CN113093052A - Method for judging turn number of turn-to-turn short circuit motor stator winding fault - Google Patents

Method for judging turn number of turn-to-turn short circuit motor stator winding fault Download PDF

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CN113093052A
CN113093052A CN202110356253.2A CN202110356253A CN113093052A CN 113093052 A CN113093052 A CN 113093052A CN 202110356253 A CN202110356253 A CN 202110356253A CN 113093052 A CN113093052 A CN 113093052A
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谢颖
李道璐
蔡蔚
陈鹏
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Harbin University of Science and Technology
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    • 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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Abstract

A method for judging the number of fault turns of a turn-to-turn short circuit motor stator winding relates to the field of motors. The invention aims to detect turn-to-turn short circuit and diagnose the number of fault turns. According to the method for judging the number of the turn-to-turn short circuit motor stator winding fault turns, the ash content sequence is utilized, the limitation of the traditional Duncus local control whole is broken, and the accuracy of judging the motor fault is improved; the inter-cell positioning method greatly reduces the calculated amount, improves the motor diagnosis speed and provides a theoretical basis for the operation, the overhaul and the fault tolerance of the motor.

Description

Method for judging turn number of turn-to-turn short circuit motor stator winding fault
Technical Field
The invention belongs to the field of motors, and particularly relates to a detection technology for turn-to-turn short circuit faults of a motor.
Background
The turn-to-turn short circuit fault is a common fault in the motor, and the conventional diagnosis method of the turn-to-turn short circuit fault is a signal processing method, which can well detect the existence of the turn-to-turn short circuit fault but cannot accurately detect the fault degree of the turn-to-turn short circuit fault. Practice shows that slight turn-to-turn short circuit fault has little influence on the operation of the motor, but once the fault is enlarged, larger eddy current can be generated, the operation efficiency of the motor is greatly reduced, even more serious inter-phase short circuit and ground short circuit are caused, and irreversible damage is caused to the motor. Therefore, if the fault degree can be accurately diagnosed while detecting the turn-to-turn short circuit, the method has important practical significance on the operation, the overhaul and the fault tolerance of the motor.
Disclosure of Invention
The invention provides a method for judging the number of fault turns of a stator winding of a turn-to-turn short circuit motor, which aims to detect the turn-to-turn short circuit and diagnose the number of fault turns.
A method for judging the number of fault turns of a stator winding of a turn-to-turn short circuit motor comprises the following steps:
initialization: establishing a motor model, wherein the total number of turns of a stator winding in the motor model is N,
the method comprises the following steps: m fault turn points are uniformly selected in the fault turn interval, wherein m is more than or equal to 2 and less than or equal to Nk，NkThe total number of fault turn points in a fault turn interval is shown, and the initial fault turn interval is [0, N]，
Step two: respectively arrange the corresponding fault turn number of m fault turn points in a motor model, collect the corresponding characteristic signal of every fault turn number point and regard as a set of standard characteristic parameter, utilize m groups of standard characteristic parameter to construct m groups of standard grey content sequences, constitute the standard database with m groups of standard grey content sequences, include in the characteristic signal: an electrical signal, a magnetic signal and a vibration signal,
step three: respectively collecting the characteristic signals of the motor with the fault to be detected as the characteristic parameters to be detected, constructing a content sequence of the detected ash by using the characteristic parameters to be detected,
step four: calculating the correlation degree of the measured gray content sequence and each group of standard gray content sequences in a standard database, sequencing the obtained m groups of correlation degrees from large to small,
step five: using the fault turns corresponding to the first correlation degree and the second correlation degree as interval end points to form a first heavy interval,
