Wind generating set and state monitoring method and device thereof

Abstract

The invention provides a wind generating set and a state monitoring method and device thereof. The state monitoring method comprises the steps that electrical signals of the wind generating set are obtained in real time, and the electrical signals comprise rotating speed signals of a generator of the wind generating set and / or three-phase current signals of the generator; the electrical signal is analyzed, an amplitude statistical value corresponding to a target frequency range is obtained, and the target frequency range is determined based on the mechanical characteristic frequency of a target mechanical part of the wind generating set; and based on the amplitude statistical value corresponding to the target frequency range, identifying the operation state of the target mechanical part.

Description

Technical Field

[0001] The present disclosure generally relates to the technical field of wind power generation, and more specifically, to a wind turbine generator set and a method and device for monitoring its state. Background Art

[0002] For the operating state of mechanical components of a wind turbine generator set (hereinafter, also simply referred to as a wind turbine), it is usually monitored by adding corresponding sensors (such as vibration sensors, temperature sensors, etc.).

[0003] However, due to the limited physical space of the measuring point arrangement of the sensors, it is difficult to effectively and comprehensively monitor the mechanical components of the wind turbine that need to be monitored. For example, it is difficult to monitor each position of the mechanical components of the wind turbine that need to be monitored (such as the vibration state of the gears inside the gearbox, the vibration state of the generator stator winding, etc.), resulting in the inability to fully cover the state monitoring of the mechanical components of the wind turbine, and leading to the inability to identify problems at the early stage when the mechanical components are in an abnormal state.

[0004] Moreover, the method of adding sensors to monitor the working state of the mechanical components of the wind turbine will lead to problems such as increased cost, increased volume of the mechanical components, and limited transportation and installation. Summary of the Invention

[0005] An exemplary embodiment of the present disclosure is to provide a wind turbine generator set and a method and device for monitoring its state, which can effectively, comprehensively, and accurately monitor the operating state of the mechanical components of the wind turbine without additional sensors.

[0006] According to a first aspect of an embodiment of the present disclosure, there is provided a method for monitoring the state of a wind turbine generator set, the state monitoring method including: obtaining electrical signals of the wind turbine generator set in real time, where the electrical signals include a rotational speed signal of a generator of the wind turbine generator set and / or three-phase current signals of the generator; analyzing the electrical signals to obtain an amplitude statistical value corresponding to a target frequency range, where the target frequency range is determined based on a mechanical characteristic frequency of a target mechanical component of the wind turbine generator set; and identifying the operating state of the target mechanical component based on the amplitude statistical value corresponding to the target frequency range.

[0007] Optionally, the step of analyzing the electrical signals to obtain an amplitude statistical value corresponding to a target frequency range includes: performing an online fast Fourier transform on the electrical signals to obtain the amplitude statistical value corresponding to the target frequency range.

[0008] Optionally, the steps of performing online fast Fourier transform analysis on the electrical signal to obtain the amplitude statistical value corresponding to the target frequency range include: for each frequency within the target frequency range, determining the total number of samplings corresponding to the frequency based on the frequency and the sampling frequency of the electrical signal; sampling the sampling values of the electrical signal according to the total number of samplings to obtain a plurality of sampling values corresponding to the frequency; determining the real part and the imaginary part of the complex form of the electrical signal at the frequency based on the plurality of sampling values corresponding to the frequency, and calculating the amplitude of the electrical signal at the frequency based on the real part and the imaginary part; and statistically analyzing the amplitudes of the electrical signal at each frequency within the target frequency range to obtain the amplitude statistical value corresponding to the target frequency range.

[0009] Optionally, the steps of identifying the operating state of the target mechanical component based on the amplitude statistical value corresponding to the target frequency range include: determining the threshold range to which the amplitude statistical value corresponding to the target frequency range belongs; and determining the operating state of the target mechanical component as: the operating state type corresponding to the threshold range to which it belongs; wherein, the threshold range corresponding to each operating state type is determined based on the historical operating data of the target mechanical component.

[0010] Optionally, the operating state type includes at least one of the following items: normal operation, abnormal prompt, alarm, and fault.

