CN115856512A - Power distribution network fault positioning method, system, equipment and storage medium - Google Patents
Power distribution network fault positioning method, system, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a power distribution network fault positioning method, a system, equipment and a storage medium, S1, monitoring information in a monitoring node direction, and constructing an expected function of a switch according to the monitoring information; s2, constructing a fitness function of power distribution network fault location according to the expected function of the switch, calculating the fitness value of the particles according to the fitness function, recording the fitness value as an initial individual extreme value, and assigning the smallest one to a global minimum extreme value; s3, updating the speed and the position of the particle group in the fitness function; and S4, repeating the step S3 until the global optimal position of the particle swarm is output, namely the actual fault state of each feeder line section of the target power distribution network. The method greatly improves the positioning speed when the power distribution network containing the distributed power supply fails, increases the reliability of fault positioning, and has great significance for eliminating fault points and safe and reliable operation of the power grid.
Description
Technical Field
The invention belongs to the field of power distribution network line fault monitoring, and relates to a power distribution network fault positioning method, system, equipment and storage medium.
Background
A large number of distributed power sources begin to access the distribution grid. Therefore, the grid structure of the power distribution network is more and more complex, and the normal operation performance, maintenance and control of the grid structure are obviously changed. In addition, the distribution network is positioned between the transmission network and the users, which plays an important bridge role for communicating the transmission network and the users, but various line faults frequently occur due to the complex network structure, the severe operating environment and more terminal elements. Therefore, the power supply recovery can be timely eliminated only by quickly and accurately finding the fault position, and the stability of the power system is favorably improved.
The traditional fault location and treatment by instrument action based on reclosers and sectionalizers is becoming impoverished and unable to meet the expectations and requirements of high efficiency and stability of power supply. At present, algorithms for performing intelligent optimization and fault location on a power distribution network are numerous, such as a neural network, a genetic algorithm, a particle swarm algorithm and the like, although the algorithms have certain fault tolerance, the algorithms mostly have the problems of complex iterative computation and location rules, for example, the genetic algorithm has complex computation operation and large computation amount, and the traditional binary particle swarm algorithm has the problems of premature convergence and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a power distribution network fault positioning method, a system, equipment and a storage medium, which greatly improve the positioning speed when a power distribution network containing distributed power supplies fails, increase the reliability of fault positioning and have great significance for eliminating fault points and safely and reliably operating the power network.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a power distribution network fault positioning method comprises the following steps:
s1, monitoring information in the direction of a monitoring node, and constructing an expected function of a switch according to the monitoring information;
s2, constructing a fitness function of power distribution network fault location according to the expected function of the switch, calculating the fitness value of the particles according to the fitness function, recording the fitness value as an initial individual extreme value, and assigning the smallest one to a global minimum extreme value;
s3, updating the speed and the position of the particle group in the fitness function;
and S4, repeating the step S3 until the global optimal position of the particle swarm is output, namely the actual fault state of each feeder line section of the target power distribution network.
Preferably, in S1, the process of monitoring the information in the monitoring node direction is as follows: when the fault overcurrent information in the direction from a main power supply end of the system to a monitoring node is detected, recording the fault overcurrent information as 1; when the information in the direction from the distributed power supply end to the monitoring node is detected, recording the information as-1; and when no overcurrent information is monitored, marking as 0.
Further, the desired function of the switch is:
S j (x)＝L 1 +L 2 +L 3 +…+L j
I j (x)＝k 1 (1-S 1 (j))*S 1 (j)-k 2 (1-S 3 (j))*S 4 (j)；
wherein "+" is a logical OR operation; l is a radical of an alcohol 1 Indicating the state of the line between the circuit breaker 1 and the circuit breaker 2, L 2 The state of the line between the circuit breaker 2 and the circuit breaker 3 is represented, and so on, if the line breaks down, the state value is '1', otherwise, the state value is '0'; assuming that the circuit breaker No. j is a breaking point, the distribution network is divided into an upper half area where a system power supply is located and a lower half area where a distributed power supply is located at the moment; k1 and k2 respectively represent the switching coefficients of the regional power supply, 1 is taken to represent that the regional power supply is connected, and 0 represents that the regional power supply is not connected; s j (x) Detected line section state calculation result, S 1 (j) Results of operations representing all line sections from the circuit breaker j to the upper half up to the main power source, S 3 (j) The result of the calculation, S, representing all the sections of the circuit breaker j from the lower half up to the distributed power supply 2 (j) And S 4 (j) Respectively representing the operation results of all the line sections of the upper half area and the lower half area.
