CN113625118B - Single-phase earth fault line selection method based on optimized pulse neural membrane system - Google Patents
Single-phase earth fault line selection method based on optimized pulse neural membrane system Download PDFInfo
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- 239000006185 dispersion Substances 0.000 claims abstract description 46
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- 238000010606 normalization Methods 0.000 claims abstract description 13
- 238000003745 diagnosis Methods 0.000 abstract description 2
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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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- G—PHYSICS
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- 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
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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
- G01R31/50—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
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- 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 application discloses a single-phase earth fault line selection method based on an optimized pulse neural membrane system, which relates to the technical field of power system distribution network fault diagnosis and comprises the following steps: acquiring fault line data and non-fault line data under different conditions; performing dispersion calculation and normalization processing on phase current and reactive power in a line, and establishing a fault line selection model; solving optimization parameters of an 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 larger than a preset value, judging that the line has a ground fault. The established fault identification model can effectively identify a fault line only through steady-state current and power, so that the practical application range of the model is improved; the fault line can be effectively identified by optimizing parameters determined by the pulse neural membrane 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 traditional manual line selection.
Description
Technical Field
The application relates to the technical field of power system distribution network fault diagnosis, in particular to a single-phase grounding fault line selection method based on an optimized pulse neural membrane system.
Background
The domestic low-medium voltage distribution network is widely operated in a small-current grounding system mode, and in the operation mode, when single-phase grounding faults occur, the distribution network can still operate for 1-2 hours with the faults, so that unnecessary power failure loss of the faults generated instantly to users can be avoided, and the reliability of power supply of the distribution network is effectively improved.
In the power distribution network faults, single-phase earth faults account for about 80%, when single-phase earth faults occur, faults need to be timely and accurately removed, and the faults are prevented from running for a long time so as to damage equipment, and 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, the fault accuracy of single-phase grounding is difficult to be effectively improved, and a manual trial-pull method is still widely adopted for line selection in a plurality of substations, so that the fault processing efficiency is extremely low.
Therefore, in the prior art, the demand of effectively selecting lines for single-phase earth faults through a dispatching SCADA system is continuously increased, however, a large amount of data exists in the dispatching system, a related mathematical model is required to be established for processing a large amount of steady state information, and fault line selection is performed through the mathematical model. However, the selection of the relevant parameters by data analysis through experiments has a great influence on the accuracy of establishing a mathematical model, so that a fault line selection method capable of accurately judging single-phase earth faults is needed.
Disclosure of Invention
In view of weak single-phase earth fault information characteristics in the prior art, in a method for selecting a line for a single-phase earth fault by a scheduling SCADA system, the selection of related parameters has a great influence on the accuracy of establishing a mathematical model, but because of a large amount of data, calculation result errors caused by parameter deviation cannot be eliminated, so the following application is proposed.
A single-phase earth fault line selection method based on an optimized pulse neural membrane system comprises the following steps.
And acquiring fault line data and non-fault line data under different conditions.
And performing dispersion calculation and normalization processing on the phase current and reactive power in the line, and establishing a fault line selection model.
And solving the optimization parameters of the objective function of the fault line selection model by using the optimized pulse neural membrane system.
And 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, the establishing of the fault line selection model specifically comprises the following steps.
Phase current data and non-power data in the line are collected.
The amount of change in phase current and reactive power is calculated.
And selecting the phase current and reactive power before and after the fault to perform dispersion calculation and normalization processing.
Further, the method for calculating the dispersion degree of the phase current before and after the fault specifically further comprises the following steps.
Three-phase current data in the line is collected.
And performing dispersion calculation on the phase current of the single-phase grounding, wherein the phase current dispersion calculation formula is as follows:wherein (1)>For each sample data, < >>The average value of the sample data is represented by n, which is the number of the sample data.
And calculating the phase current dispersion before and after the fault in the line according to the phase current dispersion, and calculating the phase current dispersion difference before and after the fault in the line.
And carrying out normalization processing on the phase current dispersion, wherein the calculation formula of the normalization processing is as follows:wherein->Indicating the difference of the phase current dispersion before and after the fault in the ith line, +.>Indicating 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 further comprises the following steps:
according to the reactive power change value, the reactive power is normalized, and the calculation formula is as follows:wherein->For the reactive power variation in the ith line, < > for>Is the reactive power variation in the kth line.
Further, the objective function specifically includes:wherein: />Is the probability of failure of the feeder line.
m is a weight parameter to be optimized;normalizing the processed value for the phase current dispersion; />The processed values are normalized for reactive power.
Further, the value of the objective function is obtained by optimizing the impulse neural membrane system to obtain an optimized parameter, and the value of the objective function is determined by using the optimized parameter.
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 probability of failure of all feeder lines at the busbar.
Further, a calculation formula for calculating the phase current dispersion difference before and after the fault in the line is as follows:wherein->For the phase current dispersion after failure, +.>Is the phase current dispersion before failure.
The application aims to provide a steady-state information single-phase grounding fault line selection method based on an optimized pulse neural membrane 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 fault lines and non-fault lines under different conditions; establishing a fault line selection module according to fault data and non-fault dataType to reflect line fault conditions; solving an objective function of the fault line selection model by using an optimized pulse neural membrane system; outputting the final optimized parameter (determining m value) to obtain Specific values of m. Calculation from test dataCalculated->If the voltage is more than 0.5, the single-phase earth fault of the line is judged. The method provided by the application 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 improved.
(2) The fault line can be effectively identified by optimizing parameters determined by the pulse neural membrane system, and the identification accuracy reaches 99.79%.
(3) The method provides selection for the traditional manual line selection method, and can effectively improve the efficiency of traditional manual line selection.
Drawings
FIG. 1 shows a single-phase ground fault line selection method based on an optimized pulse neural membrane system;
fig. 2 is a flowchart of step S2 according to the present application.
Fig. 3 is a simulation model of a 10kV small-current grounding system distribution network constructed by PSCAD/EMTDC.
FIG. 4 is a schematic diagram of an expanded impulse neural membrane system of the present application.
FIG. 5 is a schematic diagram of an optimized impulse membrane system of the present application.
FIG. 6 is a graph showing the trend of the m values of the optimized parameters after 20 independent optimizations.
Description of the embodiments
Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
Examples
The single-phase earth fault line selection method based on the optimized pulse neural membrane system, as shown in fig. 1, comprises the following steps:
and S1, acquiring fault line data and non-fault line data under different conditions.
In the embodiment of the application, PSCAD/EMTDC is established for a system with a neutral point not grounded of the power distribution network, and a Power Systems Computer Aided Design simulation model is fully called, 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 EMTDC simulation model, and the PSCAD provides a graphical operation interface for EMTDC (Electromagnetic Transients including DC).
Fig. 3 is a schematic diagram of a 10kV small current grounding system distribution network simulation model constructed by using a PSCAD/EMTDC in the embodiment of the present application, where the model includes 5 lines, line 1-Line 5, line types including overhead lines, cable lines, and overhead cable hybrid lines, and simulation parameters related to the model are shown in the following table.
TABLE 1 simulation model parameter table
To obtain data of failure occurring when the system was stably operated, the failure time was set to 2s and the duration was 0.4s. The conditions when single-phase earth fault occurs are: (1) the fault line is lines L1-L5; (2) Setting faults at the end of the line in the simulation model for setting faults with different line lengths; (3) The ground fault is set to 0Ω, 20Ω, 100deg.OMEGA, 400Ω, 800Ω, 1000Ω. Because only steady-state effective value data in the dispatching system are considered, simulation is not performed by considering different fault closing angles, and 120 groups of data are obtained in total.
Of the 120 sets of data, each set of data primarily includes tag data, steady state cable data, and steady state reactive power data, with some sample data as shown in the table below.
And S2, performing dispersion calculation and normalization processing on the phase current and 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 step S2 of establishing the fault line selection model specifically further includes the following steps.
Step S210, collecting phase current data and non-functional data in the line.
Collecting three-phase current data and reactive power data in a line to obtain an effective value in the line, and simultaneously calculating phase currentAnd reactive power variation value->。
Step S220, calculating the variation 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 perform dispersion calculation and normalization processing.
Further, in the embodiment of the present application, in step S230, the dispersion calculation is performed on the phase currents before and after the fault, and specifically the following steps are further included.
Step S2301, collecting three-phase current data in the line.
