A wind turbine stator fault detection method, device and fault early warning system

Abstract

The application discloses a kind of wind generator stator fault detection method, device and fault early warning system, the method includes: obtaining the operation data set of wind generator stator to be diagnosed, after frequency tracking algorithm and pre-design calculation method, the fault characteristic quantity and phase difference of wind generator stator to be diagnosed are obtained;Whether the stator winding asymmetry fault of wind generator stator to be diagnosed occurs based on fault characteristic quantity determination;When wind generator stator to be diagnosed occurs the stator winding asymmetry fault, the fault phase of wind generator stator to be diagnosed is determined based on phase difference.The application obtains the fault characteristic quantity and phase difference of wind generator stator to be diagnosed by using frequency tracking algorithm and pre-design calculation method, and whether the stator winding asymmetry fault occurs based on fault characteristic quantity determination, corresponding fault phase is determined based on phase difference, can continuously and quickly identify the fault defect of stator winding, improve the stator fault detection efficiency and detection accuracy.

Description

Technical Field

[0001] This invention relates to the field of wind turbine testing technology, specifically to a method, device, and fault early warning system for detecting stator faults in wind turbines. Background Technology

[0002] For a long time, wind turbines have been managed using planned maintenance and reactive maintenance methods. In planned maintenance, maintenance personnel rely heavily on their subjective experience to assess the condition of the wind turbines and then take appropriate repair measures. This method cannot provide a comprehensive and timely understanding of the wind turbine's condition, resulting in long repair times and high maintenance costs. When a fault occurs, the relevant components are often already malfunctioning or even severely damaged, making repairs at this point extremely labor-intensive.

[0003] In large permanent magnet direct-drive motors, the stator winding current is high, and the motor is subjected to significant electromagnetic forces. These forces compress and stretch the windings, easily damaging the insulation layer. Vibration causes friction and breakage between the turns of the stator winding insulation, leading to various faults. Due to the randomness of wind speed, the generator's speed varies with wind speed. Therefore, the zero-sequence voltage and current amplitudes are affected by wind speed. Directly using changes in zero-sequence voltage or current amplitudes to diagnose stator faults can easily lead to misdiagnosis. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus and fault early warning system for detecting stator faults in wind turbine generators, in order to solve the technical problems of untimely detection of stator faults in wind turbine generators and low accuracy of detection results in the prior art.

[0005] The technical solution proposed in this invention is as follows:

[0006] In a first aspect, embodiments of the present invention provide a method for detecting stator faults in a wind turbine generator. This method includes: acquiring an operational dataset of the stator of the wind turbine generator to be diagnosed; obtaining fault characteristic quantities and phase differences of the stator based on the operational dataset using a frequency tracking algorithm and a preset calculation method; determining whether a stator winding asymmetry fault has occurred in the stator based on the fault characteristic quantities; and determining the fault phase of the stator based on the phase difference when the stator experiences the stator winding asymmetry fault.

[0007] In conjunction with the first aspect, in one possible implementation of the first aspect, based on the operational dataset, the fault characteristic quantities and phase difference of the stator of the wind turbine generator to be diagnosed are obtained through a frequency tracking algorithm and a preset calculation method, including: based on the operational dataset, the zero-sequence voltage, initial phase angle, and fundamental initial phase angle of the three-phase stator current of the wind turbine generator to be diagnosed are obtained through the frequency tracking algorithm; based on the zero-sequence voltage, the initial phase angle, and the fundamental initial phase angle, the fault characteristic quantities and phase difference of the stator of the wind turbine generator to be diagnosed are obtained through the preset calculation method.

[0008] In conjunction with the first aspect, in another possible implementation of the first aspect, determining whether the stator of the wind turbine generator to be diagnosed has a stator winding asymmetry fault based on the fault feature quantity includes: comparing the fault feature quantity with a preset threshold; when the fault feature quantity is greater than the preset threshold, determining that the stator of the wind turbine generator to be diagnosed has a stator winding asymmetry fault; when the fault feature quantity is less than the preset threshold, determining that the stator of the wind turbine generator to be diagnosed has not a stator winding asymmetry fault.