Preferably, before S2, parameters of the particle swarm are initialized according to the structure of the power distribution network.
Further, each parameter of the particle swarm comprises the size of the particle swarm, the dimension of the particle, the maximum iteration number in the swarm, the grouping number of the swarm and the number of the particles contained in the swarm.
Preferably, in S2, according to the fitness function value fThe sizes of all the particles are arranged in an ascending order from small to large, and the whole particle population with the population number of m is divided into n groups every j particles; the position of each particle adopts X i ＝(x i1 ，x i2 ，x i3 ，…，x in ) Is expressed by the speed V i ＝(v i1 ，v i2 ，v i3 ，…，v in ) To represent; from the n sets of factors available to the grouping operator:
M 1 ＝(v i1 ，v i1+j ，v i1+2j ，…，v i1+(n-1)j )
M 2 ＝(v i2 ，v i2+j ，v i2+2j ，…，v i2+(n-1)j )
M n ＝(v in ，v in+j ，v in+2j ，…，v in+(n-1)j )。
preferably, according to
To update the particle group speed and position, wherein>
For the position of particle i after k +1 iterations>
Based on its speed, is>
Is the position of the particle, is>
Found for particle i through k +1 iterationsThe location of the population history optimal fitness value; />
And &>
Is the optimal velocity and position of the particle i in the set of factors. ω denotes its inertial weight->
t is the current number of iterations, t max To the maximum number of iterations, ω max And ω min Maximum and minimum values of the inertial weight, respectively; c1 and c2 are learning factors; r1 and r2 are random numbers.
A power distribution network fault location system, comprising:
the switch expectation function construction module is used for monitoring information in the monitoring node direction and constructing a switch expectation function according to the monitoring information;
the fitness function building module is used for building a fitness function for power distribution network fault location according to the expected function of the switch, calculating the fitness value of the particles according to the fitness function, recording the fitness value as an initial individual extreme value, and assigning the smallest value to a global minimum extreme value;
the particle swarm updating module is used for updating the speed and the position of a particle swarm in the fitness function;
and the iteration output module is used for repeating the particle swarm updating module process until the global optimal position of the particle swarm is output, namely the actual fault state of each feeder line section of the target power distribution network.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the power distribution network fault location method when executing the computer program.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the power distribution network fault localization method.
Compared with the prior art, the invention has the following beneficial effects:
the particles in each module group can keep good population diversity in the continuous optimizing process, are not easy to fall into local optimization, and when the optimizing of the ethnic group is finished, the module groups are recombined to form a new population for the grouping optimizing of the module groups. The method can realize information transmission between individuals, ethnic groups and groups, so that the groups have good diversity, global optimality is more favorably searched, the positioning speed of the distribution network containing the distributed power supply when the distribution network fails is greatly improved, the adaptability is strong, the fault points can still be positioned under the conditions that information uploaded by the intelligent circuit breaker fails or cannot be uploaded due to a certain reason on the line, the reliability of fault positioning is improved, and the method has great significance for eliminating the fault points and safely and reliably operating the power grid.
