Multi-criterion fusion fault line selection method, device, equipment, medium and product

Abstract

The invention discloses a fault line selection method and device based on multi-criterion fusion, equipment, a medium and a product. The method comprises the steps of determining a first fault measurement value of each line under at least two fault criteria when it is determined that the power distribution network has a fault; according to the difference between each first fault measurement value and each fault criterion, determining a second fault measurement value of each line under different fault criteria; determining a third fault measurement value of each line under different fault criteria according to the difference condition of the second fault measurement values between the lines; performing multi-criterion fusion based on the basic probability distribution function and the D-S evidence theory, and determining a corresponding fault line selection result according to the fault probability value of each line after the multi-criterion fusion; wherein the basic probability distribution function is constructed based on each third fault measurement value. Fault measurement can be effectively processed by using transverse and longitudinal relations between fault criteria and between lines, and reliable line selection of power distribution network faults is realized based on multi-criterion fusion.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular, to a fault line selection method, device, equipment, medium and product that integrates multiple criteria. Background Art

[0002] At present, 80% of the power outages in the power system are caused by distribution network accidents. Among all distribution network accidents, 70% are caused by single-phase grounding short circuits. Therefore, the accuracy of fault line selection in the distribution network is crucial. However, in the natural environment, fault feeders often fall on some non-metallic media, such as concrete floors, tree branches, and sandy soils. These media have high resistivity, resulting in a large transition resistance between the short circuit point and the ground. Therefore, the fault information existing in the system is weak, and the relay protection device often fails to detect the fault. Therefore, especially in a resonant grounding system, reliable fault line selection for high-impedance faults (HIF) in the distribution network has always been a difficult problem to solve.

[0003] Existing fault line selection methods mainly rely on analyzing the time domain, frequency domain, time-frequency domain, and energy characteristics of voltage and current to detect HIF. However, in the case of HIF, the voltage and current signals are weak, the measurement errors and noise pollution are large, and a single fault criterion is prone to misjudgment. In contrast, the fault line selection method that integrates multiple criteria can make full use of the advantages of each fault criterion and overcome the limitations of a single criterion. However, in the existing fault line selection methods based on the integration of multiple criteria, the measures of each fault criterion are usually independent of each other, lacking the horizontal and vertical connections between the criteria and between the lines, resulting in the accuracy and credibility of the final fault line selection result not being ideal enough. Summary of the Invention

[0004] The present invention provides a fault line selection method, device, equipment, medium and product that integrates multiple criteria to solve the problem that the accuracy and credibility of the fault line selection result in the existing fault line selection method based on the integration of multiple criteria are not ideal enough.

[0005] According to one aspect of the present invention, a fault line selection method that integrates multiple criteria is provided. The method includes:

[0006] When it is determined that a fault has occurred in the distribution network, determine the first fault measurement values of each line under at least two fault criteria;

[0007] According to each first fault measurement value and the difference situation between each fault criterion, determine the second fault measurement values of each line under different fault criteria;

[0008] According to the difference situation of the second fault measurement values between each line, determine the third fault measurement values of each line under different fault criteria;

[0009] Multi-criterion fusion is performed based on the basic probability assignment function and D-S evidence theory, and the corresponding fault line selection result is determined according to the fault probability values of each line after multi-criterion fusion; wherein, the basic probability assignment function is constructed based on each third fault measure value.

[0010] According to another aspect of the present invention, a fault line selection device for multi-criterion fusion is provided, and the device includes:

[0011] A first fault measure value determination module, configured to determine the first fault measure values of each line under at least two fault criteria when it is determined that a fault occurs in the distribution network;

[0012] A second fault measure value determination module, configured to determine the second fault measure values of each line under different fault criteria according to the differences between each first fault measure value and each fault criterion;

[0013] A third fault measure value determination module, configured to determine the third fault measure values of each line under different fault criteria according to the differences between the second fault measure values of each line;

[0014] A fault line selection result determination module, configured to perform multi-criterion fusion based on the basic probability assignment function and D-S evidence theory, and determine the corresponding fault line selection result according to the fault probability values of each line after multi-criterion fusion; wherein, the basic probability assignment function is constructed based on each third fault measure value.

[0015] According to another aspect of the present invention, an electronic device is provided, and the electronic device includes:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the fault line selection method for multi-criterion fusion according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, and the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the fault line selection method for multi-criterion fusion according to any embodiment of the present invention when executed.

[0020] According to another aspect of the present invention, a computer program product is provided, and the computer program product includes a computer program, and the computer program implements the fault line selection method for multi-criterion fusion according to any embodiment of the present invention when executed by a processor.

[0021] In the technical solution of the embodiment of the present invention, when it is determined that a fault occurs in the distribution network, the first fault measure values of each line under at least two fault criteria are determined; according to the difference between each first fault measure value and each fault criterion, the second fault measure values of each line under different fault criteria are determined; according to the difference between the second fault measure values of each line, the third fault measure values of each line under different fault criteria are determined; multi-criterion fusion is performed based on the basic probability assignment function and the D-S evidence theory, and the corresponding fault line selection results are determined according to the fault probability values of each line after multi-criterion fusion; wherein, the basic probability assignment function is constructed based on each third fault measure value. Through the effective further processing of the first fault measure values of each line under at least two fault criteria, the third fault measure values that simultaneously consider the horizontal and vertical connections between fault criteria and between lines are obtained, and then the D-S evidence theory is used for multi-criterion fusion to obtain the fault probability values corresponding to each line, so as to realize the rapid and accurate determination of the fault line and improve the credibility of fault line selection at the same time.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 is a flowchart of a fault line selection method for multi-criterion fusion provided in Embodiment 1 of the present invention;

[0025] Figure 2 is a flowchart of a fault line selection method for multi-criterion fusion provided in Embodiment 2 of the present invention;

[0026] Figure 3 is a schematic diagram of a standard fuzzy number and a membership function curve provided in Embodiment 2 of the present invention;

[0027] Figure 4 is a flowchart of another fault line selection method for multi-criterion fusion provided in Embodiment 2 of the present invention;

[0028] Figure 5 is a schematic diagram of a radial simulation model of a distribution network provided in Embodiment 2 of the present invention;

[0029] Figure 6 is a comparison schematic diagram provided according to Embodiment 2 of the present invention;

[0030] Figure 7 is a structural schematic diagram of a fault line selection device with multi-criterion fusion provided according to Embodiment 3 of the present invention;

[0031] Figure 8 is a structural schematic diagram of an electronic device for implementing the multi-criterion fusion fault line selection method of the embodiments of the present invention. Detailed implementation manners

[0032] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment 1

[0035] Figure 1 This is a flowchart of a multi-criterion fusion fault line selection method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of reliable line selection for high-resistance faults in a distribution network based on multi-criterion information fusion. This method can be executed by a multi-criterion fusion fault line selection device, which can be implemented in the form of hardware and / or software, and the multi-criterion fusion fault line selection device can be configured in an electronic device. As Figure 1 shown, a multi-criterion fusion fault line selection method provided in Embodiment 1 specifically includes the following steps:

[0036] S110. When it is determined that a fault has occurred in the distribution network, determine the first fault measurement values of each line under at least two fault criteria.

