CN116381413A - Active power distribution network fault identification method based on wavelet singular entropy - Google Patents

Active power distribution network fault identification method based on wavelet singular entropy Download PDF

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CN116381413A
CN116381413A CN202310450936.3A CN202310450936A CN116381413A CN 116381413 A CN116381413 A CN 116381413A CN 202310450936 A CN202310450936 A CN 202310450936A CN 116381413 A CN116381413 A CN 116381413A
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fault
current
array
wavelet
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陈灵
翟博龙
黄见虹
宋福海
吕庭钦
林美华
林叶青
余定文
童纯洁
吴晨阳
叶雷
郑益琴
郑南章
郭健生
邱碧丹
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State Grid Fujian Electric Power Co Ltd
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    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
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Abstract

The invention provides an active power distribution network fault identification method based on wavelet singular entropy, which comprises the following steps: firstly, constructing a starting criterion of a fault identification algorithm by using three-phase current data obtained by sampling, and after the fault identification algorithm is started, performing filtering treatment on a three-phase current array obtained by sampling by using a median origin moment filtering algorithm; then calculating and comparing the mutation current derivative, the mutation current effective value and the mutation current waveform consistency degree by utilizing the fault three-phase current array obtained after filtering, and judging a fault point front section and a normal line section according to the mutation current derivative, the mutation current effective value and the mutation current waveform consistency degree; and finally, calculating wavelet singular entropy of three phases of fault three-phase current A, B, C obtained after filtering, constructing a fault phase selection criterion based on the wavelet singular entropy by utilizing the characteristic that the wavelet singular entropy value of a fault phase is larger than that of a non-fault phase, and judging the fault phase.

Description

Active power distribution network fault identification method based on wavelet singular entropy
Technical Field
The invention belongs to the technical field of fault identification of an active power distribution network with a distributed power supply, and particularly relates to a method for identifying faults of the active power distribution network based on wavelet singular entropy.
Background
Currently, the permeability of distributed power sources in power distribution networks is increasing. With the continuous increase of the access and capacity of distributed power supplies (DG) of a power distribution network, the network structure and the operation mode of the DG are increasingly complex, so that the conventional fault positioning and identifying method is difficult to meet the requirements. After the power distribution network breaks down, the fault position is rapidly and effectively diagnosed, the fault type is identified, and the method has important significance in reducing the fault recovery time and improving the safe and stable operation and the power supply reliability of the power distribution network.
In a traditional single-power radial structure power distribution network, the fault section is generally positioned by utilizing the phenomenon that fault current can be detected at an upstream switch of a fault point and fault current cannot be detected at a downstream switch of the fault point. And judging the types of short-circuit faults such as single-phase grounding, two-phase short-circuit, two-phase grounding short-circuit, three-phase short-circuit and the like according to the power frequency steady-state quantities such as three-phase current, three-phase voltage and the like of the fault line.
However, the methods have the advantages of low fault detection sensitivity, low fault type identification speed and easiness in being influenced by factors such as transition resistance, fault distance and the like. More importantly, after the distributed power supply is connected, the characteristics of short-circuit current can be obviously changed due to the topological structure and the morphological change of the power distribution network, so that the traditional fault positioning method is invalid, the fault identification result is inaccurate, and a great challenge is brought to the relay protection reliability of the power distribution system.
Aiming at the problems, improvement on the fault positioning and identifying method of the traditional distribution network is needed, and the action accuracy of relay protection of the distribution network with the distributed power supply is improved.
Disclosure of Invention
The invention provides an active power distribution network fault identification method based on wavelet singular entropy, which aims at solving the problems that a traditional fault identification scheme is difficult to accurately and rapidly locate a fault section and a fault type judgment result is inaccurate because the topological structure and short-circuit current distribution characteristics of a power distribution network are complicated after the power distribution network is connected with the distributed power supply, and greatly improves the capability of carrying out fault identification on the power distribution network containing the distributed power supply.
Aiming at the problems that the network structure and the operation mode of the power distribution network are increasingly complex due to distributed power supply access, and the traditional fault positioning and identification method is difficult to meet the requirements, the invention comprehensively utilizes reliable fault identification algorithm starting criteria, a filtering algorithm with superior performance, three-phase current abrupt change and wavelet singular entropy of fault three-phase current to form an active power distribution network fault identification method based on the wavelet singular entropy:
firstly, constructing a starting criterion of a fault identification algorithm by using three-phase current data obtained by sampling, and after the fault identification algorithm is started, performing filtering treatment on a three-phase current array obtained by sampling by using a median origin moment filtering algorithm; then calculating and comparing the mutation current derivative, the mutation current effective value and the mutation current waveform consistency degree by utilizing the fault three-phase current array obtained after filtering, and judging a fault point front section and a normal line section according to the mutation current derivative, the mutation current effective value and the mutation current waveform consistency degree; and finally, calculating wavelet singular entropy of three phases of fault three-phase current A, B, C obtained after filtering, constructing a fault phase selection criterion based on the wavelet singular entropy by utilizing the characteristic that the wavelet singular entropy value of a fault phase is larger than that of a non-fault phase, and judging the fault phase.
