CN108957246A - A kind of electrical power distribution network fault location method based on population - Google Patents
A kind of electrical power distribution network fault location method based on population Download PDFInfo
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- CN108957246A CN108957246A CN201811134087.6A CN201811134087A CN108957246A CN 108957246 A CN108957246 A CN 108957246A CN 201811134087 A CN201811134087 A CN 201811134087A CN 108957246 A CN108957246 A CN 108957246A
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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
- G01R31/081—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
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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
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- 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
Abstract
The present invention relates to the fault location technology fields of AC network, more particularly, to a kind of electrical power distribution network fault location method based on population.The topological structure of the fault current information and power distribution network that report when the present invention is by using Feeder Terminal Unit FTU failure forms evaluation function, fast convergence rate, particle swarm algorithm easy to accomplish are recycled to search the optimal solution of evaluation function, achievees the purpose that fault location.It is a large amount of and the sample calculation analysis of system the result shows that: single-point in multiple source power distribution net and multipoint fault can be accurately positioned in new method fast convergence rate, and in the case where partial fault information distortion, can show that correct result, error resilience performance are good.
Description
Technical field
The present invention relates to the fault location technology fields of AC network, more particularly, to a kind of matching based on population
Electric network fault localization method.
Background technique
Distribution network failure positioning is one of the important content for realizing power distribution automation, and the purpose is to be set according to each line feed terminals
The fault message of standby (feeder terminal unit, FTU) acquisition carrys out the section of comprehensive descision failure generation, is accident analysis
Condition is provided with service restoration.Therefore, asked when it has a power failure for shortening, reduce scope of power outage and improve power supply reliability etc.
It is of great significance.
There are two main classes for the distribution network failure location algorithm studied at present: direct algorithm and Explicit Algorithm.In direct algorithm
Most typically matrix algorithm is to form fault verification matrix according to the operation of network topology matrix and fault message matrix, should
Algorithm calculating speed is fast, but FTU is required to upload accurate fault message, and poor fault tolerance, cannot handle FTU upload information has distortion
The case where.Explicit Algorithm is exactly so-called all kinds of intelligent algorithms, and Typical Representative is genetic algorithm, which believes in the failure of upload
Breath is when distorting, and can provide more accurately as a result, zmodem, but it is computationally intensive, parameter setting is complicated, affect failure
The real-time of positioning.Electrical power distribution network fault location method based on Optimized model belongs to one kind of intelligent algorithm, it can be problem
It is expressed as 0~1 integer programming problem, on this basis, finding by optimization algorithm keeps the objective function of construction minimum (or most
Optimal solution greatly) finds out the faulty equipment and fault type that can most explain fault message.Optimization-type method for diagnosing faults can be asked
Multiple possible solutions are obtained, but it needs to research and solve the problems such as there are optimizing algorithm superiority and inferiority and the real-times of optimization.
In numerous optimization algorithms, the particle swarm algorithm of development utilization is one of preferable optimization algorithm, grain in recent years
Swarm optimization (Particle Swarm Optimization, PSO) be simulate nature in flock of birds predation and formed one
Kind optimization algorithm constructs " population " in multidimensional solution space by the simulation to simple social system.With genetic algorithm phase
Than PSO algorithm has the advantages that simple and easy, easy to accomplish without being selected, being handed over justice and variation.Therefore, the present invention proposes
A kind of electrical power distribution network fault location method based on particle swarm algorithm.
Summary of the invention
The distribution network failure positioning based on population that it is an object of the invention to overcome the deficiencies of the prior art and provide a kind of
Method.
In order to solve the above technical problems, the technical solution adopted by the present invention is that: a kind of distribution network failure based on population
Localization method the described method comprises the following steps:
S1: regarding Feeder Terminal Unit FTU in power distribution network as node, and the route between node is the region that will be positioned, and
The direction of route is the direction of trend on route, and power distribution network is mapped as a digraph；
S2: according to the digraph and topological structure of power distribution network, constructing evaluation function, obtains evaluation letter using particle swarm algorithm
Optimal solution when number is minimum, determines fault zone with this；
S3: it proposes the constringent measure of innovatory algorithm, and builds emulation platform and verified.
Preferably, the step S1 specifically includes the following steps:
S11: regarding the Feeder Terminal Unit FTU in power distribution network as node and number, and carries out between the region each node
Number；
S12: determining the positive direction of distribution network, and the power outflow direction that reference power source is powered to the whole network is distribution network
Positive direction；
S13: if Feeder Terminal Unit FTU measure overcurrent information it is identical as network positive direction when, to control main website report
1, otherwise report 0.
Preferably, the expression formula of the evaluation function isThe value of expression formula
For the corresponding fitness value of each potential solution, it is more excellent to be worth smaller expression solution, therefore evaluation function answers minimalization；In formula, Ij
For the fault message that j-th of Feeder Terminal Unit FTU is uploaded, value thinks that Feeder Terminal Unit FTU has flowed through failure electricity for 1
Stream does not flow through for 0；Ij *(SB) be each Feeder Terminal Unit FTU node expectation state, if Feeder Terminal Unit FTU flow
Crossed fault current, then expectation state is 1, Instead, it is desirable to state is 0, it is the function of each equipment state, Feeder Terminal Unit
There is arbitrary region failure in the downstream FTU, then otherwise it is 0 that the value, which is 1,；N is the sum of feeder line section in power distribution network；SBFor power distribution network
In each equipment state, show equipment fault for 1, take 0 equipment normal；Indicate that a weight coefficient is set multiplied by failure
Standby number, w are the weight coefficients being arranged according to " minimal set " concept in Troubleshooting Theory, 0, < w < 1；
Preferably, the particle swarm algorithm specifically: the solution of each optimization problem is position of the particle in search space
It sets, particle determines the direction and distance that they circle in the air there are one velocity amplitude, and then particle is followed current optimal particle and solved
It is searched in space, in search process, the up to the present optimal location of itself that each particle is found is known as the individual of particle
Extreme value pbest, the optimal location in all particles is denoted as global extremum gbest, and updates itself speed and position according to the following formula
It sets
In formula:WithRespectively particle i is in+1 iteration of kth in the position of m-dimensional space
And speed；ω is inertia weight；c1And c2It is all positive real number for accelerated factor；r1And r2For be randomly generated one between [0,
1] random number between；The individual optimum particle position found until for particle i to kth time iteration in m-dimensional space；For the group's optimum particle position found until kth time iteration in m-dimensional space.
Preferably, to solve discrete or binary variable optimization problem, using Binary Particle Swarm Optimization；By grain
The individual values that the position and particle of son are optimal are defined as 0 or 1, and without restriction to the speed of particle；According to velocity magnitude come
It selects to be 0 or 1 on particle corresponding position, speed is larger, then it represents that corresponding position selects 1 probability big；Speed is smaller then
Mean that corresponding position may select 0, fundamental formular is shown below:
In formulaFor the random number between [0,1] being randomly generated；To preventFunction is full
With the speed of particle is set in [- 4,4] range, correspondingFunction are as follows:
Preferably, the constringent measure of the innovatory algorithm in step s3, using constriction factor algorithm, by the speed of particle
It is modified according to the following formula:
In formula: AndGenerally takec1=c2=2.05.It is imitative
Very the result shows that this method can greatly improve PSO convergence speed of the algorithm.Parameter before correspondence chooses ω=k=0.729,
c1=c2=1.494.Inertia weight ω has great role, ω for the ability of searching optimum and local search ability of balanced algorithm
Be worth it is larger, algorithm have stronger ability of searching optimum, but convergence decline；ω value is smaller, and algorithm tends to local search, easily
Fall into local extremum.The method that the present invention uses is ω to be initially to 0.9, and be decremented to 0.4 with the increase of the number of iterations, to reach
To preferable global and local search capability.
Compared with prior art, the beneficial effects of the present invention are:
The present invention is a kind of electrical power distribution network fault location method based on population, and compared with matrix method, serious forgiveness mentions significantly
It is high；It is simpler efficient compared with genetic algorithm, fast convergence rate；It proposes the measure of improvement PSO Algorithm Convergence, effectively solves
It has determined easy the problem of falling into local convergence；Single-point and multipoint fault can be positioned.
