CN113702760A - Method and system for identifying transverse fault and ferromagnetic resonance state of distribution line - Google Patents
Method and system for identifying transverse fault and ferromagnetic resonance state of distribution line Download PDFInfo
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/52—Testing for short-circuits, leakage current or ground faults
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
The invention provides a method and a system for identifying transverse faults and ferromagnetic resonance states of a distribution line, which comprise the following steps: acquiring wavelet singular entropy of first-layer low-frequency energy and three-phase voltage of zero-sequence voltage after fault, and preliminarily distinguishing ground fault, ferromagnetic resonance and interphase fault; judging the sizes of the three-phase voltage wavelet singular entropy and a preset threshold of the wavelet singular entropy, and identifying a fault phase of an interphase fault; calculating a wavelet energy ratio, and distinguishing a ground fault and ferromagnetic resonance by combining a preset threshold value of the wavelet energy ratio; and judging the wavelet singular entropy of the three-phase voltage and the preset threshold value of the wavelet singular entropy, and identifying the fault phase of the ground fault. From the perspective of system engineering, the identification condition of the fault state of the distribution line is comprehensively reflected by combining with the actual operation condition according to the principles of objectivity, accuracy and strong operability.
Description
Technical Field
The disclosure belongs to the technical field of power distribution network fault identification, and particularly relates to a method and a system for identifying transverse faults and ferromagnetic resonance states of a power distribution line.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the increasing capacity and voltage class of distribution networks, the operation mode and network structure of the distribution networks become more and more complex. Once the distribution line has a fault, the fault type is quickly identified, and the method has important significance for accurately positioning a fault point, quickly repairing the fault line, improving the power supply reliability and reducing the power failure loss. The traditional method for judging the fault type of the distribution line is mainly summarized according to an operation signal of a protection device and long-term experience of a dispatcher, so that the result obtained in the fault type judgment of the distribution network is inaccurate, and accurate description by using a mathematical model is difficult.
To the knowledge of the inventors, most of the existing research on fault type identification methods is based on high voltage transmission network implementation. Most of methods applying time-frequency domain analysis are based on transient current magnitude as fault characteristic quantity for analysis; the artificial intelligence theory also has wide application in fault identification, such as expert system, genetic algorithm, artificial neural network, artificial immune system, Petri network, etc. When the structure of the distribution line is complex, the methods have the defects of long identification process time, poor accuracy and the like; and the information such as voltage, current and the like acquired when the distribution line has a fault is generally influenced by a plurality of random factors such as the operation mode of the power system, the fault position, the transition impedance and the fault time. Therefore, it is important to find a distribution line fault identification method that can quickly extract fault feature quantities and is not affected by random factors such as a system operation mode, a fault position, transition impedance, fault time and the like.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for identifying the transverse fault and the ferromagnetic resonance state of the distribution line, which are used for comprehensively reflecting the identification condition of the fault state of the distribution line in an objective, accurate and strong-operability principle from the perspective of system engineering and by combining with the actual operation condition.
According to some embodiments, a first aspect of the present disclosure provides a method for identifying a lateral fault and a ferroresonance state of a distribution line, which adopts the following technical solutions:
a method for identifying transverse faults and ferromagnetic resonance states of a distribution line comprises the following steps:
acquiring wavelet singular entropy of first-layer low-frequency energy and three-phase voltage of zero-sequence voltage after fault, and preliminarily distinguishing ground fault, ferromagnetic resonance and interphase fault;
judging the sizes of the three-phase voltage wavelet singular entropy and a preset threshold of the wavelet singular entropy, and identifying a fault phase of an interphase fault;
calculating a wavelet energy ratio, and distinguishing a ground fault and ferromagnetic resonance by combining a preset threshold value of the wavelet energy ratio;
and judging the wavelet singular entropy of the three-phase voltage and the preset threshold value of the wavelet singular entropy, and identifying the fault phase of the ground fault.
As a further technical limitation, J-layer discrete wavelet decomposition is carried out on the zero sequence voltage signal after the fault at the moment k by using a Mallat algorithm to obtain a wavelet coefficient high-frequency component Dj(k) The low frequency component is Aj(k) (ii) a The wavelet energy under a single scale is the square sum of wavelet coefficients under the scale, and then the low-frequency energy E of zero-sequence voltage existsl＝||Aj(k)||2Zero sequence voltage high frequency energy Eh＝||Dj(k)||2Where J is 1,2, …, J is the decomposition scale and J is the maximum decomposition scale.
As a further technical limitation, the process of preliminarily distinguishing the ground fault, the ferroresonance and the phase-to-phase fault is as follows: judging the magnitude of the low-frequency energy of the first layer of the zero-sequence voltage and a preset threshold value of the low-frequency energy, and if the low-frequency energy of the first layer of the zero-sequence voltage is larger than the preset threshold value of the low-frequency energy, judging that an inter-phase fault occurs; otherwise, it is a ground fault or ferroresonance.
As a further technical limitation, the wavelet singular entropy of the three-phase voltage of the interphase fault is related to the singular value and the information entropy of the three-phase voltage signal on the time-frequency domain.
Further, regarding the interphase fault, taking the three-phase voltage wavelet singular entropy as a fault characteristic value, judging the size of the fault characteristic value and a preset threshold of the wavelet singular entropy, and when the fault characteristic value is larger than the preset threshold of the wavelet singular entropy, determining that the phase to which the fault characteristic value belongs in the interphase fault is a fault phase, otherwise, determining that the phase is a non-fault phase.
As a further technical limitation, the ground fault and the ferroresonance are distinguished by using the voltage amplitude in combination with the wavelet energy value because of the difference between the three-phase voltage waveform and the zero-sequence voltage waveform of the distinguished ground fault and the ferroresonance.
Further, the wavelet energy ratio is a ratio of the wavelet energy value after the fault to the wavelet energy value before the fault.
Further, the wavelet energy ratio is compared with a preset wavelet energy ratio threshold, when the wavelet energy ratio is larger than the preset wavelet energy ratio threshold, ferromagnetic resonance is achieved, and otherwise, the ground fault is achieved.
Further, regarding the ground fault, the three-phase voltage wavelet singular entropy is used as a fault characteristic value, the size of the fault characteristic value and a preset threshold of the wavelet singular entropy is judged, when the fault characteristic value is larger than the preset threshold of the wavelet singular entropy, the phase to which the fault characteristic value belongs in the phase-to-phase fault is a fault phase, and otherwise, the phase is a non-fault phase.
According to some embodiments, a second aspect of the present disclosure provides a system for identifying a transverse fault and a ferroresonance state of a distribution line, which adopts the following technical solutions:
a system for identifying lateral faults and ferroresonant states of a distribution line, comprising:
the primary distinguishing module is used for acquiring the wavelet singular entropy of the first layer low-frequency energy and the three-phase voltage of the zero-sequence voltage after the fault, and preliminarily distinguishing the ground fault, the ferromagnetic resonance and the interphase fault;
the interphase fault identification module is used for judging the sizes of the three-phase voltage wavelet singular entropy and a preset threshold of the wavelet singular entropy and identifying a fault phase of an interphase fault;
the ground fault identification module is used for calculating a wavelet energy ratio and distinguishing a ground fault and ferromagnetic resonance by combining the wavelet energy ratio with a preset threshold; and judging the wavelet singular entropy of the three-phase voltage and the preset threshold value of the wavelet singular entropy, and identifying the fault phase of the ground fault.
Compared with the prior art, the beneficial effect of this disclosure is:
the method and the device comprehensively consider the actual operation condition of the distribution line, and provide sufficient basis for comprehensive analysis of the fault state of the distribution line for distribution line operators. The characteristic of whether the earth fault exists can be well reflected by using the wavelet transform low-frequency band energy of zero-sequence voltage; the wavelet singular entropy value of the ABC three-phase voltage can better reflect the fault phase; the occupation ratio of each frequency band signal in the total signal is determined by proposing a wavelet energy weight coefficient, and for the frequency band with the maximum weight coefficient, the wavelet energy ratio of the voltage signals of two cycles before and after the reconstructed signal fault is calculated, so that the ferromagnetic resonance and the grounding fault are distinguished. The fault identification method can quickly and accurately identify various fault types and is not influenced by the transition resistance, the fault position and the fault initial phase angle.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of a method for identifying a transverse fault and a ferroresonance state of a distribution line according to a first embodiment of the disclosure;
FIG. 2 is a diagram of wavelet singular entropy values in a second embodiment of the disclosure;
FIG. 3 is a diagram of wavelet energy weight coefficients in a second embodiment of the disclosure;
fig. 4 is a block diagram of a system for identifying a lateral fault and a ferroresonant state of a distribution line according to a third embodiment of the present disclosure.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example one
The first embodiment of the disclosure introduces a method for identifying a transverse fault and a ferromagnetic resonance state of a distribution line.
In order to solve the problems in the background art, the embodiment provides a method for identifying the transverse fault and the ferromagnetic resonance state of the distribution line, wavelet transformation is carried out on three-phase voltage and zero-sequence voltage at a bus after the fault, and the fault is pre-classified by adopting a first-layer low-frequency energy value of the zero-sequence voltage; for the phenomena of grounding fault and ferromagnetic resonance, carrying out quantitative calculation of wavelet energy ratio for distinguishing; for the specific fault phase classification of the ground fault and the interphase fault, wavelet singular entropy values of three-phase voltage are used as fault characteristic values, and threshold values are selected for specific classification. Through the analysis to distribution lines operating data, can effectual judgement distribution lines operating condition's change rule to carry out reasonable classification to the different fault types in trouble region.
The method for identifying the transverse fault and the ferromagnetic resonance state of the distribution line shown in fig. 1 comprises the following steps:
step S01: acquiring wavelet singular entropy of first-layer low-frequency energy and three-phase voltage of zero-sequence voltage after a fault;
step S02: judging the magnitude of a preset threshold value of the low-frequency energy and the low-frequency energy of the first layer of the zero-sequence voltage, and preliminarily distinguishing a ground fault, a ferromagnetic resonance fault and an interphase fault;
if the low-frequency energy of the first layer of the zero-sequence voltage is greater than the preset threshold value of the low-frequency energy, the fault is an inter-phase fault, and the step S03 is executed; otherwise, it is a ground fault or ferroresonance, and the process proceeds to step S04;
step S03: judging the wavelet singular entropy of the three-phase voltage and the size of a first preset threshold value, and identifying a fault phase of the interphase fault; when the wavelet singular entropy of the three-phase voltage is larger than the preset threshold of the wavelet singular entropy, the phase to which the fault characteristic value belongs in the phase-to-phase fault is a fault phase, otherwise, the phase is a non-fault phase;
step S04: calculating a wavelet energy ratio, judging the size of the wavelet energy ratio and a preset threshold value of the wavelet energy ratio, and distinguishing a ground fault and ferromagnetic resonance;
if the wavelet energy ratio is larger than the preset wavelet energy ratio threshold, the fault is a ground fault, and the step S05 is carried out; otherwise, ferromagnetic resonance;
step S05: judging the sizes of the three-phase voltage wavelet singular entropy and a preset threshold of the wavelet singular entropy, and identifying a fault phase of the ground fault; and when the wavelet singular entropy of the three-phase voltage is larger than the preset threshold of the wavelet singular entropy, the phase to which the fault characteristic value belongs in the phase-to-phase fault is a fault phase, otherwise, the phase is a non-fault phase.
In one or more embodiments, in step S01, the first layer low-frequency energy value E is calculated by calculating the zero sequence voltage at the bus thereof0To preliminarily distinguish between a ground fault and an inter-phase short-circuit fault. Since the fault characteristics of the ground fault and the ferroresonance are very similar, the ferroresonance and the phase-to-phase fault can be indirectly distinguished.
For zero sequence voltage signal U0(n) performing J-layer discrete wavelet decomposition at the time k by using Mallat algorithm to obtain wavelet coefficient high-frequency component Dj(k) The low frequency component is Aj(k) In that respect The wavelet energy under a single scale is the square sum of wavelet coefficients under the scale, and then the obtained wavelet low-frequency energy and wavelet high-frequency energy are respectively:
where J is 1,2, …, J is the decomposition scale, and J is the maximum decomposition scale.
The singular value decomposition is essentially an orthogonal transformation. For any matrix with linear correlation of rows or columns, the left side and the right side of the matrix are multiplied by an orthogonal matrix respectively, the original matrix is converted into a diagonal matrix, and the number of independent row vectors or column vectors in the original matrix can be reflected by the obtained singular value number. The singular value decomposition is used as an important matrix decomposition tool in linear algebra, and is combined with multi-resolution analysis of wavelets to construct a multi-resolution singular value decomposition method, so that the advantage of zero phase shift of the method is very useful for extracting weak fault characteristic information. In order to more intuitively represent the characteristics of the fault phase waveform on a time-frequency domain, L-layer wavelet packet decomposition is carried out on three-phase voltage signals U (n), and m decomposed components with the length of n form a time-frequency distribution matrix Dm×n. For matrix Dm×nSingular value decomposition is carried out to obtain:
Dm×n＝UΛVT
wherein, U and V are unitary matrixes of m multiplied by m and n multiplied by n orders respectively; and Λ is a semi-positive m × n generalized diagonal matrix. The value on the main diagonal of matrix Λ is the singular value σi(i＝1,…,m)。
The information entropy is a measure of the information quantity and is a quantification of the system information. If a system is ordered, the entropy value of the system is lower; and if the system is disordered, the entropy value of the system is high. Therefore, the information entropy theory is utilized, and the system order degree can be well quantified and counted. After the three-phase voltage is subjected to singular value decomposition, if diagonal elements are more average, the system fault characteristics are more obvious, and the system entropy value is larger; otherwise, the system entropy value is smaller if the signal fault amount is small or no fault signal is contained.
The probability of the feature value obtained by the singular value decomposition is as follows:
wherein, Pi＝P{X＝σiJ ═ 1,2, …, m and
the entropy of the information can thus be found to be:
in summary, the calculation formula of the wavelet singular entropy value can be defined as follows:
and k represents a certain phase in the three phases, and the specific fault phase is judged by taking the obtained wavelet singular entropy as a fault characteristic quantity.
In one or more embodiments, in step S02, when the system is disturbed by the outside world, the excitation inductance enters the saturation region due to excitation, and generally, since which phase is saturated occurs randomly, there will be a difference in the excitation inductances of the three-phase windings, so that the three-phase load becomes unbalanced, which will cause the voltage of the system to be increased, and the voltage of the system to be increased represents that the neutral point generates a power frequency displacement zero-sequence voltage with a higher amplitude.
When the ground fault disappears, the excitation inductance curve becomes saturated, the excitation inductance is sharply reduced, and thus a resonant circuit is formed and a ferromagnetic resonance phenomenon occurs. When the ferroresonance phenomenon occurs, overvoltage and overcurrent phenomena are generated, and the stable operation of the distribution line is seriously influenced.
As one or more embodiments, in step S04, for the ground fault and the ferroresonance state, since the waveform difference of the three-phase voltage with respect to the zero-sequence voltage is more significant, the voltage amplitude value can be used to distinguish with the wavelet energy value. Because the frequency range of the fault voltage signal is wide, only multi-resolution analysis is carried out, only frequency information of each frequency band after decomposition can be obtained, the occupation ratio of each frequency band signal in the total signal cannot be determined, and the concept of the wavelet energy weight coefficient is given.