The invention adopts the following technical scheme:
an active power distribution network fault identification method based on wavelet singular entropy comprises the following steps: firstly, using three-phase current data obtained by sampling as a starting criterion of a fault identification algorithm, and after the fault identification algorithm is started, performing filtering processing on a three-phase current array obtained by sampling by using a median origin moment filtering algorithm; then calculating and comparing the mutation current derivative, the mutation current effective value and the mutation current waveform consistency degree by utilizing the fault three-phase current array obtained after filtering, and judging a fault point front section and a normal line section according to the mutation current derivative, the mutation current effective value and the mutation current waveform consistency degree; and finally, calculating wavelet singular entropy of three phases of fault three-phase current A, B, C obtained after filtering, and judging the fault phase by using a fault phase selection criterion based on the wavelet singular entropy by utilizing the characteristic that the wavelet singular entropy value of the fault phase is larger than that of the non-fault phase.
Further, the method specifically comprises the following steps:
step S1, performing real-time three-phase current data sampling by utilizing intelligent electronic protection equipment IED to form a sampling array I a ＝[I a1 ,I a2 ,···I ak ]、I b ＝[I b1 ,I b2 ,···I bk ]、I c ＝[I c1 ,I c2 ,···I ck ]；
S2, constructing a starting criterion of a fault identification algorithm：I a 、I b 、I c Whether the sum of current sampling values of any phase array in a period is larger than a set current value or not is judged, the data of the sampling array in the step S1 are substituted into a starting criterion, and whether a fault identification algorithm based on wavelet singular entropy is started or not is judged;
step S3, after the starting condition of the fault identification algorithm is met, performing median origin moment filtering processing on the sampled fault three-phase current array: firstly, selecting a sliding window, and sequencing elements of the sliding window from small to large to obtain a new array; and the element in the original array is replaced by the middle digit to obtain a new fault three-phase current array
Figure BDA0004197458660000021
Taking a sliding window for Y to obtain an array +.>
Figure BDA0004197458660000022
Computing array->
Figure BDA0004197458660000023
To obtain new array I after median origin moment filtering a '＝[I a1 ',I a2 ',···I ak ']The method comprises the steps of carrying out a first treatment on the surface of the Finally obtaining the filtered fault three-phase current array as
Figure BDA0004197458660000024
S4, utilizing the fault three-phase current array obtained after filtering
Figure BDA0004197458660000031
Calculating a discrimination index comprising a mutation current derivative, a mutation current effective value and a correlation coefficient representing the consistency degree of mutation current waveforms; comparing the fault point front section with the threshold value, and judging a normal line section according to the comparison; thereby realizing the positioning of the fault section;
s5, calculating fault three-phase current obtained after filtering
Figure BDA0004197458660000032
A. B, C three-phase wavelet singular entropy formed by wavelet transformation, singular value decomposition and information entropy combination; then, by utilizing the characteristic that the wavelet singular entropy value of the fault phase is larger than that of the non-fault phase, processing the wavelet singular entropy of each order of A, B, C three-phase signals by superposing and accumulating the relative difference of the singular entropy of the fault phase and the non-fault Xiang Xiaobo; and finally, constructing and obtaining a fault phase selection criterion based on wavelet singular entropy and judging a fault phase.
Further, in step S1, the A, B, C three-phase current of the line is sampled in real time, which specifically includes:
a, B, C three-phase current of real-time sampling line of intelligent electronic protection device IED to form sampling array I a ＝[I a1 ,I a2 ,···I ak ]、I b ＝[I b1 ,I b2 ,···I bk ]、I c ＝[I c1 ,I c2 ,···I ck ]；
Wherein k is the number of the sampling arrays, the sampling frequency is 25kHz, namely the number of sampling points in 20ms of each power frequency cycle is 500, the sampling arrays totally store sampling data of 2 cycles, namely the value of k is 2 times of the number of sampling of each cycle, and the value of k corresponds to 1000.
Further, the specific process of calculating the starting criterion of the fault identification algorithm in step S2 is as follows:
constructing a starting criterion of a fault identification algorithm, substituting the data of the sampling array in the step S1 into the starting criterion, and judging whether the fault identification algorithm based on wavelet singular entropy is started or not;
step S2, a fault identification algorithm starting criterion specifically comprises the following steps:
when I a 、I b 、I c The fault identification algorithm is started when the sum of the current sampling values of one period of any phase of the arrays is larger than a setting value, namely the fault identification algorithm is started when the following value is 1, and the fault identification algorithm is not started when the value is 0:
Figure BDA0004197458660000033
wherein the method comprises the steps ofN is the number of current sampling points per cycle, and is recommended to be 500; n+1 is not less than i and not more than k, i is E Z; i set To set the current value, the following is set:
I set ＝5I max
wherein I is max And when the circuit operates normally, the corresponding phase current is the maximum sampling value in each cycle.