[0011] Optionally, the state monitoring method further includes: analyzing the recognition results of the operating states of the target mechanical component during the entire life cycle to obtain the operating rule information of the target mechanical component.

[0012] Optionally, the target mechanical component includes at least one of the following items: gearbox, bearing, generator; and / or, the mechanical characteristic frequency includes at least one of the following items: meshing frequency, resonance frequency.

[0013] According to a second aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the state monitoring method of the wind turbine as described above.

[0014] According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, which includes: a processor; a memory storing a computer program, which, when executed by the processor, causes the processor to execute the state monitoring method of the wind turbine as described above.

[0015] According to a fourth aspect of the embodiments of the present disclosure, a wind turbine generator is provided. The controller of the wind turbine generator includes: a processor; a memory storing a computer program, which, when executed by the processor, causes the processor to execute the state monitoring method of the wind turbine generator as described above.

[0016] The wind turbine generator, its state monitoring method and device according to the exemplary embodiments of the present disclosure propose a method for monitoring the operating states of mechanical components (such as gearboxes, bearings, generators, etc.) through the electrical circuit of the wind turbine. By monitoring and analyzing the characteristic frequencies of the mechanical components during the operation of the wind turbine, the monitoring, early warning and protection of the operating states of the mechanical components are realized. On the one hand, the monitoring is more effective, comprehensive and accurate. On the other hand, there is no need to additionally install sensors, reducing the need for external sensors for the wind turbine.

[0017] In the following description, some aspects and / or advantages of the general concept of the present disclosure will be elaborated, and some aspects and / or advantages will be learned from the following description or the implementation of the general concept of the present disclosure. Description of the Drawings

[0018] From the following detailed description of the embodiments of the present application in conjunction with the drawings, these and / or other aspects and advantages of the present application will become clearer and easier to understand, where:

[0019] Figure 1 A flowchart showing the state monitoring method of a wind turbine generator according to an exemplary embodiment of the present disclosure;

[0020] Figure 2 A flowchart showing the method for obtaining the amplitude statistical value corresponding to the target frequency range by performing online fast Fourier transform analysis on electrical signals according to an exemplary embodiment of the present disclosure;

[0021] Figure 3 A flowchart showing the state monitoring method of a wind turbine generator according to another exemplary embodiment of the present disclosure. Detailed Embodiments

[0022] Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein the same reference numerals always refer to the same components. The following embodiments will be described with reference to the accompanying drawings to explain the present disclosure.

[0023] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present disclosure are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are only examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0024] It should be noted here that "at least one of several items" in the present disclosure all represents three parallel situations, including "any one of the several items", "a combination of any multiple of the several items", and "all of the several items". For example, "including at least one of A and B" includes the following three parallel situations: (1) including A; (2) including B; (3) including A and B. Another example, "performing at least one of step one and step two" means the following three parallel situations: (1) performing step one; (2) performing step two; (3) performing step one and step two.

[0025] Figure 1 The flowchart showing the state monitoring method of the wind turbine according to the exemplary embodiment of the present disclosure.

[0026] As an example, the state monitoring method of the wind turbine according to the exemplary embodiment of the present disclosure can be executed by an electronic device with data processing capabilities. For example, the electronic device can be a controller of the wind turbine (such as a main controller or a converter controller), or a field-level controller. In addition, it can also be a terminal (such as a personal notebook, a desktop computer, etc.) or a server (such as an independent server, a server cluster, a cloud platform, etc.). The embodiments of the present disclosure do not limit this.

[0027] Referring to Figure 1 , in step S101, the electrical signals of the wind turbine are acquired in real time.

[0028] As an example, the above electrical signals may include but are not limited to at least one of the following items: the rotational speed signal of the generator of the wind turbine, the three-phase current signal of the generator.

[0029] As an example, the rotational speed and three-phase current of the generator can be measured in real time by corresponding hardware measurement devices. Correspondingly, step S101 may include: acquiring in real time the rotational speed signal and three-phase current signal measured by the hardware measurement device.

