CN113625118A - Single-phase earth fault line selection method based on optimized pulse neurolemma system - Google Patents

Single-phase earth fault line selection method based on optimized pulse neurolemma system Download PDF

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CN113625118A
CN113625118A CN202110942145.3A CN202110942145A CN113625118A CN 113625118 A CN113625118 A CN 113625118A CN 202110942145 A CN202110942145 A CN 202110942145A CN 113625118 A CN113625118 A CN 113625118A
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fault
line
phase current
phase
line selection
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CN113625118B (en
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田君杨
李海勇
蒋连钿
杨彦
沈梓正
巫聪云
刘斌
韩冰
黄超
秦蓓
何洪
覃丙川
黄鹏飞
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Guangxi Power Grid Co Ltd
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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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/088—Aspects of digital computing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/52—Testing for short-circuits, leakage current or ground faults
    • 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
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/58—Testing of lines, cables or conductors
    • 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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Abstract

The invention discloses a single-phase earth fault line selection method based on an optimized pulse neurolemma system, which relates to the technical field of power distribution network fault diagnosis of a power system and comprises the following steps: acquiring fault line data and non-fault line data under different conditions; carrying out dispersion degree calculation and normalization processing on phase current and reactive power in a line, and establishing a fault line selection model; solving the optimization parameters of the objective function of the fault line selection model by using an optimized pulse neural membrane system; and calculating the value of the objective function, and if the value of the objective function is greater than a preset value, judging that the line has a ground fault. The established fault identification model can effectively identify the fault line only through steady-state current and power, so that the actual application range of the model is enlarged; the fault line can be effectively identified by optimizing the parameters determined by the pulse neurolemma system, and the identification accuracy rate reaches 99.79%; the method provides selection for the traditional manual line selection method, and can effectively improve the efficiency of the traditional manual line selection.

Description

Single-phase earth fault line selection method based on optimized pulse neurolemma system
Technical Field
The invention relates to the technical field of power distribution network fault diagnosis of a power system, in particular to a single-phase earth fault line selection method based on an optimized pulse neurolemma system.
Background
The domestic medium and low voltage distribution network is widely operated in a low current grounding system mode, and under the operation mode, when a single-phase grounding fault occurs, the distribution network can still operate for 1-2 hours with the fault, so that unnecessary power failure loss of the user caused by the instantaneously generated fault can be avoided, and the power supply reliability of the distribution network is effectively improved.
In the faults of the power distribution network, the single-phase earth faults account for about 80%, and when the single-phase earth faults occur, the faults need to be timely and accurately removed, so that the condition that the equipment is damaged due to fault operation in a long time is avoided, and the overvoltage of the whole system is caused. However, as the single-phase grounding fault information features are weak, the operation mode of the power distribution network is complex, and the accuracy of the single-phase grounding fault is difficult to effectively improve all the time, a manual trial-and-pull method is still widely adopted for line selection of a plurality of substations, and the fault processing efficiency is extremely low.
Therefore, in the prior art, the requirement for effectively selecting the line of the single-phase earth fault through the dispatching SCADA system is continuously increased, however, a large amount of data exists in the dispatching system, a relevant mathematical model needs to be established for processing a large amount of steady-state information, and the fault line selection is performed through the mathematical model. However, data analysis is carried out through experiments to find that the selection of related parameters has a significant influence on the accuracy of establishing a mathematical model, so that a fault line selection method capable of accurately judging the single-phase earth fault is urgently needed.
Disclosure of Invention
In view of the fact that single-phase earth fault information features are weak in the prior art, in the method for selecting the single-phase earth fault line by scheduling the SCADA system, selection of related parameters has a great influence on accuracy of establishing a mathematical model, but due to the fact that a large amount of data exists, calculation result errors caused by parameter deviation cannot be eliminated, the following invention is provided:
the single-phase earth fault line selection method based on the optimized pulse neurolemma system comprises the following steps:
acquiring fault line data and non-fault line data under different conditions;
carrying out dispersion degree calculation and normalization processing on phase current and reactive power in a line, and establishing a fault line selection model;
solving the optimization parameters of the objective function of the fault line selection model by using an optimized pulse neural membrane system;
and calculating the value of the objective function, and if the value of the objective function is greater than a preset value, judging that the line has a ground fault.