Further, in step S3, after the starting condition of the fault identification algorithm is satisfied, sampling the obtained fault three-phase current array I a ＝[I a1 ,I a2 ,···I ak ]、I b ＝[I b1 ,I b2 ,···I bk ]、I c ＝[I c1 ,I c2 ,···I ck ]Filtering the neutral origin moment, and setting an A-phase current array I a The specific filtering algorithm is as follows:
step S31: selecting a sliding window of length h, i.e. selecting I a The h data in (a) form an array I ah ＝[I a(i) ,I a(i+1) ,…,I a(i+h-1) ]Wherein i=1, 2, …, k-h+1, and ordering the elements in each array in order from small to large to obtain a new array
Figure BDA0004197458660000041
Step S32: the method for calculating the median of the new array comprises the following steps:
Figure BDA0004197458660000042
the calculated median y i Substitute for primitive array I a ＝[I a1 ,I a2 ,···I ak ]Element I in (a) a(i) Updating array I from i=1 to i=k-h+1 a K-h+1 elements in the array to obtain a new array
Figure BDA0004197458660000043
Step S33: according to the array Y obtained in step S32, the length is still taken ash, obtaining an array
Figure BDA0004197458660000044
Wherein i=1, 2, …, k-h+1; computing array->
Figure BDA0004197458660000045
The specific algorithm is as follows:
Figure BDA0004197458660000046
in the above-mentioned description of the invention,
Figure BDA0004197458660000047
for array->
Figure BDA0004197458660000048
The power of K of the j-th element in (a); array->
Figure BDA0004197458660000049
K-th order origin moment->
Figure BDA00041974586600000410
Substitution of the ith element of array Y +.>
Figure BDA00041974586600000411
In this way, k-h+1 elements in array Y are updated from i=1 to i=k-h+1, resulting in a new array I a '＝[I a1 ',I a2 ',···I ak ']Namely, an array after median origin moment filtering;
B. the phase A treatment process of the C two-phase current filtering process is the same, and finally the filtered fault three-phase current array is obtained as
Figure BDA00041974586600000412
Further, the specific steps of step S4 are as follows:
step S41: according to the post-filteringThe fault three-phase current data of the transformer and the three-phase current data collected during normal operation are calculated to obtain a current abrupt change, and the abrupt change current is derived; and comparing the abrupt current derivative with a threshold value delta i of the abrupt current derivative set ' size;
the fault section judging flow of the line L section A phase current is as follows:
the A phase current collected in the L section of the line in normal operation is i aL The method comprises the steps of carrying out a first treatment on the surface of the The fault A phase current after the L section filtering of the line is i aL 'A'; the fault a-phase current sudden amount of line L segment is:
Δi aL ＝i aL '-i aL
wherein Δi aL The fault A phase current abrupt change of the line L section;
for Δi aL Deriving to obtain the mutation current derivative delta i' aL According to the same processing mode as the phase A, carrying out mutation current derivative calculation on B, C two phases to obtain delta i' bL 、Δi' cL Combining the maximum value of the three-phase abrupt current derivative with the threshold value delta i of the abrupt current derivative set ' size comparison:
max(Δi' aL ,Δi' bL ,Δi' cL )≥Δi s ' et
if the above is true, go to step S42 to make further judgment, otherwise, judge line L as normal line section; wherein Δi set ' is a setting value, generally (0.1-0.6) I max ；
Step S42: calculating the effective value of each phase of abrupt current, and comparing the effective value with the threshold value of the abrupt current;
the calculation formula of the effective value of the phase A abrupt current is as follows:
Figure BDA0004197458660000051
the effective value delta I of B, C two-phase abrupt current is obtained by adopting the same calculation mode BL 、ΔI CL The method comprises the steps of carrying out a first treatment on the surface of the And comparing the three-phase abrupt current effective value with an abrupt current threshold value:
Figure BDA0004197458660000052
wherein max is defined as the maximum value of the sequence of values in brackets; ΔI max Is the maximum value in the effective values of the phase A, B and C abrupt current; ΔI set For abrupt current threshold, it is generally preferable to take (0.1-0.5) I max ；
If the relation is not established after substituting the data, judging that the section line is a normal section; if the above relation is satisfied, step S43 is performed to determine the degree of coincidence of the abrupt current waveform;
step S43: calculating a correlation coefficient representing the consistency degree of the abrupt current waveform, and comparing the correlation coefficient with a correlation coefficient threshold value:
the formula for calculating the correlation coefficient between the A, B phases is as follows:
Figure BDA0004197458660000061
wherein Σ represents the summation symbol; i.e a (k) Is the phase A current abrupt quantity; i.e b (k) Phase B current abrupt; beta ab Is the correlation coefficient of A, B two-phase abrupt current waveform;
calculating A, C two-phase correlation coefficient beta by using the same structural formula ac B, C two-phase correlation coefficient beta bc The method comprises the steps of carrying out a first treatment on the surface of the Comparing with a correlation coefficient threshold value:
Figure BDA0004197458660000062
wherein beta is min Is the minimum value in the correlation coefficient; beta set The threshold value of the correlation coefficient is generally (0.1-0.3);
if the above is true, the segment is a normal line segment; otherwise, the section is indicated as the section before the fault point.