The wavelet energy defining a band is:
the total energy of the signal is therefore:
wherein p isjFor each scale wavelet coefficient, high frequency component D is representedj(k) Wavelet coefficients and low frequency component a ofj(k) The wavelet coefficients of (a). Normalizing the energy of each frequency band to obtain a weight coefficient of the energy of each frequency band, wherein the expression is as follows:
wherein E isjRepresents the energy of the reconstructed signal of each scale of the wavelet, E represents the total energy of the signal, EpjWeight coefficients representing wavelet energy at each scale.
For the frequency band with the maximum weight coefficient, the wavelet energy value of the voltage signal of two cycles before the reconstructed signal failure is recorded as EfThe wavelet energy value of the voltage signal of two cycles after the fault is ElDefining the wavelet energy ratio after the fault and before the fault as:
therefore, the ground fault and the ferromagnetic resonance phenomenon are identified according to the size of the wavelet energy ratio.
Example two
Based on the first embodiment, detailed descriptions about the method for identifying the transverse fault and the ferromagnetic resonance state of the distribution line are given by taking monitoring data of a certain area as an example.
The method comprises the steps of monitoring the bus voltage of a distribution line in a certain area, and establishing a model by taking the collected post-fault zero-sequence voltage and three-phase voltage as analysis bases. With reference to the first embodiment, the first layer low-frequency energy value of the zero-sequence voltage after the fault and the wavelet singular entropy value of the A, B, C three-phase voltage are obtained through calculation and are respectively marked as E0、SA、SB、SCThe results are shown in table 1 below, and for clearer comparison of the magnitude of the values, a histogram of the singular entropy of wavelets is shown in fig. 2.
TABLE 1 Fault eigenvalues
According to table 1, it can be known that the first-layer low-frequency energy value of the zero-sequence voltage under the ground fault is very large and can be approximate to infinity, and the first-layer low-frequency energy value of the zero-sequence voltage under the interphase fault is almost zero, so that the ground fault or the interphase fault is judged; the wavelet singular entropy value of the faulted phase is much larger than that of the non-faulted phase, and a threshold value of 0.5 can be set for the result, namely the faulted phase is determined when the value is larger than 0.5, and the non-faulted phase is determined when the value is smaller than 0.5.
After the voltage signal is subjected to wavelet transform processing, the obtained band division results are shown in the following tables 2 and 3:
TABLE 2 high frequency partition results
TABLE 3 Low frequency division results
According to the characteristics of the harmonic waves of the power distribution network, if the harmonic waves of 16 th order and below are steady-state harmonic waves, and the harmonic waves of more than 16 th order are unsteady-state harmonic waves, the a2 frequency band contains steady-state information, the dl and d2 frequency bands contain unsteady-state information, and the a5 frequency band contains fundamental wave information.
The energy value of the signal in each frequency band after screening is obtained by calculation in the first embodiment and is expressed as:
U＝[E0,E1,E2,E3,E4,E5]
order to
E＝E0+E1+E2+E3+E4+E5
The wavelet energy weight coefficients obtained after normalization are shown in table 4. For a clearer comparison of the magnitude of the values, a histogram is obtained as shown in fig. 3:
TABLE 4 wavelet energy weight coefficients for each band
As can be seen from Table 4, the energy weight values at the a5 frequency band are relatively large, while the energy weight values at the d1-d5 frequency band are relatively low. For the frequency band a5 with the largest weight coefficient, the ratio of the wavelet energy values of the voltage signals of two cycles after the reconstructed signal failure to the wavelet energy values of the voltage signals of two cycles before the failure is calculated as shown in table 5.
TABLE 5 wavelet energy ratios
It can be seen from table 5 that the energy value before the fault is greater than the energy value after the fault in both the single-phase earth fault and the ferroresonance. The energy ratio before and after the fault is calculated, and the energy ratio under the single-phase earth fault is far smaller than the energy ratio under the ferromagnetic resonance and is close to zero; and for ferroresonance, the energy ratio is greater than 0 and less than 1. A threshold of 0.05 can be set for this result, i.e., a single-phase ground fault occurs when K < 0.05, and a ferroresonance occurs when K < 1 and 0.05.
Because the operation parameters of the distribution line in actual operation are not fixed, for a simulation system, whether the influence on the identification result is caused or not is judged by changing the values of the fault position, the fault initial phase angle and the transition resistance, so that the reliability of the identification result is verified. The simulation results after the parameters are changed in the identification process of the ground fault and the ferromagnetic resonance and the identification process of the ground fault and the interphase fault are shown in tables 6 and 7, and the conditions of the identification algorithm which are not affected can be known from the data in tables 6 and 7.
TABLE 6 Effect of changing three conditions on the criteria
TABLE 7 Effect of varying three conditions on the singular entropy of wavelets
Therefore, the fault identification method introduced in the embodiment can effectively distinguish the transverse fault and the ferromagnetic resonance phenomenon of the line, is not influenced by the change of system conditions such as fault position, fault initial phase angle and transition resistance, and has good applicability.
EXAMPLE III
The third embodiment of the disclosure introduces a system for identifying a transverse fault and a ferromagnetic resonance state of a distribution line.
A system for identifying lateral faults and ferroresonant states of a distribution line as shown in fig. 4, comprising:
the primary distinguishing module is used for acquiring the wavelet singular entropy of the first layer low-frequency energy and the three-phase voltage of the zero-sequence voltage after the fault, and preliminarily distinguishing the ground fault, the ferromagnetic resonance and the interphase fault;
the interphase fault identification module is used for judging the sizes of the three-phase voltage wavelet singular entropy and a preset threshold of the wavelet singular entropy and identifying a fault phase of an interphase fault;
the ground fault identification module is used for calculating a wavelet energy ratio and distinguishing a ground fault and ferromagnetic resonance by combining the wavelet energy ratio with a preset threshold; and judging the wavelet singular entropy of the three-phase voltage and the preset threshold value of the wavelet singular entropy, and identifying the fault phase of the ground fault.
The detailed steps are the same as those of the method for identifying the transverse fault and the ferroresonance state of the distribution line provided in the first embodiment, and are not repeated herein.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A method for identifying transverse faults and ferromagnetic resonance states of a distribution line is characterized by comprising the following steps:
acquiring wavelet singular entropy of first-layer low-frequency energy and three-phase voltage of zero-sequence voltage after fault, and preliminarily distinguishing ground fault, ferromagnetic resonance and interphase fault;
judging the sizes of the three-phase voltage wavelet singular entropy and a preset threshold of the wavelet singular entropy, and identifying a fault phase of an interphase fault;
calculating a wavelet energy ratio, and distinguishing a ground fault and ferromagnetic resonance by combining a preset threshold value of the wavelet energy ratio;
and judging the wavelet singular entropy of the three-phase voltage and the preset threshold value of the wavelet singular entropy, and identifying the fault phase of the ground fault.
2. A method for identifying transverse faults and ferroresonant conditions of a distribution line as claimed in claim 1, wherein the zero sequence voltage signal after the fault is detectedThe number utilizes Mallat algorithm to carry out J-layer discrete wavelet decomposition at the time k to obtain the high-frequency component D of the wavelet coefficientj(k) The low frequency component is Aj(k) (ii) a The wavelet energy under a single scale is the square sum of wavelet coefficients under the scale, and then the low-frequency energy E of zero-sequence voltage existsl＝||Aj(k)||2Zero sequence voltage high frequency energy Eh＝||Dj(k)||2Where J is 1,2, …, J is the decomposition scale and J is the maximum decomposition scale.
3. The method for identifying transverse fault and ferroresonance states of a distribution line as claimed in claim 1, wherein the preliminary distinguishing between ground fault, ferroresonance and interphase fault is performed by: judging the magnitude of the low-frequency energy of the first layer of the zero-sequence voltage and a preset threshold value of the low-frequency energy, and if the low-frequency energy of the first layer of the zero-sequence voltage is larger than the preset threshold value of the low-frequency energy, judging that an inter-phase fault occurs; otherwise, it is a ground fault or ferroresonance.
4. The method for identifying transverse faults and ferroresonance states of a power distribution line as claimed in claim 1, wherein the wavelet singular entropy of the three-phase voltage of the phase-to-phase fault is related to the singular value and the information entropy of the three-phase voltage signal in the time-frequency domain.
5. The method for identifying the transverse fault and the ferromagnetic resonance state of the power distribution line as claimed in claim 4, wherein for the inter-phase fault, the wavelet singular entropy of the three-phase voltage is used as the fault characteristic value, the size of the fault characteristic value and the preset threshold of the wavelet singular entropy is judged, when the fault characteristic value is greater than the preset threshold of the wavelet singular entropy, the phase to which the fault characteristic value belongs in the inter-phase fault is the fault phase, otherwise, the phase is the non-fault phase.
6. A method for identifying transverse faults and ferroresonance states of a power distribution line as claimed in claim 1, wherein the ground faults and ferroresonance are distinguished by using voltage amplitude in combination with wavelet energy values due to differences between the three-phase voltage waveforms and the zero-sequence voltage waveforms of the distinguished ground faults and ferroresonance.
7. A method of identifying transverse faults and ferroresonant conditions of a power distribution line as claimed in claim 6 wherein the wavelet energy ratio is the ratio of the value of the wavelet energy after fault to the value of the wavelet energy before fault.
8. A method for identifying the lateral fault and ferroresonance condition of a power distribution line as claimed in claim 7, wherein the wavelet energy ratio is compared with a preset wavelet energy ratio threshold, and when the wavelet energy ratio is greater than the preset wavelet energy ratio threshold, it is ferroresonance, otherwise it is a ground fault.
9. The method for identifying the transverse fault and the ferromagnetic resonance state of the distribution line as recited in claim 8, wherein for the ground fault, the wavelet singular entropy of the three-phase voltage is used as the fault characteristic value, the size of the fault characteristic value and the preset threshold of the wavelet singular entropy is determined, when the fault characteristic value is greater than the preset threshold of the wavelet singular entropy, the phase to which the fault characteristic value belongs in the phase-to-phase fault belongs is the fault phase, otherwise, the phase is the non-fault phase.
10. A system for identifying lateral faults and ferroresonant conditions in a power distribution line, comprising:
the primary distinguishing module is used for acquiring the wavelet singular entropy of the first layer low-frequency energy and the three-phase voltage of the zero-sequence voltage after the fault, and preliminarily distinguishing the ground fault, the ferromagnetic resonance and the interphase fault;
the interphase fault identification module is used for judging the sizes of the three-phase voltage wavelet singular entropy and a preset threshold of the wavelet singular entropy and identifying a fault phase of an interphase fault;
the ground fault identification module is used for calculating a wavelet energy ratio and distinguishing a ground fault and ferromagnetic resonance by combining the wavelet energy ratio with a preset threshold; and judging the wavelet singular entropy of the three-phase voltage and the preset threshold value of the wavelet singular entropy, and identifying the fault phase of the ground fault.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114910744A (en) * | 2022-06-01 | 2022-08-16 | 福州大学 | High-resistance ground fault detection method based on S transformation and self-adaptive average singular entropy |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101452038A (en) * | 2007-11-29 | 2009-06-10 | 上海蓝瑞软件技术有限公司 | Low current neutral grounding electric network single-phase earth fault diagnostic method |
| KR20120100539A (en) * | 2011-03-04 | 2012-09-12 | 한국전력공사 | Apparatus for detecting and controlling ferro-resonance of potential transformer |
| CN103454528A (en) * | 2013-08-30 | 2013-12-18 | 华南理工大学 | Method for detecting and identifying electric system element fault based on form singular entropy |
| CN105738773A (en) * | 2016-04-19 | 2016-07-06 | 中国矿业大学 | Method for identifying fault types of undercurrent grounding system |
| CN106249101A (en) * | 2016-06-30 | 2016-12-21 | 湖南大学 | A kind of intelligent distribution network fault identification method |
| CN106405339A (en) * | 2016-11-11 | 2017-02-15 | 中国南方电网有限责任公司 | Power transmission line fault reason identification method based on high and low frequency wavelet feature association |
| CN106771520A (en) * | 2016-12-15 | 2017-05-31 | 福州大学 | A kind of power distribution network temporary overvoltage classifying identification method and device |
| KR102036788B1 (en) * | 2018-09-06 | 2019-10-25 | 연세대학교 산학협력단 | System and method for estimating the fault position of the real-time power system based on the PMU measurement signal |
| CN111965486A (en) * | 2020-08-07 | 2020-11-20 | 国网江苏省电力有限公司电力科学研究院 | Power distribution network fault identification method and system based on intelligent data fusion analysis |
| CN112986754A (en) * | 2021-05-11 | 2021-06-18 | 国网江西省电力有限公司电力科学研究院 | Small current grounding system fault identification method and device based on data driving |
-
2021
- 2021-08-26 CN CN202110988626.8A patent/CN113702760B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101452038A (en) * | 2007-11-29 | 2009-06-10 | 上海蓝瑞软件技术有限公司 | Low current neutral grounding electric network single-phase earth fault diagnostic method |
| KR20120100539A (en) * | 2011-03-04 | 2012-09-12 | 한국전력공사 | Apparatus for detecting and controlling ferro-resonance of potential transformer |
| CN103454528A (en) * | 2013-08-30 | 2013-12-18 | 华南理工大学 | Method for detecting and identifying electric system element fault based on form singular entropy |
| CN105738773A (en) * | 2016-04-19 | 2016-07-06 | 中国矿业大学 | Method for identifying fault types of undercurrent grounding system |
| CN106249101A (en) * | 2016-06-30 | 2016-12-21 | 湖南大学 | A kind of intelligent distribution network fault identification method |
| CN106405339A (en) * | 2016-11-11 | 2017-02-15 | 中国南方电网有限责任公司 | Power transmission line fault reason identification method based on high and low frequency wavelet feature association |
| CN106771520A (en) * | 2016-12-15 | 2017-05-31 | 福州大学 | A kind of power distribution network temporary overvoltage classifying identification method and device |
| KR102036788B1 (en) * | 2018-09-06 | 2019-10-25 | 연세대학교 산학협력단 | System and method for estimating the fault position of the real-time power system based on the PMU measurement signal |
| CN111965486A (en) * | 2020-08-07 | 2020-11-20 | 国网江苏省电力有限公司电力科学研究院 | Power distribution network fault identification method and system based on intelligent data fusion analysis |
| CN112986754A (en) * | 2021-05-11 | 2021-06-18 | 国网江西省电力有限公司电力科学研究院 | Small current grounding system fault identification method and device based on data driving |
Non-Patent Citations (2)
| Title |
|---|
| 王艳松等: "基于小波奇异熵和支持向量机的配电网故障类型识别", 电力系统保护与控制, vol. 39, no. 23, pages 16 - 20 * |
| 龚庆武等: "基于原子分解法的中性点不接地系统铁磁谐振检测", 电工技术学报, vol. 33, no. 05, pages 1114 - 1124 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114910744A (en) * | 2022-06-01 | 2022-08-16 | 福州大学 | High-resistance ground fault detection method based on S transformation and self-adaptive average singular entropy |
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