Further, the establishing of the fault line selection model specifically includes the following steps:
collecting phase current data and non-functional rate data in a line;
calculating the variable quantity of the phase current and the reactive power;
and selecting the phase current and the reactive power before and after the fault to carry out dispersion calculation and normalization processing.
Further, calculating the dispersion of the phase currents before and after the fault, specifically comprising the following steps:
collecting three-phase current data in a line;
and calculating the dispersion of the phase current of the single-phase grounding, wherein the phase current dispersion calculation formula is as follows:
Figure BDA0003215491730000021
wherein x isiFor each of the sample data sets, the data sets,
Figure BDA0003215491730000022
the average value of the sample data is shown, and n is the number of the sample data;
calculating the phase current dispersion degree before and after the fault in the line according to the phase current dispersion degree, and calculating the phase current dispersion degree difference before and after the fault in the line;
and carrying out normalization processing on the dispersion of the phase current, wherein a calculation formula of the normalization processing is as follows:
Figure BDA0003215491730000031
wherein Δ σiShows the dispersion difference of phase current before and after the fault in the ith line, delta sigmakAnd (4) representing the phase current dispersion difference before and after the fault in the kth line.
Further, the normalization processing is performed on the reactive power, and the method specifically includes:
according to the reactive power change value, the reactive power is normalized, and the calculation formula is as follows:
Figure BDA0003215491730000032
wherein Δ QiFor the amount of change in reactive power, Δ Q, in the ith linekIs the reactive power variation in the kth line.
Further, the objective function is specifically:
Pi＝m·αi+(1-m)·βi
wherein: piIs the fault probability of the feeder line;
m is a weight parameter needing to be optimized;
αithe dispersion of the phase current is normalized;
βiand normalizing the processed value of the reactive power.
Further, the value of the objective function is optimized through a pulse neurolemma system to obtain an optimization parameter, and the optimization parameter is used for determining the value of the objective function.
Further, the preset value is 0.5.
Further, the faulty line data and the non-faulty line data include phase current data and reactive power data in the line.
Further, the objective function calculates the fault probability of all the feeder lines at the bus.
Further, a calculation formula for calculating the current dispersion difference between the pre-fault phase and the post-fault phase in the line is as follows:
Δσi＝σi2-σi1
wherein sigmai2For the dispersion of phase current after fault, σi1The dispersion of the phase current before the fault.
The invention aims to provide a steady-state information single-phase earth fault line selection method based on an optimized pulse neurolemma system, which comprises the steps of establishing a PSCAD/EMTDC simulation model aiming at a neutral point ungrounded system of a power distribution network, and acquiring fault data of a fault line and a non-fault line under different conditions; establishing a fault line selection model according to fault data and non-fault data to reflect line fault conditions; solving an objective function of the fault line selection model by using an optimized pulse neural membrane system; the final optimization parameters are output (the value of m is determined),namely to obtain Pi＝m·αi+(1-m)·βiThe specific value of m in (1). Calculating P from the test dataiCalculated PiIf the current is greater than 0.5, the line is judged to have the single-phase earth fault. The method provided by the invention has the following advantages:
(1) the established fault identification model can effectively identify the fault line only through steady-state current and power, and the practical application range of the model is enlarged.
(2) The fault line can be effectively identified by optimizing the parameters determined by the pulse neurolemma system, and the identification accuracy reaches 99.79 percent
(3) The method provides selection for the traditional manual line selection method, and can effectively improve the efficiency of the traditional manual line selection.