Further, the specific steps of step S5 are as follows:
step S51: calculating A, B, C three-phase wavelet singular entropy:
the calculation process of the A Xiang Xiaobo singular entropy comprises the following steps:
firstly, carrying out wavelet transformation on the A-phase current obtained after filtering to obtain a wavelet transformation coefficient matrix H A M×n order matrix H A Is expressed as:
H A ＝MQ A N T
wherein M is m×m order orthogonal matrix; n is an N multiplied by N order orthogonal matrix; q (Q) A ＝diag(λ 1 ,λ 2 ,...λ p ) Is a diagonal matrix, the non-negative diagonal elements of which are arranged in descending order, namely a wavelet transformation coefficient matrix H A Is a singular eigenvalue of (2);
the wavelet transformation, singular value decomposition and information entropy are organically combined to form wavelet singular entropy, and a specific formula for calculating the x-order wavelet singular entropy is shown as follows:
Figure BDA0004197458660000071
in the method, in the process of the invention,
Figure BDA0004197458660000072
for the ith non-zero singular value lambda i Is a singular entropy of the incremental wavelet;
substituting singular eigenvalues of corresponding diagonal elements into a formula to obtain A Xiang Xiaobo singular entropy WSE A (x) Then adopting the same process as the phase A to obtain B, C two-phase wavelet singular entropy WSE B (x)、WSE C (x)；
Step S52: processing the singular entropy of each order wavelet of each phase signal;
since the wavelet singular entropy of the fault phase is larger than that of the non-fault phase, the wavelet singular entropy relative ratio of each phase is selected, the wavelet singular entropy of the 2-gamma order (recommended gamma=6) of the first gamma singular eigenvalues is overlapped, and the relative difference of the fault phase and the non-fault Xiang Xiaobo singular entropy is accumulated through overlapping, and the specific processing process is as follows:
Figure BDA0004197458660000073
Figure BDA0004197458660000074
Figure BDA0004197458660000075
wherein h is a 、h b 、h c As the basis of fault options; WSE (Wireless sensor array) A (x)、WSE B (x)、WSE C (x) A, B, C three-phase wavelet singular entropy;
step S53: constructing a fault phase selection criterion based on wavelet singular entropy and judging a fault phase:
step S531: according to the condition (h) of three-phase ground fault of the transmission line a ,h b ,h c ) Determining epsilon 1 、ε 2 ，ε 1 ＝min(h a ,h b ,h c )，ε 2 ＝max(h a ,h b ,h c )；
Wherein min is defined as the minimum value of the value sequence in brackets, and max is defined as the maximum value of the value sequence in brackets;
step S532: calculate h a 、h b 、h c The specific judging flow is as follows:
if the calculation result satisfies epsilon 1 ≤h a ,h b ≤ε 2 ,h c ≤ε 2 Judging that the three-phase short circuit fault exists; if the calculated result is h a ≤ε 1 ,h b ≤ε 1 ,h c ≤ε 1 Judging that no fault phase exists; if the calculated result is h p ＞ε 2 (p=a, b, c) and p=1, i.e. any one of a, b, c, then judging that the single-phase earth fault exists, and the p phase is the fault phase; if the calculated result is h p ＞ε 2 (p=a, b, c) and p=2, i.ea. b, c, then further judging whether two phases are short-circuited or two phases are short-circuited to ground: if there is
Figure BDA0004197458660000081
Judging that the two-phase short circuit fault exists; if there is->
Figure BDA0004197458660000082
Judging that the two phases are in short circuit fault; wherein p1 and p2 denote two phases with short-circuit failure, +.>
Figure BDA0004197458660000083
Refers to the non-faulted phase.
Compared with the prior art, the scheme provided by the invention comprehensively utilizes reliable fault identification algorithm starting criteria, filtering algorithms with superior performance, three-phase current abrupt change and wavelet singular entropy of fault three-phase current, can accurately identify fault sections only by measuring current, can quickly and accurately identify various faults without network communication participation, and has good adaptability to factors such as fault positions, fault moments, transition resistances and the like.
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The invention is described in further detail below with reference to the attached drawings and detailed description:
FIG. 1 is a flow chart of the operation of an embodiment of the present invention;
FIG. 2 is a simulation model diagram built by the SCAD simulation platform in the step P according to the embodiment of the invention;
FIG. 3 is a schematic diagram of an embodiment of the present invention for determining whether a fault identification algorithm is activated according to a activation criterion;
FIG. 4 is a comparison graph of three phase current waveforms before and after median origin moment filtering in an embodiment of the present invention.
Detailed Description
In order to make the features and advantages of the present patent more comprehensible, embodiments accompanied with figures are described in detail below:
the following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The components generally described and illustrated in the figures herein may be combined in different configurations. Thus, the following detailed description of selected embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention based on the embodiments of the present invention.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
The following description of the present invention will be further presented with reference to a specific example using the present invention as shown in fig. 1.
Fig. 2 is a simulation model of a power distribution network including a distributed power supply built based on a PSCAD simulation platform. The system main network power supply is connected to the power distribution network through a transformer with the transformation ratio of 35KV/10KV, and various distributed power supplies are connected to the power distribution network in a wiring mode of 10KV T connected with a public power network line, 10KV T connected with a public power network bus, 380V T connected with the public power network bus and the like. The rated operating frequency of the power distribution network is 50Hz, and the parameter r of the positive sequence line is 1 ＝3.45×10 -4 Ω/m，xl 1 ＝2.7×10 -4 Ω/m,xc 1 = 381.7mΩ×m; zero sequence line parameter r 0 ＝1.035×10 -3 Ω/m，xl 1 ＝8.1×10 -4 Ω/m,xc 1 = 1145.1mΩ×m. The protection is arranged on two sides of each section of line close to the bus or on one side close to the bus. Taking line fault identification of the simulation model based on wavelet singular entropy as an example, the implementation of the embodiment of the invention is described.
Assume that an a-phase ground short fault occurs between circuit breaker 13 and circuit breaker 14.
According to the operating steps as shown in fig. 1:
step S1, utilizing an intelligent electronic protection device (IED) to sample A of a line in real time,B. C three-phase current, sampling the real-time three-phase current data by using the sampling frequency of 25kHz and the sampling point number of 1000 to form a sampling array I a ＝[I a1 ,I a2 ,···I ak ]、I b ＝[I b1 ,I b2 ,···I bk ]、I c ＝[I c1 ,I c2 ,···I ck ]。
And S2, substituting the data of the sampling array in the step S1 into a fault identification algorithm starting criterion, and judging whether the fault identification algorithm based on wavelet singular entropy is started or not.
I set ＝5I max ＝5×0.32＝1.6(KA)
Figure BDA0004197458660000091
Figure BDA0004197458660000092
Accordingly, the start of the fault identification algorithm can be judged. FIG. 3 is a diagram of an embodiment for determining whether a fault identification algorithm is activated based on a activation criterion.
