Permanent magnet wind power generator demagnetization fault detection positioning method and device and electronic equipment

Abstract

The disclosure provides a permanent magnet wind generator demagnetization fault detection and positioning method, device and electronic equipment, the method comprising: determining the air gap magnetic field intensity signal energy value corresponding to each magnetic intensity probe according to the rotor position signal and the plurality of air gap magnetic field intensity signals, determining the energy value standard deviation of the plurality of air gap magnetic field intensity signal energy values, determining the plurality of correlation coefficient information according to the energy value standard deviation and the reference standard deviation, and positioning the magnetic pole in the wind generator that occurs local demagnetization fault according to the detection result information, the rotor position signal and the plurality of air gap magnetic field intensity signals. Through the disclosure, the magnetic pole that occurs demagnetization fault can be found in time and the position of the demagnetization magnetic pole can be accurately positioned, the magnetic pole demagnetization fault is quickly and accurately detected, the joint use of the collected plurality of signal data for fault detection processing can avoid the misjudgment phenomenon caused by single sensor data, and the reliability of the fault diagnosis result is improved.

Description

Methods, devices and electronic equipment for detecting and locating demagnetization faults in permanent magnet wind turbines Technical Field

[0001] This disclosure relates to the field of fault diagnosis technology for permanent magnet wind turbines, and in particular to a method, device, and electronic equipment for detecting and locating demagnetization faults in permanent magnet wind turbines. Background Technology

[0002] Currently, direct-drive permanent magnet wind turbines are widely used due to their reliable operation and high efficiency. However, in actual operation, due to the influence of factors such as vibration, temperature, electromagnetic interference and aging, the permanent magnets inevitably demagnetize, resulting in a decrease in the no-load electromotive force of the generator and an increase in losses, which seriously affects the operating performance of the permanent magnet wind turbine. Therefore, it is necessary to detect and locate the demagnetization fault in direct-drive permanent magnet wind turbines.

[0003] In related technologies, the diagnosis of local demagnetization faults in direct-drive permanent magnet wind turbines mainly involves analyzing signals such as stator current, back electromotive force, and vibration to determine whether a local demagnetization fault has occurred.

[0004] In this method, it is impossible to accurately locate the position of the locally demagnetized magnetic poles. At the same time, due to the manufacturing characteristics of the sensor and the influence of the surrounding environment, the measurement data may be biased, which will directly affect the final judgment result of the demagnetization fault of the permanent magnet wind turbine, easily causing misjudgment and affecting the reliability of the fault diagnosis result. Summary of the Invention

[0005] This disclosure aims to at least partially address one of the technical problems in the related art.

[0006] Therefore, the purpose of this disclosure is to provide a method, device, electronic equipment, storage medium, and computer program product for detecting and locating demagnetization faults in permanent magnet wind turbines.

[0007] The first aspect of this disclosure proposes a method for detecting and locating demagnetization faults in a permanent magnet wind turbine, comprising: acquiring a rotor position signal and air gap magnetic field strength signals corresponding to multiple magnetic field probes within one rotation cycle of the wind turbine; determining the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe based on the rotor position signal and the multiple air gap magnetic field strength signals; determining the standard deviation of the energy values ​​of the multiple air gap magnetic field strength signals; determining the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation based on the energy value standard deviation and the reference standard deviation; performing local demagnetization fault detection processing on the wind turbine based on the multiple correlation coefficient information to obtain detection result information; and locating the magnetic pole in the wind turbine where a local demagnetization fault has occurred based on the detection result information, the rotor position signal, and the multiple air gap magnetic field strength signals.

[0008] A second aspect of this disclosure provides a device for detecting and locating demagnetization faults in a permanent magnet wind turbine, comprising: a first acquisition module for acquiring rotor position signals and air gap magnetic field strength signals corresponding to multiple magnetic field probes within one rotation cycle of the wind turbine; a first determination module for determining the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe based on the rotor position signals and the multiple air gap magnetic field strength signals; a second determination module for determining the standard deviation of the energy values ​​of the multiple air gap magnetic field strength signals; a third determination module for determining the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation based on the energy value standard deviation and the reference standard deviation; a first processing module for performing local demagnetization fault detection processing on the wind turbine based on the multiple correlation coefficient information to obtain detection result information; and a second processing module for locating the magnetic pole in the wind turbine where a local demagnetization fault has occurred based on the detection result information, the rotor position signals, and the multiple air gap magnetic field strength signals.

[0009] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for detecting and locating demagnetization faults in permanent magnet wind turbines as proposed in the first aspect of this disclosure.

[0010] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for detecting and locating demagnetization faults in permanent magnet wind turbines as proposed in the first aspect of this disclosure.

[0011] The fifth aspect of this disclosure provides a computer program product that, when executed by a processor, performs the method for detecting and locating demagnetization faults in permanent magnet wind turbines as described in the first aspect of this disclosure.

[0012] The permanent magnet wind turbine demagnetization fault detection and location method, device, electronic equipment, storage medium, and computer program product disclosed herein have at least the following beneficial effects: By acquiring the rotor position signal of the wind turbine within one rotation cycle and the air gap magnetic field strength signals corresponding to multiple magnetic field probes, the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe is determined based on the rotor position signal and multiple air gap magnetic field strength signals. The standard deviation of the energy values ​​of the multiple air gap magnetic field strength signals is determined. Based on the standard deviation of the energy values ​​and the reference standard deviation, the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation is determined. Based on the multiple correlation coefficient information, the wind turbine is processed for local demagnetization fault detection to obtain detection result information. Based on the detection result information, rotor position signal, and multiple air gap magnetic field strength signals, the magnetic pole in the wind turbine where local demagnetization fault occurs is located. This enables timely detection of the magnetic pole with demagnetization fault and accurate location of the demagnetized magnetic pole, achieving rapid and accurate detection of magnetic pole demagnetization fault. The combined use of multiple acquired signal data for fault detection processing can avoid misjudgment caused by single sensor data and improve the reliability of fault diagnosis results.

[0013] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0014] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0015] Figure 1 is a flowchart illustrating a method for detecting and locating demagnetization faults in a permanent magnet wind turbine according to an embodiment of this disclosure.

[0016] Figure 2 is a flowchart illustrating a method for detecting and locating demagnetization faults in a permanent magnet wind turbine according to another embodiment of this disclosure.

[0017] Figure 3 is a flowchart illustrating a method for detecting and locating demagnetization faults in a permanent magnet wind turbine according to another embodiment of this disclosure.

[0018] Figure 4 is a two-dimensional model diagram of demagnetization fault in an embodiment of this disclosure;

[0019] Figure 5 is a schematic diagram of the demagnetization fault detection and location process in an embodiment of this disclosure.

[0020] Figure 6 is a schematic diagram of the rotor position change of the generator during one rotation cycle in an embodiment of this disclosure.

[0021] Figure 7 is a time-domain waveform of the air gap magnetic field monitored by the magnetic probe 1 in an embodiment of this disclosure.

[0022] Figure 8 is a time-domain waveform of the air gap magnetic field monitored by the magnetic probe 2 in an embodiment of this disclosure.

[0023] Figure 9 is a radar diagram of the air gap magnetic field energy of the magnetic field probe 1 in the embodiment of this disclosure;

[0024] Figure 10 is a radar diagram of the air gap magnetic field energy of the magnetic field probe 2 in the embodiment of this disclosure;

[0025] Figure 11 is a schematic diagram of the structure of a permanent magnet wind turbine demagnetization fault detection and location device according to an embodiment of the present disclosure;

[0026] Figure 12 is a schematic diagram of the structure of a permanent magnet wind turbine demagnetization fault detection and location device according to another embodiment of the present disclosure;

[0027] Figure 13 shows a block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure. Detailed Implementation

[0028] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0029] Figure 1 is a flowchart illustrating a method for detecting and locating demagnetization faults in a permanent magnet wind turbine according to an embodiment of this disclosure.

[0030] It should be noted that the execution subject of the permanent magnet wind turbine demagnetization fault detection and location method in this embodiment is the permanent magnet wind turbine demagnetization fault detection and location device. This device can be implemented by software and / or hardware, and can be configured in electronic devices, without any limitation.

[0031] As shown in Figure 1, the method for detecting and locating demagnetization faults in this permanent magnet wind turbine includes:

[0032] S101: Acquire the rotor position signal of the wind turbine and the air gap magnetic field strength signal corresponding to multiple magnetic probes during one rotation cycle.

