CN112952738A - Fault detection method and device for power distribution network, computer equipment and storage medium - Google Patents

Fault detection method and device for power distribution network, computer equipment and storage medium Download PDF

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Publication number
CN112952738A
CN112952738A CN202110383554.4A CN202110383554A CN112952738A CN 112952738 A CN112952738 A CN 112952738A CN 202110383554 A CN202110383554 A CN 202110383554A CN 112952738 A CN112952738 A CN 112952738A
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particle
value
particles
fault
population
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CN112952738B (en
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魏存良
李延亮
刘国强
邓文科
曹德发
温玲蔚
李志华
黄海坤
高小征
邬奇林
邱舒峰
张新伟
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Meizhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Meizhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
    • H02H1/00—Details of emergency protective circuit arrangements
    • H02H1/0007—Details of emergency protective circuit arrangements concerning the detecting means
    • 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

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  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

The embodiment of the invention provides a fault detection method and device for a power distribution network, computer equipment and a storage medium, wherein the method comprises the following steps: coding each switch on each feeder line in the power distribution network in a binary mode under the dimension of current, taking the switch as a particle, initializing a particle swarm, taking equipment with a fault in the power distribution network as constraint, calculating an adaptive value of the particle according to the position of the particle, traversing the adaptive value of the particle to update an individual extreme value of each particle and a population extreme value of the particle swarm, updating the speed of the particle according to the individual extreme value and the population extreme value under the condition that the search range of a solution space is adjusted by using the distance between the position of the particle and the position with the fault, superposing the speed of the particle on the basis of the position of the particle to update the position of the particle, and judging whether an end condition is met; and if so, determining the particles corresponding to the group extreme value as the failed switch. The method can accurately position single-point and multi-point faults in the distribution network.

Description

Fault detection method and device for power distribution network, computer equipment and storage medium
Technical Field
The embodiment of the invention relates to the technical field of power distribution networks, in particular to a fault detection method and device of a power distribution network, computer equipment and a storage medium.
Background
With the gradual expansion of the scale of the power distribution network, more and more elements are contained in the power distribution network, and the fault occurrence rate is improved.
In order to ensure the safe operation of the power distribution network and the power utilization safety of users, the fault information reported by the power distribution switch monitoring terminal FTU on the feeder line can be comprehensively analyzed, so that the fault section of the power distribution network is positioned.
At present, a corresponding network topology matrix and a corresponding fault information matrix are generated according to a topology structure and fault information of a power distribution network and are operated to obtain a fault judgment matrix.
However, the accuracy requirement of the direct algorithm on the fault information uploaded by the FTU is very high, and the fault tolerance is poor when a fault signal is distorted.
Disclosure of Invention
The embodiment of the invention provides a fault detection method and device for a power distribution network, computer equipment and a storage medium, and aims to solve the problem that if part of information is distorted, the fault positioning precision of the power distribution network is maintained.
In a first aspect, an embodiment of the present invention provides a method for detecting a fault of a power distribution network, including:
coding each switch on each feeder in the distribution network in a binary manner under the dimension of the current;
initializing a particle group by taking the switch as a particle, wherein the particle is provided with a speed and a position;
calculating an adaptive value of the particle according to the position of the particle by taking a device with a fault in the power distribution network as a constraint;
traversing the adapted values of the particles to update individual extrema of each of the particles, population extrema of the population of particles;
updating the speed of the particle according to the individual extremum and the population extremum under the condition that the distance between the position of the particle and the position where the fault occurs is used for adjusting the search range of the solution space;
if the updating of the speed of the particle is completed, the speed of the particle is superposed on the position of the particle to update the position of the particle;
if the updating of the positions of the particles is completed, judging whether an ending condition is met; if not, returning to execute the device with the fault in the power distribution network as constraint, calculating the adaptive value of the particle according to the position of the particle, and if so, determining the particle corresponding to the group extreme value as a switch with the fault.
Optionally, the updating the speed of the particle according to the individual extremum and the group extremum under the condition that the distance between the position of the particle and the position where the fault occurs is used for adjusting the search range of the solution space includes:
calculating the average value of the adaptive values of all the particles as an average adaptive value;
comparing the adapted value of the particle with the average adapted value to measure a distance between the location of the particle and the location of the fault;
setting an inertia based on a result of the comparison, the inertia being positively correlated with the distance;
updating the velocity of the particle on a condition that the velocity of the particle, the distance between the position of the particle and the individual extremum, and the distance between the position of the particle and the population extremum are maintained along the inertia as updates.
Optionally, the setting inertia based on the result of the comparison includes:
if the adaptive value of the particles is larger than the average adaptive value, setting inertia as a preset upper limit value;
if the adaptive value of the particle is smaller than the average adaptive value, calculating inertia by the following formula:
Figure BDA0003013968330000021
where ω is inertia and ω ismaxUpper limit of inertia, ωminLower limit of inertia, fiIs the adaptation value of particle i, fminIs the minimum of the fitness values of all particles, favgIs the average adaptation value.
Optionally, said updating the velocity of the particle with the condition of maintaining the velocity of the particle, the distance between the position of the particle and the individual extremum, and the distance between the position of the particle and the population extremum along the inertia as updates comprises:
updating the velocity of the particle by the following equation:
Figure BDA0003013968330000022
wherein the content of the first and second substances,
Figure BDA0003013968330000031
is the number of velocities of the (k + 1) th iteration particle iThe component of the d-dimension is,
Figure BDA0003013968330000032
for the d-dimensional component in the velocity of the particle i at the k-th iteration,
Figure BDA0003013968330000033
is the d-dimensional component in the position of the k-th iteration particle i, ω is the inertia, c1、c2Are all learning factors, r1、r2Are all numerical values that are generated at random,
Figure BDA0003013968330000034
the individual extremum for the k-th iteration particle i,
Figure BDA0003013968330000035
the population extremum of all the particles in the particle group g is iterated for the kth time.
Optionally, the encoding, in a binary manner, each switch on each feeder in the power distribution network in the current dimension includes:
inquiring the current of each switch on each feeder line in the power distribution network;
generating a matrix, rows of the matrix representing the switches, columns of the matrix representing the feeders;
if the current of the switch is over-current, setting the element corresponding to the switch in the matrix as 1;
and if the current of the switch is not overcurrent, setting the element corresponding to the switch in the matrix to be 0.
