm as claimed in claim 1, wherein the method for determining the range of the summoning target is as follows:
step 101) judging a 10kV/400V low-voltage transformer where a trigger user is located, and directly calling and testing a coverage area of the low-voltage transformer;
step 102) calling at least two electric meters at each branch box;
step 103), in the fault positioning process, continuously judging, and if the user fault outside the area is continuously called, repeating the step 101) and the step 102) in a new area.
6. The power distribution network power failure fault location method based on the improved ant colony algorithm as claimed in claim 1, wherein the method for screening the power failure and voltage loss user group and the normal power supply user group is as follows:
acquiring user data in a summoning target range by using an AMI system, wherein the user data comprises the following steps: user voltage data, asset numbers, node numbers and the position information of the power distribution network where the nodes are located; the summoning users are all ant users;
and (3) judging the voltage data of the ant user: if the user voltage data are normal, classifying the user voltage data as a negative ant colony; if the user voltage data is 0, it is classified as a forward ant colony.
7. The power distribution network power failure fault location method based on the improved ant colony algorithm as claimed in claim 1, wherein the power distribution network topology of the target range of the test is obtained by using a GIS system connected with a user, and the method comprises the step of obtaining node numbers of the power distribution network and user node numbers.
8. The power distribution network power failure fault location method based on the improved ant colony algorithm as claimed in claim 1, wherein the specific method for establishing the overall path matrix is as follows:
let the total path matrix be P, split into medium-voltage network path matrix PHAnd a low voltage network path matrix PLRespectively establishing path matrixes as follows:
constructing a medium voltage network path matrix PH: the total number of nodes is n, all the nodes are arranged in sequence in the horizontal direction and the vertical direction, elements of the matrix respectively represent a path between the two nodes, and subscripts of the elements are numbers of the nodes at two ends of the path:
Figure FDA0002480487020000041
for medium voltage network path matrix P in performing pheromone iteration by ant colony algorithmHIf the partial elements correspond to paths which do not exist in the actual power grid, the element values are set to be 0 all the time; medium voltage network path matrix PHWith respect to diagonal pair matrices, symmetric elements represent the same path;
constructing a low voltage network path matrix PL: the total number of nodes is m, all the nodes are arranged in sequence in the horizontal and vertical directions, and the elements of the matrix are respectively listedShowing a path between two nodes, wherein the subscripts of the elements are the numbers of the nodes at two ends of the path:
Figure FDA0002480487020000042
for low voltage electrical network path matrix P in performing pheromone iteration using ant colony algorithmLIf the partial elements correspond to paths which do not exist in the actual power grid, the element values are set to be 0 all the time; low-voltage power network path matrix PLWith respect to the diagonal pair matrix, symmetric elements represent the same path.
CN201810400810.4A 2018-04-28 2018-04-28 Power distribution network power failure fault positioning method based on improved ant colony algorithm Active CN108594075B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810400810.4A CN108594075B (en) 2018-04-28 2018-04-28 Power distribution network power failure fault positioning method based on improved ant colony algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810400810.4A CN108594075B (en) 2018-04-28 2018-04-28 Power distribution network power failure fault positioning method based on improved ant colony algorithm

Publications (2)

Publication Number Publication Date
CN108594075A CN108594075A (en) 2018-09-28
CN108594075B true CN108594075B (en) 2020-08-21

Family

ID=63619215

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810400810.4A Active CN108594075B (en) 2018-04-28 2018-04-28 Power distribution network power failure fault positioning method based on improved ant colony algorithm

Country Status (1)

Country Link
CN (1) CN108594075B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111125694B (en) * 2019-12-20 2023-01-20 杭州安恒信息技术股份有限公司 Threat information analysis method and system based on ant colony algorithm
CN114113948B (en) * 2021-12-03 2023-10-20 中达安股份有限公司 Power distribution network fault monitoring method
CN115754578A (en) * 2022-08-30 2023-03-07 国网辽宁省电力有限公司电力科学研究院 Active power distribution network fault positioning method and system based on self-adaptive ant colony algorithm

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7693049B2 (en) * 2004-10-29 2010-04-06 Honeywell International Inc. Self-organization of sensor networks using ant colony optimization
CN102254070A (en) * 2011-07-15 2011-11-23 福州大学 Method for optimally designing electromagnetic valve based on ant colony optimization
CN105067956A (en) * 2015-08-26 2015-11-18 云南电网有限责任公司电力科学研究院 Anti-colony-algorithm-based distribution network fault positioning method
CN106646100A (en) * 2016-09-14 2017-05-10 浙江群力电气有限公司 Incomplete measurement and control power distribution network fault locating method
CN106684869A (en) * 2017-03-17 2017-05-17 燕山大学 Active distribution network failure recovery strategy considering inside and outside games