[0033] The rotor position signal is obtained by measuring an absolute photoelectric angular position sensor that is coaxially connected to the rotor.

[0034] The air gap magnetic field strength signal is obtained by measuring m (m is a positive integer) magnetic probes that are uniformly distributed on the stator yoke. Multiple magnetic probes can be numbered, i.e., magnetic probe 1 to magnetic probe m.

[0035] In this embodiment of the disclosure, when acquiring the rotor position signal of the wind turbine generator in one rotation cycle and the air gap magnetic field strength signal corresponding to multiple magnetic probes, the rotor position signal of the generator in one rotation cycle and the air gap magnetic field strength signal of multiple magnetic probes (magnetic probe 1 to magnetic probe m) can be acquired simultaneously. The rotor position signal is measured by an absolute photoelectric angular position sensor coaxially connected to the rotor, and the air gap magnetic field strength signal is measured by m magnetic probes installed on the stator yoke in a uniform distribution manner for measuring the air gap magnetic field strength.

[0036] In this embodiment of the disclosure, synchronous acquisition means that when the rotor passes through the reference zero degree (which can be a pre-set designated position), the magnetic field strength signal of the air gap is collected by the magnetic field probe 1 to the magnetic field probe m. The left end of the magnetic field probe 1 coincides with the reference zero degree of the rotor, and the other magnetic field probes are spaced from the magnetic field probe 1 by an integer multiple of the pole distance.

[0037] S102: Determine the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe based on the rotor position signal and multiple air gap magnetic field strength signals.

[0038] The energy value of the air gap magnetic field strength signal can be represented by the integral of the air gap magnetic field strength signal with respect to the rotor position signal within 1 / 2 electrical cycle. One rotation cycle can contain multiple 1 / 2 electrical cycles.

[0039] In this embodiment of the disclosure, when determining the energy value of the air gap magnetic field intensity signal corresponding to each magnetic field probe based on the rotor position signal and multiple air gap magnetic field intensity signals, the integral of the air gap magnetic field intensity signal with respect to the rotor position signal within each 1 / 2 electrical cycle can be calculated to obtain the air gap magnetic field intensity signal energy value E. ij The integral calculation expression is as follows: Where j represents the j-th air gap magnetic field probe, , is the initial value of the rotor angle in the i-th half-electric cycle. , is the end value of the rotor angle in the i-th half-electric cycle, p is the number of pole pairs, and half-electric cycle refers to the time it takes to completely pass through the magnetic pole along the circumference from one end of any magnetic pole. One rotation cycle can contain multiple half-electric cycles.

[0040] S103: Determine the standard deviation of the energy values ​​of multiple air gap magnetic field strength signals.

[0041] In this embodiment of the present disclosure, after determining the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe based on the rotor position signal and multiple air gap magnetic field strength signals, and obtaining multiple air gap magnetic field strength signal energy values, the standard deviation of the energy values ​​of the multiple air gap magnetic field strength signal energy values ​​can be determined.

[0042] In this embodiment of the disclosure, when determining the standard deviation of the energy values ​​of multiple air gap magnetic field strength signal energy values, a standard deviation calculation expression can be introduced. The standard deviation of the multiple air gap magnetic field strength signal energy values ​​is calculated, and the expression for calculating the standard deviation of the air gap magnetic field strength signal energy values ​​of all magnetic probes within one rotation cycle of the generator is as follows: Where j represents the j-th air gap magnetic field probe, i represents the i-th half-electric cycle, and s j n represents the standard deviation of the air gap magnetic field strength signal energy value measured by the j-th magnetic field probe within one rotation cycle of the generator. t It is the number of half-electric cycles in one rotational period, and its value is equal to twice the number of pole pairs p.

[0043] S104: Based on the standard deviation of the energy value and the reference standard deviation, determine the correlation coefficient information between the air gap magnetic field strength signal and the reference standard deviation corresponding to each magnetic field probe.

[0044] The reference standard deviation includes: the standard deviation of the air gap magnetic field strength signal when the wind turbine is operating normally, and the standard deviation of the air gap magnetic field strength signal that can ensure that the wind turbine has experienced a local demagnetization fault.

[0045] The correlation coefficient information refers to the confidence level of the air gap magnetic field strength signal of the magnetic field probe in a certain operating mode of the wind turbine (normal operating mode and local demagnetization fault operating mode) within one rotation cycle. The higher the correlation coefficient for local demagnetization fault, the more likely the wind turbine is to have a local demagnetization fault.

[0046] In this embodiment of the disclosure, after determining the standard deviation of the energy values ​​of multiple air gap magnetic field strength signals as described above, the correlation coefficient information between the air gap magnetic field strength signal and the reference standard deviation corresponding to each magnetic field probe can be determined based on the standard deviation of the energy values ​​and the reference standard deviation.

[0047] In this embodiment of the disclosure, when determining the correlation coefficient information between the air gap magnetic field strength signal and the reference standard deviation corresponding to each magnetic field probe, a correlation coefficient calculation formula can be introduced to determine the correlation coefficient between the standard deviation of the energy value and the standard deviation of the air gap magnetic field strength signal under normal operating conditions, and the correlation coefficient between the standard deviation of the energy value and the standard deviation of the air gap magnetic field strength signal under local demagnetization fault conditions. The calculated correlation coefficients are used as correlation coefficient information.

[0048] S105: Based on multiple correlation coefficients, perform local demagnetization fault detection processing on the wind turbine to obtain detection results.

[0049] In this implementation, when performing local demagnetization fault detection processing on a wind turbine based on multiple correlation coefficient information and obtaining the detection results, the reliability values ​​of the air gap magnetic field strength signals measured by different magnetic field probes for local demagnetization faults and normal operating conditions can be calculated based on multiple correlation coefficient information. Then, the Dempster / Shafer evidence theory (DS evidence theory) can be used to perform data fusion processing on the diagnostic results of the reliability values ​​of multiple magnetic field probes using a data fusion expression to obtain the final reliability value of the air gap magnetic field strength for different operating conditions (local demagnetization fault condition and normal operating condition). The data fusion expression is as follows:

[0050]

[0051] In the data fusion expression, m is the basic probability allocation function. Then, a pre-set confidence threshold A can be obtained. The final confidence value of the local demagnetization fault is compared with the confidence threshold A. If the final confidence value of the local demagnetization fault is greater than the confidence threshold A and the uncertainty value is less than the pre-set threshold B, then it is determined that a local demagnetization fault has occurred, and the detection result information is that the wind turbine has experienced a local demagnetization fault. Otherwise, it is determined that the wind turbine is in normal operation, and the detection result information is that the wind turbine has not experienced a local demagnetization fault.

[0052] S106: Based on the detection results, rotor position signal, and multiple air gap magnetic field strength signals, locate the magnetic pole in the wind turbine that has experienced a local demagnetization fault.

[0053] In this embodiment of the present disclosure, after performing local demagnetization fault detection processing on the wind turbine based on multiple correlation coefficient information to obtain detection result information, local demagnetization fault detection processing on the wind turbine can be performed based on multiple correlation coefficient information to obtain detection result information.

[0054] Optionally, in some embodiments, when locating the magnetic pole in the wind turbine generator that has a local demagnetization fault based on the detection result information, rotor position signal, and multiple air gap magnetic field strength signals, the magnetic pole that has a local demagnetization fault can be located based on the rotor position signal and multiple air gap magnetic field strength signals when the detection result information indicates that the wind turbine generator has a local demagnetization fault.

[0055] In this embodiment of the disclosure, when locating the magnetic pole with a local demagnetization fault based on the rotor position signal and multiple air gap magnetic field strength signals, the magnetic pole with a local demagnetization fault can be located separately for magnetic field probes 1 to m. For the first magnetic field probe, the percentage c of the air gap magnetic field strength signal deviating from the normal value in each 1 / 2 electric cycle is calculated, and a percentage threshold C is preset. If the percentage c is greater than the threshold C, it is determined that the magnetic pole corresponding to the 1 / 2 electric cycle has a local demagnetization fault. Then, the left interval angle value and the right interval angle value of the rotor angle value corresponding to the abnormal 1 / 2 electric cycle are recorded according to the rotor position signal, and the number of the demagnetized magnetic pole is determined according to the left interval angle value and the right interval angle value.