Step S3, sampling the obtained fault three-phase current array I a ＝[I a1 ,I a2 ,···I ak ]、I b ＝[I b1 ,I b2 ,···I bk ]、I c ＝[I c1 ,I c2 ,···I ck ]Filtering the neutral origin moment by using an A-phase current array I a For example, a specific filter processing algorithm is as follows:
step one: selecting a sliding window of length 10, i.e. selecting I a Of 10 data to form array I a10 ＝[I a(i) ,I a(i+1) ,…,I a(i+9) ]Wherein i=1, 2, …,991, the elements in each array are ordered in order from small to large to obtain a new array
Figure BDA0004197458660000093
Step two: the method for calculating the median of the new array comprises the following steps:
Figure BDA0004197458660000094
the median y obtained by the calculation is calculated i Substitute for primitive array I a ＝[I a1 ,I a2 ,···I ak ]Element I in (a) a(i) . In this way, array I is updated from i=1 to i=991 a 991 elements in the array to obtain a new array
Figure BDA0004197458660000101
Step three: according to the array Y obtained in the second step, the sliding window length with the length of 10 is still taken to obtain an array
Figure BDA0004197458660000102
Where i=1, 2, …,991. Computing array->
Figure BDA0004197458660000103
The specific algorithm is as follows:
Figure BDA0004197458660000104
in the above-mentioned description of the invention,
Figure BDA0004197458660000105
for array->
Figure BDA0004197458660000106
To the power of 2 of the j-th element. Array->
Figure BDA0004197458660000107
2 nd order origin moment +.>
Figure BDA0004197458660000108
Substitution of the ith element of array Y +.>
Figure BDA0004197458660000109
In this way, 991 elements in array Y are updated from i=1 to i=991, resulting in a new array I a '＝[I a1 ',I a2 ',···I a1000 ']The array is the array after the median origin moment filtering.
The B, C two-phase current is processed as well, and finally the filtered fault three-phase current array is obtained
Figure BDA00041974586600001010
Fig. 4 is a comparison graph of three-phase current waveforms before and after median origin moment filtering in the embodiment.
S4, utilizing the fault three-phase current array obtained after filtering
Figure BDA00041974586600001011
And calculating discrimination indexes such as mutation current derivative, mutation current effective value, correlation coefficient representing mutation current waveform consistency degree and the like. Comparing it with threshold, the line between circuit breaker 13 and circuit breaker 14 is designated as section L, and the calculation process is exemplified.
Step one: the fault a-phase current sudden variable of the line section is:
Δi aL ＝i aL '-i aL ＝1.26(KA)
for Δi aL The derivative of the mutation current delta i 'can be obtained by deriving' aL The mutation current derivative calculation was performed on B, C two phases to obtain Δi 'in the same manner, which was=63.4a/ms' bL ＝12.6A/ms、Δi' cL =11.3A/ms, and the obtained maximum value of the three-phase abrupt current derivative and threshold value Δi of the abrupt current derivative are used set Comparison of' =20a/ms found:
max(63.4A/ms,12.6A/ms,11.3A/ms)＝63.4A/ms≥Δi s ' et ＝20A/ms
step two: the effective value of the abrupt current of each phase is calculated, and the specific process of calculating the effective value of the abrupt current is as follows:
Figure BDA00041974586600001012
the effective value delta I of B, C two-phase abrupt current can be obtained by the same method BL ＝0.24KA、ΔI CL =0.27 KA. The three-phase abrupt current effective value and the abrupt current threshold value delta I are calculated set Comparison =0.65 KA:
Figure BDA0004197458660000111
step three: and calculating a correlation coefficient representing the consistency degree of the abrupt current waveform, and comparing the correlation coefficient with a correlation coefficient threshold value.
The correlation coefficient can measure the consistency of the three-phase abrupt current waveform, taking the calculation of the correlation coefficient between the two phases of A, B as an example, and the specific calculation formula is as follows:
Figure BDA0004197458660000112
the same principle can sequentially calculate A, C two-phase correlation coefficient beta AC =0.71, b, C two-phase correlation coefficient β BC ＝1.47。
Figure BDA0004197458660000113
Therefore, the section is the section before the fault point, and the step judgment is performed on other sections in the same way, so that the section L (i.e. the line between the breaker 13 and the breaker 14) is finally found to be the section where the fault point is located.
S5, calculating fault three-phase current obtained after filtering
Figure BDA0004197458660000114
A. B, C, performing fault phase judgment based on a fault phase selection criterion of wavelet singular entropy.
Step one: and calculating A, B, C three-phase wavelet singular entropy.
Firstly, carrying out wavelet transformation on the A-phase current obtained after filtering to obtain a wavelet transformation coefficient matrix H A . Singular value decomposition is carried out on the wavelet transformation coefficient matrix to obtain a diagonal matrix Q A . Specific m×n order matrix H A The singular value decomposition of (2) can be expressed as:
H A ＝MQ A N T
wherein M is m×m order orthogonal matrix; n is an N multiplied by N order orthogonal matrix; q (Q) A ＝diag(λ 1 ,λ 2 ,...λ p ) Is a diagonal matrix, the non-negative diagonal elements of which are arranged in descending order, namely a wavelet transformation coefficient matrix H A Is used for the singular eigenvalues of (a).
The wavelet transformation, singular value decomposition and information entropy are organically combined to form wavelet singular entropy, and a specific formula for calculating the x-order wavelet singular entropy is shown as follows:
Figure BDA0004197458660000121
in the method, in the process of the invention,
Figure BDA0004197458660000122
for the ith non-zero singular value lambda i Is used for the incremental wavelet singular entropy.