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7693049B2 (en) * 2004-10-29 2010-04-06 Honeywell International Inc. Self-organization of sensor networks using ant colony optimization
CN102254070A (en) * 2011-07-15 2011-11-23 福州大学 Method for optimally designing electromagnetic valve based on ant colony optimization
CN105067956A (en) * 2015-08-26 2015-11-18 云南电网有限责任公司电力科学研究院 Anti-colony-algorithm-based distribution network fault positioning method
CN106646100A (en) * 2016-09-14 2017-05-10 浙江群力电气有限公司 Incomplete measurement and control power distribution network fault locating method
CN106684869A (en) * 2017-03-17 2017-05-17 燕山大学 Active distribution network failure recovery strategy considering inside and outside games

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于蚁群算法的配电网故障恢复重构;刘学琴 等;《广东电力》;20090331;第15-18页 *

Also Published As

Publication number Publication date
CN108594075A (en) 2018-09-28

Similar Documents

Publication Publication Date Title
CN109460833B (en) Method and system for processing equipment data and repair work order data of distribution network fault
CN108594075B (en) Power distribution network power failure fault positioning method based on improved ant colony algorithm
CN106124935A (en) Middle and low voltage network Fault Locating Method
CN106100579B (en) A kind of photovoltaic plant method for diagnosing faults based on data analysis
CN103592567B (en) The electric network fault positioning system integrated based on multi-system information and method
CN112187518B (en) Intelligent fusion terminal area topology identification method and system
CN109474067B (en) Power grid dispatching fault processing aid decision-making method
CN110658415B (en) Low-voltage distribution line fault detection method and system
CN108594076B (en) Power failure fault study and judgment method for power distribution network
CN110095661B (en) Distribution transformer high-voltage side open-phase fault first-aid repair method
CN103683511A (en) Method and system for processing fault of integrated monitoring platform based on power distribution network
CN112467724A (en) Low-voltage distribution network fault studying and judging method
CN109949178B (en) Method for judging and complementing power failure event of medium-voltage distribution network based on support vector machine
CN110763953B (en) Troubleshooting line patrol path planning method under distribution automation condition
CN111896845A (en) Power distribution network fault diagnosis method and system based on multi-source information fusion
CN115545479A (en) Method and device for determining important nodes or important lines of power distribution network
CN113987724A (en) Power grid risk identification method and system based on topology analysis
CN111157842B (en) Power distribution line distribution transformer power failure fault studying and judging method based on power grid topology
CN109980787B (en) Fault interval positioning method based on distribution network real-time data and equipment topology
CN109165837B (en) Power protection and supply area grading operation risk analysis method based on main distribution integration
CN115542070A (en) Distribution network line fault positioning method and storage medium
CN115186974A (en) Power distribution network power supply quality comprehensive evaluation method and system based on business middling station
CN112256922B (en) Method and system for quickly identifying faults and power failure
CN109034538A (en) A kind of evaluation analysis method towards automation of transformation substations equipment running quality
CN114596171A (en) Power failure event intelligent analysis method based on multi-source data fusion

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB03 Change of inventor or designer information

Inventor after: Jiang Heshun

Inventor after: Zhang Liang

Inventor after: Jiang Haihui

Inventor after: Wu Wenguang

Inventor after: Zhou Yongzhen

Inventor after: Wang Honggang

Inventor after: Zhao Yongsheng

Inventor after: Qin Hao

Inventor after: Gan Dezhi

Inventor after: Gao Fangjing

Inventor after: Zhang Weixiang

Inventor after: Zhu Junwei

Inventor after: Chen Hui

Inventor before: Jiang Heshun

Inventor before: Zhang Liang

Inventor before: Jiang Haihui

Inventor before: Wu Wenguang

Inventor before: Zhou Yongzhen

Inventor before: Wang Honggang

Inventor before: Zhao Yongsheng

Inventor before: Qin Hao

Inventor before: Gan Dezhi

Inventor before: Gao Fangjing

Inventor before: Zhang Weixiang

Inventor before: Zhu Junwei

Inventor before: Chen Hui

CB03 Change of inventor or designer information
GR01 Patent grant
GR01 Patent grant