[0009] In conjunction with the first aspect, in another possible implementation of the first aspect, the method further includes: acquiring fault vibration data of the stator of the wind turbine generator to be diagnosed; processing the fault vibration data to obtain the vibration pattern of the stator of the wind turbine generator to be diagnosed; and determining whether an inter-turn short circuit fault has occurred in the stator of the wind turbine generator to be diagnosed based on the vibration pattern.

[0010] In conjunction with the first aspect, in another possible implementation of the first aspect, after acquiring the fault vibration data of the stator of the wind turbine to be diagnosed, the method further includes: monitoring the operating status of the wind turbine to be diagnosed based on the operating dataset and the fault vibration data.

[0011] Secondly, embodiments of the present invention provide a wind turbine stator fault early warning system, comprising: a data acquisition unit for acquiring operational data sets and fault vibration data of the wind turbine stator, and sending the operational data sets and the fault vibration data to a fault detection unit; the fault detection unit for obtaining a fault detection result of the wind turbine stator based on the operational data sets and the fault vibration data, using the wind turbine stator fault detection method as described in the first aspect and any one of the embodiments of the present invention, and sending the fault detection result to a fault early warning unit; and the fault early warning unit for issuing an early warning signal based on the fault detection result.

[0012] In conjunction with the second aspect, in one possible implementation of the second aspect, the system further includes: a status monitoring unit, configured to receive the operating dataset and the fault vibration data sent by the acquisition unit, and to monitor the operating status of the wind turbine stator based on the operating dataset and the fault vibration data.

[0013] Thirdly, embodiments of the present invention provide a wind turbine stator fault detection device, which includes: an acquisition module for acquiring an operational dataset of the wind turbine stator to be diagnosed; a processing and calculation module for obtaining fault characteristic quantities and phase differences of the wind turbine stator to be diagnosed based on the operational dataset, using a frequency tracking algorithm and a preset calculation method; a judgment module for judging whether the wind turbine stator to be diagnosed has a stator winding asymmetry fault based on the fault characteristic quantities; and a determination module for determining the fault phase of the wind turbine stator to be diagnosed based on the phase difference when the stator winding asymmetry fault occurs.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions for causing the computer to perform the wind turbine stator fault detection method as described in the first aspect and any one of the embodiments of the present invention.

[0015] Fifthly, embodiments of the present invention provide an electronic device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the wind turbine stator fault detection method as described in the first aspect and any one of the embodiments of the present invention.

[0016] The technical solution provided by this invention has the following effects:

[0017] The wind turbine stator fault detection method provided in this invention employs a frequency tracking algorithm and a preset calculation method to obtain fault characteristic quantities and phase differences of the wind turbine stator to be diagnosed. Based on the fault characteristic quantities, it determines whether an asymmetrical stator winding fault has occurred, and based on the phase difference, it identifies the corresponding faulty phase. This invention enables continuous and rapid identification of stator winding faults, improving stator fault detection efficiency and accuracy.

[0018] The wind turbine stator fault early warning system provided in this embodiment of the invention uses the wind turbine stator fault detection method provided in this embodiment of the invention to detect faults and issue early warnings based on the fault detection results. It can serve as a reference for adjusting the on-site operation mode, help the production site to reasonably arrange the operation mode, and improve the reliability and stability of the wind turbine generator set. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a wind turbine stator fault detection method according to an embodiment of the present invention;

[0021] Figure 2A This is a schematic diagram of a star-connected winding according to an embodiment of the present invention;

[0022] Figure 2B This is a schematic diagram of a triangular connection winding provided according to an embodiment of the present invention;

[0023] Figure 3 This is a diagram of a wind turbine stator fault vibration data acquisition scheme provided according to an embodiment of the present invention;

[0024] Figure 4 This is a flowchart of a wind turbine stator fault identification method according to an embodiment of the present invention;

[0025] Figure 5 This is a structural block diagram of a wind turbine stator fault early warning system according to an embodiment of the present invention;

[0026] Figure 6 This is a structural block diagram of a wind turbine stator fault detection device according to an embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of the structure of a computer-readable storage medium provided according to an embodiment of the present invention;

[0028] Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] This invention provides a method for detecting stator faults in wind turbine generators, such as... Figure 1 As shown, the method includes the following steps:

[0032] Step 101: Obtain the operating dataset of the stator of the wind turbine to be diagnosed.

