CN114814467A - Power distribution network fault positioning method - Google Patents

Power distribution network fault positioning method Download PDF

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CN114814467A
CN114814467A CN202210557744.8A CN202210557744A CN114814467A CN 114814467 A CN114814467 A CN 114814467A CN 202210557744 A CN202210557744 A CN 202210557744A CN 114814467 A CN114814467 A CN 114814467A
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value
ant
distribution network
particle
state
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林其友
葛愿
杨乐新
陈彦斌
乔向阳
余诺
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State Grid Anhui Electric Power Co Ltd Wuhu Fanchang District Power Supply Co
Anhui Polytechnic University
Wuhu Power Supply Co of State Grid Anhui Electric Power Co Ltd
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State Grid Anhui Electric Power Co Ltd Wuhu Fanchang District Power Supply Co
Anhui Polytechnic University
Wuhu Power Supply Co of State Grid Anhui Electric Power Co Ltd
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Priority to CN202210557744.8A priority Critical patent/CN114814467A/en
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    • 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
    • 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
    • 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]

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Abstract

The invention discloses a power distribution network fault positioning method, which comprises the following steps: s1, encoding the feeder line section state in the monitoring power grid area to form a position sequence; s2, converting the fitness value of the particle swarm at each position into an initial increment of the pheromone after the iteration times of the swarm particle algorithm reach the maximum iteration times, and assigning the initial increment to each ant in the ant swarm; and S3, assigning the position of each particle in the particle swarm to each ant in the ant colony, finding out the optimal position based on an ant colony algorithm, and taking the state of each section of the optimal position as the current state of each feeder line section in the monitoring grid area. According to the method, the convergence speed is improved through the particle swarm algorithm at the initial stage, the coarse search is carried out by utilizing the particle swarm algorithm, the fine search is carried out by utilizing the ant colony algorithm at the later stage, so that the fault location of the power distribution network is more accurate and rapid, and the method has great significance for recovering the power supply of the power distribution network more accurately and rapidly.