step six: judging whether the total number of the fault turn points in the first heavy interval is greater than 2, if so, executing a seventh step, otherwise, taking the fault turn number corresponding to the first relevance degree as the fault turn number of the stator winding of the tested motor,
step seven: using the fault turn number corresponding to the first relevance and the midpoint of the first heavy interval as interval endpoints to form a second heavy interval,
step eight: judging whether the total number of the fault turn number points in the second heavy interval is greater than 2, if so, replacing the second heavy interval with the fault turn number interval, then returning to the step one, otherwise, executing the step nine,
step nine: calculating the gray content sequence corresponding to the midpoint of the first heavy interval, calculating the correlation degree between the gray content sequence and the detected gray content sequence to obtain the intermediate correlation degree,
step ten: and judging whether the intermediate correlation degree is greater than the first correlation degree, if so, taking the fault turn number corresponding to the intermediate correlation degree as the fault turn number of the stator winding of the motor to be detected, and otherwise, taking the fault turn number corresponding to the first correlation degree as the fault turn number of the stator winding of the motor to be detected.
Further, the specific method for constructing m groups of standard gray content sequences by using m groups of standard characteristic parameters comprises the following steps:
setting a behavior sequence:
Figure BDA0003003990310000021
wherein, XiFor the standard characteristic parameter, x, corresponding to the ith point of turn in faulti(1),xi(2),…,xi(n) is the characteristic signal in the i-th set of standard characteristic parameters,
construction of x according to the formulai(k) At XiRatio y in the mean of all characteristic signalsi(k)：
Figure BDA0003003990310000022
Wherein x isi(k) Is the k characteristic signal in the i-th group of standard characteristic parameters, n is the total number of characteristic signals in the i-th group of standard characteristic parameters, k is 1,2, the.
Then the expression of the m groups of standard gray content sequences is:
Figure BDA0003003990310000023
wherein, YiRepresenting the ith group of gray connotation sequences.
Further, the degree of correlation R is performed according to the following formulaiAnd (3) calculating:
Figure BDA0003003990310000031
wherein, gamma (Y)0,Yi) Is Y0And YiCoefficient of correlation between, Y0For the measured ash content sequence, omegakAnd the weight coefficient is the weight coefficient of the kth characteristic signal in the ith group of gray content sequences.
Further, the method for selecting the midpoint of the first heavy interval comprises the following steps:
setting M fault turn number points in the first heavy interval,
when M is odd number, the fault turn number point in the middle of the first heavy interval is the middle point of the first heavy interval,
when M is an even number, the fault turn number point close to the fault turn number point corresponding to the first relevance degree is the middle point of the first heavy interval.
The method for judging the number of the turn-to-turn short circuit motor stator winding fault turns fuses electromechanical signals, and solves the problem of large deviation of single detection characteristic quantity; the ash content sequence is utilized, the limitation of the traditional Deng correlation local control whole is broken through, and the motor fault discrimination accuracy is improved; the inter-cell positioning method greatly reduces the calculated amount, improves the motor diagnosis speed and provides a theoretical basis for the operation, the overhaul and the fault tolerance of the motor.
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Fig. 1 is a flowchart of a method for determining the number of fault turns of a stator winding of a turn-to-turn short circuit motor according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The first embodiment is as follows: the method for judging the number of fault turns of the stator winding of the turn-to-turn short circuit motor in the embodiment comprises the following steps of:
initialization: and establishing a motor model, wherein the total number of turns of the stator winding in the motor model is N.
The method comprises the following steps: m fault turn points are uniformly selected in the fault turn interval, wherein m is more than or equal to 2 and less than or equal to Nk，NkThe total number of fault turn points in a fault turn interval is shown, and the initial fault turn interval is [0, N]。
Step two: respectively arranging the fault turns corresponding to the m fault turn points in a motor model, collecting a counter potential signal, a short-circuit current signal, a magnetic flux density signal, a double-frequency vibration displacement signal, a quadruple-frequency vibration displacement signal, a double-frequency vibration speed signal and a quadruple-frequency vibration speed signal corresponding to each fault turn point, taking the characteristic signals as a group of standard characteristic parameters, and setting a behavior sequence with m rows and n columns of standard characteristic parameters by utilizing the characteristic signals:
Figure BDA0003003990310000041
wherein, XiA set of standard characteristic parameters corresponding to the ith point of turn in fault, i.e. a standard characteristic parameter sequence, xi(1),xi(2),…,xiAnd (n) is a characteristic signal in the ith group of standard characteristic parameters.
Construction of x according to the formulai(k) At XiRatio y in the mean of all characteristic signalsi(k)：
Figure BDA0003003990310000042
Wherein x isi(k) The characteristic signal is the kth characteristic signal in the ith group of standard characteristic parameters, n is the total number of the characteristic signals in the ith group of standard characteristic parameters, and k is 1, 2.
Then the expression of the m groups of standard gray content sequences constructed by the m groups of standard characteristic parameters is as follows:
Figure BDA0003003990310000043
wherein, YiRepresenting the ith group of gray connotation sequences.
And then forming a standard database by m groups of standard gray connotation sequences, wherein the standard database is shown in the following table:
standard database
Figure BDA0003003990310000044
Figure BDA0003003990310000051
Step three: respectively collecting a back electromotive force signal, a short circuit current signal, a flux density signal, a double-frequency vibration displacement signal, a quadruple-frequency vibration displacement signal, a double-frequency vibration speed signal and a quadruple-frequency vibration speed signal of the detected fault motor as detected characteristic parameters, and constructing a detected ash connotation sequence by using the detected characteristic parameters according to the method in the second step.
Step four: and calculating the association degree of the measured gray content sequences and each group of standard gray content sequences in the standard database, and sequencing the obtained m groups of association degrees from large to small.
The degree of correlation R is carried out according to the following formulaiAnd (3) calculating:
Figure BDA0003003990310000052
wherein, gamma (Y)0,Yi) Is Y0And YiCoefficient of correlation between, Y0For the measured ash content sequence, omegakAnd the weight coefficient is the weight coefficient of the kth characteristic signal in the ith group of gray content sequences.
Step five: and taking the number of fault turns corresponding to the first relevance and the second relevance as an interval endpoint to form a first heavy interval. The first heavy interval is a closed interval.
Step six: and judging whether the total number of the fault turn points in the first heavy interval is greater than 2, if so, executing a seventh step, and otherwise, taking the fault turn number corresponding to the first relevance degree as the fault turn number of the stator winding of the tested motor.
Step seven: setting M fault turn number points in the first heavy interval,
when M is odd number, the fault turn number point in the middle of the first heavy interval is the middle point of the first heavy interval,
when M is an even number, the fault turn number point close to the fault turn number point corresponding to the first relevance degree is the middle point of the first heavy interval.
And taking the number of fault turns corresponding to the first relevance and the midpoint of the first heavy interval as interval endpoints to form a second heavy interval. The second heavy interval is a closed interval.
Step eight: and judging whether the total number of the fault turn number points in the second heavy interval is greater than 2, if so, replacing the second heavy interval with the fault turn number interval, and then returning to the first step, otherwise, executing the ninth step.
Step nine: and (3) placing the number of fault turns corresponding to the midpoint in the first heavy interval into a motor model, collecting a counter potential signal, a short-circuit current signal, a flux density signal, a double-frequency vibration displacement signal, a quadruple-frequency vibration displacement signal, a double-frequency vibration speed signal and a quadruple-frequency vibration speed signal corresponding to the number of fault turns, and constructing a gray connotation sequence according to the characteristic signals.
And calculating the correlation degree of the gray content sequence and the detected gray content sequence to obtain the intermediate correlation degree.
Step ten: and judging whether the intermediate correlation degree is greater than the first correlation degree, if so, taking the fault turn number corresponding to the intermediate correlation degree as the fault turn number of the stator winding of the motor to be detected, and otherwise, taking the fault turn number corresponding to the first correlation degree as the fault turn number of the stator winding of the motor to be detected.
To verify the feasibility of the present embodiment, the method of the present embodiment is subjected to simulation analysis, and the feasibility of the present embodiment is further described in detail with reference to the simulation result table.
In the embodiment, the construction of the gray connotation is based on the gray correlation and entropy weight fusion theory, and the defects that the local points of the traditional gray correlation model are easy to control the overall correlation degree, the average value is easy to submerge valuable information and the like are overcome, and the specific establishment scheme is as follows:
selecting a permanent magnet synchronous motor as a prototype machine for simulation analysis, wherein the parameters of the prototype machine are as follows: the power is 3kW, the linear built-in type is realized, the number of conductors in each slot is 62, and the double-layer winding is realized. Selecting 6 fault turn points, wherein the fault turn points are respectively as follows: 5. 10, 15,20, 25 and 30 turns, and setting the characteristic signals as follows: counter potential EAShort-circuit current IfMagnetic density BrDouble frequency vibration displacement X2sQuadruple frequency vibration displacement X4sDouble frequency vibration velocity V2sAnd quadruple frequency vibration velocity V4s. To better verify the feasibility of this embodiment, the motor to be tested was set to a 16-turn short circuit.
And (3) modeling and simulating the motor to obtain a behavior sequence of the characteristic parameters under different fault degrees, and calculating a gray content sequence of the behavior sequence to obtain a standard database as shown in table 1.
TABLE 1 Standard database
Figure BDA0003003990310000061
Table 2 motor ash content sequence table with detected fault
Figure BDA0003003990310000062
And (3) performing correlation calculation and comparison on the gray connotation sequence of the signal to be detected and a standard database, wherein the result is shown in table 3.
TABLE 3 calculation of correlation of faults to be tested
Figure BDA0003003990310000071
As can be seen from table 3, the unknown fault level is most correlated to 15 turn faults and second correlated to 20 turn faults. Then the first interval with unknown fault degree is [15,20], the second interval is [15,17], the output is not single data, the second interval is calculated again by using the dichotomy idea, namely, the fault point is set as: 15. and 16 and 17, calculating that the correlation degree of the gray connotation sequence of the double interval and 16 turns of faults in the standard database is the maximum, and accurately positioning the number of the fault turns to be 16.
The simulation analysis aims at a prototype with few turns of the stator winding, so that the implementation method and the feasibility of the simulation analysis method are convenient to observe, the calculation advantages of the simulation analysis method can be more prominent in a motor with many turns of the stator winding of the motor, and the time for judging the number of the fault turns is greatly saved.
The invention fuses electromechanical signals, and solves the problem of larger deviation of single detection characteristic quantity; the ash content sequence is utilized, the limitation of the traditional Deng correlation local control whole is broken through, and the motor fault discrimination accuracy is improved; the inter-cell positioning method greatly reduces the calculated amount, improves the motor diagnosis speed and provides a theoretical basis for the operation, the overhaul and the fault tolerance of the motor.

Claims (6)

1. A method for judging the number of fault turns of a stator winding of a turn-to-turn short circuit motor is characterized by comprising the following steps of:
initialization: establishing a motor model, wherein the total number of turns of a stator winding in the motor model is N,
the method comprises the following steps: m fault turn points are uniformly selected in the fault turn interval, wherein m is more than or equal to 2 and less than or equal to Nk，NkThe total number of fault turn points in a fault turn interval is shown, and the initial fault turn interval is [0, N]，
Step two: respectively arrange the corresponding fault turn number of m fault turn points in a motor model, collect the corresponding characteristic signal of every fault turn number point and regard as a set of standard characteristic parameter, utilize m groups of standard characteristic parameter to construct m groups of standard grey content sequences, constitute the standard database with m groups of standard grey content sequences, include in the characteristic signal: an electrical signal, a magnetic signal and a vibration signal,
step three: respectively collecting the characteristic signals of the motor with the fault to be detected as the characteristic parameters to be detected, constructing a content sequence of the detected ash by using the characteristic parameters to be detected,
step four: calculating the correlation degree of the measured gray content sequence and each group of standard gray content sequences in a standard database, sequencing the obtained m groups of correlation degrees from large to small,
step five: using the fault turns corresponding to the first correlation degree and the second correlation degree as interval end points to form a first heavy interval,
step six: judging whether the total number of the fault turn points in the first heavy interval is greater than 2, if so, executing a seventh step, otherwise, taking the fault turn number corresponding to the first relevance degree as the fault turn number of the stator winding of the tested motor,
step seven: using the fault turn number corresponding to the first relevance and the midpoint of the first heavy interval as interval endpoints to form a second heavy interval,
step eight: judging whether the total number of the fault turn number points in the second heavy interval is greater than 2, if so, replacing the second heavy interval with the fault turn number interval, then returning to the step one, otherwise, executing the step nine,
step nine: calculating the gray content sequence corresponding to the midpoint of the first heavy interval, calculating the correlation degree between the gray content sequence and the detected gray content sequence to obtain the intermediate correlation degree,
step ten: and judging whether the intermediate correlation degree is greater than the first correlation degree, if so, taking the fault turn number corresponding to the intermediate correlation degree as the fault turn number of the stator winding of the motor to be detected, and otherwise, taking the fault turn number corresponding to the first correlation degree as the fault turn number of the stator winding of the motor to be detected.
2. The method for judging the number of the fault turns of the stator winding of the turn-to-turn short circuit motor according to claim 1, wherein the specific method for constructing m groups of standard gray content sequences by using m groups of standard characteristic parameters comprises the following steps:
setting a behavior sequence:
Figure FDA0003003990300000021
wherein, XiFor the standard characteristic parameter, x, corresponding to the ith point of turn in faulti(1),xi(2),…,xi(n) is the characteristic signal in the i-th set of standard characteristic parameters,
construction of x according to the formulai(k) At XiRatio y in the mean of all characteristic signalsi(k)：
Figure FDA0003003990300000022
Wherein x isi(k) Is the k characteristic signal in the i-th group of standard characteristic parameters, n is the total number of characteristic signals in the i-th group of standard characteristic parameters, k is 1,2, the.
Then the expression of the m groups of standard gray content sequences is:
Figure FDA0003003990300000023
wherein, YiRepresenting the ith group of gray connotation sequences.
3. The method for determining the number of failed turns of the stator winding of the turn-to-turn short circuit motor according to claim 2, wherein the correlation degree R is performed according to the following formulaiAnd (3) calculating:
Figure FDA0003003990300000024
wherein, gamma (Y)0,Yi) Is Y0And YiCoefficient of correlation between, Y0And omega k is the weight coefficient of the kth characteristic signal in the ith group of gray content sequences.
4. The method for judging the number of the fault turns of the stator winding of the turn-to-turn short circuit motor according to claim 1, wherein the selection method of the midpoint of the first heavy interval is as follows:
setting M fault turn number points in the first heavy interval,
when M is odd number, the fault turn number point in the middle of the first heavy interval is the middle point of the first heavy interval,
when M is an even number, the fault turn number point close to the fault turn number point corresponding to the first relevance degree is the middle point of the first heavy interval.
5. The method for judging the number of the fault turns of the stator winding of the turn-to-turn short circuit motor according to claim 1 or 2, wherein in the ninth step, the specific method for calculating the gray content sequence corresponding to the midpoint of the first heavy interval is as follows:
and placing the number of fault turns corresponding to the midpoint in the first heavy interval in a motor model, collecting a characteristic signal corresponding to the number of fault turns, and constructing a gray connotation sequence according to the characteristic signal.
6. The method for determining the number of failed turns of the stator winding of the turn-to-turn short circuit motor according to claim 5, wherein the electrical signal in the characteristic signal comprises: a back-emf signal and a short-circuit current signal,
the magnetic signals in the characteristic signals include: the magnetic density signal is used for indicating the magnetic density,
the vibration signal in the characteristic signal comprises: a frequency doubling vibration displacement signal, a frequency quadrupling vibration displacement signal, a frequency doubling vibration speed signal and a frequency quadrupling vibration speed signal.
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Publication number Priority date Publication date Assignee Title
CN114035126A (en) * 2021-10-25 2022-02-11 电子科技大学 Method for determining turn-to-turn short circuit turns of permanent magnet synchronous motor winding in off-line state

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CN101221206A (en) * 2007-08-03 2008-07-16 西北工业大学 Method for diagnosing turn-to-turn short circuit of permanent magnet fault-tolerant motor
CN102360054A (en) * 2011-09-20 2012-02-22 华北电力大学（保定） Diagnostic method for estimating turn-to-turn short circuit fault degree of large generator exciting windings
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114035126A (en) * 2021-10-25 2022-02-11 电子科技大学 Method for determining turn-to-turn short circuit turns of permanent magnet synchronous motor winding in off-line state
CN114035126B (en) * 2021-10-25 2022-07-26 电子科技大学 Method for determining turn number of turn-to-turn short circuit of permanent magnet synchronous motor winding in off-line state

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