[0033] In this embodiment of the invention, the wind turbine to be diagnosed can be a three-phase motor;

[0034] The running dataset can include the phase voltage, phase current, resistance matrix, inductance matrix, no-load back EMF, and additional resistance connected in series with the three-phase stator windings of the wind turbine to be diagnosed.

[0035] Step 102: Based on the running dataset, the fault characteristic quantities and phase difference of the stator of the wind turbine to be diagnosed are obtained through the frequency tracking algorithm and the preset calculation method.

[0036] Among them, the frequency tracking algorithm represents an algorithm with automatic frequency adjustment performance; the phase difference represents the difference between the phases of two periodically changing physical quantities.

[0037] By processing the running dataset using the frequency tracking algorithm and the preset calculation method, the fault characteristic quantities and phase difference of the stator of the wind turbine to be diagnosed can be obtained.

[0038] Step 103: Based on the fault characteristic quantities, determine whether the stator winding asymmetry fault of the wind turbine generator to be diagnosed has occurred.

[0039] Among them, stator winding asymmetry fault refers to an asymmetry fault caused by an increase in the resistance value of the stator winding. This fault may be caused by a combination of factors such as poor manufacturing process, thermal cycling and vibration, or damage (corrosion or contamination) to the wiring terminals. The fault will lead to an imbalance in stator voltage or current, an increase in torque pulsation, a decrease in average torque, and an increase in losses and heat.

[0040] Specifically, the fault characteristic quantity is used to represent the fault characteristics corresponding to the stator of the wind turbine to be diagnosed. Therefore, based on the fault characteristic quantity, it can be determined whether the stator winding asymmetry fault of the wind turbine to be diagnosed has occurred.

[0041] Step 104: When the stator winding asymmetry fault occurs in the stator of the wind turbine generator to be diagnosed, the fault phase of the stator of the wind turbine generator to be diagnosed is determined based on the phase difference.

[0042] Among them, the faulty phase refers to the phase in a three-phase or multi-phase circuit that has experienced a fault.

[0043] Specifically, in the case of an asymmetrical fault in the stator winding, the faulty phase is determined based on the phase difference.

[0044] The wind turbine stator fault detection method provided in this invention employs a frequency tracking algorithm and a preset calculation method to obtain fault characteristic quantities and phase differences of the wind turbine stator to be diagnosed. Based on the fault characteristic quantities, it determines whether an asymmetrical stator winding fault has occurred, and based on the phase difference, it identifies the corresponding faulty phase. This invention enables continuous and rapid identification of stator winding faults, improving stator fault detection efficiency and accuracy.

[0045] As an optional implementation of this invention, step 102 includes: based on the running dataset, using the frequency tracking algorithm, obtaining the zero-sequence voltage, initial phase angle, and fundamental initial phase angle of the three-phase stator current of the wind turbine generator to be diagnosed; based on the zero-sequence voltage, the initial phase angle, and the fundamental initial phase angle, using the preset calculation method, obtaining the fault characteristic quantity and the phase difference of the wind turbine generator stator to be diagnosed.

[0046] In this embodiment of the invention, an additional resistor is connected in series in one phase of the stator winding to simulate a stator winding asymmetry fault. Without considering motor saturation, the voltage equation of the three-phase motor under stator winding asymmetry fault conditions can be expressed as the following relationship (1):

[0047]

[0048] In the formula: [V s,abc [R] represents the phase voltage of the three-phase stator winding; sf [i] represents the resistance matrix of the three-phase stator winding; s,abc [L] represents the phase current of the three-phase stator winding; s [e] represents the inductance matrix of the three-phase stator windings; s,abc [V0] represents the no-load back electromotive force of the three-phase stator winding; [V0] represents the zero-sequence voltage.

[0049] Based on the above relationship, the corresponding zero-sequence voltage can be calculated.

[0050] Among them, [V s,abc The following relation (2) is shown:

[0051] [V s,abc ] = [V a V b V c ] t (2)

[0052] In the formula: V a V represents the phase voltage of phase a stator winding; b V represents the phase voltage of the b-phase stator winding; c The phase voltage of the c-phase stator winding is represented by t; the phase voltage coefficient is represented by t.

[0053] [R sf The following relationship (3) is shown:

[0054]

[0055] In the formula: R s R represents the phase stator resistance; sadd_a This represents the additional resistance connected in series with the stator winding of phase A during a simulated fault.

[0056] [i s,abc The following relation (4) is shown:

[0057] [i s,abc ] = [i a i b i c ] t (4)

[0058] In the formula: i a Indicates the phase current of phase a stator winding; i b Indicates the phase current of phase b of the stator winding; i c This represents the phase current of the c-phase stator winding.

[0059] [Ls The following relation (5) is shown:

[0060]

[0061] In the formula: L represents the self-inductance of the stator winding; M represents the mutual inductance between stator windings;

[0062] [e s,abc The following relation (6) is shown:

[0063]

[0064] In the formula: λ PM,a λ represents the permanent magnet flux linkage of the a-phase stator winding; PM,b λ represents the permanent magnet flux linkage of the b-phase stator winding; PM,c This indicates the permanent magnet flux linkage of the c-phase stator winding.

[0065] Furthermore, considering rotor magnetic field harmonics, λ PM,a , λ PM,b , λ PM,c This can be expressed as the following relation (7):

[0066]

[0067] In the formula: k represents an integer; λ PM,1 λ represents the amplitude of the fundamental wave in the permanent magnet flux linkage. PM,v λ represents the amplitude of the v-th harmonic in the permanent magnet flux linkage; PM,r θ represents the amplitude of the r-th harmonic in the permanent magnet flux linkage; θ represents the rotor electrical angular position, i.e., the initial phase angle; θ v It represents the angle between the fundamental wave and the vth harmonic in the permanent magnet flux linkage, i.e., the initial phase angle of the fundamental wave.

[0068] Furthermore, the zero-sequence voltage [V0] represents the voltage at the midpoint between the stator winding neutral point and the DC bus voltage, as shown in the following equation (8):

[0069]

[0070] In the formula: λ PM,0 This represents the amplitude of the zero-sequence voltage fundamental wave in the permanent magnet flux linkage.

[0071] Furthermore, based on the zero-sequence voltage [V0], initial phase angle θ, and fundamental initial phase angle θ calculated above... v The fault characteristic quantities and phase difference of the stator of the wind turbine to be diagnosed can be calculated, as shown in the following equations (9) and (10):

[0072] P U =V0θ v (9)

[0073]

[0074] In the formula: P U V0 represents the zero-sequence voltage; V represents the fault characteristic quantity. This indicates the phase difference.

[0075] As an optional implementation of this invention, step 103 includes: comparing the fault feature quantity with a preset threshold; when the fault feature quantity is greater than the preset threshold, determining that the stator winding asymmetry fault has occurred in the stator of the wind turbine generator to be diagnosed; when the fault feature quantity is less than the preset threshold, determining that the stator winding asymmetry fault has not occurred in the stator of the wind turbine generator to be diagnosed.

[0076] Specifically, it is determined whether the fault characteristic quantity is greater than the set threshold. If the fault characteristic quantity is greater than the set threshold, then there is a stator winding asymmetry fault; otherwise, there is no fault.

[0077] As an optional embodiment of the present invention, the method further includes: acquiring fault vibration data of the stator of the wind turbine generator to be diagnosed; processing the fault vibration data to obtain the vibration pattern of the stator of the wind turbine generator to be diagnosed; and determining whether the stator of the wind turbine generator to be diagnosed has an inter-turn short circuit fault based on the vibration pattern.

[0078] In this invention, stator inter-turn short-circuit faults and irreversible demagnetization faults are the main causes of motor shutdown. These faults generate circulating currents greater than twice the stall current, causing winding overheating, which is highly detrimental to permanent magnet synchronous motors. Large fault currents will generate magnetic flux linkages in the opposite direction to the normal direction, potentially leading to irreversible demagnetization. Stator inter-turn short-circuit faults are even more harmful and destructive to permanent magnet synchronous motors.

[0079] In this embodiment of the invention, it is assumed that an inter-turn short-circuit fault occurs in the a-phase stator winding of the permanent magnet synchronous motor. The equivalent schematic diagram of the motor winding is as follows: Figure 2A-2B As shown in the figure, a short-circuit loop is added to the motor winding. The short-circuit resistor divides the phase a winding into two parts, a1 and a2. The current i flowing through the short-circuit resistor... f This is called short-circuit current.

[0080] In this embodiment of the invention, a method for diagnosing inter-turn short-circuit faults in the stator of a wind turbine generator based on radial vibration characteristics is used to diagnose whether an inter-turn short-circuit fault has occurred in the stator of the wind turbine generator to be diagnosed.

[0081] Specifically, when a motor stator is subjected to three-phase alternating current, it generates a traveling wave (rotating magnetic field) that varies with time and space in an approximately sinusoidal manner. When the stator experiences faults such as inter-turn short circuits or loosening, the asymmetry of the magnetic field causes the vibration on the stator to become more intense. By picking up the vibration signal at this time and analyzing it according to the vibration pattern, it is possible to determine whether a stator fault has occurred.

[0082] Among them, obtaining fault vibration data of the stator of the wind turbine to be diagnosed can be achieved through methods such as... Figure 3 The synthesis method shown is used to obtain it.

[0083] As an optional implementation of the present invention, after obtaining the fault vibration data of the stator of the wind turbine to be diagnosed, the method further includes: monitoring the operating status of the wind turbine to be diagnosed based on the operating dataset and the fault vibration data.

[0084] Specifically, in this embodiment of the invention, the operating status of the wind turbine to be diagnosed can also be monitored based on the acquired operating dataset and fault vibration data of the wind turbine to be diagnosed.

[0085] In one example, a method for diagnosing stator winding faults is provided, comprising the following steps:

[0086] Step 1: Use the frequency tracking algorithm to calculate the zero-sequence voltage % , the initial phase angle, and the initial phase angle of the fundamental wave in the three-phase stator current, and then calculate the fault characteristic quantities and phase difference;

[0087] Step 2: Determine whether the fault characteristic quantity is greater than the set threshold. If the fault characteristic quantity is greater than the set threshold, then there is a stator winding asymmetry fault.

[0088] Step 3: In the case of an asymmetrical fault in the stator winding, determine the faulty phase based on the phase difference.

[0089] In another example, a method for identifying stator faults in wind turbine generators is provided, the specific identification process of which is as follows: Figure 4 As shown.

[0090] This invention also provides a wind turbine stator fault early warning system, such as... Figure 5 As shown, the wind turbine stator fault early warning system 2 includes: a data acquisition unit 21, a fault detection unit 22, a fault early warning unit 23, and a status monitoring unit 24.

[0091] The fault detection unit 22 is connected to the acquisition unit 21 and the fault early warning unit 23 respectively; the acquisition unit 21 is also connected to the status monitoring unit 24.

[0092] Furthermore, the functions of each device in the above system are described.

[0093] Specifically, the acquisition unit 21 is used to acquire the operating dataset and fault vibration data of the wind turbine stator, and to send the operating dataset and fault vibration data to the fault detection unit 22;

[0094] The fault detection unit 22 uses the wind turbine stator fault detection method provided in this embodiment of the invention to detect and process the received operating dataset and fault vibration data, obtain the fault detection result of the wind turbine stator, and send the fault detection result to the fault warning unit 23.

[0095] The fault early warning unit 23 can issue a corresponding early warning signal based on the received fault detection results.

[0096] Furthermore, the acquisition unit 21 can also send the operating dataset and fault vibration data to the condition monitoring unit 24, so that the condition monitoring unit 24 can monitor the operating status of the wind turbine stator in real time based on the operating dataset and fault vibration data.

[0097] The wind turbine stator fault early warning system provided in this embodiment of the invention uses the wind turbine stator fault detection method provided in this embodiment of the invention to detect faults and issue early warnings based on the fault detection results. It can serve as a reference for adjusting the on-site operation mode, help the production site to reasonably arrange the operation mode, and improve the reliability and stability of the wind turbine generator set.

[0098] In one example, a wind turbine stator condition intelligent sensing and early fault warning system is provided, consisting of two modules: hardware and software.

[0099] (1) Hardware section

[0100] The hardware consists of vibration sensors, current sensors, temperature sensors, fiber optic grating sensors, signal conditioning modules, data acquisition cards, and a computer. The torque signal of the transmission mechanism's shaft is acquired using a reflective fiber optic grating sensor. This invention primarily monitors the real-time operating status of the wind turbine generator based on the acquired vibration signals.

[0101] (2) Software Part

[0102] The core of the software system of this invention is the fault early warning method; therefore, the software component is a key element of the monitoring, diagnosis, and early warning system. The functions that this monitoring system can achieve are:

[0103] Data Acquisition: When acquiring data, you can set relevant parameters such as sampling frequency and sampling duration.

[0104] Data analysis: Perform Fourier transform on the acquired signals and obtain the signal envelope.

[0105] Determine operating status: Based on the real-time collected vibration, current and torque signals, the system contacts the database to determine the operating status and issues a warning if a fault is detected.

[0106] Early warning: Based on various data and using intelligent algorithms, early warning of defects is provided.

[0107] Results display and data storage: The collected data and processing results are visualized, and the processing results and data are saved in file formats, etc.

[0108] This invention also provides a wind turbine stator fault detection device, such as... Figure 6 As shown, the device includes:

[0109] The acquisition module 301 is used to acquire the operating dataset of the stator of the wind turbine to be diagnosed; for details, please refer to the relevant description of step 101 in the above method embodiment.

[0110] The processing and calculation module 302 is used to obtain the fault characteristic quantity and phase difference of the stator of the wind turbine to be diagnosed based on the running dataset, through the frequency tracking algorithm and the preset calculation method; for details, please refer to the relevant description of step 102 in the above method embodiment.

[0111] The judgment module 303 is used to determine whether the stator winding asymmetry fault of the wind turbine generator to be diagnosed has occurred based on the fault characteristic quantity; for details, please refer to the relevant description of step 103 in the above method embodiment.

[0112] The determination module 304 is used to determine the fault phase of the stator of the wind turbine generator to be diagnosed based on the phase difference when the stator winding asymmetry fault occurs in the stator of the wind turbine generator to be diagnosed; for details, please refer to the relevant description of step 104 in the above method embodiment.

[0113] The wind turbine stator fault detection device provided in this invention employs a frequency tracking algorithm and a preset calculation method to obtain the fault characteristic quantities and phase differences of the stator of the wind turbine to be diagnosed. Based on the fault characteristic quantities, it determines whether an asymmetrical stator winding fault has occurred, and based on the phase difference, it identifies the corresponding faulty phase. This invention enables continuous and rapid identification of stator winding faults, improving the efficiency and accuracy of stator fault detection.

[0114] As an optional implementation of this invention, the processing and calculation module includes: a processing submodule, used to obtain, based on the running dataset and through the frequency tracking algorithm, the zero-sequence voltage, initial phase angle, and fundamental initial phase angle of the three-phase stator current of the wind turbine generator to be diagnosed; and a calculation submodule, used to obtain, based on the zero-sequence voltage, the initial phase angle, and the fundamental initial phase angle, through the preset calculation method, the fault characteristic quantity and the phase difference of the wind turbine generator to be diagnosed.

[0115] As an optional implementation of this invention, the judgment module includes: a comparison submodule, used to compare the fault feature quantity with a preset threshold; a first determination submodule, used to determine that the stator winding asymmetry fault of the wind turbine generator to be diagnosed has occurred when the fault feature quantity is greater than the preset threshold; and a second determination submodule, used to determine that the stator winding asymmetry fault of the wind turbine generator to be diagnosed has not occurred when the fault feature quantity is less than the preset threshold.

[0116] As an optional embodiment of the present invention, the device further includes: a first acquisition module, used to acquire fault vibration data of the stator of the wind turbine generator to be diagnosed; a processing module, used to process the fault vibration data to obtain the vibration pattern of the stator of the wind turbine generator to be diagnosed; and a first judgment module, used to judge whether the stator of the wind turbine generator to be diagnosed has an inter-turn short circuit fault based on the vibration pattern.

[0117] As an optional embodiment of the present invention, the device further includes: a monitoring module, used to monitor the operating status of the wind turbine to be diagnosed based on the operating dataset and the fault vibration data.

[0118] For a detailed description of the function of the wind turbine stator fault detection device provided in this embodiment of the invention, please refer to the description of the wind turbine stator fault detection method in the above embodiments.

[0119] This invention also provides a storage medium, such as... Figure 7 As shown, a computer program 401 is stored thereon. When executed by a processor, this program implements the steps of the wind turbine stator fault detection method described in the above embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0120] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0121] This invention also provides an electronic device, such as... Figure 8 As shown, the electronic device may include a processor 51 and a memory 52, wherein the processor 51 and the memory 52 may be connected via a bus or other means. Figure 8 Taking the example of a connection between China and Israel via a bus.

[0122] Processor 51 can be a central processing unit (CPU). Processor 51 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0123] The memory 52, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the corresponding program instructions / modules in the embodiments of the present invention. The processor 51 executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 52, thereby realizing the wind turbine stator fault detection method in the above method embodiments.

[0124] The memory 52 may include a program storage area and a data storage area. The program storage area may store applications required for operating the device and at least one function; the data storage area may store data created by the processor 51, etc. Furthermore, the memory 52 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 52 may optionally include memory remotely located relative to the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0125] The one or more modules are stored in the memory 52, and when executed by the processor 51, they perform the following: Figure 1-4 The wind turbine stator fault detection method in the illustrated embodiment.

[0126] For specific details regarding the aforementioned electronic devices, please refer to the relevant documentation. Figures 1 to 4 The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here.

[0127] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for detecting stator faults in a wind turbine generator, characterized in that, The method includes: Obtain the operational dataset of the stator of the wind turbine generator to be diagnosed; Based on the aforementioned operational dataset, the fault characteristic quantities and phase difference of the stator of the wind turbine to be diagnosed are obtained through a frequency tracking algorithm and a preset calculation method. Based on the fault characteristic quantities, determine whether the stator of the wind turbine generator to be diagnosed has a stator winding asymmetry fault; When the stator winding asymmetry fault occurs in the stator of the wind turbine generator to be diagnosed, the fault phase of the stator of the wind turbine generator to be diagnosed is determined based on the phase difference; Based on the operational dataset, and through a frequency tracking algorithm and a preset calculation method, the fault characteristic quantities and phase differences of the stator of the wind turbine to be diagnosed are obtained, including: Based on the aforementioned operational dataset, the zero-sequence voltage, initial phase angle, and fundamental initial phase angle of the three-phase stator current of the wind turbine generator to be diagnosed are obtained through the aforementioned frequency tracking algorithm. Based on the zero-sequence voltage, the initial phase angle, and the fundamental initial phase angle, the fault characteristic quantity and the phase difference of the stator of the wind turbine generator to be diagnosed are obtained through the preset calculation method. The preset calculation method is expressed as the following relation: In the formula: Indicates fault characteristic quantities; Represents zero-sequence voltage; Fundamental initial phase angle; Indicates phase difference; This represents the initial phase angle.