[0030] In step S102, the above electrical signal is analyzed to obtain the amplitude statistical value corresponding to the target frequency range. That is, the amplitude statistical value corresponding to the electrical signal under the target frequency range is obtained.

[0031] The target frequency range is determined based on the mechanical characteristic frequency of the target mechanical component of the wind turbine generator set.

[0032] As an example, the target mechanical component may include, but is not limited to, at least one of the following items: gearbox, bearing, generator body.

[0033] As an example, the mechanical characteristic frequency may include, but is not limited to, at least one of the following items: meshing frequency, resonance frequency.

[0034] Regarding that the target frequency range is determined based on the mechanical characteristic frequency of the target mechanical component, as an example, the target frequency range may be a frequency range covering the mechanical characteristic frequency of the target mechanical component. It should be understood that one or more target frequency ranges may be set for each target mechanical component, and each target frequency range covers a mechanical characteristic frequency of the target mechanical component. For example, one target frequency range corresponding to the gearbox may be a frequency range covering the meshing frequency of the gearbox, and another target frequency range corresponding to the gearbox may be a frequency range covering the resonance frequency of the gearbox.

[0035] As an example, step S102 may include: performing an online fast Fourier transform analysis on the electrical signal to obtain the amplitude statistical value corresponding to the target frequency range.

[0036] It should be understood that other methods of analyzing the electrical signal (such as fast Fourier transform analysis, etc.) may also be used to obtain the amplitude statistical value corresponding to the target frequency range, and the present disclosure places no limitation on this.

[0037] As an example, when the electrical signal includes the rotational speed signal of the generator and the three-phase current signal of the generator, an online fast Fourier transform analysis may be performed on the rotational speed signal to obtain the amplitude statistical value corresponding to the rotational speed signal under the target frequency range; and an online fast Fourier transform analysis may be performed on the three-phase current signal to obtain the amplitude statistical value corresponding to the three-phase current signal under the target frequency range. For example, an online fast Fourier transform analysis may be performed on the rotational speed signal to obtain the amplitude statistical value corresponding to the rotational speed signal under each lower target frequency range (such as the target frequency range below 2.5 Hz); and an online fast Fourier transform analysis may be performed on the three-phase current signal to obtain the amplitude statistical value corresponding to the three-phase current signal under each higher target frequency range (such as the target frequency range above 2.5 Hz).

[0038] As an example, online fast Fourier transform analysis can be performed on electrical signals to obtain a spectrum in the range from 0 to the switching frequency (for example, 2 kHz), and then an amplitude statistical value corresponding to the target frequency range can be obtained based on this spectrum. The switching frequency is the switching frequency of the three-phase power module in the converter of the wind turbine generator set.

[0039] Next, an exemplary embodiment of a method for obtaining an amplitude statistical value corresponding to a target frequency range by performing online fast Fourier transform analysis on electrical signals will be described in conjunction with Figure 2 This will not be elaborated here for the time being.

[0040] In step S103, based on the amplitude statistical value corresponding to the target frequency range, the operating state of the target mechanical component is identified.

[0041] As an example, step S103 may include: determining the threshold range to which the amplitude statistical value corresponding to the target frequency range belongs; determining the operating state of the target mechanical component as: the operating state type corresponding to the threshold range to which it belongs.

[0042] As an example, the operating state type may include but is not limited to at least one of the following items: normal operation, abnormal prompt, alarm, and fault.

[0043] As an example, the threshold range corresponding to each operating state type can be determined based on the historical operating data of the target mechanical component. For example, the historical operating data of the target mechanical component may include but is not limited to at least one of the following items: the electrical signals of the wind turbine generator set when the target mechanical component is operating normally; the electrical signals of the wind turbine generator set when the target mechanical component fails. By analyzing these historical operating data, the threshold range corresponding to each operating state type can be determined.

[0044] As an example, the threshold range corresponding to normal operation can be determined by analyzing the electrical signals of the wind turbine generator set when the target mechanical component is operating normally; the threshold range corresponding to a fault can be determined by analyzing the electrical signals of the wind turbine generator set when the target mechanical component fails; based on the threshold range corresponding to normal operation and the threshold range corresponding to a fault, with corresponding safety margins, the threshold range corresponding to an abnormal prompt and the threshold range corresponding to an alarm can be formulated.