Detailed description of the invention
Fig. 1 is Fault Locating Method flow chart of the present invention；
Fig. 2 is the topology diagram of more electrical power distribution nets of the invention.
Specific embodiment
The present invention is further illustrated With reference to embodiment.Wherein, attached drawing only for illustration,
What is indicated is only schematic diagram, rather than pictorial diagram, should not be understood as the limitation to this patent；Reality in order to better illustrate the present invention
Example is applied, the certain components of attached drawing have omission, zoom in or out, and do not represent the size of actual product；To those skilled in the art
For, the omitting of some known structures and their instructions in the attached drawings are understandable.
The same or similar label correspond to the same or similar components in the attached drawing of the embodiment of the present invention；It is retouched in of the invention
In stating, it is to be understood that if the orientation or positional relationship for having the instructions such as term " on ", "lower", "left", "right" is based on attached drawing
Shown in orientation or positional relationship, be merely for convenience of description of the present invention and simplification of the description, rather than indication or suggestion is signified
Device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore positional relationship is described in attached drawing
Term only for illustration, should not be understood as the limitation to this patent, for the ordinary skill in the art, can
To understand the concrete meaning of above-mentioned term as the case may be.
Embodiment
Fig. 1 is a kind of electrical power distribution network fault location method based on population, be the described method comprises the following steps:
S1: regarding Feeder Terminal Unit FTU in power distribution network as node, and the route between node is the region that will be positioned, and
The direction of route is the direction of trend on route, and power distribution network is mapped as a digraph；
S2: according to the digraph and topological structure of power distribution network, constructing evaluation function, obtains evaluation letter using particle swarm algorithm
Optimal solution when number is minimum, determines fault zone with this；
S3: it proposes the constringent measure of innovatory algorithm, and builds emulation platform and verified.
Wherein, step S1 specifically includes the following steps:
S11: regarding the Feeder Terminal Unit FTU in power distribution network as node and number, and carries out between the region each node
Number；
S12: determining the positive direction of distribution network, and the power outflow direction that reference power source is powered to the whole network is distribution network
Positive direction；
S13: if Feeder Terminal Unit FTU measure overcurrent information it is identical as network positive direction when, to control main website report
1, otherwise report 0.
In addition, the expression formula of evaluation function isThe value of expression formula is each
It is potential to solve corresponding fitness value, it is more excellent to be worth smaller expression solution, therefore evaluation function answers minimalization；In formula, IjIt is j-th
The fault message that Feeder Terminal Unit FTU is uploaded, value think that Feeder Terminal Unit FTU has flowed through fault current for 1, are 0
It does not flow through then；Ij *(SB) be each Feeder Terminal Unit FTU node expectation state, if Feeder Terminal Unit FTU flowed through therefore
Hindering electric current, then expectation state is 1, Instead, it is desirable to state is 0, it is the function of each equipment state, under Feeder Terminal Unit FTU
Trip has arbitrary region failure, then otherwise it is 0 that the value, which is 1,；N is the sum of feeder line section in power distribution network；SBRespectively to be set in power distribution network
Standby state shows equipment fault for 1, takes 0 equipment normal；Indicate a weight coefficient multiplied by faulty equipment number, w
Be according in Troubleshooting Theory " minimal set " concept be arranged weight coefficient, 0, < w < 1；
Wherein, particle swarm algorithm specifically: the solution of each optimization problem is position of the particle in search space, then
Particle is followed current optimal particle and is searched in solution space, in search process, each particle up to the present find from
The optimal location of body is known as the individual extreme value p of particlebest, the optimal location in all particles is denoted as global extremum gbest, and according to
Following formula updates speed and the position of itself:
In formula:WithRespectively particle i is in+1 iteration of kth in the position of m-dimensional space
And speed；ω is inertia weight；c1And c2It is all positive real number for accelerated factor；r1And r2For be randomly generated one between [0,
1] random number between；The individual optimum particle position found until for particle i to kth time iteration in m-dimensional space；For the group's optimum particle position found until kth time iteration in m-dimensional space.
In addition, to solve discrete or binary variable optimization problem, using Binary Particle Swarm Optimization；By particle
Position and the optimal individual values of particle be defined as 0 or 1, and it is without restriction to the speed of particle；It is selected according to velocity magnitude
Selecting is 0 or 1 on particle corresponding position, and speed is larger, then it represents that corresponding position selects 1 probability big；Speed is smaller, anticipates
Taste corresponding position may select 0, fundamental formular is shown below:
In formulaFor the random number between [0,1] being randomly generated；To preventFunction is full
With the speed of particle is set in [- 4,4] range, correspondingFunction are as follows:
Wherein, the constringent measure of the innovatory algorithm in step s3 is pressed the speed of particle using constriction factor algorithm
It is modified according to following formula:
In formula: AndGenerally takec1=c2=2.05.It is imitative
Very the result shows that this method can greatly improve PSO convergence speed of the algorithm.Parameter before correspondence chooses ω=k=0.729,
c1=c2=1.494.Inertia weight ω has great role, ω for the ability of searching optimum and local search ability of balanced algorithm
Be worth it is larger, algorithm have stronger ability of searching optimum, but convergence decline；ω value is smaller, and algorithm tends to local search, easily
Fall into local extremum.The method that the present invention uses is ω to be initially to 0.9, and be decremented to 0.4 with the increase of the number of iterations, to reach
To preferable global and local search capability.
Fig. 2 is the topology diagram of the more electrical power distribution nets of the present invention, and the arrow in figure is the positive direction of power distribution network, and a~i is
The state in 9 regions of positioning, faulty is 1, fault-free 0；S1~S11For the switch equipped with FTU.According to the knot of power distribution network
Structure and the expectation state I respectively switchedj *(SB) definition, the available expectation state I respectively switchedj *(SB) it is as follows:
When short trouble occurs for route, FTU detects over-current phenomenon avoidance and reports to control main website.Main website analyzes failure letter
Breath, determines fault section, and the result statistics of partial picture is as follows:
| FTU reports situation | Positioning scenarios |
| FTU uploads errorless | Positioning result is all correct |
| Section f failure, S2It fails to report | Section f is faulty |
| Section f failure, S5It reports by mistake | Section f is faulty |
| Section c failure, S11It reports by mistake | Section c is faulty |
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (6)
1. a kind of electrical power distribution network fault location method based on population, which is characterized in that the described method comprises the following steps:
S1: regarding Feeder Terminal Unit FTU in power distribution network as node, and the route between node is the region that will be positioned, and route
Direction be trend on route direction, power distribution network is mapped as a digraph；
S2: according to the digraph and topological structure of power distribution network, evaluation function is constructed, obtains evaluation function most using particle swarm algorithm
The optimal solution of hour, determines fault zone with this；
S3: it proposes the constringent measure of innovatory algorithm, and builds emulation platform and verified.
2. a kind of electrical power distribution network fault location method based on population according to claim 1, which is characterized in that the step
Rapid S1 specifically includes the following steps:
S11: regard the Feeder Terminal Unit FTU in power distribution network as node and number, and the region each node is numbered；
S12: determining the positive direction of distribution network, power that reference power source is powered to the whole network outflow direction be distribution network just
Direction；
S13: if Feeder Terminal Unit FTU measure overcurrent information it is identical as network positive direction when, report 1 to control main website, it is no
Then report 0.
3. a kind of electrical power distribution network fault location method based on population according to claim 2, which is characterized in that institute's commentary
The expression formula of valence function isThe value of expression formula is that each potential solution is corresponding suitable
Angle value is answered, it is more excellent to be worth smaller expression solution, therefore evaluation function answers minimalization；In formula, IjFor j-th of Feeder Terminal Unit
The fault message that FTU is uploaded, value think that Feeder Terminal Unit FTU has flowed through fault current for 1, do not flow through for 0；Ij *
(SB) be each Feeder Terminal Unit FTU node expectation state, if Feeder Terminal Unit FTU has flowed through fault current, phase
Prestige state is 1, Instead, it is desirable to state is 0, it is the function of each equipment state, there is arbitrary region in the downstream Feeder Terminal Unit FTU
Failure, then otherwise it is 0 that the value, which is 1,；N is the sum of feeder line section in power distribution network；SBIt is 1 table for equipment state each in power distribution network
Bright equipment fault takes 0 equipment normal；A weight coefficient is indicated multiplied by faulty equipment number, w is examined according to failure
The weight coefficient of " minimal set " concept setting in disconnected theory, 0, < w < 1.
4. a kind of electrical power distribution network fault location method based on population according to claim 3, which is characterized in that the grain
Swarm optimization specifically: the solution of each optimization problem is position of the particle in search space, and then particle is followed current
Optimal particle is searched in solution space, and in search process, the up to the present optimal location of itself that each particle is found claims
For the individual extreme value p of particlebest, the optimal location in all particles is denoted as global extremum gbest, and itself is updated according to the following formula
Speed and position: In formula:WithRespectively particle i is in+1 iteration of kth in the position and speed of m-dimensional space；ω is inertia weight；c1And c2To add
The fast factor is all positive real number；r1And r2For a random number between [0,1] being randomly generated；Extremely for particle i
The individual optimum particle position found until kth time iteration in m-dimensional space；To be tieed up until kth time iteration in m
Group's optimum particle position that space is found.
5. a kind of electrical power distribution network fault location method based on population according to claim 4, which is characterized in that solve
Discrete or binary variable optimization problem, using Binary Particle Swarm Optimization；The position of particle and particle is optimal
Individual values are defined as 0 or 1, and without restriction to the speed of particle；It is selected according to velocity magnitude on particle corresponding position
It is 0 or 1, speed is larger, then it represents that corresponding position selects 1 probability big；Speed is smaller, means that corresponding position may
0 is selected, fundamental formular is shown below:
In formulaFor the random number between [0,1] being randomly generated；To preventFunction saturation, grain
The speed of son is set in [- 4,4] range, correspondingFunction are as follows:
6. a kind of electrical power distribution network fault location method based on population according to claim 4 or 5, which is characterized in that
The speed of particle is modified by the constringent measure of innovatory algorithm described in step S3 according to the following formula using constriction factor algorithm:In formula: AndInertia weight ω has great role for the ability of searching optimum and local search ability of balanced algorithm, and ω value is larger,
Algorithm has stronger ability of searching optimum, but convergence declines；ω value is smaller, and algorithm tends to local search, easily falls into office
Portion's extreme value.
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| CN112014686A (en) * | 2020-08-14 | 2020-12-01 | 国网河南省电力公司封丘县供电公司 | Low-voltage distribution network fault positioning method based on shortest path of adjacency matrix |
| CN112147458A (en) * | 2020-04-09 | 2020-12-29 | 南京理工大学 | Fault section positioning method of DG-containing power distribution network based on improved universal gravitation algorithm |
| CN112505532A (en) * | 2020-12-14 | 2021-03-16 | 电子科技大学 | Analog circuit single fault diagnosis method based on improved particle swarm optimization |
| CN112557811A (en) * | 2020-11-19 | 2021-03-26 | 安徽理工大学 | Fault location method based on improved genetic algorithm and used for power distribution network with distributed power supplies |
| CN112684281A (en) * | 2020-11-12 | 2021-04-20 | 国网河北省电力有限公司电力科学研究院 | Power distribution network single-phase earth fault section positioning method and device and terminal equipment |
| CN112952738A (en) * | 2021-04-09 | 2021-06-11 | 广东电网有限责任公司梅州供电局 | Fault detection method and device for power distribution network, computer equipment and storage medium |
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| CN113675877A (en) * | 2021-06-11 | 2021-11-19 | 国网冀北电力有限公司承德供电公司 | Fault diagnosis method for distribution network with distributed power supply based on deep learning |
| CN113675877B (en) * | 2021-06-11 | 2023-06-30 | 国网冀北电力有限公司承德供电公司 | Deep learning-based distributed power supply distribution network fault diagnosis method |
| CN116298686A (en) * | 2023-03-16 | 2023-06-23 | 广东电网有限责任公司广州供电局 | Fault positioning method, device, equipment and medium applied to power distribution network |
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Application publication date: 20181207 |