CN113702760B - Method and system for identifying transverse faults and ferromagnetic resonance states of distribution line - Google Patents
Method and system for identifying transverse faults and ferromagnetic resonance states of distribution line Download PDFInfo
- Publication number
- CN113702760B CN113702760B CN202110988626.8A CN202110988626A CN113702760B CN 113702760 B CN113702760 B CN 113702760B CN 202110988626 A CN202110988626 A CN 202110988626A CN 113702760 B CN113702760 B CN 113702760B
- Authority
- CN
- China
- Prior art keywords
- fault
- wavelet
- phase
- energy
- preset threshold
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
The present disclosure provides a method and a system for identifying a transverse fault and a ferromagnetic resonance state of a distribution line, comprising the following steps: acquiring low-frequency energy of a first layer of zero sequence voltage and singular entropy of three-phase voltage wavelet after faults, and distinguishing grounding faults, ferromagnetic resonance faults and interphase faults at a primary stage; judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, and identifying a fault phase of an inter-phase fault; calculating a wavelet energy ratio, and combining a preset threshold value of the wavelet energy ratio to distinguish a grounding fault from ferromagnetic resonance; judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, 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 the actual operation condition and using 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 continuous rise of the capacity and voltage level of the power distribution network, the operation mode and the network structure of the power distribution network become more and more complex. Once the distribution line fails, the method has important significance in quickly identifying the failure type, quickly repairing the failure line with the accurate positioning failure point, improving the power supply reliability and reducing the power failure loss. The traditional distribution line fault type distinguishing method is mainly summarized according to the operation signals of the protection device and the long-term experience of a dispatcher, so that the result obtained in the distribution network fault type distinguishing is inaccurate, and accurate description is difficult to carry out by using a mathematical model.
To the best of our knowledge, existing studies on fault type identification methods are mostly implemented based on high voltage power transmission networks. The method for applying time-frequency domain analysis is mostly based on transient current as fault characteristic quantity for analysis; artificial intelligence theory is also widely used in fault identification, such as expert systems, genetic algorithms, artificial neural networks, artificial immune systems, petri networks, 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 when the distribution line breaks down, the obtained information such as voltage, current and the like is generally influenced by a plurality of random factors such as the operation mode, the fault position, the transition impedance, the fault moment and the like of the power system. Therefore, it is important to find a distribution line fault identification method which can extract fault characteristic quantity rapidly and is not influenced 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 present disclosure provides a method and a system for identifying a transverse fault and a ferromagnetic resonance state of a distribution line, which comprehensively reflects the identification condition of the fault state of the distribution line in the principles of objectivity, accuracy and strong operability from the perspective of system engineering 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 ferromagnetic resonance state of a distribution line, which adopts the following technical scheme:
a method for identifying transverse faults and ferromagnetic resonance states of a distribution line comprises the following steps:
acquiring low-frequency energy of a first layer of zero sequence voltage and singular entropy of three-phase voltage wavelet after faults, and distinguishing grounding faults, ferromagnetic resonance faults and interphase faults at a primary stage;
judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, and identifying a fault phase of an inter-phase fault;
calculating a wavelet energy ratio, and combining a preset threshold value of the wavelet energy ratio to distinguish a grounding fault from ferromagnetic resonance;
judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, and identifying the fault phase of the ground fault.
As a further technical limitation, performing J-layer discrete wavelet decomposition on the zero-sequence voltage signal after the fault at the time k by using a Mallat algorithm to obtain a wavelet coefficient high-frequency component D j (k) The low frequency component is A j (k) The method comprises the steps of carrying out a first treatment on the surface of the The wavelet energy under a single scale is the square sum of wavelet coefficients under the scale, and then the zero sequence voltage low-frequency energy E exists l ＝||A j (k)|| 2 Zero sequence voltage high frequency energy E h ＝||D j (k)|| 2 Where j=1, 2, …, J is the decomposition scale and J is the maximum decomposition scale.