[0056] The implementation principle of the permanent magnet wind turbine demagnetization fault detection and location in this embodiment is as follows: When a local demagnetization fault occurs in the permanent magnet wind turbine, the air gap magnetic field strength under the demagnetized pole will decrease, while the air gap magnetic field strength under the normal pole will not change much. The occurrence of a demagnetization fault can be detected by measuring the change in the energy value of the air gap magnetic field strength during different electrical cycles of rotor rotation. By setting a reference zero degree for rotor rotation and numbering the magnetic poles, the rotor position signal corresponds to the magnetic pole number, facilitating the location of the demagnetized pole. Since the number of generator poles is 2p, each... A magnetic pole corresponds to an angle range of 360 / 2p degrees. That is, every time the rotation is 360 / 2p degrees, a magnetic pole will completely pass through the magnetic intensity probe. If the magnetic intensity probe 1 is set to coincide with the rotor reference zero degree position, then the angle range of 0-360 / 2p degrees corresponds to the first magnetic pole. The correspondence between the subsequent magnetic pole numbers and the rotor angle can be calculated based on the degree interval between the first magnetic pole and other magnetic poles. If a local demagnetization fault is determined, the abnormal 1 / 2 electrical cycle is found based on the air gap magnetic field energy value and corresponding to the rotor position, thereby accurately locating the demagnetized magnetic pole.

[0057] The permanent magnet wind turbine demagnetization fault detection and location method proposed in this embodiment can determine whether a wind turbine has demagnetized through rotor position signals and air gap magnetic field signals, and can accurately locate the demagnetized magnetic poles. By using air gap magnetic field intensity signals from multiple magnetic intensity probes and employing multi-sensor information fusion, it can avoid misdiagnosis caused by fault detection based on single sensor data, thereby effectively improving the reliability of fault diagnosis. Furthermore, this method can be used for online monitoring, effectively shortening the subsequent operation and maintenance time and cost after diagnosis.

[0058] In this embodiment, the rotor position signal of the wind turbine and the air gap magnetic field strength signals corresponding to multiple magnetic field probes are acquired within one rotation cycle. Based on the rotor position signal and multiple air gap magnetic field strength signals, the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe is determined, and the standard deviation of the energy values ​​of multiple air gap magnetic field strength signals is determined. Based on the standard deviation of the energy values ​​and the reference standard deviation, the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation is determined. Based on the multiple correlation coefficient information, the wind turbine is processed for local demagnetization fault detection, and the detection result information is obtained. Based on the detection result information, the rotor position signal, and multiple air gap magnetic field strength signals, the magnetic pole in the wind turbine where the local demagnetization fault occurs is located. This allows for timely detection of the magnetic pole with the demagnetization fault and accurate location of the demagnetized magnetic pole, achieving rapid and accurate detection of magnetic pole demagnetization faults. The combined use of multiple acquired signal data for fault detection processing can avoid misjudgment caused by single sensor data and improve the reliability of fault diagnosis results.

[0059] Figure 2 is a flowchart illustrating a method for detecting and locating demagnetization faults in a permanent magnet wind turbine according to another embodiment of this disclosure.

[0060] As shown in Figure 2, the method for detecting and locating demagnetization faults in this permanent magnet wind turbine includes:

[0061] S201: Acquire the rotor position signal of the wind turbine and the air gap magnetic field strength signal corresponding to multiple magnetic probes during one rotation cycle.

[0062] S202: Determine the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe based on the rotor position signal and multiple air gap magnetic field strength signals.

[0063] S203: Determine the standard deviation of the energy values ​​of multiple air gap magnetic field strength signals.

[0064] For a detailed description of S201 to S203, please refer to the above embodiments, which will not be repeated here.

[0065] S204: Based on the standard deviation of the energy value and the reference standard deviation, determine the correlation coefficient information between the air gap magnetic field strength signal and the reference standard deviation corresponding to each magnetic field probe.

[0066] Optionally, in some embodiments, the reference standard deviation includes: a first reference standard deviation and a second reference standard deviation, wherein the first reference standard deviation is the standard deviation of the air gap magnetic field strength signal within one rotation cycle of the wind turbine under normal operating conditions, and the second reference standard deviation is the standard deviation of the air gap magnetic field strength signal within one rotation cycle of the wind turbine under local demagnetization fault conditions. When determining the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation based on the energy value standard deviation and the reference standard deviation, the first correlation coefficient of the air gap magnetic field strength signal corresponding to each magnetic field probe under normal operating conditions can be determined based on the energy value standard deviation, the first reference standard deviation, and the second reference standard deviation. The second correlation coefficient of the air gap magnetic field strength signal corresponding to each magnetic field probe under local demagnetization fault conditions can be determined based on the energy value standard deviation, the first reference standard deviation, and the second reference standard deviation. The first correlation coefficient and the second correlation coefficient are used as correlation coefficient information.

[0067] The reference standard deviation includes: the first reference standard deviation and the second reference standard deviation. The first reference standard deviation refers to the standard deviation of the air gap magnetic field strength signal within one rotation cycle of the wind turbine under normal operating conditions.

[0068] The first correlation coefficient refers to the correlation coefficient between the energy standard deviation and the first reference standard deviation of the wind turbine under normal operating conditions.

[0069] The second reference standard deviation refers to the standard deviation of the air gap magnetic field strength signal within one rotation cycle of a wind turbine under local demagnetization fault conditions.

[0070] The second correlation coefficient refers to the correlation coefficient between the energy standard deviation and the second reference standard deviation of a wind turbine under the condition of local demagnetization fault.

[0071] In this embodiment of the disclosure, when determining the first correlation coefficient between the air gap magnetic field strength signal corresponding to each magnetic field probe and the wind turbine under normal operating conditions based on the energy value standard deviation, the first reference standard deviation, and the second reference standard deviation, a calculation expression for the first correlation coefficient can be introduced. ,in, Let sj be the correlation coefficient between the air gap magnetic field strength signal and normal operating conditions within one rotation cycle of the j-th sensor, and s0 be the first reference standard deviation of the air gap magnetic field strength signal during normal operation of the wind turbine. b To ensure the second reference standard deviation of the air gap magnetic field strength signal in the event of a partial demagnetization fault in a wind turbine, s b The value of can be obtained from the expression It is determined that b is the tolerance coefficient, and its actual application value should be determined according to the accuracy required for diagnosis.

[0072] In this embodiment of the disclosure, when determining the second correlation coefficient of the air gap magnetic field strength signal corresponding to each magnetic field probe to the wind turbine generator under local demagnetization fault condition based on the energy value standard deviation, the first reference standard deviation, and the second reference standard deviation, a calculation expression for the second correlation coefficient can be introduced. ,in, s represents the correlation coefficient between the air gap magnetic field strength signal and the local demagnetization fault within one rotation cycle of the j-th sensor, and s0 is the first reference standard deviation of the air gap magnetic field strength signal when the generator is operating normally. b This serves as a second reference standard deviation for the air gap magnetic field strength signal, ensuring that a partial demagnetization fault has occurred in the generator.

[0073] In this embodiment of the present disclosure, after determining the first correlation coefficient and the second correlation coefficient based on the energy value standard deviation, the first reference standard deviation, and the second reference standard deviation, the first correlation coefficient and the second correlation coefficient can be used as correlation coefficient information to realize the determination of the correlation coefficient information between the air gap magnetic field strength signal and the reference standard deviation corresponding to each magnetic field probe based on the energy value standard deviation and the reference standard deviation.

[0074] S205: Determine multiple confidence values ​​for the occurrence of local demagnetization faults in wind turbines based on multiple correlation coefficient information.

[0075] In this embodiment of the disclosure, when determining multiple confidence values ​​for local demagnetization faults in wind turbines based on multiple correlation coefficient information, a target-type algorithm can be introduced for calculation and processing. An identification framework can be constructed first. Where A1 refers to the local demagnetization condition and A2 refers to the normal condition, the expression for calculating the maximum correlation coefficient is then introduced. Calculate the maximum correlation coefficient of the magnetic field probe j with the fault mode, where N c It is the number of failure modes. Let be the correlation coefficients between each sensor and different fault modes calculated in the formula, and then introduce the correlation allocation value calculation expression. Calculate the correlation assignment value of the magnetic field probe j to the fault mode, where W j To obtain the environmental weighting coefficient for the j-th sensor data, whose value ranges from [0, 1], a reliability coefficient calculation expression is introduced. Calculate the reliability coefficient of the j-th magnetic field probe to obtain the confidence level m of the magnetic field probe j against local demagnetization faults. j (A k ), , Where N is the number of fault modes and K is the correction coefficient, so as to obtain multiple confidence values ​​of multiple correlation coefficient information for the local demagnetization fault of wind turbine.