[0037] Among them, the fault criterion can be understood as the fault line selection criterion index used to determine the fault line after a fault occurs in the distribution network. Exemplarily, the fault criterion can include but is not limited to: the criterion based on the transient energy of the feeder, the criterion based on the zero-state response current of the feeder at the initial moment of the fault, the criterion based on the waveform similarity, etc.

[0038] The first fault measurement value can refer to the original measurement value corresponding to a certain fault criterion for the line, and each line corresponds to a first fault measurement value under a certain fault criterion, where the fault measurement value is a metric used to describe the suspected fault degree shown by a certain fault criterion.

[0039] In the embodiment of the present invention, after it is monitored that a fault has occurred in the distribution network, the line fault data transmitted back by each measurement point of the distribution network can be obtained, such as the zero-sequence current signal and the bus zero-sequence voltage signal, etc. At the same time, in order to solve the problem that the accuracy of the fault line selection result of a single fault criterion is not high, the first fault measurement values of each line under at least two fault criteria can be determined based on the above line fault data, so as to realize reliable line selection for high-resistance faults in the distribution network based on multi-criterion information fusion subsequently. It can be understood that the monitoring conditions for determining whether a fault has occurred in the distribution network in this embodiment are not specifically limited. Exemplarily, it can be determined that a fault has occurred when the bus zero-sequence voltage of the distribution network exceeds a preset threshold. In addition, the selection quantity (at least two) and types of the fault criteria in this embodiment are not specifically limited, and can be set accordingly according to the actual distribution network situation.

[0040] S120. According to the difference situation between each first fault measurement value and each fault criterion, determine the second fault measurement values of each line under different fault criteria.

[0041] Among them, the second fault measurement value can be understood as the corresponding fault measurement value obtained by correcting the first fault measurement value considering the mutual difference situation between each fault criterion, that is, the horizontal connection between the criteria.

[0042] In the embodiments of the present invention, in the existing fault line selection method based on multi-criterion fusion, the measures of each fault criterion are usually independent of each other, without considering the mutual differences between the fault criteria, that is, the horizontal relationship between the criteria, resulting in unreliable fault line selection results after multi-criterion fusion. To solve the above problems, in this embodiment, the first fault measure values of each line under different fault criteria can be corrected according to the differences between the fault criteria to obtain the corresponding second fault measure values. Among them, the method of correcting the first fault measure value according to the differences between the fault criteria may include, but is not limited to: determining the mutual difference coefficient between each fault criterion based on the fuzzy theory, then using the mutual difference coefficient to determine the corresponding proportion coefficient of each fault criterion, and finally correcting the first fault measure value of each line under the corresponding fault criterion based on the proportion coefficient to obtain the second fault measure value; or configuring corresponding weight coefficients for each fault criterion in advance based on expert experience knowledge, and then correcting the first fault measure value of each line under the corresponding fault criterion based on the weight coefficient to obtain the second fault measure value.

[0043] S130. Determine the third fault measure value of each line under different fault criteria according to the differences between the second fault measure values of each line.

[0044] Among them, the third fault measure value can be understood as the corresponding fault measure value obtained by correcting the second fault measure value considering the differences between the second fault measure values of each line, that is, the vertical relationship between the lines.

[0045] In the embodiments of the present invention, this embodiment will further consider the vertical relationship between each line (the convergence of fault judgment between each line), that is, it will correct the second fault measure value of each line under different fault criteria according to the differences between the second fault measure values of each line to obtain the corresponding third fault measure value. At this time, the third fault measure value takes into account both the horizontal and vertical relationships between the fault criteria and between the lines, providing an important data basis for subsequent multi-criterion fusion and obtaining reliable line selection results. In a specific embodiment, the second fault measure value of each line corresponding to each fault criterion can be first converted into a line vector, and then the fitting coefficient corresponding to each line can be determined based on each line vector. The fitting coefficient can be used to measure the difference degree between the second fault measure values of each line, and finally the second fault measure value of each line under the corresponding fault criterion is corrected based on the fitting coefficient to obtain the final third fault measure value.

[0046] S140. Perform multi-criterion fusion based on the basic probability assignment function and D-S evidence theory, and determine the corresponding fault line selection result according to the fault probability values of each line after multi-criterion fusion; among them, the basic probability assignment function is constructed based on each third fault measure value.

[0047] Among them, the basic probability assignment function can be used to determine the basic probability of each line having a fault, and it can be constructed based on the third fault measurement value corresponding to each line. The D-S evidence theory (Dempster-Shafer Evidence Theory) is a mathematical framework for dealing with uncertain reasoning and decision-making, and has been widely used in the fault line selection method of multi-evidence fusion.

[0048] In the embodiment of the present invention, the corresponding basic probability assignment function can be constructed based on the third fault measurement values of each line under different fault criteria, and then the D-S evidence theory and the constructed basic probability assignment function are used for multi-criterion fusion to obtain the fault probability values of each line after fusion, and the line corresponding to the largest fault probability value is determined as the fault line, thereby completing the fault line selection.

[0049] The technical solution of the embodiment of the present invention, when it is determined that a fault occurs in the distribution network, determines the first fault measurement value of each line under at least two fault criteria; determines the second fault measurement value of each line under different fault criteria according to the difference between each first fault measurement value and each fault criterion; determines the third fault measurement value of each line under different fault criteria according to the difference between the second fault measurement values of each line; performs multi-criterion fusion based on the basic probability assignment function and the D-S evidence theory, and determines the corresponding fault line selection result according to the fault probability values of each line after multi-criterion fusion; among them, the basic probability assignment function is constructed based on each third fault measurement value. This technical solution further effectively processes the first fault measurement values of each line under at least two fault criteria to obtain the third fault measurement value that simultaneously considers the horizontal and vertical connections between fault criteria and between lines, and then uses the D-S evidence theory for multi-criterion fusion to obtain the fault probability values corresponding to each line, thereby realizing the rapid and accurate determination of the fault line and improving the credibility of the fault line selection at the same time.