2. The method according to claim 1, characterized in that, Determining whether the stator of the wind turbine generator to be diagnosed has a stator winding asymmetry fault based on the fault characteristic quantities includes: The fault characteristic quantity is compared with a preset threshold. When the fault characteristic quantity is greater than the preset threshold, it is determined that the stator winding asymmetry fault has occurred in the stator of the wind turbine to be diagnosed. When the fault characteristic quantity is less than the preset threshold, it is determined that the stator winding asymmetry fault has not occurred in the stator of the wind turbine to be diagnosed.

3. The method according to claim 1, characterized in that, The method further includes: Obtain the fault vibration data of the stator of the wind turbine generator to be diagnosed; The fault vibration data is processed to obtain the vibration pattern of the stator of the wind turbine generator to be diagnosed; Based on the vibration pattern, it is determined whether the stator of the wind turbine generator to be diagnosed has an inter-turn short circuit fault.

4. The method according to claim 3, characterized in that, After acquiring the fault vibration data of the stator of the wind turbine generator to be diagnosed, the method further includes: The operating status of the wind turbine to be diagnosed is monitored based on the operational dataset and the fault vibration data.

5. A wind turbine stator fault early warning system, characterized in that, The system includes: The acquisition unit is used to acquire the operating dataset and fault vibration data of the wind turbine stator, and to send the operating dataset and the fault vibration data to the fault detection unit. The fault detection unit is used to obtain the fault detection result of the wind turbine stator based on the operating dataset and the fault vibration data, using the wind turbine stator fault detection method as described in any one of claims 1-4, and to send the fault detection result to the fault early warning unit. The fault early warning unit is used to issue an early warning signal based on the fault detection result.

6. The system according to claim 5, characterized in that, The system also includes: The status monitoring unit is used to receive the operating dataset and the fault vibration data sent by the acquisition unit, and to monitor the operating status of the wind turbine stator based on the operating dataset and the fault vibration data.

7. A wind turbine stator fault detection device, characterized in that, The device includes: The acquisition module is used to acquire the operational dataset of the stator of the wind turbine to be diagnosed; The processing and calculation module is used to obtain the fault characteristic quantities and phase difference of the stator of the wind turbine to be diagnosed based on the running dataset, through a frequency tracking algorithm and a preset calculation method; The judgment module is used to determine whether the stator winding asymmetry fault of the wind turbine generator to be diagnosed has occurred based on the fault characteristic quantity. The determination module is used to determine the fault phase of the stator of the wind turbine generator under diagnosis based on the phase difference when the stator winding asymmetry fault occurs in the stator of the wind turbine generator under diagnosis. The processing and calculation module includes: The processing submodule is used to obtain the zero-sequence voltage, initial phase angle, and fundamental initial phase angle of the three-phase stator current of the wind turbine generator to be diagnosed, based on the running dataset and the frequency tracking algorithm. The calculation submodule is used to obtain the fault characteristic quantity and the phase difference of the stator of the wind turbine generator to be diagnosed based on the zero-sequence voltage, the initial phase angle and the fundamental initial phase angle, through the preset calculation method. The preset calculation method is expressed as the following relation: In the formula: Indicates fault characteristic quantities; Represents zero-sequence voltage; Fundamental initial phase angle; Indicates phase difference; This represents the initial phase angle.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the wind turbine stator fault detection method as described in any one of claims 1 to 4.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the wind turbine stator fault detection method as described in any one of claims 1 to 4.

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