As a further technical definition, the process of initially distinguishing between a ground fault, a ferromagnetic resonance and an interphase fault is: judging the magnitude of the low-frequency energy of the first layer of zero sequence voltage and the preset threshold value of the low-frequency energy, and if the low-frequency energy of the first layer of zero sequence voltage is not larger than the preset threshold value of the low-frequency energy, judging that the first layer of low-frequency energy is an interphase fault; otherwise it is a ground fault or ferroresonance.
As a further technical definition, the wavelet singular entropy of the phase-to-phase fault three-phase voltage is related to the singular value and the information entropy of the three-phase voltage signal in the time-frequency domain.
Further, aiming at interphase faults, the three-phase voltage wavelet singular entropy is used as a fault characteristic value, the magnitude of the fault characteristic value and the magnitude of a wavelet singular entropy preset threshold are judged, when the fault characteristic value is larger than the wavelet singular entropy preset threshold, the phase to which the fault characteristic value belongs in the interphase faults at the moment is a fault phase, and otherwise, the phase is a non-fault phase.
As a further technical definition, the voltage amplitude is used in combination with the wavelet energy value to distinguish between the ground fault and the ferroresonance due to the difference between the three-phase voltage waveform and the zero-sequence voltage waveform of the distinguished ground fault and ferroresonance.
Further, the wavelet energy ratio is the ratio of the wavelet energy value after the failure to the wavelet energy value before the failure.
Further, the microwave energy ratio is compared to a preset threshold of the microwave energy ratio, and when the microwave energy ratio is greater than the preset threshold of the microwave energy ratio, the resonance is ferromagnetic resonance, otherwise, the resonance is a ground fault.
Further, aiming at the ground fault, the three-phase voltage wavelet singular entropy is used as a fault characteristic value, the magnitude of the fault characteristic value and the magnitude of the wavelet singular entropy preset threshold are judged, when the fault characteristic value is larger than the wavelet singular entropy preset threshold, the phase to which the fault characteristic value belongs in the inter-phase fault at the moment is the fault phase, and otherwise, the phase is the non-fault phase.
According to some embodiments, a second aspect of the present disclosure provides a system for identifying a lateral fault and a ferromagnetic resonance state of a distribution line, which adopts the following technical scheme:
a system for identifying a lateral fault and a ferromagnetic resonance condition of a distribution line, comprising:
the primary distinguishing module is used for acquiring the low-frequency energy of a first layer of zero-sequence voltage and the singular entropy of three-phase voltage wavelet after the fault, and primarily distinguishing the grounding fault, the ferromagnetic resonance fault and the interphase fault;
the interphase fault identification module is used for judging the magnitude of the three-phase voltage wavelet singular entropy and the magnitude of a wavelet singular entropy preset threshold value and identifying the fault phase of the interphase fault;
the ground fault identification module is used for calculating the wavelet energy ratio and combining a preset threshold value of the wavelet energy ratio to distinguish the ground fault from the ferroresonance; judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, and identifying the fault phase of the ground fault.
Compared with the prior art, the beneficial effects of the present disclosure are:
the method comprehensively considers the actual operation condition of the distribution line, and provides a sufficient basis for the distribution line operators to comprehensively analyze the fault state of the distribution line. The characteristic that whether the ground fault happens can be better reflected by wavelet transformation low-frequency band energy of zero sequence voltage is utilized; the wavelet singular entropy value of the ABC three-phase voltage can better reflect the fault phase; the proposal of the wavelet energy weight coefficient determines the occupancy rate of signals of each frequency band in the total signal, and calculates the wavelet energy ratio of the voltage signals of two cycles before and after the reconstruction signal fault of the frequency band with the maximum weight coefficient, thereby distinguishing the ferromagnetic resonance and the grounding fault. The fault identification method can quickly and accurately identify various fault types and is not influenced by transition resistance, fault position and fault initial phase angle.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain the disclosure, and do not constitute an undue limitation on the disclosure.