Drawings
FIG. 1 is a schematic flow chart of a power distribution network fault location method of the present invention
Fig. 2 is a block diagram of a distribution substation system including an intelligent circuit breaker and a distributed power source according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that as used in the following description, the terms "front," "back," "left," "right," "upper" and "lower" refer to directions in the drawings, and the terms "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular component.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The invention is mainly applied to a distribution network containing a low-voltage intelligent circuit breaker, and takes distributed energy access into consideration for embodying more generality. The system network structure mainly comprises a distribution transformer area monitoring center, distributed new energy sources (such as solar energy, wind energy and the like), a plurality of distribution transformers, at least one low-voltage bus outgoing line branch circuit connected with the distribution transformers, a plurality of low-voltage intelligent circuit breakers, section switches and the like, wherein the low-voltage intelligent circuit breakers and the section switches are arranged on branch circuits. The low-voltage intelligent circuit breaker integrates a circuit breaker, a residual current action protector and a collector, is of a zero-one company TMM3LZ series, has the functions of current and voltage protection, reclosing, remote operation, topology identification, information acquisition, phase sequence automatic identification and communication, and can upload acquired node information to a distribution transformer area monitoring center.
As shown in fig. 1, the method for positioning the fault of the power distribution network based on the mixed frog-leap algorithm can overcome the premature convergence problem of the conventional algorithm, and further improve the convergence and stability of the algorithm. The invention is realized by the following technical scheme, and the fault positioning method comprises the following steps:
the method comprises the steps that firstly, various parameters of particle swarm initialization according to the structure of a power distribution network comprise particle swarm size, particle dimension, maximum iteration times in a group, the number of swarm groups and the number of particles contained in the swarm.
And step two, defining switch state information and constructing an expectation function of the switch. The intelligent circuit breaker can monitor fault overcurrent information on a switch node, and when the fault overcurrent information in the direction from a main power supply end of the system to the monitoring node is detected, the fault overcurrent information is marked as 1; when the information in the direction from the distributed power supply end to the monitoring node is detected, recording the information as-1; and when no overcurrent information is monitored, marking as 0. The desired function of the switch may reflect the presence of over-current information at a node section and downstream sections. The desired function of the corresponding switch is expressed as
S j (x)＝L 1 +L 2 +L 3 +…+L j
I j (x)＝k 1 (1-S 1 (j))*S 1 (j)-k 2 (1-S 3 (j))*S 4 (j)；
Wherein "+" is a logical OR operation; l is 1 Indicating the state of the line between the circuit breaker 1 and the circuit breaker 2, L 2 The state of the line between the circuit breaker 2 and the circuit breaker 3 is represented, and by analogy, if the line breaks down, the state value is '1', otherwise, the state value is '0'; assuming that the circuit breaker No. j is a breaking point, the distribution network is divided into an upper half area where a system power supply is located and a lower half area where a distributed power supply is located at the moment; k1 and k2 respectively represent the switching coefficients of the regional power supply, 1 is taken to represent that the regional power supply is connected, and 0 represents that the regional power supply is not connected; s j (x) Detected line section state calculation result, S 1 (j) Results of operations, S, representing all the line sections from the circuit breaker j to the upper half up to the main power supply 3 (j) Results of operations, S, representing all line sections from the breaker j to the lower half up to the distributed power source 2 (j) And S 4 (j) Respectively representing the operation results of all the line sections of the upper half area and the lower half area.
And thirdly, constructing a fitness function, forming an expected function of a switch according to the fault information uploaded by the circuit breaker, and constructing the fitness function of the fault location of the power distribution network. And introducing a grouping operator and a module, and arranging all the particles in an ascending order from small to large according to the size of the fitness function value f, and dividing the whole particle population with the population number m into n groups every j particles. Where the position of each particle is represented by X i ＝(x i1 ，x i2 ，x i3 ，…，x in ) Is expressed by V, its speed i ＝(v i1 ，v i2 ，v i3 ，…，v in ) To indicate. N number of modules M obtainable from a grouping operator 1 ＝(v i1 ，v i1+j ，v i1+2j ，…，v i1+(n-1)j )
M 2 ＝(v i2 ，v i2+j ，v i2+2j ，…，v i2+(n-1)j )
M n ＝(v in ，v in+j ，v in+2j ，…，v in+(n-1)j ). And calculating the fitness value of the particle according to the fitness function, recording the fitness value as initial individual extrema, and assigning the minimum value to the global minimum extremum.
Step four, according to
To update the particle cluster velocity and position. In the formula (II)>
For the position of particle i after k +1 iterations, <' > H>
Based on its speed, is>
Is the position of the particle, is>
The position of the optimal value of the group history found from the particle i to the (k + 1) th iteration is located; />
And &>
Is the optimal velocity and position of the particle i in the set of factors. ω denotes its inertial weight->
t is the current number of iterations, t max Is the maximum number of iterations, ω max And ω min Maximum and minimum values of the inertial weight, respectively; c1 and c2 are learning factors; r1 and r2 are random numbers.
Step five, if the algorithm reaches the maximum iteration times, stopping calculation, and outputting the global optimal position of the particle swarm, namely the actual fault state of each feeder line section of the target power distribution network; otherwise, returning to the step four, and iterating the calculation again.
Aiming at a distribution network containing a distributed power supply and a low-voltage intelligent circuit breaker, the invention is based on a mixed frog-leaping algorithm (a swarm intelligence algorithm for simulating frog foraging), and a method for arranging particle populations from small to large according to fitness function values by introducing grouping operators and obtaining a model group according to an equal-difference value grouping rule is adopted, so that the whole particle populations can be subjected to ordered and directed optimizing evolution. According to information uploaded to a distribution transformer monitoring center by a low-voltage intelligent circuit breaker in a fault network, initializing the state of a section corresponding to the information by taking the state as a particle position, and taking the total number of the sections as a particle dimension, so that the state problem of a feeder line section can be converted into a particle swarm optimal solution problem; and evaluating the position of the current particle through an evaluation function, so that the speed and the position of the particle are continuously changed and are continuously close to the global optimal position, and the global optimal position, namely the actual state of the fault section is finally obtained.
The following is a detailed description of the embodiments of the present invention in terms of practical applications.
The connection relationship between various components and faults is established based on the structure of a distribution network, as shown in fig. 2, the connection relationship is a distribution station system diagram containing an intelligent circuit breaker and a distributed power supply, and specifically comprises 110 kV bus, 1 branch bus, 1 station distribution transformer and 12C low-voltage intelligent circuit breakers for the intelligent circuit breaker i I =1,2, …,12 denotes that 3 distributed power sources are DG i i =1,2,3. The low-voltage intelligent circuit breaker is a circuit breakerThe intelligent circuit breaker integrating the residual current operated protector and the collector has the functions of current and voltage protection, reclosing, remote operation, topology identification, information acquisition, automatic phase sequence identification and communication, and can upload acquired node information to a distribution transformer area monitoring center. The invention focuses on the description of the fault location method, so that in the embodiment, only a part of branch sections in a simple power distribution network are drawn, and actually, the network branches can be expanded for many times, and the invention still belongs to the protection scope of the invention on the premise of not changing the idea of the invention.
The method comprises the steps that firstly, various parameters of a particle swarm are initialized according to the structure of a power distribution network, wherein the parameters comprise the size of a particle swarm, the dimension of a particle, the maximum iteration number in a group, the group grouping number and the number of particles contained in the group. The method comprises the following steps that the particle population size F =30, the particle dimension D =12, the maximum iteration number is MaxMum =120, the maximum iteration number Ne =30 in a group, and the population grouping number n =5; each module group comprises m =6 particles, 3.0 variation probability code, and 2.05 learning factor C1= C2.
And step two, the expected function of the switch can reflect the relation between the fault overcurrent information of a certain switch node and the state of the line, namely whether overcurrent information exists in a feeder line section and a downstream section of the certain switch node or not, and the overcurrent information of the switch node can be analyzed by an algorithm by constructing the expected function of the switch so as to judge the position of the fault point. Thus, the desired functional expression of the switch is as follows
S j (x)＝L 1 +L 2 +L 3 +…+L j
Wherein "+" is a logical OR operation; l is a radical of an alcohol 1 Indicating the state of the line between the circuit breaker 1 and the circuit breaker 2, L 2 The state of the line between the circuit breaker 2 and the circuit breaker 3 is represented, and so on, if the line breaks down, the state value is '1', otherwise, the state value is '0'; suppose that the J-th circuit breaker is a breaking pointAt the moment, the distribution network is divided into an upper half area where a system power supply is located and a lower half area where a distributed power supply is located; k1 and k2 respectively represent the switching coefficients of the regional power supply, 1 is taken to represent that the regional power supply is connected, and 0 represents that the regional power supply is not connected; s j (x) Detected line section state calculation result, S 1 (j) Results of operations, S, representing all the line sections from the circuit breaker j to the upper half up to the main power supply 3 (j) Results of operations, S, representing all line sections from the breaker j to the lower half up to the distributed power source 2 (j) And S 4 (j) Respectively representing the operation results of all the line sections of the upper half area and the lower half area.
And step three, when the distribution network fault is positioned by using the algorithm, the fault result needs to be evaluated, so that a corresponding fitness function is constructed to be used for expressing the difference between the fault information uploaded by the intelligent circuit breaker and the expected value of the expected function of the corresponding switch, and the optimal solution can be found according to the difference. The fitness function taken is as follows:
in the formula: m represents the number of all line segments in the station area, generally M = N; I.C. A j (x) Indicating real-time fault information detected by the intelligent circuit breaker, i.e. when the jth circuit breaker node detects fault over-current information j (x) =1, otherwise 0;
the expected value of switch j is represented as the expected function of the switch. S (j) is the line segment state and β is a random positive coefficient, taken herein as 0.5.
Step four, optimizing the speed updating formula of the particles by introducing a grouping operator and a module group:
in the formula
For the position of particle i after k +1 iterations, <' > H>
Based on its speed, is>
Is the position of the particle; />
For the position at which the optimal value of the population history found up to the (k + 1) th iteration is located, a->
And &>
Is the optimal velocity and position of the particle i in the set of factors. ω denotes its inertial weight->
t is the current number of iterations, t max Is the maximum number of iterations, ω max And ω min Maximum and minimum values of the inertial weight, respectively; c1 and c2 are learning factors; r1 and r2 are random numbers.
The particle position update formula is as follows:
wherein
Where rand () is a function that randomly generates a positive real number between [0,1 ].
As shown in fig. 2, in a radial power distribution network with a single power supply 12 node and a distributed power supply, a power distribution network fault location method based on a hybrid frog-leaping algorithm is subjected to example verification, and the result shows that the improved algorithm has better convergence and stability and overcomes the premature convergence problem of the traditional algorithm.
And step five, calculating the conditions of single fault, multiple faults and uploading information fault of the power distribution area network, continuously operating for 50 times, and obtaining the calculation result shown in table 1. If the algorithm reaches the maximum iteration times, stopping calculation, and outputting the global optimal position of the particle swarm, namely the actual fault state of each feeder line section of the target power distribution network; otherwise, returning to the step four, and iterating the calculation again.
Table 1 distribution network fault location list with distributed power supply
When a single fault occurs, if a single-phase earth short circuit fault occurs in the feeder line section C4, the intelligent circuit breaker reports the fault current as [111100111000], the node switches 1,2,3, 4, 7, 8 and 9 are displayed to experience fault current, distortion-free information is obtained, the fault current is calculated through a hybrid leapfrogging algorithm, the output result is [000001000000], the fault of the feeder line section C4 is displayed, and the fault section positioning is accurately achieved. Under the same fault condition, when the information reported by the intelligent circuit breaker has a small amount of distortion [100100111000], namely the circuit breakers 2 and 3 report by mistake, the final output is still [00000100000], the fault of the feeder line section C4 is displayed, and the fault section is still accurately positioned.
When multiple faults occur, if a feeder line section C4 has a single-phase earth short circuit fault, the intelligent circuit breaker reports the fault as [111110110000], the node switches 1,2,3, 4, 5, 7 and 8 are displayed to experience fault current, distortion-free information is obtained, the fault current is calculated through a hybrid leapfrog algorithm, the output result is [000010010000], the faults of the feeder line sections C5 and C8 are displayed, and the fault section positioning is accurately achieved. Under the same fault condition, when the information reported by the intelligent circuit breaker has a small amount of distortion [100110110000], namely the circuit breakers 2 and 3 are in false alarm, the final output is still [000010010000], the faults of feeder line sections C5 and C8 are displayed, and the fault sections are still accurately positioned.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details of non-careless mistakes in the embodiment of the apparatus, please refer to the embodiment of the method of the present invention.
In another embodiment of the present invention, a power distribution network fault location system is provided, which can be used to implement the power distribution network fault location method described above, and specifically, the power distribution network fault location system includes a desired function construction module of a switch, a fitness function construction module, a particle swarm update module, and an iterative output module.
The expected function building module of the switch is used for monitoring the information in the direction of the monitoring node and building the expected function of the switch according to the monitoring information.
The fitness function building module is used for building a fitness function of power distribution network fault location according to the expected function of the switch, calculating the fitness value of the particles according to the fitness function, recording the fitness value as an initial individual extreme value, and assigning the smallest value to the global minimum extreme value.
And the particle swarm updating module is used for updating the speed and the position of the particle swarm in the fitness function.
The iterative output module is used for repeating the particle swarm updating module process until the globally optimal position of the particle swarm is output, namely the actual fault state of each feeder line section of the target power distribution network.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of the power distribution network fault positioning method, and comprises the following steps: s1, monitoring information in the direction of a monitoring node, and constructing an expected function of a switch according to the monitoring information; s2, constructing a fitness function of power distribution network fault positioning according to the expected function of the switch, calculating the fitness value of the particles according to the fitness function, recording the fitness value as an initial individual extreme value, and assigning the minimum value to a global minimum extreme value; s3, updating the speed and the position of the particle group in the fitness function; and S4, repeating the step S3 until the global optimal position of the particle swarm is output, namely the actual fault state of each feeder line section of the target power distribution network.
In still another embodiment, the present invention also provides a computer-readable storage medium (Memory) which is a Memory device in a terminal device and stores programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor can load and execute one or more instructions stored in the computer readable storage medium to realize the corresponding steps of the power distribution network fault positioning method in the embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of: s1, monitoring information in the direction of a monitoring node, and constructing an expected function of a switch according to the monitoring information; s2, constructing a fitness function of power distribution network fault location according to the expected function of the switch, calculating the fitness value of the particles according to the fitness function, recording the fitness value as an initial individual extreme value, and assigning the smallest one to a global minimum extreme value; s3, updating the speed and the position of the particle group in the fitness function; and S4, repeating the step S3 until the globally optimal position of the particle swarm is output, namely the actual fault state of each feeder line section of the target power distribution network.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the patent should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. The disclosures of all articles and references, including patent applications and publications, are hereby incorporated by reference for all purposes. The omission in the foregoing claims of any aspect of subject matter that is disclosed herein is not intended to forego such subject matter, nor should the applicant consider that such subject matter is not considered part of the disclosed subject matter.
Claims (10)
1. A power distribution network fault positioning method is characterized by comprising the following steps:
s1, monitoring information in the direction of a monitoring node, and constructing an expected function of a switch according to the monitoring information;
s2, constructing a fitness function of power distribution network fault location according to the expected function of the switch, calculating the fitness value of the particles according to the fitness function, recording the fitness value as an initial individual extreme value, and assigning the smallest one to a global minimum extreme value;
s3, updating the speed and the position of the particle group in the fitness function;
and S4, repeating the step S3 until the global optimal position of the particle swarm is output, namely the actual fault state of each feeder line section of the target power distribution network.
2. The power distribution network fault location method according to claim 1, wherein in S1, the process of monitoring the information of the monitoring node direction is as follows: when the fault overcurrent information in the direction from a main power supply end of the system to a monitoring node is detected, recording the fault overcurrent information as 1; when the information in the direction from the distributed power supply end to the monitoring node is detected, recording the information as-1; and when no overcurrent information is monitored, marking as 0.
3. Method for fault location in an electric distribution network according to claim 2, characterized in that the desired function of the switches is:
S j (x)＝L 1 +L 2 +L 3 +…+L j
I j (x)＝k 1 (1-S 1 (j))*S 1 (j)-k 2 (1-S 3 (j))*S 4 (j)；
wherein "+" is a logical OR operation; l1 represents the state of the line between the circuit breaker 1 and the circuit breaker 2, L 2 The state of the line between the circuit breaker 2 and the circuit breaker 3 is represented, and by analogy, if the line breaks down, the state value is '1', otherwise, the state value is '0'; assuming that the circuit breaker No. j is a breaking point, the distribution network is divided into an upper half area where a system power supply is located and a lower half area where a distributed power supply is located at the moment; k1 and k2 represent the switching coefficients of the area power supply, respectively, and "1" represents the switching coefficientThe regional power is accessed, and 0 indicates that the regional power is not accessed; s j (x) Detected line section state calculation result, S 1 (j) Results of operations, S, representing all the line sections from the circuit breaker j to the upper half up to the main power supply 3 (j) The result of the calculation, S, representing all the sections of the circuit breaker j from the lower half up to the distributed power supply 2 (j) And S 4 (j) Respectively representing the operation results of all the line sections of the upper half area and the lower half area.
4. The power distribution network fault location method of claim 1, wherein before S2, parameters of the particle swarm are initialized according to a structure of a power distribution network.
5. The power distribution network fault location method according to claim 4, wherein each parameter of the particle swarm comprises a particle swarm size, a particle dimension, a maximum iteration number in a group, a swarm grouping number and a number of particles contained in the swarm.
6. The power distribution network fault location method according to claim 1, wherein in S2, all the particles are arranged in ascending order from small to large according to the magnitude of the fitness function value f, and the whole particle population with the population number m is divided into n groups every j particles; position of each particle is assumed to be X i ＝(x i1 ，x i2 ，x i3 ，…，x in ) Is shown, its speed adopts V i ＝(v i1 ，v i2 ，v i3 ，…，v in ) To represent; from the n sets of factors available to the grouping operator:
M 1 ＝(v i1 ，v i1+j ，v i1+2j ，…，v i1+(n-1)j )
M 2 ＝(v i2 ，v i2+j ，v i2+2j ，…，v i2+(n-1)j )
M n ＝(v in ，v in+j ，v in+2j ，…，v in+(n-1)j )。
7. method for fault location in an electric distribution network according to claim 1, characterised in that it is based on
To update the particle group speed and position, wherein>
For the position of particle i after k +1 iterations, <' > H>
Based on its speed, is>
Is the position of the particle, is>
The position of the optimal value of the group history found from the particle i to the (k + 1) th iteration is located; />
And &>
The optimal speed and position of the particle i in the module; ω represents its inertial weight
t is the current number of iterations, t max Is the maximum number of iterations, ω max And ω min Are respectively asMaximum and minimum values of inertial weight; c1 and c2 are learning factors; r1 and r2 are random numbers.
8. A power distribution network fault location system, comprising:
the expected function building module of the switch is used for monitoring the information in the direction of the monitoring node and building an expected function of the switch according to the monitoring information;
the fitness function building module is used for building a fitness function of power distribution network fault positioning according to the expected function of the switch, calculating the fitness value of the particles according to the fitness function, recording the fitness value as an initial individual extreme value, and assigning the minimum value to a global minimum extreme value;
the particle swarm updating module is used for updating the speed and the position of a particle swarm in the fitness function;
and the iteration output module is used for repeating the particle swarm updating module process until the global optimal position of the particle swarm is output, namely the actual fault state of each feeder line section of the target power distribution network.
9. Computer arrangement comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor realizes the steps of the method for fault localization of an electric power distribution network according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for fault location of a power distribution network according to any one of claims 1 to 7.
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| CN117031214B (en) * | 2023-10-10 | 2024-01-23 | 国网山东省电力公司曲阜市供电公司 | Intelligent monitoring method, system, medium and equipment for power grid faults |
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