And step S2302, performing dispersion calculation on the phase current of the single-phase grounding, wherein a phase current dispersion calculation formula is as follows.
Wherein, the liquid crystal display device comprises a liquid crystal display device,for each sample data, < >>The average value of the sample data is represented by n, which is the number of the sample data.
Step S2303, calculating the phase current dispersion before and after the fault in the line according to the phase current dispersion, and calculating the phase current dispersion difference before and after the fault in the line.
Step S2304, performing normalization processing on the phase current dispersion, where a calculation formula of the normalization processing is as follows:wherein->Indicating the difference of the phase current dispersion before and after the fault in the ith line, +.>Indicating 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:wherein->For the phase current dispersion after failure, +.>Is the phase current dispersion before failure.
Thereby, the phase current dispersion before and after the fault in the ith line is calculatedThereby obtainingNormalizing the value after the dispersion to obtain +.>. Reactive power value normalized to reactive power is +.>。
Further, in a preferred embodiment of the present application, the normalization processing is performed on the reactive power in step S230, which specifically further includes.
Step S2310, according to the reactive power change value, the reactive power is normalized, and the calculation formula is as follows:wherein->For the reactive power variation in the ith line, < > for>Is the reactive power variation in the kth line.
And S3, solving optimization parameters of an objective function of the fault line selection model by using an optimization pulse neural membrane system.
As shown in fig. 4, fig. 4 is a schematic diagram of an extended pulse neural membrane system in which the ESNPS is capable of generating a string of binary codes of length n to represent an individual or a chromosome in an optimization problem.
As can be seen from FIG. 4, the ESNPS is composed of a sub-system of m+2 neurons, in which the neuronsJust like, every execution of one step of neurons +.>The ignition rule is executed once and pulses are mutually supplied to each other. At the same time, neurons->To neurons->Is sent to a pulse, neuron +.>Is->With probability->Execute ignition rule->With probability->Execute ignition rule->If neuron is->Executing the ignition rule and emitting a pulse, outputting a '1'; otherwise, neuron->Executing the forgetting rule, a "0" is output. Thus, adjusting the probability matrix during ESNPS execution controls the binary pulse train output by the system.
Fig. 5 is an optimized pulsed neural membrane system (AOSNPS) provided by an embodiment of the application. Wherein the AOSNPS introduces a director for adaptively adjusting the probability of evolution rules on the basis of ESNPSs for adjusting the probability of rules within each neuron in each ESNPS. The input to the director is a pulse train containing H-row m-column binary codesThe output is a probability matrix consisting of neuron rule probabilities of H ESNPSs +.> 。
And 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 the ground fault.
Further, in a preferred embodiment of the present application, the objective function in the step S4 is specifically:
wherein:is the probability of failure of the feeder line.
m is a weight parameter to be optimized.
The processed values are normalized for phase current dispersion.
Is the reactive powerAnd (5) unifying the processed values.
As shown in fig. 6, a value-taking line graph of the value of the optimization parameter m is obtained after 20 independent runs, and the average value thereof is obtained as the final value of m according to the result after 20 independent optimizations. Thereby obtaining the objective function asThe 40 sets of test data were validated according to equation 8, resulting in an accuracy of 99.79%. The resulting route selection model established herein and the applied parameter optimization method are viable and effective for steady state information single phase to earth fault route selection.
In the built modelIn (I)>From the three-phase current data and the reactive power data, the calculation in step S2 is known, m and +.>Unknown, the corresponding ++can be determined given or optimized value of m>Values, therefore, reactive power data and tag data, based on the three-phase current data obtained from the training data set>An objective function of optimizing the impulse membrane system is established. The establishment thinking is as follows: let the tag value of each group of data in training set +.>And (m) calculated after determination of m>The sum of squares of the differences is the smallest, i.e. the formula is shown as (1). />Wherein the method comprises the steps ofFor final optimization->Value of->For training set data number, +.>。
Further, in a preferred embodiment of the application, the value of the objective function is derived by optimizing the impulse membrane system to obtain an optimization parameter, and the value of the objective function is determined using the optimization parameter.
The specific steps for optimizing parameters by using the optimized impulse membrane system include.
S121: setting the number of neurons participating in evolution as M according to the value precision of M and establishing a network frame; inputting learning probability valuesPulse train +.>Rearranged into a regular probability matrix->Input mutation probability->Population number H, initial iteration number gen=0; wherein j is more than or equal to 1 and less than or equal to M, M is a rule probability matrix +.>Each row of the matrix has probability values from the same ESNPS, i.e. an expanded impulse membrane system, for representing one individual of the optimization problem.
S122: execution of gen=gen+1 starts.
S123: the row indicator i is given an initial value of 1.
S124: if the row indicator i is greater than its maximum value H, then go to S1215; wherein H is a regular probability matrixIs a number of rows of (a).
S125: the column indicator j is given an initial value of 1.
S126: if the column indicator j is greater than its maximum value M, then proceed to S1212.
S127: generating a random numberIf random number->Less than learning probability value->Then continue, otherwise, go to S1210.
S128: at the position ofAmong individuals, select +.>Is->I.e. < -> If the individual is->Fitness function value->There is a relationship-> Then the current individual i is->Learning, i.e.)>Otherwise, the current individual i is +.>Learning, i.e.)> The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Intermediate variable, th->Person and->The j-th binary code of each individual.
S129: if it isThe current rule probability value is +.> Otherwise, the current rule probability value is +.>The method comprises the steps of carrying out a first treatment on the surface of the S1210: if the j-th binary code of the best solution is searched as +.>The probability value of the current rule isOtherwise-> The method comprises the steps of carrying out a first treatment on the surface of the S1211: the column indicator j increases by 1, proceeding to S126;
s1212: the row indicator i increases by 1 and goes to S124 to continue.
S1213: calculation ofValue, and at H +.>Value selection minimum +.>And preserve->And its corresponding m value.
S1214 if it isGo to S122, otherwise continue; wherein (1)>Is the maximum number of iterations.
S1215 outputAnd an m value.
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 application, the objective function calculates the probability of failure of all feeder lines at the bus.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform 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, random access memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Claims (7)
1. The single-phase earth fault line selection method based on the optimized pulse neural membrane system is characterized by comprising the following steps of:
acquiring fault line data and non-fault line data under different conditions;
the method for calculating the dispersion degree of the phase current before and after the fault specifically comprises the following steps:
collecting three-phase current data in a line;
and performing dispersion calculation on the phase current of the single-phase grounding, wherein the phase current dispersion calculation formula is as follows:wherein (1)>For each sample data, < >>The average value of the sample data is obtained, and n is the number of the sample data;
calculating the phase current dispersion before and after the fault in the line according to the phase current dispersion, and calculating the phase current dispersion difference before and after the fault in the line;
and carrying out normalization processing on the phase current dispersion, wherein the calculation formula of the normalization processing is as follows:wherein the method comprises the steps ofIndicating the difference of the phase current dispersion before and after the fault in the ith line, +.>Representing the phase current dispersion difference before and after the fault in the kth line;
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:wherein->For the reactive power variation in the ith line, < > for>The reactive power variation in the kth line is the reactive power variation;
establishing a fault line selection model;
solving optimization parameters of an 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 larger 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 impulse neural system of claim 1, wherein the objective function is specifically:wherein: />The fault probability of the feeder line;
m is a weight parameter to be optimized;normalizing the processed value for the phase current dispersion; />The processed values are normalized for reactive power.
3. The optimized impulse neural system-based single-phase earth fault line selection method of claim 1, wherein the value of the objective function is used to determine the value of the objective function by optimizing the impulse neural system to obtain an optimization parameter.
4. The optimized impulse neural system-based single-phase earth fault line selection method of claim 1, wherein the preset value is 0.5.
5. The optimized impulse neural system-based single-phase earth fault line selection method of claim 1, wherein the faulty line data and the non-faulty line data include phase current data and reactive power data in a line.
6. The optimized impulse neural system-based single-phase earth fault line selection method of claim 1, wherein the objective function calculates the probability of faults for all feeders at the bus.
7. The optimized impulse neural system-based single-phase earth fault line selection method of claim 1, wherein the calculation formula for calculating the pre-fault and post-fault phase current dispersion differences in the line is:wherein the method comprises the steps ofFor the phase current dispersion after failure, +.>Is the phase current dispersion before failure.
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