Description

Power distribution network fault positioning method
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to a power distribution network fault positioning method.
Background
The power distribution network is the most important link in the transmission of electric energy from the power grid to users, but the distribution network is the most complex in the whole power grid and is also a part of the most frequent accidents due to wide distribution, complex topology and large number of various devices. Therefore, the method is very important for accurately positioning, timely isolating and recovering the fault position of the power distribution network, improving the power supply reliability and reducing the power failure loss.
Most of the existing feeder line faults are positioned based on a group particle algorithm, but the positioning method is relatively high in positioning speed and low in positioning accuracy.
Disclosure of Invention
The invention provides a power distribution network fault positioning method, aiming at solving the problems.
The invention is realized in such a way that a power distribution network fault positioning method specifically comprises the following steps:
s1, encoding the feeder line section state in the monitoring power grid area to form a position sequence;
s2, converting the fitness value of the particle swarm at each position into an initial increment of the pheromone after the iteration times of the swarm particle algorithm reach the maximum iteration times, and assigning the initial increment to each ant in the ant swarm;
and S3, assigning the position of each particle in the particle swarm to each ant in the ant swarm, finding out the optimal position based on an ant swarm algorithm, and taking the state of each section of the optimal position as the current state of each feeder section in the monitoring grid area.
Further, the fitness calculation formula of the particle at each position is as follows:
Figure BDA0003652925610000021
where N is the total number of feeder sections in the monitored grid area, I j Indicating that the jth switch FTU stores fault current information, a value of 1 indicating that the switch has experienced a fault current, a value of 0 indicating no fault current,
Figure BDA0003652925610000022
indicates the desired state, S, of the jth switch FTU B (j) The state of the jth switch FTU is represented, the value of the state is 1 or 0, the failure and the normality of the equipment are respectively represented, w is a failure diagnosis weight coefficient, and the value is [0,1 ]]In the meantime.
Further, in the above-mentioned case,
Figure BDA0003652925610000023
the method for determining the expected state of the jth switch FTU is specifically as follows:
Figure BDA0003652925610000024
wherein S is B (j) To S B (j + m) is the downstream node of the jth switch FTU, and if the node has fault current, the fault current is transmitted
Figure BDA0003652925610000025
Value 1, at S B (j) To S B (j + m) have not experienced fault current, then
Figure BDA0003652925610000026
The value is 0.
Further, the fitness value of the particle at each position is determined Fit Converting into increment value of initial pheromone, calculating increment value of initial pheromoneThe formula is as follows:
Figure BDA0003652925610000027
wherein k is a constant greater than 0, and a is greater than 0 and less than 1.
According to the method, the convergence speed is improved through the particle swarm algorithm at the initial stage, the coarse search is carried out by utilizing the particle swarm algorithm, the fine search is carried out by utilizing the ant colony algorithm at the later stage, so that the fault location of the power distribution network is more accurate and rapid, and the method has great significance for recovering the power supply of the power distribution network more accurately and rapidly.
Drawings
Fig. 1 is a flowchart of a power distribution network fault location method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a topology structure of a power distribution network according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be given in order to provide those skilled in the art with a more complete, accurate and thorough understanding of the inventive concept and technical solutions of the present invention.
Fig. 1 is a flowchart of a power distribution network fault location method provided in an embodiment of the present invention, where the method specifically includes the following steps:
s1, encoding the feeder line section state in the monitoring power grid area to form a position sequence;
in the embodiment of the invention, feeder terminal equipment (FTU) in a power distribution network is regarded as a node, and the state of a feeder section between two nodes comprises the following steps: the method comprises the steps of (1) carrying out fault and non-fault, wherein a value 0 represents no fault, a value 1 represents fault, and assuming that 20 feeder line sections exist in a current detection power grid area, possible states of the 20 feeder line sections are coded and combined to form a position sequence, and each position point in the position sequence is one combination of the states of the 20 feeder line sections;
s2, converting the fitness value of the particle swarm at each position into an initial increment of the pheromone after the iteration times of the swarm particle algorithm reach the maximum iteration times, and assigning the initial increment to each ant in the ant swarm;
in the embodiment of the present invention, the group particle algorithm is specifically as follows:
s11, determining particle swarm parameters including particle swarm size M and maximum iteration number t max ；
S12, calculating the fitness value of each particle at the current position according to the fitness function;
in the embodiment of the invention, the fitness function F it The expression (c) is specifically as follows:
Figure BDA0003652925610000031
where N is the total number of feeder sections in the monitored grid area, I j Indicating that the jth switch FTU stores fault current information, a value of 1 indicating that the switch has experienced a fault current, a value of 0 indicating no fault current,
Figure BDA0003652925610000032
indicates the desired state, S, of the jth switch FTU B (j) The state of the jth switch FTU is represented, the value of the state is 1 or 0, the failure and the normality of the equipment are respectively represented, w is a failure diagnosis weight coefficient, and the value is [0,1 ]]In the meantime.
Figure BDA0003652925610000041
Wherein S is B (j) To S B (j + m) is the downstream node of the jth switch FTU, and after one node only experiences fault current, the value is 1, and then
Figure BDA0003652925610000042
Value 1, only at S B (j) To S B (j + m) has not experienced fault current, and the values are all 0, then
Figure BDA0003652925610000043
The value is 0.
Desired state determining method of jth switch FTUThe method is explained with reference to FIG. 2, S represents power supply, k 1 ～k 14 Representative node, s 1 ～s 14 Representing desired states of switches of feeder section
Figure BDA0003652925610000044
The values of (A) are as follows:
Figure BDA0003652925610000045
to is directed at
Figure BDA0003652925610000046
In other words, the downstream node is s 4 、s 5 And s 6 Only at node s 4 、s 5 And s 6 When there is no fault current in the circuit,
Figure BDA0003652925610000047
is 0, at node s 4 、s 5 And s 6 When a fault current is present at one of the nodes,
Figure BDA0003652925610000048
is 1.
S13, updating the individual optimal value p best The current individual optimal value p of each particle is calculated best Comparing the fitness value of each particle with the current fitness value of each particle, and if the fitness value of each particle is larger than the individual optimal value p best Then the fitness value is taken as the individual optimum value p of the particle best And storing, otherwise, the volume optimal value p of the particle best Keeping the same;
s14, updating the global optimal value g best The global optimum value g is set best Comparing with the currently obtained maximum global adaptability value, if the current maximum global adaptability value is larger than the global optimum value g best Then the fitness value is taken as the global optimum value g best And is saved, otherwise, the current global optimum value g is kept best The change is not changed;
s15, setting the iteration number t to t +1, and detecting whether the iteration number t reaches the maximum iteration number t max If the result of the detection is positive, the fitness value of the particle swarm at each position is converted into the increment of the initial pheromone, the initial pheromone is given to each ant in the ant swarm, and if the result of the detection is negative, the step S12 is executed.
In the embodiment of the invention, the number of ants in the ant colony is the same as the number of particles in the particle swarm, the positions of the particles in the particle swarm are assigned to all the ants in the corresponding ant colony, meanwhile, the fitness value of the particles at the positions is converted into the increment of the initial pheromone, and the initial pheromone tau of all the ants is initialized c Then pheromone tau of each ant s ＝τ c +τ p ；
Particles are placed at various positions based on the following formula Xi Fitness value of Fit Converting the incremental value into the incremental value of the initial pheromone, wherein the calculation formula is as follows:
Figure BDA0003652925610000051
wherein k is a constant greater than 0, a is greater than 0 and less than 1, and the larger the fitness value is, the more pheromones are left in the corresponding position.
And S3, assigning the position of each particle in the particle swarm to each ant in the ant colony, finding out the optimal position based on an ant colony algorithm, and taking the state of each section of the optimal position as the current state of each feeder line section in the monitoring grid area.
The particle swarm optimization convergence speed is improved in the early stage, the coarse search is carried out by using the particle swarm optimization, the fine search is carried out by using the ant colony optimization in the later stage to achieve the more accurate and rapid fault location of the power distribution network, and the fault location can be more accurate and has great significance to the restoration of the power distribution network rapidly.
The invention has been described by way of example, and it is obvious that the invention is not limited to the embodiments described above, but it is within the scope of the invention to employ various insubstantial modifications of the inventive concepts and techniques, or to apply them directly to other applications without such modifications.

Claims (4)

1. A power distribution network fault positioning method is characterized by specifically comprising the following steps:
s1, encoding the feeder line section state in the monitoring power grid area to form a position sequence;
s2, converting the fitness value of the particle swarm at each position into an initial increment of the pheromone after the iteration times of the swarm particle algorithm reach the maximum iteration times, and assigning the initial increment to each ant in the ant swarm;
and S3, assigning the position of each particle in the particle swarm to each ant in the ant colony, finding out the optimal position based on an ant colony algorithm, and taking the state of each section of the optimal position as the current state of each feeder line section in the monitoring grid area.
2. The method for locating faults in a power distribution network according to claim 1, wherein the fitness calculation formula of the particles at each position is as follows:
Figure FDA0003652925600000011
where N is the total number of feeder sections in the monitored grid area, I j Indicating that the jth switch FTU stores fault current information, a value of 1 indicating that the switch has experienced a fault current, a value of 0 indicating no fault current,
Figure FDA0003652925600000015
indicates the desired state, S, of the jth switch FTU B (j) The state of the jth switch FTU is represented, the value of the state is 1 or 0, the failure and the normality of the equipment are respectively represented, w is a failure diagnosis weight coefficient, and the value is [0,1 ]]In the meantime.
3. The power distribution network fault location method of claim 2,
Figure FDA0003652925600000016
the method for determining the expected state of the jth switch FTU is specifically as follows:
Figure FDA0003652925600000012
wherein S is B (j) To S B (j + m) is the downstream node of the jth switch FTU, and if the node has fault current, the fault current is transmitted
Figure FDA0003652925600000013
Value 1, at S B (j) To S B (j + m) have not experienced fault current, then
Figure FDA0003652925600000014
The value is 0.
4. Method for fault location in an electric distribution network according to claim 1, characterized in that the fitness value F of the particles at each location is used it Converting the incremental value into an incremental value of the initial pheromone, wherein an incremental value calculation formula of the initial pheromone is as follows:
Figure FDA0003652925600000021
wherein k is a constant greater than 0, and a is greater than 0 and less than 1.
CN202210557744.8A 2022-05-19 2022-05-19 Power distribution network fault positioning method Pending CN114814467A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN115986744A (en) * 2022-12-28 2023-04-18 国网安徽省电力有限公司芜湖供电公司 Power flow optimization method for power distribution network containing shared energy storage

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN115986744A (en) * 2022-12-28 2023-04-18 国网安徽省电力有限公司芜湖供电公司 Power flow optimization method for power distribution network containing shared energy storage
CN115986744B (en) * 2022-12-28 2024-02-06 国网安徽省电力有限公司芜湖供电公司 Power distribution network power flow optimization method containing shared energy storage

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