FIG. 1 is a flow chart of a method of identifying a lateral fault and a ferromagnetic resonance condition of a distribution line in a first embodiment of the present disclosure;
fig. 2 is a wavelet singular entropy diagram in a second embodiment of the present disclosure;
FIG. 3 is a wavelet energy weighting factor graph in a second embodiment of the present disclosure;
fig. 4 is a block diagram of a system for identifying a lateral fault and a ferromagnetic resonance condition of a distribution line in a third embodiment of the present disclosure.
The specific embodiment is as follows:
the disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present 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 exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
Example 1
An embodiment of the disclosure first 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 faults and ferromagnetic resonance states of a distribution line, wavelet transformation is carried out on three-phase voltage and zero-sequence voltage at a bus after the faults, and the faults are pre-classified by adopting a first-layer low-frequency energy value of the zero-sequence voltage; carrying out quantitative calculation of the wavelet energy ratio for the ground fault and the ferromagnetic resonance phenomenon to distinguish; for the specific fault phase classification of the ground fault and the interphase fault, the wavelet singular entropy value of the three-phase voltage is used as a fault characteristic value, and the threshold value is selected for specific classification. Through the analysis of the distribution line operation data, the change rule of the distribution line operation state can be effectively judged, and different fault types of the fault area can be reasonably classified.
A method for identifying a lateral fault and a ferromagnetic resonance state of a distribution line as shown in fig. 1, comprising the steps of:
step S01: acquiring low-frequency energy of a first layer of zero sequence voltage and singular entropy of three-phase voltage wavelets after faults;
step S02: judging the magnitude of a zero sequence voltage first layer low frequency energy and a low frequency energy preset threshold value, and distinguishing a grounding fault, a ferromagnetic resonance fault and an interphase fault in a preliminary stage;
if the zero sequence voltage first layer low frequency energy is not greater than the low frequency energy preset threshold value, the phase-to-phase fault exists, and step S03 is entered; otherwise, the method is ground fault or ferroresonance, and the step S04 is carried out;
step S03: judging the singular entropy of the three-phase voltage wavelet and the first preset threshold value, and identifying the fault phase of the interphase fault; when the three-phase voltage wavelet singular entropy is larger than the wavelet singular entropy preset threshold, the phase to which the fault characteristic value belongs in the interphase fault is the fault phase, otherwise, the phase is the non-fault phase;
step S04: calculating a wavelet energy ratio, judging the magnitude of a preset threshold value of the wavelet energy ratio and the wavelet energy ratio, and distinguishing a grounding fault and ferromagnetic resonance;
if the wavelet energy ratio is greater than the preset threshold, ferromagnetic resonance is adopted; otherwise, the ground fault is detected, and the step S05 is carried out;
step S05: judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, and identifying a fault phase of the ground fault; when the three-phase voltage wavelet singular entropy is larger than the wavelet singular entropy preset threshold, the phase to which the fault characteristic value belongs in the interphase fault is the fault phase, otherwise, the phase is the non-fault phase.
As one or more embodiments, in step S01, the first layer low frequency energy value E of the zero sequence voltage at its bus is calculated 0 Is sized to initially distinguish between ground faults and phase-to-phase short faults. Since the fault characteristics of the ground fault and the ferromagnetic resonance are very similar, the ferromagnetic resonance and the interphase fault can be distinguished indirectly.
For zero sequence voltage signal U 0 (n) performing J-layer discrete wavelet decomposition at k time by using Mallat algorithm to obtain wavelet coefficient high-frequency component D j (k) The low frequency component is A j (k) A. The invention relates to a method for producing a fibre-reinforced plastic composite Wavelet energy under a single scale is the sum of squares of wavelet coefficients under the scale, and then the low-frequency energy and the high-frequency energy of the wavelet can be obtained respectively as follows:
where j=1, 2, …, J is the decomposition scale, J is the maximum decomposition scale.