[0050] Embodiment Two

[0051] Figure 2 The flowchart of a fault line selection method for multi-criterion fusion provided by the second embodiment of the present invention is further optimized and extended based on the above embodiment, and can be combined with each optional technical solution in the above embodiment. As Figure 2 shown, a fault line selection method for multi-criterion fusion provided by the second embodiment of the present invention specifically includes the following steps:

[0052] S210. When it is monitored that the zero-sequence voltage of the busbar of the distribution network exceeds the preset threshold, obtain the zero-sequence current signal and the zero-sequence voltage signal collected at each measurement point in the distribution network within a preset number of power frequency cycles after the fault occurs.

[0053] Among them, the preset threshold can refer to the bus zero-sequence voltage monitoring threshold set for the distribution network fault monitoring, which can be set according to the actual distribution network situation. Exemplarily, the preset threshold can be set to 0.15 times the rated voltage. When the bus zero-sequence voltage exceeds the preset threshold, it indicates that a fault has occurred in the distribution network.

[0054] In the embodiment of the present invention, the bus zero-sequence voltage of the distribution network can be monitored in real time. When it is monitored that the bus zero-sequence voltage exceeds the preset threshold, it is determined that a fault has occurred in the distribution network. At this time, the fault recording devices at each measurement point in the distribution network will be immediately started to record the (feeder) zero-sequence current signal and the bus zero-sequence voltage signal within a preset number of power frequency cycles (such as 20) after the fault occurs, and transmit them back to the multi-criterion fusion fault line selection device of this embodiment.

[0055] S220. Based on the zero-sequence current signal and the bus zero-sequence voltage signal, different fault criteria are used to generate the corresponding first fault measure values for each line; among them, the fault criteria at least include: the criterion based on the transient energy of the feeder, the criterion based on the zero-state response current of the feeder at the initial moment of the fault, and the criterion based on the waveform similarity.

[0056] In the embodiment of the present invention, the zero-sequence current signal and the bus zero-sequence voltage signal collected after the fault occurs can be used to sequentially determine the corresponding first fault measure values for each line by using different fault criteria. Among them, the fault criteria at least include: the criterion based on the transient energy of the feeder, the criterion based on the zero-state response current of the feeder at the initial moment of the fault, and the criterion based on the waveform similarity. It should be understood that the specific process of generating the first fault measure value by using different fault criteria in this embodiment can refer to the prior art, that is, the corresponding feeder transient energy value can be generated by using the criterion based on the transient energy of the feeder, the corresponding feeder zero-state response current value can be generated by using the criterion based on the zero-state response current of the feeder at the initial moment of the fault, the corresponding waveform similarity value can be generated by using the criterion based on the waveform similarity, etc. The specific calculation process is not elaborated in this embodiment. In addition, the 3 fault criteria adopted in the above embodiment are only examples. In practical applications, the types of fault criteria can fully include the above-mentioned various dimension information, or only include some of the above-mentioned dimension information, or can also include more dimension information. The embodiment of the present invention does not limit this.

[0057] Exemplarily, taking the criterion based on the waveform similarity as an example, for the i-th line l i the corresponding first fault measure value fm(l i ) can be expressed as follows:

[0058]

[0059] Among them,

[0060] Where ρ represents the Pearson correlation coefficient; n represents the total number of sampling points corresponding to the zero-sequence current signal; represents the zero-sequence current signal of the i-th line l i corresponding to the p-th sampling point. It can be understood that when the value of fm(l i ) is larger, it indicates that the probability of the corresponding line l i having a fault is higher.

[0061] Furthermore, based on the above-described invention embodiments, after generating the first fault measure value, it further includes: performing a normalization process on the first fault measure value. Specifically, the following formula can be called for the normalization process:

[0062]

[0063] Where X is the original first fault measure value, X min and X max are respectively the minimum value and the maximum value in the first fault measure value, and X normal is the first fault measure value after normalization.

[0064] S230. Based on the fuzzy theory and each first fault measure value, generate standard fuzzy numbers corresponding to each fault criterion; wherein, the standard fuzzy number is a regular triangular fuzzy number.

[0065] In fuzzy theory, if the membership function of a fuzzy number satisfies the following relational expression, then this fuzzy number is called a triangular fuzzy number:

[0066]

[0067] Where x is the fuzzy number, and a, b, and c are respectively the upper bound, the median value, and the lower bound of the fuzzy number. Further, if ω = 1, then this fuzzy number is a regular triangular fuzzy number, denoted as If ω ∈ [0, 1), then this fuzzy number is a generalized triangular fuzzy number, denoted as

[0068] In the embodiments of the present invention, the regular triangular fuzzy number is adopted, that is, ω = 1. The standard fuzzy number corresponding to each fault criterion can be expressed as Where:

[0069]

[0070] Where a j , b j and c j respectively represent the upper bound, the median value, and the lower bound of the standard fuzzy number corresponding to the j-th fault criterion; fm j (l i ) represents the zero-sequence current signal of the i-th line l iThe first fault measurement value under the fault criterion j; N represents the total number of line numbers in the distribution network, and the line number i = 0 represents the bus, and i = 1, 2,..., N represents the feeder; ε() represents the step function, and ε(0) = 1.

[0071] S240. Based on the preset reference fuzzy number and the membership function corresponding to the standard fuzzy number, determine the proportion coefficient corresponding to each fault criterion, and use each proportion coefficient as the difference situation between each fault criterion.

[0072] In the embodiment of the present invention, as Figure 3 shown, the preset reference fuzzy number may include: the first preset reference fuzzy number and the second preset reference fuzzy number At the same time, the membership function curve on the left half side of the standard fuzzy number is denoted as the left membership function curve g 1 (x), and the membership function curve on the right half side of the standard fuzzy number is denoted as the right membership function curve g 2 (x), then S240 may include the following steps:

[0073] S2401. For each fault criterion, the area of the region enclosed by the first preset reference fuzzy number and the left membership function curve is denoted as the left adjacent area, the area of the region enclosed by the first preset reference fuzzy number and the right membership function curve is denoted as the left far area, the area of the region enclosed by the second preset reference fuzzy number and the right membership function curve is denoted as the right adjacent area, and the area of the region enclosed by the second preset reference fuzzy number and the left membership function curve is denoted as the right far area; wherein, the first preset reference fuzzy number is a regular triangular fuzzy number with an upper bound, a median, and a lower bound all being 0, the second preset reference fuzzy number is a regular triangular fuzzy number with an upper bound, a median, and a lower bound all being 1, the left membership function curve is the membership function curve on the left side of the median in the membership function, and the right membership function curve is the membership function curve on the right side of the median in the membership function;

[0074] S2402. For each fault criterion, the average value of the left adjacent area and the left far area is denoted as the left average area, and the average value of the right adjacent area and the right far area is denoted as the right average area;

[0075] S2403. Based on the left average area and the right average area corresponding to each fault criterion, call the preset mutual difference coefficient calculation formula for criteria to determine the mutual difference coefficient between any two different fault criteria;

[0076] S2404. Based on the mutual difference coefficient, call the preset proportion coefficient calculation formula for criteria to determine the proportion coefficient corresponding to each fault criterion.