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FIG. 1 is a single-phase earth fault line selection method based on an optimized pulse neurolemma system according to the present invention;
FIG. 2 is a flowchart illustrating the step S2 according to the present invention;
FIG. 3 is a simulation model of a distribution network of a 10kV low-current grounding system built by the PSCAD/EMTDC of the invention;
FIG. 4 is a schematic view of an extended pulse neurolemma system of the present invention;
FIG. 5 is a schematic view of an optimized spiking neural membrane system according to the present invention;
FIG. 6 is a trend graph of the optimized parameter m values after 20 independent optimizations according to the present invention.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
Example one
The single-phase earth fault line selection method based on the optimized pulse neurolemma system is shown in fig. 1 and comprises the following steps:
step S1, acquiring fault line data and non-fault line data under different conditions;
in the embodiment of the application, a PSCAD/EMTDC (Power System Computer Aided Design/electromagnetic transient over time) full-name Power Systems Computer aid Design simulation model is established for a distribution network neutral ungrounded system, so that fault line data and non-fault line data under different conditions are obtained. The PSCAD/EMTDC simulation model is electromagnetic transient simulation software widely used in the world, the EMTDC is a simulation calculation core of the PSCAD/EMTDC simulation model, and the PSCAD provides a graphical operation interface for the EMTDC (electromagnetic transitions including DC).
As shown in fig. 3, the embodiment of the present invention is a distribution network simulation model of a 10kV low-current grounding system built by using a PSCAD/EMTDC, and the embodiment of the present invention is a distribution network simulation model of a 10kV low-current grounding system built by using a PSCAD/EMTDC, where the model includes 5 lines, Line 1-Line 5, the Line types include an overhead Line, a cable Line, and an overhead cable hybrid Line, and simulation parameters related to the model are shown in the following table:
TABLE 1 simulation model parameter Table
Figure BDA0003215491730000051
Figure BDA0003215491730000061
In order to obtain data of faults when the system stably operates, the fault time is set to be 2s, and the duration is 0.4 s. The conditions when a single-phase earth fault occurs are as follows: (1) the fault line is a line L1-L5; (2) setting faults at the end part of the line in the simulation model for setting the faults with different line lengths; (3) the ground faults are set to 0 Ω, 20 Ω, 100 Ω, 400 Ω, 800 Ω, 1000 Ω. Because only steady-state effective value data in the dispatching system is considered, simulation is carried out without considering different fault closing angles, and 120 groups of data are obtained in total.
In 120 groups of data, each group of data mainly comprises tag data, steady-state cable data and steady-state reactive power data, and part of sample data is shown in the following table:
TABLE 2 partial sample data
Figure BDA0003215491730000062
Figure BDA0003215491730000071
Step S2, carrying out dispersion degree calculation and normalization processing on the phase current and the reactive power in the line, and establishing a fault line selection model;
further, in a preferred embodiment of the present application, as shown in fig. 2, the establishing a fault line selection model in step S2 specifically includes the following steps:
step S210, collecting phase current data and non-functional rate data in a line;
collecting three-phase current data and reactive power data in the line to obtain effective values in the line, and calculating phase current delta IiAnd a reactive power variation value DeltaQi。
Step S220, calculating the variable quantity of the phase current and the reactive power;
and step S230, selecting the phase current and the reactive power before and after the fault to carry out dispersion calculation and normalization processing.
Further, in this embodiment of the present application, in step S230, the calculating the dispersion of the phase currents before and after the fault specifically includes the following steps:
step S2301, collecting three-phase current data in a line;
step S2302, calculating the dispersion of the phase current of the single-phase grounding, wherein the calculation formula of the dispersion of the phase current is as follows:
Figure BDA0003215491730000072
wherein x isiFor each of the sample data sets, the data sets,
Figure BDA0003215491730000073
the average value of the sample data is shown, and n is the number of the sample data;
step S2303, calculating phase current dispersion degrees before and after the fault in the line according to the phase current dispersion degrees, and calculating phase current dispersion degree difference before and after the fault in the line;
step S2304, performing normalization processing on the phase current dispersion, where the normalization processing calculation formula is as follows:
Figure BDA0003215491730000081
wherein Δ σiShows the dispersion difference of phase current before and after the fault in the ith line, delta sigmakAnd (4) representing the phase current dispersion difference before and after the fault in the kth line.