Substituting the singular eigenvalues (namely diagonal elements) into a formula to obtain the A Xiang Xiaobo singular entropy WSE A (x) The wavelet singular entropy WSE of B, C two phases can be obtained by the same method B (x)、WSE C (x)。
Step two: and processing the singular entropy of each order wavelet of each phase signal.
The wavelet singular entropy relative ratio of each phase is selected, 2-6-order wavelet singular entropy of the first 6 (gamma=6 is recommended) singular eigenvalues are overlapped, and the relative difference of fault phases and non-fault Xiang Xiaobo singular entropy is accumulated through overlapping. The specific treatment process is as follows:
Figure BDA0004197458660000123
Figure BDA0004197458660000124
Figure BDA0004197458660000125
wherein h is a 、h b 、h c As the basis of fault options; WSE (Wireless sensor array) A (x)、WSE B (x)、WSE C (x) The above-obtained A, B, C three-phase wavelet singular entropy was used.
Step three: and constructing a fault phase selection criterion based on wavelet singular entropy and judging a fault phase.
1) According to the condition (h) of three-phase ground fault of the transmission line a ,h b ,h c ) Determining epsilon 1 ＝16、ε 2 ＝42。
2) The calculated result is h a ＞ε 2 And p=1, then it is determined that the single phase is faulty, and the a phase is the faulty phase.
Thus identifying the failed phase as phase a.
To sum up, it is judged that: the A-phase grounding short-circuit fault occurs in the circuit between the circuit breaker 13 and the circuit breaker 14, so that the diagnosis result of the active power distribution network fault identification method based on wavelet singular entropy is correct, and the diagnosis performance is superior.
In addition, other short-circuit fault types are set for developing more example displays. The phase selection results of different fault types of the active power distribution network fault identification method based on wavelet singular entropy are shown in the table one.
Figure BDA0004197458660000126
Figure BDA0004197458660000131
The logic programming scheme among the above schemes provided in this embodiment may be stored in a computer readable storage medium in a coded form, implemented in a computer program, and input basic parameter information required for calculation through computer hardware, and output a calculation result.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to methods, apparatus (means), and computer program products in accordance with embodiments of the present invention. It should be understood that each flow may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart or flows.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flows.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the invention in any way, and any person skilled in the art may make modifications or alterations to the disclosed technical content to the equivalent embodiments. However, any simple modification, equivalent variation and variation of the above embodiments according to the technical substance of the present invention still fall within the protection scope of the technical solution of the present invention.
The present patent is not limited to the above-mentioned best embodiment, any person can obtain other active power distribution network fault identification methods based on wavelet singular entropy in various forms under the teaching of the present patent, and all equivalent changes and modifications made according to the scope of the present patent should be covered by the present patent.

Claims (7)

1. A method for identifying faults of an active power distribution network based on wavelet singular entropy is characterized by comprising the following steps: firstly, using three-phase current data obtained by sampling as a starting criterion of a fault identification algorithm, and after the fault identification algorithm is started, performing filtering processing on a three-phase current array obtained by sampling by using a median origin moment filtering algorithm; then calculating and comparing the mutation current derivative, the mutation current effective value and the mutation current waveform consistency degree by utilizing the fault three-phase current array obtained after filtering, and judging a fault point front section and a normal line section according to the mutation current derivative, the mutation current effective value and the mutation current waveform consistency degree; and finally, calculating wavelet singular entropy of three phases of fault three-phase current A, B, C obtained after filtering, and judging the fault phase by using a fault phase selection criterion based on the wavelet singular entropy by utilizing the characteristic that the wavelet singular entropy value of the fault phase is larger than that of the non-fault phase.
2. The method for identifying the faults of the active power distribution network based on wavelet singular entropy according to claim 1 is characterized by comprising the following steps:
step S1, performing real-time three-phase current data sampling by utilizing intelligent electronic protection equipment IED to form a sampling array I a ＝[I a1 ,I a2 ,···I ak ]、I b ＝[I b1 ,I b2 ,···I bk ]、I c ＝[I c1 ,I c2 ,···I ck ]；
S2, constructing a starting criterion of a fault identification algorithm: i a 、I b 、I c Whether the sum of current sampling values of any phase array in a period is larger than a set current value or not is judged, the data of the sampling array in the step S1 are substituted into a starting criterion, and whether a fault identification algorithm based on wavelet singular entropy is started or not is judged;
step S3, after the starting condition of the fault identification algorithm is met, performing median origin moment filtering processing on the sampled fault three-phase current array: firstly, selecting a sliding window, and sequencing elements of the sliding window from small to large to obtain a new array; and the element in the original array is replaced by the middle digit to obtain a new fault three-phase current array
Figure FDA0004197458650000011
Taking a sliding window for Y to obtain an array +.>
Figure FDA0004197458650000012
Computing array->
Figure FDA0004197458650000013
Obtaining a new array after median origin moment filtering
Figure FDA0004197458650000014
Finally, the filtered fault three-phase current array is obtained as +.>
Figure FDA0004197458650000015
S4, utilizing the fault three-phase current array obtained after filtering
Figure FDA0004197458650000016
Calculating a discrimination index comprising a mutation current derivative, a mutation current effective value and a correlation coefficient representing the consistency degree of mutation current waveforms; and comparing with threshold value to determine failureA pre-point section and a normal line section; thereby realizing the positioning of the fault section;
s5, calculating fault three-phase current obtained after filtering
Figure FDA0004197458650000017
A. B, C three-phase wavelet singular entropy formed by wavelet transformation, singular value decomposition and information entropy combination; then, by utilizing the characteristic that the wavelet singular entropy value of the fault phase is larger than that of the non-fault phase, processing the wavelet singular entropy of each order of A, B, C three-phase signals by superposing and accumulating the relative difference of the singular entropy of the fault phase and the non-fault Xiang Xiaobo; and finally, constructing and obtaining a fault phase selection criterion based on wavelet singular entropy and judging a fault phase.