[0077] In the embodiment of the present invention, asFigure 3 As shown, for the standard fuzzy numbers of each fault criterion j the corresponding left adjacent area S can be determined in sequence LN , the left far area S LF , the right adjacent area S RN and the right far area S RF , and then the corresponding left average area S L =(S LN +S LF ) / 2 and the right average area S R =(S RN +S RF ) / 2 can be determined.

[0078] Next, based on the left average area and the right average area corresponding to each fault criterion determined above, the mutual difference coefficient between any two different fault criteria can be determined by invoking the preset formula for calculating the mutual difference coefficient of criteria, where the preset mutual difference coefficient of criteria can be defined as follows:

[0079] dis(u,v)=|S L (u)-S L (v)|+|S R (u)-S R (v)|

[0080] In the formula, dis(u,v) represents the mutual difference coefficient between two different fault criteria u and v, and the smaller the mutual difference coefficient, the closer the judgment results of these two fault criteria are and the higher the credibility.

[0081] Furthermore, the following preset formula for calculating the proportion coefficient of criteria can be invoked to determine the proportion coefficient corresponding to each fault criterion:

[0082]

[0083] S250. The product result of the first fault measure value of each line under different fault criteria and the proportion coefficient of the corresponding fault criterion is determined as the second fault measure value of the line under the corresponding fault criterion.

[0084] In the embodiment of the present invention, based on the proportion coefficient acc(j) of the fault criterion j determined in S240, the first fault measure value fm i of each line l j under different fault criteria j i can be corrected to obtain the corresponding second fault measure value fm j '(l i ), that is: fm j '(l i )=fm j (li )*acc(j).

[0085] S260. Concatenate the second fault measure values corresponding to different fault criteria for each line to obtain the line vector corresponding to each line.

[0086] In the embodiment of the present invention, each line l can be used i The corrected second fault measure values fm j '(l i ) are used to generate the corresponding line vector H(i), that is: H(i) = [fm 1 '(l i ), fm 2 '(l i ), …, fm j '(l i )], i = 0, 1, 2, …, N.

[0087] S270. Based on each line vector, call the preset line fitting degree calculation formula to determine the fitting degree between any two different lines.

[0088] In the embodiment of the present invention, the following preset line fitting degree calculation formula can be called to determine the fitting degree between any two different lines i and k:

[0089]

[0090] In the formula, <H(i), H(k)> represents the vector inner product operation. Among them, the greater the fitting degree, the stronger the convergence of fault judgment between these two lines.

[0091] S280. Based on each line vector and the fitting degree, call the preset line fitting coefficient calculation formula to determine the fitting coefficient corresponding to each line.

[0092] In the embodiment of the present invention, based on the line vector H(i) corresponding to each line l i and the fitting degree between each line, the following preset line fitting coefficient calculation formula can be called to sequentially determine the fitting coefficient corresponding to each line l i :

[0093]

[0094] Among them, the fitting coefficient fit(i) can be used to measure the difference degree of the second fault measure between this line l i and other lines.

[0095] S290. Determine the product result of the fitting coefficient corresponding to each line and the second fault measure value of the line under different fault criteria as the third fault measure value of the line under the corresponding fault criterion.

[0096] In the embodiment of the present invention, based on the fitting coefficients fit(i) corresponding to each line l determined in S280, the second fault measure values fm' (l i ) corresponding to each line l i under different fault criteria j can be corrected to obtain the corresponding third fault measure values fm'' (l j '), that is: fm'' (l i ) = fm' (l j ) * fit(i). i ) j i ) j i )

[0097] S2100. Based on the basic probability assignment functions constructed for each line under different fault criteria, the preset D-S evidence fusion formula is called to determine the fault probability values after fusion for each line.

[0098] In the embodiment of the present invention, based on the third fault measure values fm'' (l i ) of each line l j , the basic probability assignment function can be constructed as follows: i

[0099]

[0100] In the formula, m j (l i ) represents the basic probability assignment function (basic belief value) of the i-th line l i under the fault criterion j, which is used to characterize the degree of fault of the i-th line l i under the fault criterion j and can be used as the input for subsequent D-S evidence fusion operations; fm'' (l j ) represents the third fault measure value of the i-th line l i under the fault criterion j; acc(j) represents the proportion coefficient of the fault criterion j; m i (Θ) represents the uncertainty of the fault criterion j, and the larger m j (Θ) is, the higher the uncertainty of the fault criterion j; Θ represents the set of mutually exclusive hypotheses about whether each line has a fault. j

[0101] Further, based on the constructed basic probability assignment function, the following preset D-S evidence fusion formula can be called to determine the fault probability values after fusion for each line l i :

[0102]

[0103] In the formula​​​​ A 1 = A 2 = … = A M = {l i | i = 0, 1, 2, …, N}; M represents the number of types of fault criteria; m(l i ) represents the fused fault probability value of the i-th line l i ; m(Θ) represents the uncertainty corresponding to the faulty line; ∩ represents the intersection symbol.

[0104] S2110. Traverse the fault probability values corresponding to each line, and determine the line corresponding to the maximum fault probability value as the faulty line.

[0105] In the embodiment of the present invention, based on the fused fault probability values m(l i ) of each line determined above, the line corresponding to the maximum fault probability value can be determined as the faulty line, that is:

[0106] Figure 4 This is the flowchart of another fault line selection method for multi-criterion fusion provided in the second embodiment of the present invention. As Figure 4 shown, this method is similar to the multi-criterion fusion fault line selection method provided in the above embodiment. For details, reference can be made to the above embodiment, and details will not be repeated here.

[0107] The technical solution of the embodiment of the present invention is as follows: When it is monitored that the zero-sequence voltage of the bus exceeds the preset threshold, the zero-sequence current signal and the zero-sequence voltage signal within a preset number of power frequency cycles after the fault occur are acquired, and a plurality of fault criteria are used to generate corresponding first fault measure values, and these first fault measure values are normalized; then, based on the fuzzy theory, the mutual difference coefficient between the fault criteria is introduced, and each fault criterion is differentially processed to obtain the corrected second fault measure value; and, the concept based on the vector direction parameter and the fitting coefficient between lines are introduced to measure the judgment trend of the second fault measure values of different lines, and the fitted third fault measure value is obtained; then, a basic probability assignment function is constructed based on each third fault measure value to obtain the basic belief values of each line; finally, the D-S evidence theory is used for multi-criterion fusion to obtain the final fault probability values of each line, and the line with the maximum fault probability value is the faulty line.

[0108] This solution can effectively process the fault measure according to the judgment differences between various fault criteria and the convergence of the judgment of each line criterion, and realize reliable fault line selection for high-resistance faults in the distribution network based on multi-criterion information fusion. At the same time, the fault probability value obtained after being processed by this solution is higher than the fault probability value when this solution is not used. The maximum value of the finally obtained fault probability value is higher, the fault probability values of other lines are lower, and the uncertainty is lower, thus making the fault line selection result more credible.

[0109] To enable those skilled in the art to better understand the embodiments of the present invention, the following uses specific examples to illustrate the multi-criterion fusion fault line selection method in the embodiments of the present invention. This example uses PSCAD software for simulation testing, and the established radial distribution network is as Figure 5 shown. Among them, the 110 kV system is connected to the distribution network through a transformer with a turns ratio of 110 kV / 10.5 kV. The high-voltage side of the transformer adopts a star connection method, and the neutral point adopts a grounding method through an arc suppression coil with over-compensation, and the compensation degree is 8%, and the inductance L = 0.46 H. The distribution network lines adopt a mixed connection method of cables and overhead lines, and the total line length is 68 km. Among them, the cable adopts YJV22-3*400, with a total length of 34 km, and the overhead line adopts JKLYJ-150 with a total length of 34 km. The end of the distribution network feeder is connected to a 1 MW load through a 10 kV / 0.4 kV transformer. In addition, at the ends of l2 and l4, a 10 kV / 0.69 kV transformer is used to connect to the photovoltaic power generation systems DG1 and DG2. Since the fault measures used in this method are all based on the measured zero-sequence data, and the lines connected to the DG adopt a △ / Y connection method, the zero-sequence current will not flow through the transformer, so the access of the DG will not affect the zero-sequence current in the medium-voltage distribution network.

[0110] The distribution network fault is set as: at the position (a) 4 km from the beginning of line l 1 a single-phase ground fault occurred at the initial fault phase angle θ = 90° of phase A, and the transition resistance is R f = 3000 Ω.

[0111] Three fault criteria are selected respectively: ① FC1: a criterion based on the transient energy of the feeder; ② FC2: a criterion based on the zero-state response current of the feeder at the initial moment of the fault; ③ FC3: a criterion based on the waveform similarity. After normalizing the first fault measure values corresponding to the above three fault criteria, the obtained fault measure values are shown in Table 1.

[0112] Table 1 Normalized fault measure values

[0113]

[0114] After considering the horizontal and vertical connections between fault criteria and between lines, the third fault measure value fm obtained by this method j ”(l i ) is shown in Table 2.

[0115] Table 2 Third fault measure value after processing by this method

[0116]

[0117] Finally, the final fault probability value obtained after multi-criterion fusion is shown in Table 3.

[0118] Table 3 Final fault probability values of each line in this method

[0119]

[0120] It can be seen from Table 3 that the fault probability value (combined belief value) corresponding to line l 1 is much larger than that of other lines. Therefore, it can be determined that l 1 is the faulty line, which is consistent with the faulty line number set in the simulation. Therefore, the faulty line selection is successful, that is, the effectiveness of this method is verified.

[0121] In order to further show the final effect of this method and the advantages of the traditional method, the D-S evidence theory is directly used below to perform multi-criterion fusion on the fault measure values in Table 1, skipping the processing process of the fault measure by this method. The final fault probability values obtained are shown in Table 4.

[0122] Table 4 Final fault probability values of each line in the traditional method

[0123]

[0124]

[0125] To increase the intuitiveness of the data, the data in Table 3 and Table 4 are compared in the form of a line chart, and the comparison chart obtained is as Figure 6 shown.

[0126] From Figure 6 it can be seen that the fault probability value (combined belief value) of line l 1 in this method is significantly larger than that of the traditional method, and the fault probability values of other lines are significantly smaller than those of the traditional method. At the same time, for the uncertainty m(Θ), this method is also smaller than the traditional method, indicating that the faulty line selection result obtained by this method is more credible.

[0127] Example 3

[0128] Figure 7The following is a schematic structural diagram of a fault line selection device with multi-criterion fusion provided by Embodiment 3 of the present invention. As Figure 7 shown, the device includes:

[0129] A first fault measure value determination module 31, configured to determine first fault measure values of each line under at least two fault criteria when it is determined that a fault occurs in the distribution network;

[0130] A second fault measure value determination module 32, configured to determine second fault measure values of each line under different fault criteria according to the differences between the first fault measure values of each line and between the fault criteria;

[0131] A third fault measure value determination module 33, configured to determine third fault measure values of each line under different fault criteria according to the differences between the second fault measure values of each line;

[0132] A fault line selection result determination module 34, configured to perform multi-criterion fusion based on the basic probability assignment function and the D-S evidence theory, and determine the corresponding fault line selection result according to the fault probability values of each line after multi-criterion fusion; wherein, the basic probability assignment function is constructed based on each third fault measure value.

[0133] The technical solution of the embodiment of the present invention is as follows: when it is determined that a fault occurs in the distribution network, the first fault measure value determination module determines the first fault measure values of each line under at least two fault criteria; the second fault measure value determination module determines the second fault measure values of each line under different fault criteria according to the differences between the first fault measure values of each line and between the fault criteria; the third fault measure value determination module determines the third fault measure values of each line under different fault criteria according to the differences between the second fault measure values of each line; the fault line selection result determination module performs multi-criterion fusion based on the basic probability assignment function and the D-S evidence theory, and determines the corresponding fault line selection result according to the fault probability values of each line after multi-criterion fusion; wherein, the basic probability assignment function is constructed based on each third fault measure value. By further effectively processing the first fault measure values of each line under at least two fault criteria, this technical solution obtains the third fault measure values that simultaneously consider the horizontal and vertical connections between the fault criteria and between the lines, and then uses the D-S evidence theory for multi-criterion fusion to obtain the fault probability values corresponding to each line, thereby realizing the rapid and accurate determination of the fault line and improving the credibility of the fault line selection at the same time.

[0134] Further, on the basis of the above-mentioned embodiment of the invention, the first fault measure value determination module 31 includes:

[0135] A signal acquisition unit, configured to acquire zero-sequence current signals and bus zero-sequence voltage signals collected at each measurement point in the distribution network within a preset number of power frequency cycles after a fault occurs when it is detected that the bus zero-sequence voltage of the distribution network exceeds a preset threshold;

[0136] A first fault measure value determination unit, configured to generate first fault measure values corresponding to each line by using different fault criteria based on the zero-sequence current signals and the bus zero-sequence voltage signals; wherein, the fault criteria at least include: a criterion based on the transient energy of the feeder, a criterion based on the zero-state response current of the feeder at the initial moment of the fault, and a criterion based on waveform similarity.

[0137] Further, on the basis of the above-mentioned invention embodiment, the first fault measure value determination module 31 further includes:

[0138] A normalization unit, configured to perform normalization processing on the first fault measure values after the first fault measure values are generated.

[0139] Further, on the basis of the above-mentioned invention embodiment, the second fault measure value determination module 32 includes:

[0140] A standard fuzzy number generation unit, configured to generate standard fuzzy numbers corresponding to each fault criterion based on fuzzy theory and each first fault measure value; wherein, the standard fuzzy number is a regular triangular fuzzy number;

[0141] A proportion coefficient determination unit, configured to determine proportion coefficients corresponding to each fault criterion based on a preset reference fuzzy number and the membership function corresponding to the standard fuzzy number, and use each proportion coefficient as the difference situation between each fault criterion;

[0142] A second fault measure value determination unit, configured to determine the product result of the first fault measure values of each line under different fault criteria and the proportion coefficients corresponding to the corresponding fault criteria as the second fault measure value of the line under the corresponding fault criterion.

[0143] Further, on the basis of the above-mentioned invention embodiment, the upper bound, median, and lower bound of the standard fuzzy number are respectively expressed as:

[0144]

[0145] wherein, a j , b j and c j respectively represent the upper bound, median, and lower bound of the standard fuzzy number corresponding to the jth fault criterion; fm j (l i ) represents the first fault measure value of the ith line l i under the fault criterion j; N represents the total number of line numbers in the distribution network; ε(·) represents a step function, and ε(0) = 1.

[0146] Further, based on the above-described invention embodiments, the proportion coefficient determination unit is specifically configured to:

[0147] For each fault criterion, the area of the region enclosed by the first preset reference fuzzy number and the left membership function curve is denoted as the left adjacent area, the area of the region enclosed by the first preset reference fuzzy number and the right membership function curve is denoted as the left far area, the area of the region enclosed by the second preset reference fuzzy number and the right membership function curve is denoted as the right adjacent area, and the area of the region enclosed by the second preset reference fuzzy number and the left membership function curve is denoted as the right far area; wherein, the first preset reference fuzzy number is a regular triangular fuzzy number with an upper bound, a median, and a lower bound all being 0, the second preset reference fuzzy number is a regular triangular fuzzy number with an upper bound, a median, and a lower bound all being 1, the left membership function curve is the membership function curve located on the left side of the median in the membership function, and the right membership function curve is the membership function curve located on the right side of the median in the membership function;

[0148] For each fault criterion, the average value of the left adjacent area and the left far area is denoted as the left average area, and the average value of the right adjacent area and the right far area is denoted as the right average area;

[0149] Based on the left average area and the right average area corresponding to each fault criterion, a preset criterion mutual difference coefficient calculation formula is called to determine the mutual difference coefficient between any two different fault criteria;

[0150] Based on the mutual difference coefficient, a preset criterion proportion coefficient calculation formula is called to determine the proportion coefficient corresponding to each fault criterion.

[0151] Further, based on the above-described invention embodiments, the third fault measure value determination module 33 includes:

[0152] A line vector generation unit, configured to splice the second fault measure values corresponding to different fault criteria of each line to obtain a line vector corresponding to each line;

[0153] A fitting degree determination unit, configured to, based on each line vector, call a preset line fitting degree calculation formula to determine the fitting degree between any two different lines;

[0154] A fitting coefficient determination unit, configured to, based on each line vector and the fitting degree, call a preset line fitting coefficient calculation formula to determine the fitting coefficient corresponding to each line;

[0155] A third fault measure value determination unit, configured to determine the product result between the fitting coefficient corresponding to each line and the second fault measure value of the line under different fault criteria as the third fault measure value of the line under the corresponding fault criterion.

[0156] Further, on the basis of the above-mentioned invention embodiments, the fault line selection result determination module 34 includes:

[0157] A multi-criterion fusion unit, configured to determine the fused fault probability value of each line by calling a preset D-S evidence fusion formula based on the basic probability assignment functions constructed for each line under different fault criteria;

[0158] A fault line determination unit, configured to traverse the fault probability values corresponding to each line, and determine the line corresponding to the maximum fault probability value as the fault line.

[0159] Further, on the basis of the above-mentioned invention embodiments, the preset D-S evidence fusion formula is expressed as:

[0160]

[0161] Wherein, A 1 = A 2 =... = A M = {l i |i = 0, 1, 2,..., N}; M represents the number of types of fault criteria; m(l i ) represents the fused fault probability value of the i-th line l i ; m(Θ) represents the uncertainty corresponding to the fault line; ∩ represents the intersection symbol.

[0162] Further, on the basis of the above-mentioned invention embodiments, the basic probability assignment function is expressed as:

[0163]

[0164] Wherein, m j (l i ) represents the basic probability assignment function of the i-th line l i under the fault criterion j; fm” j (l i ) represents the third fault measure value of the i-th line l i under the fault criterion j; acc(j) represents the proportion coefficient of the fault criterion j; m j (Θ) represents the uncertainty of the fault criterion j; Θ represents the set of mutually exclusive hypotheses of whether each line has a fault.

[0165] The multi-criterion fusion fault line selection device provided by the embodiments of the present invention can execute the multi-criterion fusion fault line selection method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0166] Embodiment 4

[0167] Figure 8The structural schematic diagram of an electronic device 40 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0168] As Figure 8 shown, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc. The memory stores a computer program executable by the at least one processor. The processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. The input / output (I / O) interface 45 is also connected to the bus 44.

[0169] A plurality of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disc, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0170] The processor 41 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the fault line selection method of multi-criterion fusion.

[0171] In some embodiments, the fault line selection method with multi-criterion fusion can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the fault line selection method with multi-criterion fusion described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to execute the fault line selection method with multi-criterion fusion in any other suitable manner (e.g., by means of firmware).

[0172] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs, the one or more computer programs can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0173] In some embodiments, the fault line selection method with multi-criterion fusion can be implemented as a computer program, which is invisibly contained in a computer program product. The computer program implements the fault line selection method with multi-criterion fusion of the present invention when executed by a processor. The computer program product can be understood as a software product that mainly implements its solution through the computer program. The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0174] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0175] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0176] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0177] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0178] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0179] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A fault line selection method based on multi-criteria fusion, characterized in that: The method comprises: When it is determined that a fault occurs in the distribution network, determining a first fault measurement value of each line under at least two fault criteria; Determining a second fault measurement value of each of the lines under different fault criteria according to differences between each of the first fault measurement values ​​and each of the fault criteria; Determining a third fault measurement value of each of the lines under different fault criteria according to differences in the second fault measurement values ​​between the lines; Based on the basic probability distribution function and DS evidence theory, multiple criteria are fused, and the corresponding fault line selection result is determined according to the fault probability value of each line after the multiple criteria are fused; wherein the basic probability distribution function is constructed based on each of the third fault measurement values.

2. The method according to claim 1, characterized in that When determining that a fault occurs in the distribution network, determining a first fault measurement value of each line under at least two fault criteria includes: When it is monitored that the bus zero-sequence voltage of the distribution network exceeds a preset threshold, obtaining zero-sequence current signals and bus zero-sequence voltage signals collected at each measuring point in the distribution network within a preset number of power frequency cycles after the fault occurs; Based on the zero-sequence current signal and the bus zero-sequence voltage signal, different fault judgment criteria are used to generate the first fault measurement value corresponding to each of the lines; wherein the fault judgment criteria include at least: a judgment based on feeder transient energy, a judgment based on feeder zero-state response current at the initial moment of the fault, and a judgment based on waveform similarity.

3. The method according to claim 2, characterized in that After generating the first fault measure value, the method further includes: The first fault measurement value is normalized.

4. The method according to claim 1, characterized in that The determining, according to the differences between the first fault measurement values ​​and the fault criteria, the second fault measurement values ​​of the lines under different fault criteria comprises: Based on fuzzy theory and each of the first fault measurement values, a standard fuzzy number corresponding to each of the fault criteria is generated; wherein the standard fuzzy number is a regular triangular fuzzy number; Based on a preset reference fuzzy number and a membership function corresponding to the standard fuzzy number, determining a proportion coefficient corresponding to each of the fault criteria, and using each of the proportion coefficients as a difference between the fault criteria; The product of the first fault measurement value of each line under different fault criteria and the proportion coefficient corresponding to the fault criterion is determined as the second fault measurement value of the line under the corresponding fault criterion.

5. The method according to claim 4, characterized in that The upper bound, median and lower bound of the standard fuzzy number are expressed as: Among them, a j 、b j and c j They represent the upper bound, median and lower bound of the standard fuzzy number corresponding to the jth fault criterion; fm j (l i ) represents the i-th line l i The first fault measure value under fault criterion j; N represents the total number of line numbers in the distribution network; ε( · ) represents a step function, and ε(0)=1.

6. The method according to claim 4, characterized in that The determining of the proportion coefficient corresponding to each of the fault criteria based on the preset reference fuzzy number and the membership function corresponding to the standard fuzzy number includes: For each of the fault criteria, the area of ​​the region enclosed by the first preset reference fuzzy number and the left membership function curve is recorded as the left adjacent area, the area of ​​the region enclosed by the first preset reference fuzzy number and the right membership function curve is recorded as the left far area, the area of ​​the region enclosed by the second preset reference fuzzy number and the right membership function curve is recorded as the right adjacent area, and the area of ​​the region enclosed by the second preset reference fuzzy number and the left membership function curve is recorded as the right far area; wherein the first preset reference fuzzy number is a regular triangular fuzzy number whose upper bound, median and lower bound are all 0, the second preset reference fuzzy number is a regular triangular fuzzy number whose upper bound, median and lower bound are all 1, the left membership function curve is the membership function curve located to the left of the median in the membership function, and the right membership function curve is the membership function curve located to the right of the median in the membership function; For each of the fault criteria, the average value of the left adjacent area and the left far area is recorded as the left average area, and the average value of the right adjacent area and the right far area is recorded as the right average area; Based on the left average area and the right average area corresponding to each fault criterion, calling a preset criterion mutual difference coefficient calculation formula to determine the mutual difference coefficient between any two different fault criterions; Based on the mutual difference coefficients, a preset criterion proportion coefficient calculation formula is called to determine the proportion coefficient corresponding to each fault criterion.

7. The method according to claim 1, characterized in that Determining the third fault measurement value of each of the lines under different fault criteria according to the difference of the second fault measurement values ​​between the lines includes: splicing the second fault measurement values ​​corresponding to different fault criteria of each line to obtain a line vector corresponding to each line; Based on each of the line vectors, calling a preset line fitting degree calculation formula to determine the fitting degree between any two different lines; Based on each of the line vectors and the degree of fitting, calling a preset line fitting coefficient calculation formula to determine the fitting coefficient corresponding to each of the lines; The product of the fitting coefficient corresponding to each of the lines and the second fault measurement value of the line under different fault judgment criteria is determined as the third fault measurement value of the line under the corresponding fault judgment criterion.

8. The method according to claim 1, characterized in that The multi-criteria fusion based on the basic probability distribution function and the DS evidence theory, and determining the corresponding fault line selection result according to the fault probability value of each line after the multi-criteria fusion, include: Based on the basic probability distribution function constructed for each of the lines under different fault criteria, calling a preset DS evidence fusion formula to determine the fused fault probability value of each of the lines; The fault probability values ​​corresponding to the lines are traversed, and the corresponding line with the largest fault probability value is determined as the faulty line.

9. The method according to claim 8, characterized in that The preset DS evidence fusion formula is expressed as: in, A1=A2= … =A M = {l i i=0,1,2, … , N}; M represents the number of types of fault judgment; m(l i ) represents the i-th line l i The fused fault probability value; m(Θ) represents the uncertainty corresponding to the faulty line; ∩ represents the intersection symbol.

10. The method according to claim 1, characterized in that The basic probability distribution function is expressed as: Among them, m j (l i ) represents the i-th line l i Basic probability distribution function under fault criterion j; fm" j (l i ) represents the i-th line l i The third fault measurement value under fault criterion j; acc(j) represents the proportion coefficient of fault criterion j; m j (Θ) represents the uncertainty of fault criterion j; Θ represents a set of mutually exclusive hypotheses about whether each line has a fault.