[0045] It should be understood that when the number of target mechanical components is multiple, for each target mechanical component, the target mechanical component has its corresponding at least one target frequency range, and each target frequency range in this at least one target frequency range has its own threshold range corresponding to each operating state type. The target frequency ranges corresponding to different target mechanical components may be different, and the threshold ranges corresponding to each operating state type corresponding to different target frequency ranges may be different.

[0046] In addition, as an example, the method for monitoring the state of a wind turbine according to an exemplary embodiment of the present disclosure may further include: analyzing the recognition results of the operating states of target mechanical components during the entire life cycle to obtain the operating law information of the target mechanical components. Thus, information such as which mechanical components are prone to problems, which mechanical components are relatively reliable, and the laws of mechanical component failures can be obtained.

[0047] In addition, due to the limited memory of the converter controller chip, the converter controller can upload the above analysis data (including but not limited to specific spectra, amplitude statistical values corresponding to the target frequency range, recognition results of operating states) to the main controller of the wind turbine at regular intervals (one day or one week), and the main controller of the wind turbine uploads the above data to the field controller or the cloud for long-term storage.

[0048] Figure 2 The flowchart shows a method for performing an online fast Fourier transform analysis on an electrical signal to obtain amplitude statistical values corresponding to a target frequency range according to an exemplary embodiment of the present disclosure.

[0049] Refer to Figure 2 , in step S201, for each frequency within the target frequency range, based on this frequency and the sampling frequency of the electrical signal, determine the total number of samplings corresponding to this frequency.

[0050] As an example, the total number of samplings N corresponding to each frequency can be: the sampling frequency of the electrical signal / this frequency. For example, the sampling frequency of the electrical signal can be 4 kHz. If the frequency is 20 Hz, then the total number of samplings N corresponding to this frequency is: 4000 / 20 = 200. The sampling frequency of the electrical signal is the number of samples (such as rotational speed values, current values) that can be collected from the real-time measured electrical signal per unit time through an algorithm.

[0051] In step S202, resample the sampling values of the electrical signal according to the total number of samplings N corresponding to this frequency to obtain N sampling values corresponding to this frequency.

[0052] Specifically, the sampling values of the electrical signal are: the sampling value array obtained by sampling the electrical signal according to the above sampling frequency; then, sample N times from the sampling value array to obtain N sampling values.

[0053] In step S203, based on the N sampling values corresponding to this frequency, determine the real part and the imaginary part of the complex form of the electrical signal at this frequency, and calculate the amplitude of the electrical signal at this frequency based on this real part and this imaginary part.

[0054] As an example, the real part X of the complex form of the electrical signal at this frequency can be determined by the following formula Re and the imaginary part X Im, and based on the real part X Re and the imaginary part X Im calculate the amplitude X of the electrical signal at this frequency amp .

[0055]

[0056] where x dqh (0) represents the first sampling value among the above N sampling values, and x dqh (i) represents the (i - 1)-th sampling value among the above N sampling values.

[0057] The simplified online FFT analysis method proposed according to the exemplary embodiments of the present disclosure can effectively reduce the computational amount and shorten the calculation time, thereby improving the real-time performance of state monitoring.

[0058] In step S204, the amplitudes of the electrical signal at each frequency within the target frequency range are statistically analyzed to obtain the amplitude statistical value corresponding to the target frequency range.

[0059] As an example, the statistical method can be the statistical average or the statistical maximum. It should be understood that other statistical methods can also be used, and the present disclosure does not limit this.

[0060] The present disclosure considers that when the operating state of the mechanical components of the fan is abnormal, the corresponding characteristic frequencies of the mechanical components will be reflected in states such as rotational speed and torque. The mechanical components are connected to the generator, that is, the influence caused by the characteristic frequencies will be transmitted to the generator. The generator converts mechanical energy into electrical energy through electromechanical conversion and transmits the influence of the characteristic frequencies into the electrical signal. Therefore, by performing real-time detection and analysis on the electrical signal and rotational speed signal of the generator through the electrical circuit of the fan, the working state of the mechanical components can be indirectly identified, thereby monitoring the state of the mechanical components. After adopting this method, the cost of the fan can be not increased, but the electrical circuit of the fan can be used as a sensor for function expansion. When the fan performs real-time monitoring and analysis on the electrical signal and rotational speed signal of the generator, the results of the real-time monitoring and analysis can be uploaded to the upper computer memory for real-time display and storage, so as to monitor the state of the mechanical components throughout the life cycle in the way of long-term data accumulation, providing data support for the long-term operating state analysis of the mechanical components.

[0061] Aiming at the current situation that there is generally no mechanical component state monitoring through the electrical circuit of the fan in the fan system, the present disclosure proposes a scheme for monitoring the state of the mechanical components of the fan through the electrical circuit of the fan, realizing real-time and long-term monitoring of the state of the mechanical components of the fan through the electrical circuit of the fan, so as to perform reliable early warning and protection on the mechanical components of the fan, reduce the demand for external sensors such as vibration and temperature, improve the reliability of the operation detection of the mechanical components of the fan, and reduce the cost of the fan.

[0062] Figure 3 The flowchart of the method for monitoring the state of a wind turbine according to another exemplary embodiment of the present disclosure is shown.

[0063] Referring to Figure 3 , in step S301, it is determined whether the converter is modulated.

[0064] When the converter is modulated, it can be determined that the wind turbine is in an operating state, so that subsequent state monitoring steps can be executed.

[0065] In step S302, the three-phase currents ia, ib, ic and the rotational speed n of the generator are collected in real time.

[0066] In step S303, an online FFT analysis is performed on the collected three-phase currents ia, ib, ic and the rotational speed n, and the amplitudes corresponding to the currents and rotational speeds at each frequency (the analysis frequency range is determined according to the mechanical characteristic frequency range of the target mechanical component) are calculated.

[0067] In step S304, the amplitudes corresponding to the currents and rotational speeds at each frequency are uploaded and stored (the storage space needs to meet the requirement of long-term storage).

[0068] In step S305, the amplitude protection thresholds at the mechanical characteristic frequencies conducted from the target mechanical component to the motor end are divided into an abnormal prompt file, an alarm file and a fault shutdown file, and the amplitudes corresponding to the currents and rotational speeds at each frequency are compared with the protection thresholds in real time to realize the monitoring of the state of the target mechanical component.

[0069] In addition, by analyzing the amplitude information corresponding to the currents and rotational speeds at each frequency stored in a long period, the change trend of the operating state of the mechanical component can be judged, so as to realize the long-term monitoring of the mechanical component and the acquisition of the operating law.

[0070] According to the method for monitoring the working state of the fan mechanical component proposed by the exemplary embodiment of the present disclosure, the electrical circuit of the fan is equivalent to a sensor to detect the state of the mechanical characteristic frequency of the mechanical component, which can effectively supplement the monitoring of the current working state of the fan mechanical component, minimize the demand for external sensors to the greatest extent, and thus can reduce the cost of the fan, reduce the volume of the mechanical component, and reduce the difficulty of transportation and installation. In addition, the storage of mechanical characteristic frequency data for long-term operation can be realized to realize the long-term monitoring of the working state of the mechanical component and the analysis of the operating law.

[0071] Exemplary embodiments of the present disclosure provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the state monitoring method of a wind turbine as described in the above exemplary embodiments. The computer-readable storage medium is any data storage device capable of storing data readable by a computer system. Examples of computer-readable storage media include: read-only memory, random access memory, compact disc read-only memory, magnetic tape, floppy disk, optical data storage device, and carrier waves (such as data transmission via the Internet through wired or wireless transmission paths).

[0072] An electronic device according to an exemplary embodiment of the present disclosure includes: a processor (not shown) and a memory (not shown), wherein the memory stores a computer program, which, when executed by the processor, causes the processor to execute the state monitoring method of a wind turbine as described in the above exemplary embodiments.

[0073] As an example, the electronic device may be an electronic device with data processing capabilities. For example, the electronic device may be a controller of a wind turbine (such as a main controller or a converter controller), or a field-level controller. In addition, it may also be a terminal (such as a personal notebook, desktop computer, etc.) or a server (such as an independent server, server cluster, cloud platform, etc.). The embodiments of the present disclosure do not limit this.

[0074] A wind turbine according to an exemplary embodiment of the present disclosure, the controller of the wind turbine includes: a processor; a memory storing a computer program, which, when executed by the processor, causes the processor to execute the state monitoring method of a wind turbine as described in the above exemplary embodiments.

[0075] Although some exemplary embodiments of the present disclosure have been shown and described, those skilled in the art should understand that these embodiments can be modified without departing from the scope and spirit of the present disclosure defined by the claims and their equivalents.

Claims

1. A method for monitoring the state of a wind turbine generator, characterized in that, The state monitoring method includes: Obtaining in real time the electrical signals of the wind turbine generator set, where the electrical signals include the rotational speed signal of the generator of the wind turbine generator set and / or the three-phase current signals of the generator; Analyzing the electrical signals to obtain the amplitude statistical value corresponding to the target frequency range, where the target frequency range is determined based on the mechanical characteristic frequency of the target mechanical component of the wind turbine generator set; Identifying the operating state of the target mechanical component based on the amplitude statistical value corresponding to the target frequency range.

2. The state monitoring method according to claim 1, wherein The step of analyzing the electrical signals to obtain the amplitude statistical value corresponding to the target frequency range includes: Performing an online fast Fourier transform on the electrical signals to obtain the amplitude statistical value corresponding to the target frequency range.

3. The state monitoring method according to claim 2, wherein The step of performing an online fast Fourier transform analysis on the electrical signals to obtain the amplitude statistical value corresponding to the target frequency range includes: For each frequency within the target frequency range, determining the total number of samples corresponding to the frequency based on the frequency and the sampling frequency of the electrical signals; Sampling the sampled values of the electrical signals according to the total number of samples to obtain a plurality of sampled values corresponding to the frequency; Based on the plurality of sampled values corresponding to the frequency, determining the real part and the imaginary part of the complex form of the electrical signals at the frequency, and calculating the amplitude of the electrical signals at the frequency based on the real part and the imaginary part; Statistically analyzing the amplitudes of the electrical signals at each frequency within the target frequency range to obtain the amplitude statistical value corresponding to the target frequency range.

4. The state monitoring method according to any one of claims 1 to 3, characterized in that The step of identifying the operating state of the target mechanical component based on the amplitude statistical value corresponding to the target frequency range includes: Determining the threshold range to which the amplitude statistical value corresponding to the target frequency range belongs; Determining the operating state of the target mechanical component as: the operating state type corresponding to the threshold range to which it belongs; Wherein, the threshold range corresponding to each operating state type is determined based on the historical operating data of the target mechanical component.

5. The state monitoring method according to claim 4, characterized in that The operating state type includes at least one of the following items: normal operation, abnormal prompt, alarm, failure.

6. The state monitoring method according to claim 1, wherein The state monitoring method further includes: Analyzing the identification results of the operating states of the target mechanical component during the entire life cycle to obtain the operating rule information of the target mechanical component.

7. The state monitoring method according to any one of claims 1 to 3, characterized in that The target mechanical component includes at least one of the following items: gearbox, bearing, generator; And / or, the mechanical characteristic frequency includes at least one of the following items: meshing frequency, resonance frequency.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it causes the processor to execute the state monitoring method of the wind turbine generator set according to any one of claims 1 to 7.

9. An electronic device, characterized in that, The electronic device includes: A processor; A memory storing a computer program, when the computer program is executed by the processor, it causes the processor to execute the state monitoring method of the wind turbine generator set according to any one of claims 1 to 7.

10. A wind power generating set, characterized in that, The controller of the wind turbine generator set includes: A processor; A memory stores a computer program which, when executed by a processor, causes the processor to execute the method for monitoring the state of a wind power generation set according to any one of claims 1 to 7.

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