3. The method for identifying faults of the active power distribution network based on wavelet singular entropy according to claim 2, wherein in step S1, A, B, C three-phase currents of a line are sampled in real time, specifically comprising:
a, B, C three-phase current of real-time sampling line of intelligent electronic protection device IED to form sampling array I a ＝[I a1 ,I a2 ,···I ak ]、I b ＝[I b1 ,I b2 ,···I bk ]、I c ＝[I c1 ,I c2 ,···I ck ]；
Wherein k is the number of the sampling arrays, the sampling frequency is 25kHz, namely the number of sampling points in 20ms of each power frequency cycle is 500, the sampling arrays totally store sampling data of 2 cycles, namely the value of k is 2 times of the number of sampling of each cycle, and the value of k corresponds to 1000.
4. The method for identifying faults of the active power distribution network based on wavelet singular entropy according to claim 2, wherein the specific process of calculating the starting criterion of the fault identification algorithm in step S2 is as follows:
constructing a starting criterion of a fault identification algorithm, substituting the data of the sampling array in the step S1 into the starting criterion, and judging whether the fault identification algorithm based on wavelet singular entropy is started or not;
step S2, a fault identification algorithm starting criterion specifically comprises the following steps:
when I a 、I b 、I c The fault identification algorithm is started when the sum of the current sampling values of one period of any phase of the arrays is larger than a setting value, namely the fault identification algorithm is started when the following value is 1, and the fault identification algorithm is not started when the value is 0:
Figure FDA0004197458650000021
wherein n is the number of current sampling points per cycle; n+1 is not less than i and not more than k, i is E Z; i set To set the current value, the following is set:
I set ＝5I max
wherein I is max And when the circuit operates normally, the corresponding phase current is the maximum sampling value in each cycle.
5. The method for identifying faults of an active power distribution network based on wavelet singular entropy according to claim 2, wherein in step S3, after the starting condition of the fault identification algorithm is satisfied, the three-phase current array I of the fault obtained by sampling is sampled a ＝[I a1 ,I a2 ,···I ak ]、I b ＝[I b1 ,I b2 ,···I bk ]、I c ＝[I c1 ,I c2 ,···I ck ]Filtering the neutral origin moment, and setting an A-phase current array I a The specific filtering algorithm is as follows:
step S31: selecting a sliding window of length h, i.e. selecting I a The h data in (a) form an array I ah ＝[I a(i) ,I a(i+1) ,…,I a(i+h-1) ]Wherein i=1, 2, …, k-h+1, and ordering the elements in each array in order from small to large to obtain a new array
Figure FDA0004197458650000031
Step S32: the method for calculating the median of the new array comprises the following steps:
Figure FDA0004197458650000032
the calculated median y i Substitute for primitive array I a ＝[I a1 ,I a2 ,···I ak ]Element I in (a) a(i) Updating array I from i=1 to i=k-h+1 a K-h+1 elements in the array to obtain a new array
Figure FDA0004197458650000033
Step S33: according to the array Y obtained in the step S32, the sliding window length with the length of h is still taken to obtain an array
Figure FDA0004197458650000034
Wherein i=1, 2, …, k-h+1; computing array->
Figure FDA0004197458650000035
The specific algorithm is as follows:
Figure FDA0004197458650000036
in the above-mentioned description of the invention,
Figure FDA0004197458650000037
for array->
Figure FDA0004197458650000038
The power of K of the j-th element in (a); array->
Figure FDA0004197458650000039
K-th order origin moment->
Figure FDA00041974586500000310
Substitution of the ith element of array Y +.>
Figure FDA00041974586500000311
In this way, k-h+1 elements in array Y are updated from i=1 to i=k-h+1, resulting in a new array I a '＝[I a1 ',I a2 ',···I ak ']Namely, an array after median origin moment filtering;
B. the phase A treatment process of the C two-phase current filtering process is the same, and finally the filtered fault three-phase current array is obtained as
Figure FDA00041974586500000312
6. The method for identifying faults of the active power distribution network based on wavelet singular entropy according to claim 2, wherein the specific steps of step S4 are as follows:
step S41: calculating to obtain a current mutation quantity according to the filtered fault three-phase current data and the three-phase current data acquired during normal operation, and deriving the mutation current; and comparing the abrupt current derivative with a threshold value delta i of the abrupt current derivative set ' size;
the fault section judging flow of the line L section A phase current is as follows:
the A phase current collected in the L section of the line in normal operation is i aL The method comprises the steps of carrying out a first treatment on the surface of the The fault A phase current after the L section filtering of the line is i aL 'A'; the fault a-phase current sudden amount of line L segment is:
Δi aL ＝i aL '-i aL
wherein Δi aL The fault A phase current abrupt change of the line L section;
for Δi aL Deriving to obtain the mutation current derivative delta i' aL According to the same processing mode as the phase A, carrying out mutation current derivative calculation on B, C two phases to obtain delta i' bL 、Δi' cL Gate for comparing the maximum value of the three-phase abrupt current derivative with the abrupt current derivativeThreshold Δi set ' size comparison:
max(Δi' aL ,Δi' bL ,Δi' cL )≥Δi s ' et
if the above is true, go to step S42 to make further judgment, otherwise, judge line L as normal line section; Δi set ' is a setting value;
step S42: calculating the effective value of each phase of abrupt current, and comparing the effective value with the threshold value of the abrupt current;
the calculation formula of the effective value of the phase A abrupt current is as follows:
Figure FDA0004197458650000041
the effective value delta I of B, C two-phase abrupt current is obtained by adopting the same calculation mode BL 、ΔI CL The method comprises the steps of carrying out a first treatment on the surface of the And comparing the three-phase abrupt current effective value with an abrupt current threshold value:
Figure FDA0004197458650000042
wherein max is defined as the maximum value of the sequence of values in brackets; ΔI max Is the maximum value in the effective values of the phase A, B and C abrupt current; ΔI set Is a sudden change current threshold value;
if the relation is not established after substituting the data, judging that the section line is a normal section; if the above relation is satisfied, step S43 is performed to determine the degree of coincidence of the abrupt current waveform;
step S43: calculating a correlation coefficient representing the consistency degree of the abrupt current waveform, and comparing the correlation coefficient with a correlation coefficient threshold value:
the formula for calculating the correlation coefficient between the A, B phases is as follows:
Figure FDA0004197458650000051
wherein Σ represents the summation symbol; i.e a (k) Is the phase A current abrupt quantity; i.e b (k) Phase B current abrupt; beta ab Is the correlation coefficient of A, B two-phase abrupt current waveform;
calculating A, C two-phase correlation coefficient beta by using the same structural formula ac B, C two-phase correlation coefficient beta bc ；
Comparing with a correlation coefficient threshold value:
Figure FDA0004197458650000052
wherein beta is min Is the minimum value in the correlation coefficient; beta set Is a threshold value of the correlation coefficient;
if the above is true, the segment is a normal line segment; otherwise, the section is indicated as the section before the fault point.
7. The method for identifying faults of the active power distribution network based on wavelet singular entropy according to claim 2, wherein the specific steps of step S5 are as follows:
step S51: calculating A, B, C three-phase wavelet singular entropy:
the calculation process of the A Xiang Xiaobo singular entropy comprises the following steps:
firstly, carrying out wavelet transformation on the A-phase current obtained after filtering to obtain a wavelet transformation coefficient matrix H A M×n order matrix H A Is expressed as:
H A ＝MQ A N T
wherein M is m×m order orthogonal matrix; n is an N multiplied by N order orthogonal matrix; q (Q) A ＝diag(λ 1 ,λ 2 ,...λ p ) Is a diagonal matrix, the non-negative diagonal elements of which are arranged in descending order, namely a wavelet transformation coefficient matrix H A Is a singular eigenvalue of (2);
the wavelet transformation, singular value decomposition and information entropy are organically combined to form wavelet singular entropy, and a specific formula for calculating the x-order wavelet singular entropy is shown as follows:
Figure FDA0004197458650000053
in the method, in the process of the invention,
Figure FDA0004197458650000054
for the ith non-zero singular value lambda i Is a singular entropy of the incremental wavelet;
substituting singular eigenvalues of corresponding diagonal elements into a formula to obtain A Xiang Xiaobo singular entropy WSE A (x) Then adopting the same process as the phase A to obtain B, C two-phase wavelet singular entropy WSE B (x)、WSE C (x)；
Step S52: processing the singular entropy of each order wavelet of each phase signal;
since the wavelet singular entropy of the fault phase is larger than that of the non-fault phase, the wavelet singular entropy relative ratio of each phase is selected, the 2-gamma-order wavelet singular entropy of the first gamma singular eigenvalues is overlapped, and the relative difference of the fault phase and the non-fault Xiang Xiaobo singular entropy is accumulated through overlapping, and the specific processing process is as follows:
Figure FDA0004197458650000061
Figure FDA0004197458650000062
Figure FDA0004197458650000063
wherein h is a 、h b 、h c As the basis of fault options; WSE (Wireless sensor array) A (x)、WSE B (x)、WSE C (x) A, B, C three-phase wavelet singular entropy;
step S53: constructing a fault phase selection criterion based on wavelet singular entropy and judging a fault phase:
step S531: according to the condition (h) of three-phase ground fault of the transmission line a ,h b ,h c ) Determining epsilon 1 、ε 2 ，ε 1 ＝min(h a ,h b ,h c )，ε 2 ＝max(h a ,h b ,h c )；
Wherein min is defined as the minimum value of the value sequence in brackets, and max is defined as the maximum value of the value sequence in brackets;
step S532: calculate h a 、h b 、h c The specific judging flow is as follows:
if the calculation result satisfies epsilon 1 ≤h a ,h b ≤ε 2 ,h c ≤ε 2 Judging that the three-phase short circuit fault exists; if the calculated result is h a ≤ε 1 ,h b ≤ε 1 ,h c ≤ε 1 Judging that no fault phase exists; if the calculated result is h p ＞ε 2 (p=a, b, c) and p=1, i.e. any one of a, b, c, then judging that the single-phase earth fault exists, and the p phase is the fault phase; if the calculated result is h p ＞ε 2 (p=a, b, c) and p=2, i.e. any two of a, b, c), then it is necessary to further determine whether the two phases are shorted or shorted to ground: if there is
Figure FDA0004197458650000064
Judging that the two-phase short circuit fault exists; if there is->
Figure FDA0004197458650000065
Judging that the two phases are in short circuit fault; wherein p1 and p2 denote two phases with short-circuit failure, +.>
Figure FDA0004197458650000066
Refers to the non-faulted phase.
CN202310450936.3A 2023-04-25 2023-04-25 Active power distribution network fault identification method based on wavelet singular entropy Pending CN116381413A (en)

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