11. A multi-criteria fusion fault line selection device, characterized in that: The device comprises: A first fault measurement value determination module is used to determine a first fault measurement value of each line under at least two fault criteria when it is determined that a fault occurs in the distribution network; A second fault measurement value determination module, configured to determine a second fault measurement value of each of the lines under different fault criteria according to differences between each of the first fault measurement values ​​and each of the fault criteria; A third fault measurement value determination module, used to determine the third fault measurement value of each of the lines under different fault judgment criteria according to the difference of the second fault measurement values ​​between the lines; A fault line selection result determination module is used to perform multi-criteria fusion based on a basic probability distribution function and DS evidence theory, and determine the corresponding fault line selection result according to the fault probability value of each line after the fusion of the multi-criteria; wherein the basic probability distribution function is constructed based on each of the third fault measurement values.

12. The device according to claim 11, characterized in that The first fault measurement value determination module includes: A signal acquisition unit, configured to acquire zero-sequence current signals and bus zero-sequence voltage signals collected at each measuring point in the distribution network within a preset number of power frequency cycles after a fault occurs, when it is monitored that the bus zero-sequence voltage of the distribution network exceeds a preset threshold value; The first fault measurement value determination unit is used to generate the first fault measurement value corresponding to each of the lines based on the zero-sequence current signal and the bus zero-sequence voltage signal using different fault judgment criteria; wherein the fault judgment criteria at least include: a judgment based on feeder transient energy, a judgment based on feeder zero-state response current at the initial moment of the fault, and a judgment based on waveform similarity.

13. The device according to claim 12, characterized in that The first fault measurement value determination module also includes: A normalization unit is used to perform normalization processing on the first fault measurement value after generating the first fault measurement value.

14. The device according to claim 11, characterized in that The second fault measurement value determination module includes: A standard fuzzy number generating unit, used for generating a standard fuzzy number corresponding to each of the fault criteria based on fuzzy theory and each of the first fault measurement values; wherein the standard fuzzy number is a regular triangular fuzzy number; A proportion coefficient determination unit, used to determine the proportion coefficient corresponding to each of the fault criteria based on a preset reference fuzzy number and a membership function corresponding to the standard fuzzy number, and use each of the proportion coefficients as the difference between the fault criteria; The second fault measurement value determination unit is used to determine the product of the first fault measurement value of each line under different fault criteria and the proportion coefficient corresponding to the fault criterion as the second fault measurement value of the line under the corresponding fault criterion.

15. The device according to claim 14, characterized in that The upper bound, median and lower bound of the standard fuzzy number are expressed as: Among them, a j 、b j and c j They represent the upper bound, median and lower bound of the standard fuzzy number corresponding to the jth fault criterion; fm j (l i ) represents the i-th line l i The first fault measure value under fault criterion j; N represents the total number of line numbers in the distribution network; ε( · ) represents a step function, and ε(0)=1.

16. The device according to claim 14, characterized in that The proportion coefficient determination unit is specifically used for: For each of the fault criteria, the area of ​​the region enclosed by the first preset reference fuzzy number and the left membership function curve is recorded as the left adjacent area, the area of ​​the region enclosed by the first preset reference fuzzy number and the right membership function curve is recorded as the left far area, the area of ​​the region enclosed by the second preset reference fuzzy number and the right membership function curve is recorded as the right adjacent area, and the area of ​​the region enclosed by the second preset reference fuzzy number and the left membership function curve is recorded as the right far area; wherein the first preset reference fuzzy number is a regular triangular fuzzy number whose upper bound, median and lower bound are all 0, the second preset reference fuzzy number is a regular triangular fuzzy number whose upper bound, median and lower bound are all 1, the left membership function curve is the membership function curve located to the left of the median in the membership function, and the right membership function curve is the membership function curve located to the right of the median in the membership function; For each of the fault criteria, the average value of the left adjacent area and the left far area is recorded as the left average area, and the average value of the right adjacent area and the right far area is recorded as the right average area; Based on the left average area and the right average area corresponding to each fault criterion, calling a preset criterion mutual difference coefficient calculation formula to determine the mutual difference coefficient between any two different fault criterions; Based on the mutual difference coefficients, a preset criterion proportion coefficient calculation formula is called to determine the proportion coefficient corresponding to each fault criterion.

17. The device according to claim 11, characterized in that The third fault measure value determination module includes: A line vector generating unit, used for splicing the second fault measurement values ​​corresponding to different fault criteria of each line to obtain a line vector corresponding to each line; A fitting degree determination unit, configured to determine the fitting degree between any two different routes by calling a preset route fitting degree calculation formula based on each route vector; A fitting coefficient determination unit, configured to determine the fitting coefficient corresponding to each of the lines by calling a preset line fitting coefficient calculation formula based on each of the line vectors and the fitting degree; The third fault measurement value determining unit is used to determine the product of the fitting coefficient corresponding to each of the lines and the second fault measurement value of the line under different fault criteria as the third fault measurement value of the line under the corresponding fault criterion.

18. The device according to claim 11, characterized in that The fault line selection result determination module comprises: A multi-criteria fusion unit, configured to call a preset DS evidence fusion formula to determine the fused fault probability value of each line based on the basic probability distribution function constructed under different fault criteria for each line; The fault line determination unit is used to traverse the fault probability values ​​corresponding to the lines, and determine the corresponding line with the largest fault probability value as the fault line.

19. The device according to claim 18, characterized in that The preset DS evidence fusion formula is expressed as: in, A1=A2= … =A M = {l i i=0,1,2, … , N}; M represents the number of types of fault judgment; m(l i ) represents the i-th line l i The fused fault probability value; m(Θ) represents the uncertainty corresponding to the faulty line; ∩ represents the intersection symbol.

20. The device according to claim 11, characterized in that The basic probability distribution function is expressed as: Among them, m j (l i ) represents the i-th line l i Basic probability distribution function under fault criterion j; fm" j (l i ) represents the i-th line l i The third fault measurement value under fault criterion j; acc(j) represents the proportion coefficient of fault criterion j; m j (Θ) represents the uncertainty of fault criterion j; Θ represents a set of mutually exclusive hypotheses about whether each line has a fault.

21. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the fault line selection method based on multi-criteria fusion as described in any one of claims 1-10.

22. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the fault line selection method for multi-criteria fusion according to any one of claims 1 to 10 when executed.

23. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the fault line selection method based on multi-criteria fusion according to any one of claims 1 to 10 is implemented.

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