Optionally, initializing a particle group with the switch as a particle includes:
setting the switches as particles in a population of particles;
randomly generating a numerical value for the particles within a preset first range as the speed of the particles;
substituting the velocity of the particle into an S-shaped function to map the velocity into the probability of the particle moving;
randomly generating a numerical value for the particles in a preset second range to serve as a threshold value;
if the probability of the movement of the particles is less than or equal to the threshold value, the position of the particles is taken as 1;
if the probability of the movement of the particles is larger than the threshold value, the position of the particles is taken as 0;
wherein the sigmoid function is represented as follows:
Figure BDA0003013968330000036
wherein sigmoid is a sigmoid function,
Figure BDA0003013968330000037
is the d-dimensional component in the velocity of the particle i at the k-th iteration, A and B are both positive numbers, and the sum between A and B is 1, alpha1Is the lower limit of the speed, α2The upper limit value of the speed.
Optionally, said traversing the adapted values of the particles to update an individual extremum of each of the particles, a population extremum of the population of particles, comprises:
comparing the adapted value of the particle with adapted values of individual extrema of the particle, the individual extrema of the particle initially being null;
updating the position of the particle to be the individual extreme value of the particle if the adaptive value of the particle is larger than the adaptive value corresponding to the individual extreme value of the particle;
if the adaptive value of the particle is smaller than the adaptive value corresponding to the individual extreme value of the particle, maintaining the individual extreme value of the particle;
comparing the adaptive value of the particle with an adaptive value corresponding to a population extremum of the particle swarm, wherein the population extremum of the particle swarm is initially null;
updating the position of the particle to be the population extremum of the particle swarm if the adaptive value of the particle is larger than the adaptive value corresponding to the population extremum of the particle swarm;
and if the adaptive value of the particle is smaller than the adaptive value corresponding to the population extremum of the particle swarm, maintaining the population extremum of the particle swarm.
In a second aspect, an embodiment of the present invention further provides a device for detecting a fault of a power distribution network, including:
the encoding module is used for encoding each switch on each feeder line in the power distribution network in a binary mode under the dimension of current;
the particle swarm initialization module is used for initializing a particle swarm by taking the switch as a particle, and the particle is configured with a speed and a position;
the adaptive value calculation module is used for calculating the adaptive value of the particle according to the position of the particle by taking the equipment with faults in the power distribution network as constraint;
an extreme value updating module, configured to traverse the adapted values of the particles to update an individual extreme value of each of the particles and a population extreme value of the particle swarm;
a speed updating module, configured to update the speed of the particle according to the individual extremum and the group extremum under a condition that a search range of a solution space is adjusted using a distance between the position of the particle and a position where the fault occurs;
a position updating module, configured to, if updating the velocity of the particle is completed, superimpose the velocity of the particle on the basis of the position of the particle to update the position of the particle;
an ending condition judgment module, configured to judge whether an ending condition is met if updating the position of the particle is completed; if not, returning to call the adaptive value calculation module, and if so, calling a fault determination module;
and the fault determining module is used for determining the particles corresponding to the group extreme value as the switches with faults.
Optionally, the speed update module includes:
the average adaptive value calculating module is used for calculating the average value of the adaptive values of all the particles as an average adaptive value;
an adaptation value calculation module for comparing the adaptation value of the particle with the average adaptation value to measure a distance between the location of the particle and a location of a fault;
an inertia setting module for setting an inertia based on a result of the comparison, the inertia being positively correlated with the distance;
a constraint update module to update the velocity of the particle on a condition that maintains the velocity of the particle, the distance between the position of the particle and the individual extremum, and the distance between the position of the particle and the population extremum along the inertia as updates.
Optionally, the inertia setting module comprises:
the first state setting module is used for setting inertia as a preset upper limit value if the adaptive value of the particles is larger than the average adaptive value;
a second state setting module, configured to calculate inertia according to the following formula if the adaptive value of the particle is smaller than the average adaptive value:
Figure BDA0003013968330000051
where ω is inertia and ω ismaxUpper limit of inertia, ωminLower limit of inertia, fiIs the adaptation value of particle i, fminIs the minimum of the fitness values of all particles, favgIs the average adaptation value.
In one embodiment of the invention, the constraint update module comprises:
a particle update module for updating the velocity of the particle by the following formula:
Figure BDA0003013968330000052
wherein the content of the first and second substances,
Figure BDA0003013968330000053
for the d-dimensional component in the velocity of the (k + 1) -th iteration particle i,
Figure BDA0003013968330000054
for the d-dimensional component in the velocity of the particle i at the k-th iteration,
Figure BDA0003013968330000055
is the d-dimensional component in the position of the k-th iteration particle i, ω is the inertia, c1、c2Are all learning factors, r1、r2Are all numerical values that are generated at random,
Figure BDA0003013968330000056
the individual extremum for the k-th iteration particle i,
Figure BDA0003013968330000057
the population extremum of all the particles in the particle group g is iterated for the kth time.
Optionally, the encoding module comprises:
the current query module is used for querying the current of each switch on each feeder line in the power distribution network;
the matrix generation module is used for generating a matrix, wherein rows of the matrix represent the switches, and columns of the matrix represent the feeder lines;
the abnormal setting module is used for setting the element in the matrix corresponding to the switch to be 1 if the current of the switch is over-current;
and the normal setting module is used for setting the element in the matrix corresponding to the switch to be 0 if the current of the switch is not overcurrent.
Optionally, the particle swarm initialization module includes:
a particle setting module for setting the switch as particles in a particle swarm;
the speed random setting module is used for randomly generating a numerical value for the particles in a preset first range to serve as the speed of the particles;
the movement probability mapping module is used for substituting the speed of the particles into an S-shaped function so as to map the speed of the particles into the movement probability of the particles;
the threshold random generation module is used for randomly generating a numerical value for the particles in a preset second range to serve as a threshold;
a first position setting module, configured to take a value of the position of the particle as 1 if the probability of the movement of the particle is smaller than or equal to the threshold;
a second position setting module, configured to, if the probability of the movement of the particle is greater than the threshold, take a value of the position of the particle as 0;
wherein the sigmoid function is represented as follows:
Figure BDA0003013968330000061
wherein sigmoid is a sigmoid function,
Figure BDA0003013968330000062
is the d-dimensional component in the velocity of the particle i at the k-th iteration, A and B are both positive numbers, and the sum between A and B is 1, alpha1Is the lower limit of the speed, α2The upper limit value of the speed.