The singular value decomposition is essentially an orthogonal transformation. For a matrix with linear correlation of any row or column, the left side and the right side of the matrix are respectively multiplied by an orthogonal matrix, 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 number of the obtained singular values. Singular value decomposition is used as an important matrix decomposition tool in linear algebra, and a multi-resolution singular value decomposition method is constructed by combining multi-resolution analysis of wavelets and zero phase shift of the singular value decompositionThe method has the advantage of being very useful for extracting the weak fault characteristic information. In order to more intuitively express the characteristics of fault phase waveforms on a time-frequency domain, L-layer wavelet packet decomposition is carried out on a three-phase voltage signal U (n), and a time-frequency distribution matrix D is formed by m decomposed components with the length of n m×n . For matrix D m×n Singular value decomposition is performed to obtain:
D m×n ＝UΛV T
wherein U and V are unitary matrices of m×m and n×n orders, respectively; Λ is a half-positive m×n order generalized diagonal matrix. The value on the main diagonal of matrix lambda is the singular value sigma i (i＝1,…,m)。
Information entropy is a measure of the amount of information and is also a measure of the amount of system information. If one system is ordered, the system entropy is lower; and if the system is chaotic, the entropy value of the system is high. Therefore, the order degree of the system can be well quantified and counted by utilizing the information entropy theory. After singular value decomposition is carried out on the three-phase voltage, if diagonal elements are more average, the system fault characteristics are more obvious, and the system entropy is larger; otherwise, the signal fault amount is small, or no fault signal is contained, and the system entropy value is smaller.
The probability of the eigenvalue obtained by the singular value decomposition is as follows:
wherein P is i ＝P{X＝σ i I, j=1, 2, …, m andthe information entropy can thus be obtained as:
the calculation formula for the wavelet singular entropy value can be defined in the sum as follows:
wherein k represents one of the three phases, and the specific fault phase is judged by taking the obtained wavelet singular entropy value as a fault characteristic quantity.
As one or more embodiments, in step S02, when the system is disturbed from the outside, the excitation inductance enters the saturation region due to excitation, and generally, due to randomness of saturation of which phase occurs, the three-phase winding excitation inductances are different, so that the three-phase load becomes unbalanced, the system ground voltage is increased, and the system ground voltage increase is represented by that the neutral point generates a power frequency displacement zero sequence voltage with higher amplitude.
When the ground fault disappears, the excitation inductance curve becomes saturated, the excitation inductance is reduced sharply, and therefore a resonant circuit is formed, and ferromagnetic resonance occurs. When the ferromagnetic resonance 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 ferroresonant state, since the waveform of the three-phase voltage is more distinct from the waveform of the zero-sequence voltage, the voltage amplitude may be utilized in combination with the wavelet energy value for differentiation. Because the frequency range of the fault voltage signal is wider, only multi-resolution analysis is carried out, only the frequency information of each frequency band after decomposition can be obtained, the occupancy rate of each frequency band signal in the total signal can not be determined, and the concept of the wavelet energy weight coefficient is given.
The wavelet energy defining a certain frequency band is:
the total energy of the signal is:
wherein p is j For each scale wavelet coefficient, representing high frequency component D j (k) A kind of electronic deviceWavelet coefficients and low frequency component A j (k) Wavelet coefficients of (c) are determined. Normalizing the energy of each frequency band to obtain the energy weight coefficient of each frequency band, wherein the expression is as follows:
wherein E is j Represents the energy of the reconstructed signal of each scale of the wavelet, E represents the total energy of the signal, and Ep j A weight coefficient representing wavelet energy for each scale.
For the frequency band with the maximum weight coefficient, the wavelet energy value of the voltage signal of two cycles before the reconstruction signal failure is recorded as E f The wavelet energy value of the voltage signal of two cycles after the fault is E l The wavelet energy ratio after and before failure is defined as:
thereby distinguishing the ground fault from the ferromagnetic resonance phenomenon according to the magnitude of the wavelet energy ratio.
Example two
In the second embodiment of the present disclosure, based on the first embodiment, a detailed description of a method for identifying a lateral fault and a ferromagnetic resonance state of a distribution line is developed by taking monitoring data of a certain area as an example.