Further, in a preferred embodiment of the present application, the calculation formula for calculating the phase current dispersion difference before and after the fault in the line is as follows:
Δσi＝σi2-σi1
wherein sigmai2For the dispersion of phase current after fault, σi1The dispersion of the phase current before the fault.
Thus, the dispersion of phase current σ before and after the fault in the ith line is calculatedi1，σi2Thereby obtaining Δ σi＝σi2-σi1Normalizing the value after the dispersion to obtain
Figure BDA0003215491730000082
The reactive power value normalized by the reactive power is
Figure BDA0003215491730000083
Further, in a preferred embodiment of the present application, the normalizing the reactive power in step S230 specifically includes:
step S2310, according to the reactive power change value, carrying out normalization processing on the reactive power, wherein a calculation formula is as follows:
Figure BDA0003215491730000084
wherein Δ QiFor the amount of change in reactive power, Δ Q, in the ith linekIs the reactive power variation in the kth line.
S3, solving optimization parameters of an objective function of the fault line selection model by using an optimized pulse neural membrane system;
FIG. 4 is a schematic diagram of an extended pulse neuromembrane system, as shown in FIG. 4, in which the ESNPS is able to generate a string of binary codes of length n for representing an individual or a chromosome in the optimization problem.
As can be seen from FIG. 4, the ESNPS is a subsystem consisting of m +2 neurons, in which the neuron σ is presentm+1And σm+2Exactly the same, neuron σ is executed every time one step is performedm+1And σm+2The firing rule is executed once and pulses are mutually supplied to each other. At the same time, neuron σm+2To neuron sigma1…σmIn which each neuron sends a pulse, neuron sigma1…σmEach neuron in
Figure BDA0003215491730000097
By probability
Figure BDA0003215491730000091
Implementing firing rules
Figure BDA0003215491730000092
By probability
Figure BDA0003215491730000093
Implementing firing rules
Figure BDA0003215491730000094
If neuron σiExecuting the firing rules and firing a pulse, a "1" is output; otherwise, neuron σiA forget rule is executed, and "0" is output. Therefore, the binary pulse train output by the system can be controlled by adjusting the probability matrix in the ESNPS execution process.
Fig. 5 is a diagram of an optimized pulsatile neural membrane system (AOSNPS) according to an embodiment of the present invention. The AOSNPS introduces a guider for adaptively adjusting the probability of the evolutionary rule on the basis of the ESNPS, and is used for adjusting the rule probability in each neuron in each ESNPS. The input of the director is a pulse train T containing binary codes of H rows and m columnssThe output is a probability matrix formed by neuron regular probabilities of H ESNPS
Figure BDA0003215491730000095
Figure BDA0003215491730000096
And step S4, calculating the value of the objective function, and if the value of the objective function is larger than a preset value, judging that the line has a ground fault.
Further, in a preferred embodiment of the present application, the objective function in step S4 is specifically:
Pi＝m·αi+(1-m)·βi
wherein: piIs the fault probability of the feeder line;
m is a weight parameter needing to be optimized;
αithe dispersion of the phase current is normalized;
βiand normalizing the processed value of the reactive power.
As shown in fig. 6, a value-taking line graph of the optimized parameter m value is obtained after 20 independent operations, and the average value is obtained as the final value of m according to the result after 20 independent optimizations. Thereby obtaining an objective function of Pi＝0.5988αi+0.4012βiAnd verifying the 40 groups of test data according to the formula 8 to obtain the accuracy of 99.79 percent. Therefore, the line selection model established by the method and the parameter optimization method applied by the method are feasible and effective for the line selection of the single-phase earth fault with the steady-state information.