Optionally, the extremum updating module includes:
an individual extreme value comparison module, configured to compare the adaptive value of the particle with an adaptive value of an individual extreme value of the particle, where the individual extreme value of the particle is initially null;
an individual extreme value updating module, configured to update the position of the particle to an individual extreme value of the particle if the adaptive value of the particle is greater than the adaptive value corresponding to the individual extreme value of the particle;
an individual extreme value maintaining module, configured to maintain the individual extreme value of the particle if the adaptive value of the particle is smaller than the adaptive value corresponding to the individual extreme value of the particle;
a population extreme value comparison module, configured to compare the adaptive value of the particle with an adaptive value corresponding to a population extreme value of the particle swarm, where the population extreme value of the particle swarm is initially null;
a population extremum updating module, configured to update the position of the particle to the population extremum of the particle swarm if the adaptive value of the particle is greater than the adaptive value corresponding to the population extremum of the particle swarm;
and the population extreme value maintaining module is used for maintaining the population extreme value of the particle swarm if the adaptive value of the particle is smaller than the adaptive value corresponding to the population extreme value of the particle swarm.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of fault detection for an electrical distribution network as in any of the first aspects.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the fault detection method for the power distribution network according to any one of the first aspect.
In the embodiment, each switch on each feeder line in the power distribution network is encoded in a binary mode under the dimension of current, the switches are used as particles, a particle swarm is initialized, the particles are configured with speed and positions, a device with a fault in the power distribution network is used as a constraint, an adaptive value of the particles is calculated according to the positions of the particles, the adaptive value of the particles is traversed, an individual extreme value of each particle and a group extreme value of the particle swarm are updated, the speed of the particles is updated according to the individual extreme value and the group extreme value under the condition that the search range of a solution space is adjusted by using the distance between the positions of the particles and the position with the fault, if the speed of the particles is updated, the speed of the particles is superposed on the basis of the positions of the particles to update the positions of the particles, and if the positions of the particles are updated, whether an; if not, returning to execute the operation of taking the equipment with the fault in the power distribution network as constraint, calculating the adaptive value of the particles according to the positions of the particles, and if so, determining the particles corresponding to the group extreme value as the switches with the fault. The search range of the solution space is adjusted through the distance between the positions of the particles and the positions with faults, the global search capability can be improved, single-point faults and multipoint faults in the distribution network can be accurately positioned, meanwhile, the positioning precision is high under the condition that partial information is distorted, and the power grid stability and the power supply reliability are improved.
Drawings
Fig. 1 is a flowchart of a method for detecting a fault of a power distribution network according to an embodiment of the present invention;
fig. 2 is a diagram illustrating a topology of a power distribution network according to an embodiment of the present invention;
fig. 3 is a diagram illustrating a fault simulation of a power distribution network according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a fault detection device for a power distribution network according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a method for detecting a fault of a power distribution network according to an embodiment of the present invention, where the present embodiment is applicable to a situation where an area of the power distribution network where a fault occurs is located based on an improved binary particle swarm algorithm, and the method may be executed by a fault detection device of the power distribution network, where the fault detection device of the power distribution network may be implemented by software and/or hardware, and may be configured in a computer device, such as a server, a workstation, a personal computer, and the like, and the computer device may serve as a dispatching control center in the power distribution network, as shown in fig. 1, where the method specifically includes the following steps:
step 101, encoding each switch on each feeder in the distribution network in a binary manner under the dimension of current.
In a distribution network provided with an SCADA (Supervisory Control And Data Acquisition, Supervisory Control system), a feeder line is divided into a plurality of sections by a plurality of switches, each switch is provided with an FTU (fiber to the Unit), a current out-of-limit threshold of each switch is set, And when a current value is greater than a rated value (namely, overcurrent), a current out-of-limit remote signaling signal is sent And transmitted to a dispatching Control center.
The dispatching control center provides a power distribution network fault positioning function, positions a fault area according to fault information uploaded by each FTU, and remotely controls a corresponding switch to isolate the fault area to prevent the expansion of an accident, namely, the FTU uploads the fault information to serve as the input of the dispatching control center, the dispatching control center analyzes the fault information, and outputs a fault section to guide the action of each switch.
Fault information I uploaded by each FTUiIt is reflected whether or not a fault current (fault current 1, no fault current 0, i.e., normal) flows at each switch, IiCan be obtained by comparing the collected fault current at the switch of the ith segment with a preset rated value.
Thus, in this embodiment, the current of each switch on each feeder in the distribution network can be queried, where 1 represents fault (overcurrent) and 0 represents normal (no overcurrent), then the combined sequence of 0-1 represents fault information for the entire distribution network, and the switch state function is as follows:
Figure BDA0003013968330000091
wherein the symbol pi () represents the OR of all the items, and N represents the switch KjA set of subsequent feeders; liAnd the state of the ith feeder line is shown, the fault is 1, and the normal fault is 0.
For example, the distribution network, as shown in FIG. 2, switch K1-K5The state function of (a) is as follows:
I*(K1)＝l1Pl2Pl3Pl4Pl5
I*(K2)＝l2Pl3Pl4
I*(K3)＝l3Pl4
I*(K4)＝l4
I*(K5)＝l5
and generating a matrix, wherein rows of the matrix represent switches, columns of the matrix represent feeders, if the current of the switches is overcurrent, the elements corresponding to the switches in the matrix are set to be 1, and if the current of the switches is not overcurrent, the elements corresponding to the switches in the matrix are set to be 0.
For example, in a distribution network such as that shown in fig. 2, the switch state function may be represented as a matrix as follows:
Figure BDA0003013968330000101
wherein, the element in the ith row and the jth column indicates whether the jth feeder fault can cause the switch i to generate overcurrent.
Step 102, initializing a particle group by using a switch as a particle.
In the embodiment, fault detection can be performed on the power distribution network based on Binary Particle Swarm Optimization (BPSO).
Specifically, BPSO is applied to solve the distribution network fault location problem, the positions of particles represent the states of feeder sections in the distribution network, and the dimensions of the particles represent the total number of feeder sections of the distribution network. Each feeder line section has two states of 0 and 1, wherein 0 represents a normal state, 1 represents a fault state, and the state of the feeder line section is a pending quantity.