The method is characterized in that the model is built by monitoring the busbar voltage of a distribution line in a certain area and taking the collected zero sequence voltage and three-phase voltage after faults as analysis basis. In combination with the first embodiment, the first layer low-frequency energy value of the zero sequence voltage and the wavelet singular entropy value of A, B, C three-phase voltage after the fault are calculated and respectively marked as E 0 、S A 、S B 、S C The results obtained are shown in table 1 below, and a histogram of wavelet singular entropy is shown in fig. 2 for a clearer comparison of the magnitudes of the values.
TABLE 1 fault signature
As can be seen from table 1, the zero-sequence voltage first-layer low-frequency energy value under the ground fault is very large and can be approximated to infinity, and the zero-sequence voltage first-layer low-frequency energy value under the interphase fault is almost zero, so as to determine the ground fault or the interphase fault; the wavelet singular entropy of the failed phase is much larger than the wavelet singular entropy of the non-failed phase, and a threshold value of 0.5 can be set for the result, namely the failed phase when the value is larger than 0.5, and the non-failed phase when the value is smaller than 0.5.
After the voltage signal was subjected to wavelet transform, the frequency band division results are shown in tables 2 and 3 below:
TABLE 2 high frequency division results
TABLE 3 Low frequency division results
According to the characteristics of the power distribution network harmonic waves, if the harmonic waves of 16 times and below are steady-state harmonic waves and the harmonic waves of 16 times and above 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 signals in each frequency band after screening is obtained by calculation in the first embodiment, and is expressed as:
U＝[E 0 ,E 1 ,E 2 ,E 3 ,E 4 ,E 5 ]
order the
E＝E 0 +E 1 +E 2 +E 3 +E 4 +E 5
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 weighting coefficients for each band
As can be seen from table 4, the energy weight values at the a5 band are relatively large, while the energy weight values at the d1-d5 bands are relatively low. For the a5 frequency band with the largest weight coefficient, the ratio of the wavelet energy value of the voltage signal of the two cycles after the fault of the reconstruction signal to the wavelet energy value of the voltage signal of the two cycles before the fault is calculated as shown in table 5.
TABLE 5 wavelet energy ratio
It can be seen from table 5 that the energy value before failure is greater than the energy value after failure in both cases of single phase ground failure and ferroresonance. The energy ratio before and after the fault is calculated, so that 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 ferromagnetic resonance its energy ratio is greater than 0 and less than 1. A threshold value of 0.05 can be set for this result, i.e. single phase earth fault phenomena occur when K < 0.05, ferroresonance phenomena occur when K < 0.05 < 1.
Because the operation parameters of the distribution line are not fixed in actual operation, for a simulation system, whether the result of identification is affected is judged by changing the fault position, the fault initial phase angle and the value of the transition resistance, so that the reliability of the identification result is verified. The identification process of the grounding fault and the ferromagnetic resonance and the identification process of the grounding fault and the interphase fault are shown in table 6 and table 7, the simulation results after the parameters are changed, and the conditions that the identification algorithm is not affected can be known from the data in table 6 and table 7.
Table 6 influence of three conditions on criteria
TABLE 7 influence of three conditions on wavelet singular entropy values
Therefore, the fault identification method introduced in the embodiment can effectively distinguish the transverse faults and ferromagnetic resonance phenomena of the circuit, is not influenced by the change of system conditions such as fault positions, fault initial phase angles, transition resistances and the like, 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 a lateral fault and a ferromagnetic resonance condition of a distribution line as shown in fig. 4, comprising:
the primary distinguishing module is used for acquiring the low-frequency energy of a first layer of zero-sequence voltage and the singular entropy of three-phase voltage wavelet after the fault, and primarily distinguishing the grounding fault, the ferromagnetic resonance fault and the interphase fault;
the interphase fault identification module is used for judging the magnitude of the three-phase voltage wavelet singular entropy and the magnitude of a wavelet singular entropy preset threshold value and identifying the fault phase of the interphase fault;
the ground fault identification module is used for calculating the wavelet energy ratio and combining a preset threshold value of the wavelet energy ratio to distinguish the ground fault from the ferroresonance; judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, and identifying the fault phase of the ground fault.