[0076] S206: Perform data fusion processing on multiple confidence values ​​to obtain the target confidence value, wherein the target confidence value is the final confidence value of the wind turbine generator experiencing a local demagnetization fault.

[0077] The target confidence value is the final confidence value for a wind turbine experiencing a local demagnetization fault, which can be used to make a final diagnosis and determination of whether a wind turbine has experienced a local demagnetization fault.

[0078] In this embodiment of the disclosure, when performing data fusion processing on multiple confidence values ​​to obtain a target confidence value, the DS evidence theory can be used to perform data fusion on the diagnostic results of multiple magnetic field probes to obtain the final confidence value of the air gap magnetic field strength for different fault modes. The fusion calculation expression is as follows:

[0079]

[0080] Among them, when hour, =0, C is the sum of the products of all two base probabilities that do not intersect. The value of C represents the degree of conflict between the various sources of evidence. When there are multiple sources of evidence, the fusion expression is: .

[0081] S207: Determine the uncertainty value of a local demagnetization fault in a wind turbine.

[0082] In this embodiment of the disclosure, the uncertainty value of a local demagnetization fault in a wind turbine can be determined based on the collected data and correlation coefficient information.

[0083] S208: Based on the target confidence value and uncertainty value, perform local demagnetization fault detection processing on the wind turbine to obtain the detection result information.

[0084] In this embodiment of the present disclosure, after performing data fusion processing on multiple confidence values ​​to obtain a target confidence value and determining the uncertainty value of a local demagnetization fault in a wind turbine, a local demagnetization fault detection process can be performed on the wind turbine based on the target confidence value and the uncertainty value to obtain detection result information.

[0085] In this embodiment of the disclosure, when performing local demagnetization fault detection processing on a wind turbine based on a target confidence value and an uncertainty value to obtain detection result information, a preset threshold A can be set for the target confidence value and a preset threshold B can be set for the uncertainty value. When the target confidence value is greater than the preset threshold A and the uncertainty value is less than the preset threshold B, the detection result information is determined to be that the wind turbine has experienced a local demagnetization fault. Otherwise, under other circumstances, the detection result information is determined to be that the wind turbine is in normal operating condition and no local demagnetization fault has occurred.

[0086] Optionally, in some embodiments, when performing local demagnetization fault detection processing on the wind turbine based on the target confidence value and uncertainty value to obtain detection result information, a preset confidence value threshold and a preset uncertainty value threshold can be obtained. If the target confidence value is greater than the preset confidence value threshold and the uncertainty value is less than the preset uncertainty value threshold, the detection result information is determined to be that the wind turbine has a local demagnetization fault. If the target confidence value is less than or equal to the preset confidence value threshold, or if the uncertainty value is greater than or equal to the preset uncertainty value threshold, the detection result information is determined to be that the wind turbine has not experienced a local demagnetization fault. Thus, multiple confidence value data can be combined for local demagnetization fault detection, effectively avoiding fault misjudgment and ensuring the accuracy and reliability of fault detection results.

[0087] Among them, the preset reliability threshold refers to the reliability threshold value set in advance for the target reliability value.

[0088] Among them, the uncertainty threshold refers to the confidence threshold value set in advance for the uncertainty threshold. The preset confidence threshold and uncertainty threshold can be used to detect and process local demagnetization faults in wind turbines.

[0089] In this embodiment of the disclosure, when performing local demagnetization fault detection processing on a wind turbine based on a target confidence value and an uncertainty value to obtain detection result information, a preset confidence value threshold can be set in advance for the target confidence value, and a preset uncertainty value threshold can be set in advance for the uncertainty value. Then, the target confidence value and the preset confidence value threshold can be numerically compared, and the uncertainty value and the preset uncertainty value threshold can be numerically compared. If the target confidence value is greater than the preset confidence value threshold and the uncertainty value is less than the preset uncertainty value threshold, the detection result information is determined to be that the wind turbine has a local demagnetization fault. If the target confidence value is less than or equal to the preset confidence value threshold, or if the uncertainty value is greater than or equal to the preset uncertainty value threshold, the detection result information is determined to be that the wind turbine has not experienced a local demagnetization fault.

[0090] S209: Based on the detection results, rotor position signal, and multiple air gap magnetic field strength signals, locate the magnetic pole in the wind turbine that has experienced a local demagnetization fault.

[0091] For a detailed description of S209, please refer to the above embodiments, which will not be repeated here.

[0092] In this embodiment, the rotor position signal of the wind turbine and the air gap magnetic field strength signals corresponding to multiple magnetic field probes are acquired within one rotation cycle. Based on the rotor position signal and the multiple air gap magnetic field strength signals, the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe is determined, and the standard deviation of the energy values ​​of the multiple air gap magnetic field strength signals is determined. Based on the standard deviation of the energy values ​​and the reference standard deviation, the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation is determined. Based on the multiple correlation coefficient information, the wind turbine is processed for local demagnetization fault detection, and the detection result information is obtained. Based on the detection result information, the rotor position signal, and the multiple air gap magnetic field strength signals, the magnetic pole in the wind turbine where the local demagnetization fault occurs is located, which can promptly detect the magnetic pole with the demagnetization fault and accurately determine the position of the demagnetized magnetic pole. This system enables rapid and accurate detection of magnetic pole demagnetization faults. By combining multiple collected signal data for fault detection processing, it avoids misjudgments caused by single sensor data, improving the reliability of fault diagnosis results. By acquiring preset confidence and uncertainty thresholds, the system determines the detection result as a local demagnetization fault in the wind turbine when the target confidence value is greater than the preset confidence threshold and the uncertainty value is less than the preset uncertainty threshold. Conversely, it determines the detection result as no local demagnetization fault in the wind turbine when the target confidence value is less than or equal to the preset confidence threshold, or when the uncertainty value is greater than or equal to the preset uncertainty threshold. This allows for the combined use of multiple confidence value data for local demagnetization fault detection, effectively avoiding misjudgments and ensuring the accuracy and reliability of fault detection results.

[0093] Figure 3 is a flowchart illustrating a method for detecting and locating demagnetization faults in a permanent magnet wind turbine according to another embodiment of this disclosure.

[0094] As shown in Figure 3, the method for detecting and locating demagnetization faults in this permanent magnet wind turbine includes:

[0095] S301: Acquire the rotor position signal of the wind turbine and the air gap magnetic field strength signal corresponding to multiple magnetic probes within one rotation cycle. One rotation cycle contains multiple half-electric cycles.

[0096] The half-electric cycle refers to the time it takes to completely pass through any magnetic pole along the circumference, starting from one end of the magnetic pole. One rotation cycle contains multiple half-electric cycles.

[0097] S302: The first sampling rate for acquiring the rotor position signal and the second sampling rate for acquiring the air gap magnetic field strength signal.

[0098] The first sampling rate refers to the data sampling rate when acquiring rotor position signals.

[0099] The second sampling rate refers to the data sampling rate when collecting the air gap magnetic field strength signal.

[0100] In this embodiment of the disclosure, a first sampling rate of the rotor position signal and a second sampling rate of the air gap magnetic field strength signal can be obtained. The first sampling rate and the second sampling rate can be used to resample the air gap magnetic field strength signal. Resampling can make the rotor position signal and the air gap magnetic field strength signal have the same sampling rate.

[0101] S303: Resample the air gap magnetic field strength signal according to the first sampling rate and the second sampling rate.

[0102] Optionally, in some embodiments, when resampling the air gap magnetic field strength signal according to the first sampling rate and the second sampling rate, the air gap magnetic field strength signal can be upsampled if the second sampling rate is less than the first sampling rate, and downsampled if the second sampling rate is greater than the first sampling rate. This ensures that the rotor position signal and the air gap magnetic field strength signal have the same number of sampling points per unit time, which facilitates subsequent data processing for detecting local demagnetization faults in wind turbines.

[0103] In this embodiment of the disclosure, when resampling the air gap magnetic field strength signal according to the first sampling rate and the second sampling rate, the first sampling rate and the second sampling rate can be compared and processed. The resampling makes the rotor position signal and the air gap magnetic field strength signal have the same sampling rate. If the second sampling rate of the air gap magnetic field strength signal is less than the first sampling rate of the rotor position signal, the air gap magnetic field strength signal is upsampled. If the second sampling rate of the air gap magnetic field strength signal is greater than the first sampling rate of the rotor position signal, the air gap magnetic field strength signal is downsampled. If the first sampling rate of the air gap magnetic field strength signal is the same as the second sampling rate of the rotor position signal, no resampling operation is required, so as to ensure that the rotor position signal and the air gap magnetic field strength signal have the same number of sampling points per unit time.

[0104] S304: Determine the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe based on the rotor position signal and multiple air gap magnetic field strength signals.

[0105] S305: Determine the standard deviation of the energy values ​​of multiple air gap magnetic field strength signals.

[0106] S306: Determine the correlation coefficient information between the air gap magnetic field strength signal and the reference standard deviation for each magnetic field probe based on the standard deviation of the energy value and the reference standard deviation.

[0107] S307: Based on multiple correlation coefficients, perform local demagnetization fault detection processing on the wind turbine to obtain detection result information.

[0108] For a detailed description of S304 to S307, please refer to the above embodiments, which will not be repeated here.

[0109] S308: Based on multiple air gap magnetic field strength signals, determine the target electrical cycle in which the magnetic pole where the local demagnetization fault occurs from multiple half-electric cycles.

[0110] In this embodiment of the disclosure, when locating the magnetic pole in the wind turbine generator that has a local demagnetization fault based on the rotor position signal and multiple air gap magnetic field strength signals, the target electric cycle in which the magnetic pole with the local demagnetization fault occurs can be determined from multiple half-electric cycles based on the multiple air gap magnetic field strength signals.

[0111] Optionally, in some embodiments, the target electrical cycle in which the magnetic pole where the local demagnetization fault occurs is determined from multiple half-electric cycles based on multiple air gap magnetic field strength signals. If the percentage value of the air gap magnetic field strength signal of the half-electric cycle deviating from the normal value is greater than a preset percentage threshold, then the half-electric cycle is determined as the target electrical cycle.

[0112] In this embodiment of the disclosure, when determining the target electrical cycle in which the magnetic pole experiencing a local demagnetization fault occurs from multiple half-electric cycles based on multiple air gap magnetic field strength signals, for the first magnetic field probe, the percentage c of the air gap magnetic field strength deviating from the normal value in each half-electric cycle is calculated. If it is greater than a threshold C, then the magnetic pole corresponding to that half-electric cycle has experienced a demagnetization fault, and that half-electric cycle can be used as the target electrical cycle. The expression for calculating the percentage c of the air gap magnetic field strength deviating from the normal value in each half-electric cycle is as follows: Where E0 is the energy value of the air gap magnetic field strength within a single 1 / 2 electric cycle under normal operating conditions.

[0113] S309: Based on the rotor position signal, determine the rotor angle interval corresponding to the target electrical cycle, wherein the rotor angle interval has a corresponding left end angle value and a right end angle value.

[0114] In this embodiment of the present disclosure, after determining the target electrical cycle in which the magnetic pole with the local demagnetization fault occurs from multiple half-electric cycles based on multiple air gap magnetic field strength signals, the rotor angle interval corresponding to the target electrical cycle can be determined based on the rotor position signal, and the angle values ​​of the left and right endpoints of the interval can be recorded.

[0115] S310: Determine the pole number of the pole that has experienced a local demagnetization fault based on the angle values ​​of the left and right ends of the interval.

[0116] In this embodiment of the disclosure, the left interval l of determining the rotor angle interval corresponding to the target electrical cycle based on the rotor position signal and recording the rotor angle value corresponding to the target electrical cycle is described above. n The right interval r n Next, we can introduce interval expressions. Solve for the value of n in the expression, and n will be the number of the demagnetized magnetic pole, so as to locate the magnetic pole that has a local demagnetization fault in the wind turbine.

[0117] in, This represents the angle value of the left endpoint of the rotor position interval corresponding to the nth magnetic pole passing through magnetic field probe 1. Let be the angle value of the right end point of the rotor position interval corresponding to the nth magnetic pole passing through magnetic intensity probe 1, and p be the number of pole pairs. The demagnetizing magnetic pole is located based on the angle values ​​of the left and right ends of the interval. Then, other magnetic intensity probes are calculated and processed. If the interval between magnetic intensity probe j and magnetic intensity probe 1 is... If the degree is given, then the calculation expression for the interval corresponding to the magnetic field probe is: ,in, This represents the left endpoint of the rotor position interval corresponding to the nth magnetic pole passing through the magnetic intensity probe j. Let p be the right endpoint of the rotor position interval corresponding to the magnetic field probe j, where the nth magnetic pole passes through. .

[0118] In this embodiment, an absolute angular position sensor installed along the rotor axis and a magnetic field probe in the air gap are used to simultaneously acquire and measure the rotor position signal and the air gap magnetic field strength signal of the generator. The air gap magnetic field strength signal is resampled to ensure that the sampling rates of the rotor position signal and the air gap magnetic field strength signal are the same. The integral of the air gap magnetic field strength signal with respect to the rotor position signal is calculated in each half-electric cycle. The correlation coefficients of all magnetic field probes for fault and normal operating conditions are calculated using the standard deviation of the air gap magnetic field energy in one rotation cycle. The reliability values ​​of all magnetic field probes for fault and normal operating conditions are calculated, and multiple reliability values ​​are fused to determine whether a local demagnetization fault has occurred. Furthermore, the data from multiple magnetic field probes are fused using information fusion methods to improve the reliability of fault diagnosis. Finally, the demagnetized magnetic poles are accurately located using the rotor angle range corresponding to the abnormal electric cycle, and the positioning accuracy is not affected by changes in rotational speed, which facilitates subsequent operation and maintenance.

[0119] The implementation process of the technical solutions in the embodiments of this disclosure will be described in detail and completely below with reference to the accompanying drawings. The described embodiments are only some embodiments of this disclosure, and this disclosure is not limited thereto.

[0120] For example, taking a 44-pole permanent magnet direct-drive wind turbine as an example, the parameters of the wind turbine are shown in Table 1. This wind turbine uses two magnetic intensity probes, namely magnetic intensity probe 1 and magnetic intensity probe 2. Counterclockwise rotation is positive. Magnetic intensity probe 2 is 180° apart from magnetic intensity probe 1. The magnetic poles are numbered sequentially from 1 to 44 in a clockwise direction. The left end of the magnetic pole numbered 1 coincides with the rotor reference zero degree. The first permanent magnet is set to have a demagnetization fault, and its demagnetization degree is 20%, as shown in Figure 4. Figure 4 is a two-dimensional model diagram of the demagnetization fault in the embodiment of this disclosure. In this diagram, the permanent magnet numbered 1 is the demagnetized magnetic pole. At the same time, two observation points, magnetic intensity probe 1 and magnetic intensity probe 2, are set at 180 degrees to each other to monitor the change in the air gap magnetic field strength when the demagnetization fault occurs.

[0121] Table 1

[0122]

[0123] For example, as shown in Figure 5, which is a schematic diagram of the demagnetization fault detection and location process in an embodiment of this disclosure, the demagnetization fault detection of a wind turbine includes the following steps:

[0124] Step 1: Synchronously acquire the rotor position signal and the magnetic field strength signals of magnetic probe 1 and magnetic probe 2 within one rotation cycle of the wind turbine. For example, as shown in Figure 6, which is a schematic diagram of the rotor position change of the generator within one rotation cycle in this embodiment, the rotor position signal was acquired during the 360-degree rotation of the rotor in one rotation cycle under the above-mentioned fault condition. The changes in the air gap magnetic field strength acquired synchronously by magnetic probe 1 and magnetic probe 2 with the rotor position during one rotation cycle of the wind turbine are shown in Figures 7 and 8. Figure 7 is a schematic diagram of the rotor position change of the generator within one rotation cycle in this embodiment. Figure 8 shows the time-domain waveform of the air gap magnetic field monitored by the magnetic field probe 1 in this embodiment. As the rotor rotates, the air gap magnetic field strength at the monitoring point will change accordingly. The waveform of the air gap magnetic field strength in some half-cycles will be significantly lower than that in other half-cycles. Furthermore, the half-cycles of the abnormal waveforms detected by the magnetic field probe 1 and the magnetic field probe 2 are different. This is related to the position of the demagnetizing magnetic pole. When the demagnetizing magnetic pole passes near the monitoring point, the air gap magnetic field strength will decrease significantly.

[0125] Step 2: Resampling ensures that the rotor position signal and the air gap magnetic field strength signal have the same sampling rate.

[0126] In this embodiment, the rotor position signal and the air gap magnetic field strength signal use the same sampling rate, which is 5kHz, so resampling is not required.

[0127] Step 3: Calculate the integral of the air gap magnetic field strength signal with respect to the rotor position signal within each half-cycle to obtain the energy value E of the air gap magnetic field strength signal. ij Calculate the integral E of the air gap magnetic field strength signal with respect to the rotor position signal during each half-electric cycle of the magnetic field probe 1. i1 The results are shown in Table 2. Table 2 shows the energy values ​​of the air gap magnetic field strength in each half-electric cycle of the magnetic field probe 1. It can be seen that the energy values ​​of the air gap magnetic field strength in the 1st, 2nd, and 44th half-electric cycles are significantly lower than those in the other half-electric cycles, and the energy value in the first half-electric cycle is the smallest. This indicates that the demagnetized magnetic pole passed through the magnetic field probe 1 in the first half-electric cycle and affected the waveform of the air gap magnetic field strength in the two adjacent half-electric cycles.

[0128] Table 2

[0129]

[0130] Calculate the integral E of the air gap magnetic field strength signal with respect to the rotor position signal during each half-electric cycle of the magnetic field probe 2. i2The results are shown in Table 3. Table 3 is a table of air gap magnetic field strength energy values ​​for each half-electric cycle of magnetic field probe 2. It can be seen that the air gap magnetic field strength energy in the 22nd, 23rd, and 24th half-electric cycles is significantly lower than that in the other half-electric cycles, and the energy value in the 23rd half-electric cycle is the smallest. This indicates that the demagnetized magnetic pole passed through magnetic field probe 2 in the 23rd half-electric cycle and affected the air gap magnetic field strength waveform of the two adjacent half-electric cycles.

[0131] Table 3

[0132]

[0133] Step 4: Calculate the standard deviation of the air gap magnetic field strength signal energy value within one rotation cycle of all magnetic field probes. When a permanent magnet wind turbine experiences a local demagnetization fault, the air gap magnetic field strength under the demagnetized magnetic pole will decrease, while the air gap magnetic field strength under the normal magnetic pole will not change much. Therefore, when the demagnetized magnetic pole passes the magnetic field probe, the air gap magnetic field strength corresponding to that 1 / 2 electric cycle will decrease, resulting in the air gap magnetic field energy value of some 1 / 2 electric cycles being significantly lower than that of other 1 / 2 electric cycles within one rotation cycle. This increases the dispersion of the air gap magnetic field energy in that rotation cycle. The standard deviation of the air gap magnetic field signal energy of magnetic field probe 1 and magnetic field probe 2 under normal operating conditions and local demagnetization fault conditions within one rotation cycle is calculated as shown in Table 4. Table 4 is the standard deviation table of air gap magnetic field signal energy.

[0134] Table 4

[0135]

[0136] Step 5: Calculate the correlation coefficients of each magnetic field probe to the normal operation of the generator and to the local demagnetization fault condition. Based on the standard deviation of the air gap magnetic field energy, calculate the correlation coefficients of each magnetic field probe to the local demagnetization fault and normal operation condition, as shown in Table 5. Table 5 is a statistical table of correlation coefficients. In this embodiment, the value of b is 3 (its actual application value should be determined according to the accuracy required for diagnosis).

[0137] Table 5

[0138]

[0139] Step 6: Calculate the confidence values ​​of the air gap magnetic field strength signals measured by different magnetic intensity probes for local demagnetization faults and normal operating conditions, and calculate the confidence of each sensor for the degree of fault, where W1=W2=0.95, K=0.4. The calculated confidence values ​​of each magnetic intensity probe for local demagnetization faults and normal operating conditions are shown in Table 6. Table 6 describes the distribution of the confidence values ​​of each sensor for the degree of fault.

[0140] Table 6

[0141]

[0142] Step 7: Use DS evidence theory to fuse the diagnostic results of multiple magnetic field probes to obtain the final confidence value of the air gap magnetic field strength for different fault modes.

[0143] As shown in Table 6, after DS information fusion, the confidence value for local demagnetization faults reached 0.9690, which is a significant improvement compared with the diagnosis results of single information, and the uncertainty value was reduced to 0.0077, which can more accurately identify local demagnetization faults in permanent magnet wind turbines.

[0144] Step 8: If the final confidence value of the local demagnetization fault in Step 7 is greater than the threshold A and the uncertainty is less than the threshold B, then it is determined that a local demagnetization fault has occurred; otherwise, it is determined that the generator is in normal operation.

[0145] In this embodiment, threshold A is 0.9 and threshold B is 0.05. The value range of threshold A is generally [0.8, 1), and the value range of threshold B is generally (0, 0.1). The smaller the value of threshold A, the higher the detection sensitivity; the larger the value of threshold B, the higher the detection sensitivity. The actual application value should be adaptively determined according to the accuracy required for diagnosis. After DS evidence theory data fusion, if the confidence value of local demagnetization fault is greater than threshold A and the uncertainty is less than threshold B, it can be determined that the permanent magnet wind turbine has experienced a local demagnetization fault.

[0146] Step 9: If step 8 determines that a local demagnetization fault has occurred, then further locate the demagnetizing pole. To locate the demagnetizing pole, it is necessary to consider the rotor's position when the abnormal air gap magnetic field waveform appears. When a permanent magnet wind turbine experiences a local demagnetization fault, the air gap magnetic field strength under the demagnetizing pole will decrease, while the air gap magnetic field strength under the normal pole will not change much, as shown in Figures 9 and 10. Figure 9 is the air gap magnetic field energy radar diagram of the magnetic intensity probe 1 in this embodiment, and Figure 10 is the air gap magnetic field energy radar diagram of the magnetic intensity probe 2 in this embodiment. The energy values ​​of the air gap magnetic field strength of the magnetic intensity probe 1 in each 1 / 2 electric cycle can be used to preliminarily determine that a demagnetization fault has occurred at the pole of the magnetic intensity probe 1 in the first 1 / 2 electric cycle. Calculate the percentage c of the air gap magnetic field strength deviating from the normal value in the first 1 / 2 electric cycle. In this embodiment, the threshold C is set to 10% (the smaller the threshold C, the higher the sensitivity to the fault). (The actual application value should be adaptively determined according to the accuracy required for diagnosis.) For the air gap magnetic field strength data of the first 1 / 2 electric cycle, c is 12.97%, which is greater than the threshold C. It can be accurately determined that the magnetic pole passing through magnetic intensity probe 1 has a demagnetization fault in the first 1 / 2 electric cycle. The rotor angle range corresponding to the first 1 / 2 electric cycle is 0-8.18°. According to the expression for solving the magnetic pole number of magnetic intensity probe 1, n equals 1, that is, the magnetic pole numbered 1 has a demagnetization fault, which is consistent with the actual situation, indicating the correctness of the method. Then, using magnetic intensity probe 2, it is determined that the magnetic pole passing through magnetic intensity probe 2 has a demagnetization fault in the 23rd 1 / 2 electric cycle. The corresponding angle range is 180-196.18°. According to the expression for solving the magnetic pole number of other magnetic intensity probes, n equals 1, that is, the magnetic pole numbered 1 has a demagnetization fault, which is consistent with the result of diagnosis using the data of magnetic intensity probe 1.

[0147] In this embodiment, the above method can efficiently detect local demagnetization faults in low-speed direct-drive permanent magnet wind turbines and locate the demagnetized magnetic poles. The positioning accuracy is not affected by changes in rotational speed, which facilitates subsequent operation and maintenance.

[0148] In this embodiment, the rotor position signal of the wind turbine and the air gap magnetic field strength signals corresponding to multiple magnetic field probes are acquired within one rotation cycle. Based on the rotor position signal and the multiple air gap magnetic field strength signals, the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe is determined, and the standard deviation of the energy values ​​of the multiple air gap magnetic field strength signals is determined. Based on the standard deviation of the energy values ​​and the reference standard deviation, the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation is determined. Based on the multiple correlation coefficient information, the wind turbine is processed for local demagnetization fault detection to obtain detection result information. Based on the detection result information, the rotor position signal, and the multiple air gap magnetic field strength signals, the magnetic field strength of the wind turbine experiencing local demagnetization fault is analyzed. The system performs pole positioning processing, enabling timely detection of demagnetized poles and accurate location of their positions. This allows for rapid and accurate detection of demagnetization faults. By combining multiple collected signal data for fault detection processing, it avoids misjudgments caused by data from a single sensor, improving the reliability of fault diagnosis results. By upsampling the air gap magnetic field strength signal when the second sampling rate is lower than the first sampling rate, and downsampling the air gap magnetic field strength signal when the second sampling rate is higher than the first sampling rate, it ensures that the rotor position signal and the air gap magnetic field strength signal have the same number of sampling points per unit time. This facilitates subsequent data processing for detecting local demagnetization faults in wind turbines.

[0149] Figure 11 is a schematic diagram of the structure of a permanent magnet wind turbine demagnetization fault detection and location device according to an embodiment of the present disclosure.

[0150] As shown in Figure 11, the permanent magnet wind turbine demagnetization fault detection and location device 110 includes:

[0151] The first acquisition module 1101 is used to acquire the rotor position signal of the wind turbine in one rotation cycle and the air gap magnetic field strength signal corresponding to multiple magnetic probes.

[0152] The first determining module 1102 is used to determine the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe based on the rotor position signal and multiple air gap magnetic field strength signals.

[0153] The second determining module 1103 is used to determine the standard deviation of the energy values ​​of multiple air gap magnetic field strength signal energy values;

[0154] The third determining module 1104 is used to determine the correlation coefficient information between the air gap magnetic field strength signal and the reference standard deviation for each magnetic field probe based on the standard deviation of the energy value and the reference standard deviation.

[0155] The first processing module 1105 is used to perform local demagnetization fault detection processing on the wind turbine based on multiple correlation coefficient information to obtain detection result information;

[0156] The second processing module 1106 is used to locate the magnetic pole in the wind turbine generator that has a local demagnetization fault based on the detection result information, rotor position signal and multiple air gap magnetic field strength signals.

[0157] In some embodiments of this disclosure, the first processing module 1105 is specifically used for:

[0158] Multiple confidence values ​​for local demagnetization faults in wind turbines were determined using multiple correlation coefficient information.

[0159] Multiple confidence values ​​are fused to obtain a target confidence value, which is the final confidence value for a wind turbine experiencing a local demagnetization fault.

[0160] Determine the uncertainty value of a local demagnetization fault in a wind turbine;

[0161] Based on the target confidence value and uncertainty value, a local demagnetization fault detection process is performed on the wind turbine to obtain the detection result information.

[0162] In some embodiments of this disclosure, the first processing module 1105 is further configured to:

[0163] Obtain the preset reliability threshold and preset uncertainty threshold;

[0164] If the target confidence value is greater than the preset confidence value threshold and the uncertainty value is less than the preset uncertainty value threshold, the detection result is determined to be a local demagnetization fault in the wind turbine.

[0165] If the target confidence value is less than or equal to the preset confidence value threshold, or if the uncertainty value is greater than or equal to the preset uncertainty value threshold, the detection result is determined to be that the wind turbine has not experienced a local demagnetization fault.

[0166] In some embodiments of this disclosure, the second processing module 1106 is specifically used for:

[0167] When the detection results indicate that the wind turbine has a local demagnetization fault, the magnetic poles where the local demagnetization fault has occurred are located based on the rotor position signal and multiple air gap magnetic field strength signals.

[0168] In some embodiments of this disclosure, one rotation cycle includes multiple half-electric cycles, wherein the second processing module 1106 is further configured to:

[0169] Based on multiple air gap magnetic field strength signals, the target electrical cycle in which the magnetic pole where the local demagnetization fault occurred is determined from multiple half-electric cycles;

[0170] Based on the rotor position signal, the rotor angle interval corresponding to the target electrical cycle is determined, wherein the rotor angle interval has a corresponding left end angle value and a right end angle value.

[0171] Based on the angle values ​​at the left and right ends of the interval, determine the pole number of the pole that experienced the local demagnetization fault.

[0172] In some embodiments of this disclosure, the second processing module 1106 is further configured to:

[0173] If the percentage of the air gap magnetic field strength signal deviating from the normal value during half an electric cycle is greater than a preset percentage threshold, then half an electric cycle is determined as the target electric cycle.

[0174] In some embodiments of this disclosure, the reference standard deviation includes: a first reference standard deviation and a second reference standard deviation, wherein the first reference standard deviation is the standard deviation of the air gap magnetic field strength signal within one rotation cycle of the wind turbine under normal operating conditions, and the second reference standard deviation is the standard deviation of the air gap magnetic field strength signal within one rotation cycle of the wind turbine under local demagnetization fault conditions.

[0175] The third determining module 1104 is specifically used for:

[0176] Based on the standard deviation of energy value, the first reference standard deviation and the second reference standard deviation, determine the first correlation coefficient between the air gap magnetic field strength signal corresponding to each magnetic field probe and the wind turbine under normal operating conditions.

[0177] Based on the standard deviation of energy value, the first reference standard deviation, and the second reference standard deviation, determine the second correlation coefficient of the air gap magnetic field strength signal corresponding to each magnetic field probe with the wind turbine under the local demagnetization fault condition.

[0178] The first and second correlation coefficients are used as correlation coefficient information.

[0179] As shown in FIG12, which is a structural schematic diagram of a permanent magnet wind turbine demagnetization fault detection and location device proposed in another embodiment of the present disclosure, the device further includes:

[0180] The second acquisition module 1107 is used to acquire the first sampling rate of the rotor position signal and the second sampling rate of the air gap magnetic field intensity signal after acquiring the rotor position signal of the wind turbine in one rotation cycle and the air gap magnetic field intensity signal corresponding to multiple magnetic field probes.

[0181] The third processing module 1108 is used to resample the air gap magnetic field strength signal according to the first sampling rate and the second sampling rate.

[0182] In some embodiments of this disclosure, the third processing module 1108 is specifically used for:

[0183] When the second sampling rate is less than the first sampling rate, the air gap magnetic field strength signal is upsampled.

[0184] When the second sampling rate is greater than the first sampling rate, the air gap magnetic field strength signal is downsampled.

[0185] Corresponding to the permanent magnet wind turbine demagnetization fault detection and location method provided in the embodiments of Figures 1 to 10 above, this disclosure also provides a permanent magnet wind turbine demagnetization fault detection and location device. Since the permanent magnet wind turbine demagnetization fault detection and location device provided in this disclosure corresponds to the permanent magnet wind turbine demagnetization fault detection and location method provided in the embodiments of Figures 1 to 10 above, the implementation method of the permanent magnet wind turbine demagnetization fault detection and location method is also applicable to the permanent magnet wind turbine demagnetization fault detection and location device provided in this disclosure, and will not be described in detail in this disclosure.

[0186] In this embodiment, the rotor position signal of the wind turbine and the air gap magnetic field strength signals corresponding to multiple magnetic field probes are acquired within one rotation cycle. Based on the rotor position signal and multiple air gap magnetic field strength signals, the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe is determined, and the standard deviation of the energy values ​​of multiple air gap magnetic field strength signals is determined. Based on the standard deviation of the energy values ​​and the reference standard deviation, the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation is determined. Based on the multiple correlation coefficient information, the wind turbine is processed for local demagnetization fault detection, and the detection result information is obtained. Based on the detection result information, the rotor position signal, and multiple air gap magnetic field strength signals, the magnetic pole in the wind turbine where the local demagnetization fault occurs is located. This allows for timely detection of the magnetic pole with the demagnetization fault and accurate location of the demagnetized magnetic pole, achieving rapid and accurate detection of magnetic pole demagnetization faults. The combined use of multiple acquired signal data for fault detection processing can avoid misjudgment caused by single sensor data and improve the reliability of fault diagnosis results.

[0187] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for detecting and locating demagnetization faults in permanent magnet wind turbines as proposed in the foregoing embodiments of this disclosure.

[0188] To implement the above embodiments, this disclosure also proposes a computer program product, which, when executed by an instruction processor, performs the permanent magnet wind turbine demagnetization fault detection and location method as proposed in the foregoing embodiments of this disclosure.

[0189] Figure 13 shows a block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure.

[0190] The electronic device 13 shown in Figure 13 is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0191] As shown in Figure 13, the electronic device 13 is represented in the form of a general-purpose computing device. The components of the electronic device 13 may include, but are not limited to: one or more processors or processing units 16, memory 28, and bus 18 connecting different system components (including memory 28 and processing unit 16).

[0192] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0193] Electronic device 13 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 13, including volatile and non-volatile media, removable and non-removable media.

[0194] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache memory 32. Electronic device 13 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (not shown in Figure 13, commonly referred to as a "hard disk drive").

[0195] Although not shown in Figure 13, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0196] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0197] Electronic device 13 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with electronic device 13, and / or with any device that enables electronic device 13 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 13 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 13 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 13, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0198] The processing unit 16 executes various functional applications and parameter information determination by running the program stored in the memory 28, such as implementing the permanent magnet wind turbine demagnetization fault detection and location method mentioned in the foregoing embodiments.

[0199] It should be noted that in the description of this disclosure, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0200] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0201] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0202] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0203] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0204] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0205] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0206] Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for detecting and locating demagnetization faults in permanent magnet wind turbines, characterized in that, include: The process involves acquiring the rotor position signal of a wind turbine within one rotation cycle and the air gap magnetic field strength signals corresponding to multiple magnetic field probes, wherein one rotation cycle includes multiple half-electric cycles; determining the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe based on the rotor position signal and the multiple air gap magnetic field strength signals; determining the standard deviation of the energy values ​​of the multiple air gap magnetic field strength signals; determining the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation based on the energy value standard deviation and the reference standard deviation; performing local demagnetization fault detection processing on the wind turbine based on the multiple correlation coefficient information to obtain detection result information; when the detection result information indicates that the wind turbine has a local demagnetization fault, based on the rotor position signal and the multiple air gap magnetic field strength signals... The field strength signal is used to locate the magnetic pole in the wind turbine where a local demagnetization fault has occurred. This includes: determining the target electrical cycle of the magnetic pole experiencing the local demagnetization fault from multiple half-electric cycles based on the multiple air gap magnetic field strength signals, wherein if the percentage of the air gap magnetic field strength signal deviating from the normal value in the half-electric cycle is greater than a preset percentage threshold, then the half-electric cycle is determined as the target electrical cycle; determining the rotor angle interval corresponding to the target electrical cycle based on the rotor position signal, wherein the rotor angle interval has a corresponding left endpoint angle value and a right endpoint angle value; and determining the magnetic pole number of the magnetic pole experiencing the local demagnetization fault based on the left endpoint angle value and the right endpoint angle value, including: recording the left endpoint angle value l of the rotor angle value corresponding to the target electrical cycle. n and the angle value r at the right endpoint of the interval n Introducing interval expressions: [l n ,r n ]=[(n-1) 360 / 2p,n [360 / 2p] where p is the number of pole pairs and n is the number of the demagnetizing pole.

2. The method as described in claim 1, characterized in that, The step of performing local demagnetization fault detection processing on the wind turbine based on multiple correlation coefficient information to obtain detection result information includes: determining multiple confidence values ​​for the local demagnetization fault of the wind turbine based on the multiple correlation coefficient information; performing data fusion processing on the multiple confidence values ​​to obtain a target confidence value, wherein the target confidence value is the final confidence value for the local demagnetization fault of the wind turbine; determining the uncertainty value for the local demagnetization fault of the wind turbine; and performing local demagnetization fault detection processing on the wind turbine based on the target confidence value and the uncertainty value to obtain the detection result information.

3. The method as described in claim 2, characterized in that, The step of performing local demagnetization fault detection processing on the wind turbine based on the target confidence value and the uncertainty value to obtain the detection result information includes: acquiring a preset confidence value threshold and a preset uncertainty value threshold; determining that the detection result information indicates that the wind turbine has experienced a local demagnetization fault when the target confidence value is greater than the preset confidence value threshold and the uncertainty value is less than the preset uncertainty value threshold; and determining that the detection result information indicates that the wind turbine has not experienced a local demagnetization fault when the target confidence value is less than or equal to the preset confidence value threshold, or when the uncertainty value is greater than or equal to the preset uncertainty value threshold.

4. The method as described in claim 1, characterized in that, The reference standard deviation includes: a first reference standard deviation and a second reference standard deviation, wherein the first reference standard deviation is the standard deviation of the air gap magnetic field strength signal of the wind turbine in one rotation cycle under normal operating conditions, and the second reference standard deviation is the standard deviation of the air gap magnetic field strength signal of the wind turbine in one rotation cycle under local demagnetization fault conditions; wherein, determining the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation based on the energy value standard deviation and the reference standard deviation includes: determining a first correlation coefficient of the air gap magnetic field strength signal corresponding to each magnetic field probe under normal operating conditions with respect to the wind turbine in normal operating conditions based on the energy value standard deviation, the first reference standard deviation, and the second reference standard deviation; determining a second correlation coefficient of the air gap magnetic field strength signal corresponding to each magnetic field probe under local demagnetization fault conditions with respect to the wind turbine in normal operating conditions based on the energy value standard deviation, the first reference standard deviation, and the second reference standard deviation; and using the first correlation coefficient and the second correlation coefficient as the correlation coefficient information.

5. The method as described in claim 1, characterized in that, After acquiring the rotor position signal of the wind turbine in one rotation cycle and the air gap magnetic field strength signal corresponding to multiple magnetic field probes, the method further includes: acquiring a first sampling rate of the rotor position signal and a second sampling rate of the air gap magnetic field strength signal; and resampling the air gap magnetic field strength signal according to the first sampling rate and the second sampling rate.

6. The method as described in claim 5, characterized in that, The step of resampling the air gap magnetic field strength signal according to the first sampling rate and the second sampling rate includes: upsampling the air gap magnetic field strength signal when the second sampling rate is less than the first sampling rate; and downsampling the air gap magnetic field strength signal when the second sampling rate is greater than the first sampling rate.

7. A device for detecting and locating demagnetization faults in permanent magnet wind turbines, characterized in that, include: A first acquisition module is used to acquire the rotor position signal of the wind turbine and the air gap magnetic field strength signals corresponding to multiple magnetic field probes within one rotation cycle, wherein the one rotation cycle includes multiple half-electric cycles; a first determination module is used to determine the energy value of the air gap magnetic field strength signal corresponding to each magnetic field probe based on the rotor position signal and the multiple air gap magnetic field strength signals; a second determination module is used to determine the standard deviation of the energy values ​​of the multiple air gap magnetic field strength signals; a third determination module is used to determine the correlation coefficient information between the air gap magnetic field strength signal corresponding to each magnetic field probe and the reference standard deviation based on the energy value standard deviation and the reference standard deviation; a first processing module is used to perform local demagnetization fault detection processing on the wind turbine based on the multiple correlation coefficient information to obtain detection result information; a second processing module is used to determine the wind turbine experiencing a local demagnetization fault based on the detection result information. At the same time, based on the rotor position signal and multiple air gap magnetic field strength signals, the magnetic pole in the wind turbine generator experiencing a local demagnetization fault is located; the second processing module is specifically used for: determining the target electrical cycle in which the magnetic pole experiencing a local demagnetization fault is located from multiple half-electric cycles based on the multiple air gap magnetic field strength signals, wherein if the percentage value of the air gap magnetic field strength signal of the half-electric cycle deviating from the normal value is greater than a preset percentage threshold, then the half-electric cycle is determined as the target electrical cycle; determining the rotor angle interval corresponding to the target electrical cycle based on the rotor position signal, wherein the rotor angle interval has a corresponding left endpoint angle value and a right endpoint angle value; determining the magnetic pole number of the magnetic pole experiencing a local demagnetization fault based on the left endpoint angle value and the right endpoint angle value, including: recording the left endpoint angle value l of the interval corresponding to the rotor angle value of the target electrical cycle. n and the angle value r at the right endpoint of the interval n Introducing interval expressions: [l n ,r n ]=[(n-1) 360 / 2p,n [360 / 2p] where p is the number of pole pairs and n is the number of the demagnetizing pole.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method for detecting and locating demagnetization faults in a permanent magnet wind turbine as described in any one of claims 1-6.

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