Therefore, the state solution of the N feeder sections is converted into an N-dimensional particle swarm optimization solution, and the N-dimensional position of each particle is represented as the potential state of the N feeder sections of the power distribution network. And in each iteration process, evaluating the quality of each particle position through an evaluation function, updating the current optimal position of the particle and the optimal positions of all the particles, and further updating the speed and the positions of the particles until a program termination condition is met. The finally obtained global optimal position of the particle swarm is the actual state of each feeder line section.
Therefore, the problem of power distribution network fault location is converted into an optimization problem, and then the BPSO algorithm is adopted to solve the problem, so that a reasonable evaluation function is constructed. The constructed evaluation function can evaluate the quality of each particle position (the solution of the fault section), and finally can iterate the solution which can explain the fault information uploaded by the FTU best.
In the binary-based particle swarm algorithm, the position coding of the particles is in a binary mode, namely each dimension component of the particle position is limited to 0 or 1, the velocity of the particles is understood as the probability of position change, namely the probability that each dimension component of the particle velocity represents 0 or 1 selected by the corresponding position dimension component, and when the particle swarm is initialized, the initialized parameters comprise the swarm size m, the particle dimension D and the position x of each exampleiAnd velocity viInertia ω, learning factor c1、c2And so on.
(1) Population size m (number of particles). Under the condition of convergence of the particle swarm, all particles fly to the direction of the optimal solution, and the particles easily lose diversity, so that the population number is too small, the particles are easily trapped in the local optimal solution, and the initial population is properly increased within a certain range, so that the probability of the optimal solution is improved to a certain extent. The population size is generally 20-40, and 10 particles can achieve good results for most problems, and the number of particles can achieve a bit higher for more difficult problems or problems of a specific type, such as 100-.
(2) The particle dimension D (spatial dimension). Determined by the problem being optimized is the spatial dimension of the problem solution.
(3) The inertia ω. It keeps the particles moving inertias, making them prone to expand the search space, with the ability to explore new areas. The influence of the previous speed on the current speed is controlled by adjusting the magnitude of omega, global search and local search are compromised, and search precision and search speed are coordinated. The omega value is small, so that better solution can be mined in the space of the current solution, local search and more accurate solution can be obtained, but the search speed is slow and the solution can fall into a local extreme value sometimes; the value of omega is larger, which is beneficial to searching a larger space by the particles, finding a new solution domain, and being beneficial to global search and obtaining a faster convergence speed, but not easy to obtain an accurate solution.
(4) Learning factor c1、c2. They have a large impact on convergence speed, they reflect the role that the particles' own experience and social experience play in their motion, and represent the weight of statistical acceleration terms that push each particle to its individual historical best position and global historical best position. The suitable acceleration coefficient is beneficial to the algorithm to converge and depart from local extreme values quickly. Low c1、c2A value that allows the particle to wander outside the target region before being pulled back; and c is high1、c2Values that may cause the particles to suddenly rush toward or over the target area.
If c is1＝c2At 0, the particle will fly inertially at the current velocity until the boundary is reached.
If c is1When the number is 0, the particles have no cognitive ability and have social parts, and the convergence rate is high.
If c is2When the number of the particles is 0, the particles do not share the society, and a cognitive part exists, so that the individuals do not interact with each other, and the random search with multiple starting points is realized.
Is set to be larger c1The value of (a) may cause excessive particles to wander in a local area; in contrast, larger c2The value of (c) may cause the particle to prematurely converge to a local minimum. To balance the effect of random factors, c is typically set1＝c2＝2.1。
Further, the switches are arranged as particles in a particle group, the particles being arranged with a velocity, a position, and a value being randomly generated for the particles within a predetermined first range (e.g., -3, 3) as the velocity of the particles.
The velocity of the particle is substituted into a sigmoid function sigmoid to map to a probability of particle movement.
Illustratively, the sigmoid function is represented as follows:
Figure BDA0003013968330000111
wherein sigmoid is a sigmoid function,
Figure BDA0003013968330000121
is the d-dimensional component in the velocity of the particle i at the kth iteration, A and B are both positive numbers and the sum between A and B is 1, e.g., A is 0.05, B is 0.95, α1Is the lower limit value of speed (e.g., -3), α2The upper limit of the speed (e.g., 3).
In the Sigmoid function, the value of the Sigmoid function is closer to 1 as the speed is larger, and the value of the Sigmoid function is correspondingly closer to 0 when the speed is small. The Sigmoid function value of the particle may be regarded as the probability that the position of the particle is 1 or 0. To prevent saturation of the Sigmoid function, the velocity of the particles may be set within a first range a, B.
A value is randomly generated for the particles within a predetermined second range (e.g., [0, 1]) as a threshold value.
The probability of particle movement is compared to the threshold.
On the one hand, if the probability of the movement of the particle is less than or equal to the threshold, the position of the particle is taken as 1, and on the other hand, if the probability of the movement of the particle is greater than the threshold, the position of the particle is taken as 0, which is expressed as follows:
Figure BDA0003013968330000122
wherein sigmoid is a sigmoid function,
Figure BDA0003013968330000123
for the d-dimensional component in the velocity of the particle i at the k-th iteration,
Figure BDA0003013968330000124
for the d-th dimension component in the threshold for the k-th iteration particle i,
Figure BDA0003013968330000125
is the d-th dimension component in the location of the k-th iteration particle i.
And 103, calculating an adaptive value of the particle according to the position of the particle by taking the device with the fault in the power distribution network as a constraint.
In this embodiment, based on the principle that the deviation between the FTU information corresponding to each feeder line segment to be obtained in the actual state and the actually uploaded fault information is the minimum, the following evaluation function may be constructed:
Figure BDA0003013968330000126
wherein, IjFor the fault information uploaded by the jth switch FTU, the value of 1 is considered that the fault current (fault) flows through the switch, and 0 does not flow (normal),
Figure BDA0003013968330000127
the expected state of each switch node is 1 (fault) if fault current flows through the switch, and conversely, the expected state is 0 (normal), and the expression is a function of the state of each section; n is the total number of feeder sections in the power distribution network; siIf the state of each device in the power distribution network is 1, indicating that the device is in fault, and if 0 is selected, the device is normal;
Figure BDA0003013968330000131
the method is characterized in that a weight coefficient is multiplied by the number of fault equipment, omega is a weight coefficient set according to the concept of a minimum set in the fault diagnosis theory, the value range is between 0 and 1, the smaller the number of fault intervals is, the better the fault intervals are, and the occurrence of misdiagnosis is avoided.
The expression results represent the fitness value corresponding to each potential solution, and the smaller the result, the better the fitness value.
And step 104, traversing the adaptive values of the particles to update the individual extreme value of each particle and the group extreme value of the particle swarm.
In this embodiment, in each iteration, the adaptive values of the particles may be traversed, and the positions of the particles are evaluated according to the adaptive values, so as to update the individual extremum of each particle (i.e., the current optimal position of the particle), and the population extremum of the particle group (i.e., the optimal positions of all the particles, also referred to as global extremum) according to the situation of the adaptive values.
In one aspect, the adapted value of the particle is compared to adapted values of individual extrema of the particle, wherein the individual extrema of the particle is initially null.
If the adaptive value of the particle is larger than the adaptive value corresponding to the individual extreme value of the particle, updating the position of the particle to the individual extreme value of the particle in the iteration of the current round;
and if the adaptive value of the particle is smaller than the adaptive value corresponding to the individual extreme value of the particle, maintaining the individual extreme value of the particle unchanged in the iteration.
Comparing the adaptive value of the particle with an adaptive value corresponding to a population extreme value of the particle swarm, wherein the population extreme value in the particle swarm is initially empty;
if the adaptive value of the particle is larger than the adaptive value corresponding to the group extreme value of the particle swarm, updating the position of the particle to the group extreme value of the particle swarm in the iteration of the current round;
and if the adaptive value of the particle is smaller than the adaptive value corresponding to the population extremum of the particle swarm, maintaining the population extremum in the particle swarm unchanged in the iteration.
And 105, under the condition that the distance between the position of the particle and the position with the fault is used for adjusting the search range of the solution space, updating the speed of the particle according to the individual extreme value and the group extreme value.
In a D-dimensional target search space, BPSO randomly initializes a particle group consisting of m particles, and the position X (potential solution of optimization problem) of the ith particle can be expressed as { X }i1，xi2，……，xiDSubstituting the obtained value into an evaluation function to obtain an adaptive value for measuring the advantages and disadvantages of the particles, wherein the corresponding flight speed V can be expressed as{vi1，vi2，……，viD}. During each iteration, the particle updates its velocity and position by tracking two extreme values: one extreme is the optimal solution that the particle itself has searched so far, i.e. the individual extreme PibestDenoted as { Pibest.1，Pibest.2，……，Pibest.DThe other extreme, the best solution searched so far by the population particles, i.e. the population extreme, is denoted as { P }gbest.1，Pgbest.2，……，Pgbest.D}。
In the embodiment, improvement is provided for BPSO, the distance between the position of the particle and the position where the fault occurs is used for adjusting the search range of the solution space, so that the search range is converged, the search accuracy is improved, and the speed of the particle is updated according to the individual extremum and the population extremum under the condition of limiting the search range.
In one embodiment of the present invention, step 105 comprises the steps of:
step 1051, calculate the average value of the fitness values of all particles as the average fitness value.
Step 1052, compare the adapted value of the particle with the average adapted value to measure the distance between the location of the particle and the location of the fault.
Comparing the fitness value of the current particle with the average fitness value may reflect to some extent the distance between the position of the particle and the location of the fault, which may be used to adjust the inertia.
Setting the average adaptation value to favgIf the adaptation value f for the ith particle isiIf f isi＞favgIf f is greater than f, the particle i is far from the optimal solution position, and the inertia should be set to be largeriIs less than favgThen it means that the particle i is closer to the optimal solution position and smaller inertia should be set.
Step 1053, set inertia based on the result of the comparison.
In the present embodiment, the inertia is set with reference to the result of the comparison so that the inertia is positively correlated with the distance, i.e., the larger the distance, the larger the inertia, and conversely, the smaller the distance, the smaller the inertia.
If the adaptive value of the particles is larger than the average adaptive value, setting inertia as a preset upper limit value;
if the adaptation value of the particle is less than the average adaptation value, calculating inertia by the following formula:
Figure BDA0003013968330000141
where ω is inertia and ω ismaxUpper limit of inertia, ωminLower limit of inertia, fiIs the adaptation value of particle i, fminIs the minimum of the fitness values of all particles, favgIs the average adaptation value.
Step 1054, the velocity of the particle is updated by using the velocity of the particle, the distance between the position of the particle and the individual extremum, and the distance between the position of the particle and the population extremum as updating conditions.
In this embodiment, the velocity of the particle may be updated by a constraint of three parts, the first part is the "inertial" behavior of the particle, which represents the inertia of the previous velocity of the particle, reflects the memory ability of the particle, which may be understood as maintaining the velocity of the particle along the inertia, the second part is the "cognitive" behavior of the particle, which may be understood as the process of the particle absorbing its own experience knowledge, which may reflect the thinking ability of the particle, which may be understood as the distance between the current position of the particle and its best position (i.e., the individual extremum), and the third part is the "social" behavior of the particle, which may be understood as the process of the particle learning the experience of other particles in the population, which may be understood as the distance between the current position of the particle and the best position of the population (i.e., the population extremum).
Illustratively, the velocity of the particle is updated by the following formula:
Figure BDA0003013968330000151
wherein the content of the first and second substances,
Figure BDA0003013968330000152
for the d-dimensional component in the velocity of the (k + 1) -th iteration particle i,
Figure BDA0003013968330000153
for the d-dimensional component in the velocity of the particle i at the k-th iteration,
Figure BDA0003013968330000154
the d-component in the position of the k-th iteration particle i, ω being the inertia, c1、c2Are all learning factors, r1、r2Are all numerical values that are generated at random,
Figure BDA0003013968330000155
the individual extremum for the k-th iteration particle i,
Figure BDA0003013968330000156
the population extremum of all the particles in the particle group g is iterated for the kth time.
Further, the air conditioner is provided with a fan,
Figure BDA0003013968330000157
is the "inertial" behavior of the particle,
Figure BDA0003013968330000158
in order to "learn" the behavior of the particle,
Figure BDA0003013968330000159
is the "social" behavior of the particle.
And 106, if the updating of the particle speed is finished, overlapping the particle speed on the basis of the position of the particle to update the position of the particle.
If updating the velocity of the particle is completed, the velocity of the particle may be superimposed on the position of the particle as a new position of the particle, as shown below:
Figure BDA00030139683300001510
wherein the content of the first and second substances,
Figure BDA00030139683300001511
for the d-dimensional component in the position of the (k + 1) -th iteration particle i,
Figure BDA00030139683300001512
for the d-dimensional component in the position of the k-th iteration particle i,
Figure BDA00030139683300001513
is the d-th dimensional component in the velocity of the particle i for the k-th iteration.
Step 107, if the updating of the positions of the particles is completed, judging whether an end condition is met; if not, the process returns to step 103, and if yes, step 108 is performed.
And step 108, determining the particles corresponding to the group extreme values as the switches with faults.
In this embodiment, a condition for ending the iteration may be preset as the ending condition, for example, the number of iterations reaches a preset maximum number, a threshold of an optimal fitness value is satisfied, the fitness value is not updated for consecutive times, and the like.
If the updating of the positions of the particles is completed, whether the end conditions are met or not can be checked, when any end condition is not met, the next iteration can be carried out, and when any end condition is met, the particles corresponding to the group extreme value can be output to serve as the switches with faults.
In the embodiment, each switch on each feeder line in the power distribution network is encoded in a binary mode under the dimension of current, the switches are used as particles, a particle swarm is initialized, the particles are configured with speed and positions, a device with a fault in the power distribution network is used as a constraint, an adaptive value of the particles is calculated according to the positions of the particles, the adaptive value of the particles is traversed, an individual extreme value of each particle and a group extreme value of the particle swarm are updated, the speed of the particles is updated according to the individual extreme value and the group extreme value under the condition that the search range of a solution space is adjusted by using the distance between the positions of the particles and the position with the fault, if the speed of the particles is updated, the speed of the particles is superposed on the basis of the positions of the particles to update the positions of the particles, and if the positions of the particles are updated, whether an; if not, returning to execute the operation of taking the equipment with the fault in the power distribution network as constraint, calculating the adaptive value of the particles according to the positions of the particles, and if so, determining the particles corresponding to the group extreme value as the switches with the fault. The search range of the solution space is adjusted through the distance between the positions of the particles and the positions with faults, the global search capability can be improved, single-point faults and multipoint faults in the distribution network can be accurately positioned, meanwhile, the positioning precision is high under the condition that partial information is distorted, and the power grid stability and the power supply reliability are improved.
In order to make the skilled person better understand the embodiments of the present invention, the following describes a method for detecting a fault of a power distribution network according to the embodiments of the present invention by using a specific example.
Fig. 3 is a model of an IEEE standard 33-node power distribution network, in which after numbering 1-33 each switch and numbering 1-33 each feeder line, a program that can implement the method for detecting a fault in the power distribution network according to the embodiment of the present invention is written in Matlab, and single-point faults and multi-point faults are respectively located.
Wherein the parameters of the particle swarm are as follows: the population size is 50, the maximum number of iteration times is 10, the search space dimension is 33, and the learning factor c1＝c 22, lower limit value ω of inertiamin0.3, upper limit value ω of inertiamax＝0.9。
One, single point fault simulation
Assuming that the feeder lines 9 and 14 are short-circuited between phases respectively and considering the signal distortion, the four faults are respectively:
the fault type 1 is that the feeder line 9 has a fault, and the switching signal is not distorted;
fault type 2, feeder 9 fault, and switch 15 signal distortion;
fault type 3, feeder 14 fault, no distortion of switching signal;
fault type 4, feeder 14 fault, and switches 20, 29 distorted.
The test results are shown in the following table:
Figure BDA0003013968330000171
as can be seen from the above table, when a single-point fault occurs in the power distribution network, the embodiment of the present invention can accurately locate the faulty feeder, and when the fault information reported by the FTU has a small amount of distortion, the accuracy of the location result can still be ensured.
Two, multi-point fault simulation
Suppose that the four failures are:
the fault type 1 is that the feeder lines 7 and 22 simultaneously have faults, and the switching signals are not distorted;
fault type 2, feeder 7, 22 simultaneously fails and switch 28 signal is distorted;
the fault type 3, the feeder lines 6, 19 and 29 simultaneously have faults, and the switching signals are not distorted;
fault type 4, feeder 6, 9, 29 simultaneously fails and switches 30, 32 are distorted.
The test results are shown in the following table:
Figure BDA0003013968330000172
Figure BDA0003013968330000181
the above table shows that when the power distribution network has a multi-point fault, the embodiment of the invention still has a good positioning effect, and the accuracy of the positioning result can be still ensured for the distortion of the non-critical position switch signal.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Example two
Fig. 4 is a block diagram of a structure of a fault detection device for a power distribution network according to a second embodiment of the present invention, which may specifically include the following modules:
the encoding module 401 is configured to encode each switch on each feeder in the power distribution network in a binary manner in the current dimension;
a particle swarm initialization module 402, configured to initialize a particle swarm with the switch as a particle, the particle being configured with a speed and a position;
an adaptive value calculating module 403, configured to calculate an adaptive value of the particle according to the position of the particle, with a device in the power distribution network that has failed as a constraint;
an extremum updating module 404 for traversing the adapted values of the particles to update an individual extremum of each of the particles, a population extremum of the particle population;
a speed updating module 405, configured to update the speed of the particle according to the individual extremum and the group extremum under a condition that a search range of a solution space is adjusted using a distance between the position of the particle and a position where the fault occurs;
a position updating module 406, configured to, if updating the velocity of the particle is completed, superimpose the velocity of the particle on the basis of the position of the particle to update the position of the particle;
an ending condition determining module 407, configured to determine whether an ending condition is met if updating the position of the particle is completed; if not, returning to call the adaptive value calculation module 403, and if so, calling a fault determination module 408;
and a failure determining module 408, configured to determine that the particle corresponding to the group extremum is a failed switch.
In one embodiment of the present invention, the speed update module 405 comprises:
the average adaptive value calculating module is used for calculating the average value of the adaptive values of all the particles as an average adaptive value;
an adaptation value calculation module for comparing the adaptation value of the particle with the average adaptation value to measure a distance between the location of the particle and a location of a fault;
an inertia setting module for setting an inertia based on a result of the comparison, the inertia being positively correlated with the distance;
a constraint update module to update the velocity of the particle on a condition that maintains the velocity of the particle, the distance between the position of the particle and the individual extremum, and the distance between the position of the particle and the population extremum along the inertia as updates.
In one embodiment of the invention, the inertia setting module comprises:
the first state setting module is used for setting inertia as a preset upper limit value if the adaptive value of the particles is larger than the average adaptive value;
a second state setting module, configured to calculate inertia according to the following formula if the adaptive value of the particle is smaller than the average adaptive value:
Figure BDA0003013968330000191
where ω is inertia and ω ismaxUpper limit of inertia, ωminLower limit of inertia, fiIs the adaptation value of particle i, fminIs the minimum of the fitness values of all particles, favgIs the average adaptation value.
In one embodiment of the invention, the constraint update module comprises:
a particle update module for updating the velocity of the particle by the following formula:
Figure BDA0003013968330000192
wherein the content of the first and second substances,
Figure BDA0003013968330000201
for the d-dimensional component in the velocity of the (k + 1) -th iteration particle i,
Figure BDA0003013968330000202
for the d-dimensional component in the velocity of the particle i at the k-th iteration,
Figure BDA0003013968330000203
is the d-dimensional component in the position of the k-th iteration particle i, ω is the inertia, c1、c2Are all learning factors, r1、r2Are all numerical values that are generated at random,
Figure BDA0003013968330000204
the individual extremum for the k-th iteration particle i,
Figure BDA0003013968330000205
the population extremum of all the particles in the particle group g is iterated for the kth time.
In one embodiment of the present invention, the encoding module 401 includes:
the current query module is used for querying the current of each switch on each feeder line in the power distribution network;
the matrix generation module is used for generating a matrix, wherein rows of the matrix represent the switches, and columns of the matrix represent the feeder lines;
the abnormal setting module is used for setting the element in the matrix corresponding to the switch to be 1 if the current of the switch is over-current;
and the normal setting module is used for setting the element in the matrix corresponding to the switch to be 0 if the current of the switch is not overcurrent.
In one embodiment of the present invention, the particle swarm initialization module 402 comprises:
a particle setting module for setting the switch as particles in a particle swarm;
the speed random setting module is used for randomly generating a numerical value for the particles in a preset first range to serve as the speed of the particles;
the movement probability mapping module is used for substituting the speed of the particles into an S-shaped function so as to map the speed of the particles into the movement probability of the particles;
the threshold random generation module is used for randomly generating a numerical value for the particles in a preset second range to serve as a threshold;
a first position setting module, configured to take a value of the position of the particle as 1 if the probability of the movement of the particle is smaller than or equal to the threshold;
a second position setting module, configured to, if the probability of the movement of the particle is greater than the threshold, take a value of the position of the particle as 0;
wherein the sigmoid function is represented as follows:
Figure BDA0003013968330000206
wherein sigm0id is a sigmoid function,
Figure BDA0003013968330000211
is the d-dimensional component in the velocity of the particle i at the k-th iteration, A and B are both positive numbers, and the sum between A and B is 1, alpha1Is the lower limit of the speed, α2The upper limit value of the speed.
In one embodiment of the present invention, the extremum updating module 404 includes:
an individual extreme value comparison module, configured to compare the adaptive value of the particle with an adaptive value of an individual extreme value of the particle, where the individual extreme value of the particle is initially null;
an individual extreme value updating module, configured to update the position of the particle to an individual extreme value of the particle if the adaptive value of the particle is greater than the adaptive value corresponding to the individual extreme value of the particle;
an individual extreme value maintaining module, configured to maintain the individual extreme value of the particle if the adaptive value of the particle is smaller than the adaptive value corresponding to the individual extreme value of the particle;
a population extreme value comparison module, configured to compare the adaptive value of the particle with an adaptive value corresponding to a population extreme value of the particle swarm, where the population extreme value of the particle swarm is initially null;
a population extremum updating module, configured to update the position of the particle to the population extremum of the particle swarm if the adaptive value of the particle is greater than the adaptive value corresponding to the population extremum of the particle swarm;
and the population extreme value maintaining module is used for maintaining the population extreme value of the particle swarm if the adaptive value of the particle is smaller than the adaptive value corresponding to the population extreme value of the particle swarm.
The fault detection device for the power distribution network, provided by the embodiment of the invention, can execute the fault detection method for the power distribution network, provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a computer device according to a third embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 5 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 5, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, computer device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement the fault detection method for the power distribution network provided by the embodiment of the present invention.
Example four
The fourth embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the fault detection method for the power distribution network, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
A computer readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A fault detection method for a power distribution network is characterized by comprising the following steps:
coding each switch on each feeder in the distribution network in a binary manner under the dimension of the current;
initializing a particle group by taking the switch as a particle, wherein the particle is provided with a speed and a position;
calculating an adaptive value of the particle according to the position of the particle by taking a device with a fault in the power distribution network as a constraint;
traversing the adapted values of the particles to update individual extrema of each of the particles, population extrema of the population of particles;
updating the speed of the particle according to the individual extremum and the population extremum under the condition that the distance between the position of the particle and the position where the fault occurs is used for adjusting the search range of the solution space;
if the updating of the speed of the particle is completed, the speed of the particle is superposed on the position of the particle to update the position of the particle;
if the updating of the positions of the particles is completed, judging whether an ending condition is met; if not, returning to execute the device with the fault in the power distribution network as constraint, calculating the adaptive value of the particle according to the position of the particle, and if so, determining the particle corresponding to the group extreme value as a switch with the fault.
2. The method of claim 1, wherein updating the velocity of the particle according to the individual extremum and the population extremum under the condition that the distance between the position of the particle and the position of the fault is used to adjust a search range of a solution space comprises:
calculating the average value of the adaptive values of all the particles as an average adaptive value;
comparing the adapted value of the particle with the average adapted value to measure a distance between the location of the particle and the location of the fault;
setting an inertia based on a result of the comparison, the inertia being positively correlated with the distance;
updating the velocity of the particle on a condition that the velocity of the particle, the distance between the position of the particle and the individual extremum, and the distance between the position of the particle and the population extremum are maintained along the inertia as updates.
3. The method of claim 2, wherein setting an inertia based on a result of the comparison comprises:
if the adaptive value of the particles is larger than the average adaptive value, setting inertia as a preset upper limit value;
if the adaptive value of the particle is smaller than the average adaptive value, calculating inertia by the following formula:
Figure FDA0003013968320000021
where ω is inertia and ω ismaxUpper limit of inertia, ωminLower limit of inertia, fiIs the adaptation value of particle i, fminFor adaptation of all particlesMinimum value of value, favgIs the average adaptation value.
4. The method of claim 2, wherein updating the velocity of the particle conditioned on maintaining the velocity of the particle, the distance between the position of the particle and the individual extremum, and the distance between the position of the particle and the population extremum along the inertia as updates comprises:
updating the velocity of the particle by the following equation:
Figure FDA0003013968320000022
wherein the content of the first and second substances,
Figure FDA0003013968320000023
for the d-dimensional component in the velocity of the (k + 1) -th iteration particle i,
Figure FDA0003013968320000024
for the d-dimensional component in the velocity of the particle i at the k-th iteration,
Figure FDA0003013968320000025
is the d-dimensional component in the position of the k-th iteration particle i, ω is the inertia, c1、c2Are all learning factors, r1、r2Are all numerical values that are generated at random,
Figure FDA0003013968320000026
the individual extremum for the k-th iteration particle i,
Figure FDA0003013968320000027
the population extremum of all the particles in the particle group g is iterated for the kth time.
5. The method of any of claims 1-4, wherein encoding each switch on each feeder in the distribution network in a binary manner in the current dimension comprises:
inquiring the current of each switch on each feeder line in the power distribution network;
generating a matrix, rows of the matrix representing the switches, columns of the matrix representing the feeders;
if the current of the switch is over-current, setting the element corresponding to the switch in the matrix as 1;
and if the current of the switch is not overcurrent, setting the element corresponding to the switch in the matrix to be 0.
6. The method according to any one of claims 1-4, wherein initializing a population of particles with the switch as a particle comprises:
setting the switches as particles in a population of particles;
randomly generating a numerical value for the particles within a preset first range as the speed of the particles;
substituting the velocity of the particle into an S-shaped function to map the velocity into the probability of the particle moving;
randomly generating a numerical value for the particles in a preset second range to serve as a threshold value;
if the probability of the movement of the particles is less than or equal to the threshold value, the position of the particles is taken as 1;
if the probability of the movement of the particles is larger than the threshold value, the position of the particles is taken as 0;
wherein the sigmoid function is represented as follows:
Figure FDA0003013968320000031
wherein sigmoid is a sigmoid function,
Figure FDA0003013968320000032
for the d-th dimension in the velocity of the particle i of the k-th iterationAmount, A and B are both positive numbers and the sum between A and B is 1, α1Is the lower limit of the speed, α2The upper limit value of the speed.
7. The method of any one of claims 1-4, wherein said traversing the adapted values of the particles to update individual extrema of each of the particles, population extrema of the population of particles, comprises:
comparing the adapted value of the particle with adapted values of individual extrema of the particle, the individual extrema of the particle initially being null;
updating the position of the particle to be the individual extreme value of the particle if the adaptive value of the particle is larger than the adaptive value corresponding to the individual extreme value of the particle;
if the adaptive value of the particle is smaller than the adaptive value corresponding to the individual extreme value of the particle, maintaining the individual extreme value of the particle;
comparing the adaptive value of the particle with an adaptive value corresponding to a population extremum of the particle swarm, wherein the population extremum of the particle swarm is initially null;
updating the position of the particle to be the population extremum of the particle swarm if the adaptive value of the particle is larger than the adaptive value corresponding to the population extremum of the particle swarm;
and if the adaptive value of the particle is smaller than the adaptive value corresponding to the population extremum of the particle swarm, maintaining the population extremum of the particle swarm.
8. A fault detection device for an electrical distribution network, comprising:
the encoding module is used for encoding each switch on each feeder line in the power distribution network in a binary mode under the dimension of current;
the particle swarm initialization module is used for initializing a particle swarm by taking the switch as a particle, and the particle is configured with a speed and a position;
the adaptive value calculation module is used for calculating the adaptive value of the particle according to the position of the particle by taking the equipment with faults in the power distribution network as constraint;
an extreme value updating module, configured to traverse the adapted values of the particles to update an individual extreme value of each of the particles and a population extreme value of the particle swarm;
a speed updating module, configured to update the speed of the particle according to the individual extremum and the group extremum under a condition that a search range of a solution space is adjusted using a distance between the position of the particle and a position where the fault occurs;
a position updating module, configured to, if updating the velocity of the particle is completed, superimpose the velocity of the particle on the basis of the position of the particle to update the position of the particle;
an ending condition judgment module, configured to judge whether an ending condition is met if updating the position of the particle is completed; if not, returning to call the adaptive value calculation module, and if so, calling a fault determination module;
and the fault determining module is used for determining the particles corresponding to the group extreme value as the switches with faults.
9. A computer device, characterized in that the computer device comprises:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of fault detection for an electrical distribution network as claimed in any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out a method for fault detection of an electrical distribution network according to any one of claims 1-7.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106093713A (en) * 2016-08-24 2016-11-09 广东工业大学 A kind of power distribution network section Fault Locating Method containing distributed power source and system
CN106229964A (en) * 2016-07-22 2016-12-14 南京工程学院 A kind of based on the electrical power distribution network fault location method improving binary particle swarm algorithm
CN108957246A (en) * 2018-09-27 2018-12-07 广东电网有限责任公司 A kind of electrical power distribution network fault location method based on population
CN112485587A (en) * 2020-11-11 2021-03-12 国网福建省电力有限公司宁德供电公司 Layered positioning method for fault section of distribution-containing photovoltaic power distribution network

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106229964A (en) * 2016-07-22 2016-12-14 南京工程学院 A kind of based on the electrical power distribution network fault location method improving binary particle swarm algorithm
CN106093713A (en) * 2016-08-24 2016-11-09 广东工业大学 A kind of power distribution network section Fault Locating Method containing distributed power source and system
CN108957246A (en) * 2018-09-27 2018-12-07 广东电网有限责任公司 A kind of electrical power distribution network fault location method based on population
CN112485587A (en) * 2020-11-11 2021-03-12 国网福建省电力有限公司宁德供电公司 Layered positioning method for fault section of distribution-containing photovoltaic power distribution network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
李超文 等: "基于二进制粒子群算法的辐射状配电网故障定位", 《电力***保护与控制》 *

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