The detailed steps are the same as the identification method of the transverse fault and ferromagnetic resonance state of the distribution line provided in the first embodiment, and will not be repeated here.
The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (8)
1. The method for identifying the transverse faults and the ferromagnetic resonance states of the distribution line is characterized by comprising the following steps of:
acquiring low-frequency energy of a first layer of zero-sequence voltage and wavelet singular entropy of three-phase voltage after faults, judging the magnitude of a preset threshold value of the low-frequency energy of the first layer of zero-sequence voltage and the magnitude of a preset threshold value of the low-frequency energy, and if the low-frequency energy of the first layer of zero-sequence voltage is not larger than the preset threshold value of the low-frequency energy, judging that the first layer of low-frequency energy of the zero-sequence voltage is an interphase fault; otherwise it is a ground fault or ferroresonance;
judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, and identifying a fault phase of an inter-phase fault;
calculating a wavelet energy ratio, and combining a preset threshold value of the wavelet energy ratio to distinguish a grounding fault from ferromagnetic resonance; the wavelet energy ratio is the ratio of the wavelet energy value after the fault to the wavelet energy value before the fault;
judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, and identifying the fault phase of the ground fault.
2. A method for identifying a transverse fault and a ferromagnetic resonance condition of a distribution line as claimed in claim 1, wherein the zero sequence voltage signal after the fault is identified in the following by using the Mallat algorithmTime go->Layer discrete wavelet decomposition to obtain wavelet coefficient high frequency component +.>The low frequency component is->The method comprises the steps of carrying out a first treatment on the surface of the Wavelet energy at a single scale is the flattening of wavelet coefficients at that scaleThe sum of the squares is zero sequence voltage low frequency energy +.>Zero sequence voltage high frequency energy->Wherein->，/>For decomposing scale, ->Is the largest decomposition scale.
3. A method of identifying a transverse fault and a ferromagnetic resonance condition of a distribution line as claimed in claim 1, wherein the wavelet singular entropy of the phase-to-phase fault three-phase voltage is related to the singular value and the information entropy of the three-phase voltage signal in the time-frequency domain.
4. The method for identifying a transverse fault and a ferromagnetic resonance state of a distribution line according to claim 3, wherein, for an interphase fault, wavelet singular entropy of a three-phase voltage is used as a fault characteristic value, the magnitude of the fault characteristic value and a wavelet singular entropy preset threshold is judged, when the fault characteristic value is larger than the wavelet singular entropy preset threshold, the phase to which the fault characteristic value belongs in the interphase fault is the fault phase, otherwise, the phase is the non-fault phase.
5. A method of identifying a transverse fault and a ferroresonant condition of a power distribution line as claimed in claim 1 wherein the differential between the three phase voltage waveforms of the distinguished ground fault and ferroresonant and the zero sequence voltage waveform is used to distinguish the ground fault and ferroresonant by combining the voltage amplitude with the wavelet energy value.
6. A method of identifying a transverse fault and a ferroresonant condition of a power distribution line as claimed in claim 1 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 then the ferroresonant condition is established and otherwise the ground fault condition is established.
7. The method for identifying a transverse fault and a ferromagnetic resonance state of a distribution line according to claim 6, wherein, for a ground fault, three-phase voltage wavelet singular entropy is used as a fault characteristic value, the magnitude of the fault characteristic value and a wavelet singular entropy preset threshold is judged, when the fault characteristic value is larger than the wavelet singular entropy preset threshold, the phase to which the fault characteristic value belongs in the phase-to-phase fault is the fault phase, otherwise, the phase is the non-fault phase.