In the established model Pi＝m·αi+(1-m)·βiIn alphaiAnd betaiFrom the three-phase current data and reactive power data and the calculation of step S2, m and P are knowniGiven or optimized, unknown, m values, the corresponding P can be determinediValues, thus, based on three-phase current data, reactive power data and tag data f obtained from the training data setiAn objective function is established that optimizes the pulsatile neural membrane system. The establishment idea is as follows: let the label value f of each group of data in the training setiAnd P calculated after m is determinediThe sum of the squares of the differences is minimal, i.e. the formula is shown in (1):
Figure BDA0003215491730000101
where O _ m is the final optimized value of m, N is the training set data number,
Figure BDA0003215491730000102
further, in a preferred embodiment of the present application, the value of the objective function is determined by optimizing a pulse neurolemma system to obtain an optimization parameter, and determining the value of the objective function using the optimization parameter.
The specific steps of optimizing parameters by using the optimized pulse neurolemma system comprise:
s121: setting participation evolution according to the value precision of mThe number of the neurons is M and a network frame is established; inputting learning probability values
Figure BDA0003215491730000103
Pulse train TsRearranged into a regular probability matrix PRInput mutation probability
Figure BDA0003215491730000104
The population number H and the initial iteration number gen is 0; wherein j is more than or equal to 1 and less than or equal to M, and M is a regular probability matrix PRThe probability value of each row in the matrix is from the same ESNPS, namely an extended pulse neurolemma system, which is used for representing one individual of the optimization problems;
s122: start execution gen + 1;
s123: the line indicator i is assigned with an initial value of 1;
s124: if the row indicator i is greater than its maximum value H, go to S1215; where H is the regular probability matrix PRThe number of rows of (c);
s125: the column indicator j is assigned with an initial value of 1;
s126: if the column indicator j is greater than the maximum value M, go to S1212;
s127: generating a random number frandIf the random number frandLess than learning probability value
Figure BDA0003215491730000111
Continuing, otherwise, turning to S1210;
s128: among the H individuals, two individuals k different from the current individual i are selected1And k2I.e. k1≠k2Not equal to i if individual k1And k2Fitness function value f (C)k1) And f (C)k2) There is a relationship f (C)k1)＞f(Ck2) Then the current individual i goes to k2Learning, i.e. bj＝bk1Otherwise, the current individual is directed to k2Learning, i.e. bj＝bk2(ii) a Wherein, bj、bk1And bk2Respectively being intermediate variable, kth1The sum of k2The j bit binary code of the individual;
s129: if b isjIf the probability value is more than 0.5, the current rule probability value is
Figure BDA0003215491730000112
Figure BDA0003215491730000113
Otherwise, the current rule probability value is
Figure BDA0003215491730000114
S1210: if the j bit binary code of the best solution is searched
Figure BDA0003215491730000115
The current rule probability value is
Figure BDA0003215491730000116
Otherwise
Figure BDA0003215491730000117
Figure BDA0003215491730000118
S1211: increasing the column indicator j by 1, and turning to S126 to continue;
s1212: increasing the line indicator i by 1, and turning to S124 to continue;
s1213: calculating f (m) values, and selecting the smallest f (m) among the H f (m) values and saving f (m) and its corresponding m value.
S1214: if gen is less than or equal to genmaxGo to S122, otherwise continue; among them, genmaxIs the maximum iteration number;
s1215: outputting f (m) and m values.
Further, in a preferred embodiment of the present application, the preset value is 0.5.
Further, in a preferred embodiment of the present application, the faulty line data and the non-faulty line data comprise phase current data and reactive power data in the line.
Further, in a preferred embodiment of the present application, the objective function calculates the probability of failure of all feeders at the bus.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.

Claims (10)

1. The single-phase earth fault line selection method based on the optimized pulse neurolemma system is characterized by comprising the following steps of:
acquiring fault line data and non-fault line data under different conditions;
carrying out dispersion degree calculation and normalization processing on phase current and reactive power in a line, and establishing a fault line selection model;
solving the optimization parameters of the objective function of the fault line selection model by using an optimized pulse neural membrane system;
and calculating the value of the objective function, and if the value of the objective function is greater than a preset value, judging that the line has a ground fault.
2. The single-phase earth fault line selection method based on the optimized pulse neurolemma system according to claim 1, wherein the establishing of the fault line selection model specifically comprises the following steps:
collecting phase current data and reactive power data in a line;
calculating the variable quantity of the phase current and the reactive power;
and selecting the phase current and the reactive power before and after the fault to carry out dispersion calculation and normalization processing.
3. The single-phase earth fault line selection method based on the optimized pulse neurolemma system according to claim 2, wherein the dispersion calculation is performed on the phase currents before and after the fault, and the method specifically comprises the following steps:
collecting three-phase current data in a line;
and calculating the dispersion of the phase current of the single-phase grounding, wherein the phase current dispersion calculation formula is as follows:
Figure FDA0003215491720000011
wherein x isiFor each of the sample data sets, the data sets,
Figure FDA0003215491720000012
the average value of the sample data is shown, and n is the number of the sample data;
calculating the phase current dispersion degree before and after the fault in the line according to the phase current dispersion degree, and calculating the phase current dispersion degree difference before and after the fault in the line;
and carrying out normalization processing on the dispersion of the phase current, wherein a calculation formula of the normalization processing is as follows:
Figure FDA0003215491720000021
wherein Δ σiShows the dispersion difference of phase current before and after the fault in the ith line, delta sigmakAnd (4) representing the phase current dispersion difference before and after the fault in the kth line.
4. The single-phase earth fault line selection method based on the optimized pulse neurolemma system according to claim 2, wherein the reactive power is normalized, and the method specifically comprises the following steps:
according to the reactive power change value, the reactive power is normalized, and the calculation formula is as follows:
Figure FDA0003215491720000022
wherein Δ QiFor the amount of change in reactive power, Δ Q, in the ith linekIs the reactive power variation in the kth line.
5. The method for single-phase earth fault line selection based on an optimized pulse neurolemma system according to claim 1, wherein the objective function is specifically:
Pi＝m·αi+(1-m)·βi
wherein: piIs the fault probability of the feeder line;
m is a weight parameter needing to be optimized;
αithe dispersion of the phase current is normalized;
βiand normalizing the processed value of the reactive power.
6. The optimized pulsed neural membrane system-based single-phase ground fault line selection method of claim 1, wherein the value of the objective function is obtained by optimizing a pulsed neural membrane system to obtain an optimization parameter, and the value of the objective function is determined by using the optimization parameter.
7. The single-phase ground fault line selection method based on the optimized pulse neurolemma system according to claim 1, wherein the preset value is 0.5.
8. The optimized pulsed neural membrane system-based single-phase ground fault line selection method of claim 1, wherein the faulty line data and the non-faulty line data comprise phase current data and reactive power data in a line.
9. The optimized pulse neurolemma system-based single-phase ground fault line selection method according to claim 1, wherein the objective function calculates the fault probability of all feeder lines at a bus.
10. The single-phase earth fault line selection method based on the optimized pulse neurolemma system according to claim 3, wherein the calculation formula for calculating the phase current dispersion difference before and after the fault in the line is as follows:
Δσi＝σi2-σi1
wherein sigmai2For the dispersion of phase current after fault, σi1The dispersion of the phase current before the fault.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114113891A (en) * 2021-11-20 2022-03-01 国网四川省电力公司电力科学研究院 Single-phase earth fault determination method of pulse neurolemma system based on distributed population

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106771846A (en) * 2016-11-08 2017-05-31 西华大学 Power transmission line fault phase selection based on fuzzy reasoning pulse nerve membranous system
CN108120903A (en) * 2018-02-05 2018-06-05 西南交通大学 A kind of low-current single-phase earth fault line selection method based on pulse nerve membranous system
CN108459240A (en) * 2018-03-27 2018-08-28 国网江苏省电力有限公司苏州供电分公司 A kind of distribution network line fault deciding field method
CN109490702A (en) * 2018-10-08 2019-03-19 西南交通大学 A kind of method for diagnosing faults based on adaptive optimization pulse nerve membranous system
CN109752630A (en) * 2019-03-06 2019-05-14 云南电网有限责任公司保山供电局 A kind of common-tower double-return DC line fault selection method
CN109856515A (en) * 2019-03-20 2019-06-07 国网电力科学研究院武汉南瑞有限责任公司 A kind of direct current cables state of insulation judgment method and system
CN110488154A (en) * 2019-08-28 2019-11-22 国网四川省电力公司成都供电公司 A kind of small current earthing wire-selecting method for scheduling station end
CN110888017A (en) * 2019-10-29 2020-03-17 国电南瑞科技股份有限公司 Low-current ground fault line selection method based on power grid dispatching control system
CN111880048A (en) * 2020-08-21 2020-11-03 国网四川省电力公司成都供电公司 Low-current grounding line selection method and equipment applied to resonant grounding system
CN111898729A (en) * 2020-06-10 2020-11-06 国网江苏省电力有限公司电力科学研究院 Power transmission line fault reason identification method and system
CN112415324A (en) * 2020-10-15 2021-02-26 西安理工大学 Fault line selection method for low-current grounding system

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106771846A (en) * 2016-11-08 2017-05-31 西华大学 Power transmission line fault phase selection based on fuzzy reasoning pulse nerve membranous system
CN108120903A (en) * 2018-02-05 2018-06-05 西南交通大学 A kind of low-current single-phase earth fault line selection method based on pulse nerve membranous system
CN108459240A (en) * 2018-03-27 2018-08-28 国网江苏省电力有限公司苏州供电分公司 A kind of distribution network line fault deciding field method
CN109490702A (en) * 2018-10-08 2019-03-19 西南交通大学 A kind of method for diagnosing faults based on adaptive optimization pulse nerve membranous system
CN109752630A (en) * 2019-03-06 2019-05-14 云南电网有限责任公司保山供电局 A kind of common-tower double-return DC line fault selection method
CN109856515A (en) * 2019-03-20 2019-06-07 国网电力科学研究院武汉南瑞有限责任公司 A kind of direct current cables state of insulation judgment method and system
CN110488154A (en) * 2019-08-28 2019-11-22 国网四川省电力公司成都供电公司 A kind of small current earthing wire-selecting method for scheduling station end
CN110888017A (en) * 2019-10-29 2020-03-17 国电南瑞科技股份有限公司 Low-current ground fault line selection method based on power grid dispatching control system
CN111898729A (en) * 2020-06-10 2020-11-06 国网江苏省电力有限公司电力科学研究院 Power transmission line fault reason identification method and system
CN111880048A (en) * 2020-08-21 2020-11-03 国网四川省电力公司成都供电公司 Low-current grounding line selection method and equipment applied to resonant grounding system
CN112415324A (en) * 2020-10-15 2021-02-26 西安理工大学 Fault line selection method for low-current grounding system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
但扬清 等: "基于ABC-DNN的小电流接地故障选线方法", 智慧电力, vol. 47, no. 4, pages 46 - 52 *
葛眠俊: "基于脉冲神经膜系统的小电流单相接地故障选线", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, no. 10, pages 042 - 216 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114113891A (en) * 2021-11-20 2022-03-01 国网四川省电力公司电力科学研究院 Single-phase earth fault determination method of pulse neurolemma system based on distributed population
CN114113891B (en) * 2021-11-20 2023-04-25 国网四川省电力公司电力科学研究院 Single-phase grounding fault judging method of pulse neural membrane system based on distributed population

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