8. A system for identifying a transverse fault and a ferromagnetic resonance condition of a distribution line, comprising:
the primary distinguishing module is used for acquiring low-frequency energy of the first layer of the zero-sequence voltage and wavelet singular entropy of the three-phase voltage after the fault, judging the magnitude of the preset threshold value of the low-frequency energy and the low-frequency energy of the first layer of the zero-sequence voltage, and if the low-frequency energy of the first layer of the zero-sequence voltage is not larger than the preset threshold value of the low-frequency energy, judging that the fault is an interphase fault; otherwise it is a ground fault or ferroresonance;
the interphase fault identification module is used for judging the magnitude of the three-phase voltage wavelet singular entropy and the magnitude of a wavelet singular entropy preset threshold value and identifying the fault phase of the interphase fault;
the ground fault identification module is used for calculating the wavelet energy ratio and combining a preset threshold value of the wavelet energy ratio to distinguish the ground fault from the ferroresonance; the wavelet energy ratio is the ratio of the wavelet energy value after the fault to the wavelet energy value before the fault; judging the magnitude of a three-phase voltage wavelet singular entropy and a wavelet singular entropy preset threshold value, and identifying the fault phase of the ground fault.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110988626.8A CN113702760B (en) | 2021-08-26 | 2021-08-26 | Method and system for identifying transverse faults and ferromagnetic resonance states of distribution line |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110988626.8A CN113702760B (en) | 2021-08-26 | 2021-08-26 | Method and system for identifying transverse faults and ferromagnetic resonance states of distribution line |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113702760A CN113702760A (en) | 2021-11-26 |
| CN113702760B true CN113702760B (en) | 2023-08-25 |
Family
ID=78655205
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110988626.8A Active CN113702760B (en) | 2021-08-26 | 2021-08-26 | Method and system for identifying transverse faults and ferromagnetic resonance states of distribution line |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113702760B (en) |
Families Citing this family (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 (1)
| Title |
|---|
| 基于小波奇异熵和支持向量机的配电网故障类型识别;王艳松等;电力系统保护与控制;第39卷(第23期);第16-20页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113702760A (en) | 2021-11-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Biswal et al. | Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier | |
| Haghifam et al. | Development of a fuzzy inference system based on genetic algorithm for high-impedance fault detection | |
| CN110554274B (en) | Adaptive weight grounding line selection method based on wavelet singular information | |
| CN111308272B (en) | Positioning method for low-current ground fault section | |
| CN112098886B (en) | Single-phase grounding line selection method and system for low-current grounding system | |
| CN110007198B (en) | Single-phase earth fault starting method | |
| CN103308822A (en) | Small current earth fault line selection method for radial distribution network | |
| CN113702760B (en) | Method and system for identifying transverse faults and ferromagnetic resonance states of distribution line | |
| Akorede et al. | Wavelet transform based algorithm for high-impedance faults detection in distribution feeders | |
| CN108845177B (en) | Lightning stroke monitoring and identifying method, device and equipment for power distribution network | |
| Zhang et al. | Morphology singular entropy-based phase selector using short data window for transmission lines | |
| Tabatabaei et al. | A traveling-wave fault location technique for three-terminal lines based on wavelet analysis and recurrent neural network using GPS timing | |
| CN110579684A (en) | low-current grounding system line selection method based on fusion algorithm | |
| CN113376477B (en) | Flexible direct-current power grid single-end protection method based on traveling wave energy spectrum matrix similarity | |
| Moravej et al. | Power transformer protection scheme based on time‐frequency analysis | |
| Coban et al. | Detection and classification of short-circuit faults on a transmission line using current signal | |
| CN112578198B (en) | Ship MMC-MVDC rapid fault protection method based on transient current characteristics | |
| Mustari et al. | Fault location estimation on transmission lines using neuro-fuzzy system | |
| CN116609610A (en) | Single-phase earth fault phase selection method based on self-adaptive wavelet packet transformation | |
| Kajoijilertsakul et al. | Wavelet based fault detection, classification and location in existing 500 kV transmission line | |
| CN112529066B (en) | Voltage sag source identification method based on BAS-BP classifier model | |
| Sima et al. | A framework for automatically cleansing overvoltage data measured from transmission and distribution systems | |
| Jeong et al. | A pattern-based fault classification algorithm for distribution transformers | |
| Jiao et al. | A new approach to identify inrush current based on generalized S-transform | |
| Lopes et al. | Harmonic selection-based analysis for high impedance fault location using Stockwell transform and random forest |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |