Fault detection method and device for offshore wind turbine generator converter

Abstract

The invention provides a fault detection method and device for an offshore wind turbine generator converter, and relates to the technical field of offshore power generation. The method comprises the following steps: acquiring operation data of an offshore wind turbine generator converter, wherein the operation data at least comprises temperature data, current data, vibration data and voltage data; the operation data are input into a trained anomaly recognition model, time sequence analysis is carried out through the anomaly recognition model, fault prediction parameters output by the anomaly recognition model are obtained, and the fault prediction parameters comprise prediction fault types and prediction fault time; and generating a maintenance plan of the offshore wind turbine generator converter based on the fault prediction parameters. According to the method, the fault of the converter of the wind turbine generator can be accurately predicted based on the historical data and the real-time data by using the anomaly recognition model, so that the occurrence of sudden faults is avoided; the traditional wind turbine generator converter maintenance often depends on regular inspection or artificial judgment, and the scheme based on data driving can improve the accuracy of fault early warning.

Description

Technical Field

[0001] The present application relates to the field of offshore power generation technology, and in particular to a fault detection method and device for an offshore wind turbine converter. Background Art

[0002] As an important renewable energy source, the converter in offshore wind power system plays a vital role. The converter is the core equipment that connects wind turbines to the power grid, and is responsible for converting the DC power generated by wind turbines into AC power suitable for the power grid. With the continuous development and maturity of offshore wind power technology, the scale of wind turbines is increasing, which puts higher requirements on the performance and stability of converters. The reliable operation of converters is directly related to the power generation efficiency of wind power systems and the stability of power grids. Therefore, the maintenance and management of converters is particularly important.

[0003] However, as the scale of offshore wind power projects expands, the maintenance cost of the converter accounts for a large proportion of the total operating cost. Traditional maintenance methods mainly include regular maintenance and post-fault maintenance. Regular maintenance usually performs routine inspections and maintenance on the converter based on preset time intervals or operating hours. Although this method can ensure the basic normal operation of the equipment to a certain extent, it has problems such as the mismatch between maintenance frequency and actual needs, waste of resources, and inability to adjust maintenance plans according to the health of the equipment. Post-fault maintenance is to repair the converter when it fails. Although it can effectively deal with equipment failures, the equipment can no longer operate normally after the failure, resulting in more serious losses. In addition, this method cannot detect potential failure risks in a timely manner, resulting in longer equipment downtime and higher repair costs. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, one purpose of the present application is to propose a fault detection method for an offshore wind turbine converter, comprising: obtaining operating data of the offshore wind turbine converter, the operating data including at least temperature data, current data, vibration data and voltage data; inputting the operating data into a trained abnormality recognition model, performing time series analysis through the abnormality recognition model, and obtaining fault prediction parameters output by the abnormality recognition model, the fault prediction parameters including predicted fault type and predicted fault time; and generating a maintenance plan for the offshore wind turbine converter based on the fault prediction parameters.

[0006] The second objective of the present application is to provide a fault detection device for an offshore wind turbine converter.

[0007] The third objective of the present application is to provide an electronic device.

[0008] A fourth object of the present application is to provide a non-transitory computer-readable storage medium.

[0009] A fifth object of the present application is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first aspect of the present application proposes a fault detection method for an offshore wind turbine converter, comprising: obtaining operating data of the offshore wind turbine converter, the operating data including at least temperature data, current data, vibration data and voltage data; inputting the operating data into a trained abnormality recognition model, performing time series analysis through the abnormality recognition model, and obtaining fault prediction parameters output by the abnormality recognition model, the fault prediction parameters including predicted fault type and predicted fault time; and generating a maintenance plan for the offshore wind turbine converter based on the fault prediction parameters.

[0011] According to an embodiment of the present application, the fault detection method for an offshore wind turbine converter further includes: generating fault alarm information based on fault prediction parameters; and sending the fault alarm information and a maintenance plan to a terminal device corresponding to a maintenance personnel.

[0012] According to one embodiment of the present application, obtaining operating data of an offshore wind turbine converter includes: obtaining operating data of the offshore wind turbine converter based on wireless communication, wherein the operating data is collected by relevant sensors installed on the offshore wind turbine converter.

[0013] According to one embodiment of the present application, a training method for an abnormality recognition model includes: obtaining a training sample set, each training sample in the training sample set includes historical operating data of an offshore wind turbine converter in a preset time window and historical fault information of the offshore wind turbine converter, the historical fault information includes historical fault types and historical fault times; training an abnormality recognition model to be trained based on the training sample set to obtain a trained abnormality recognition model.

[0014] According to one embodiment of the present application, the anomaly recognition model is generated based on a machine learning algorithm.

[0015] To achieve the above-mentioned purpose, the second aspect embodiment of the present application proposes a fault detection device for an offshore wind turbine converter, including: an acquisition module, used to obtain operating data of the offshore wind turbine converter, the operating data including at least temperature data, current data, vibration data and voltage data; a prediction module, used to input the operating data into a trained abnormality recognition model, perform time series analysis through the abnormality recognition model, and obtain fault prediction parameters output by the abnormality recognition model, the fault prediction parameters including predicted fault type and predicted fault time; a generation module, used to generate a maintenance plan for the offshore wind turbine converter based on the fault prediction parameters.

[0016] According to an embodiment of the present application, the generating module is further used to: generate fault alarm information based on the fault prediction parameters; and send the fault alarm information and the maintenance plan to the terminal device corresponding to the maintenance personnel.

[0017] According to an embodiment of the present application, the acquisition module is also used to: acquire operating data of the offshore wind turbine converter based on wireless communication, where the operating data is collected by relevant sensors installed on the offshore wind turbine converter.

[0018] According to one embodiment of the present application, the fault detection device of the offshore wind turbine converter also includes a training module, which is used to obtain a training sample set, each training sample in the training sample set includes historical operation data of the offshore wind turbine converter in a preset time window and historical fault information of the offshore wind turbine converter, and the historical fault information includes historical fault types and historical fault times; based on the training sample set, the abnormality recognition model to be trained is trained to obtain a trained abnormality recognition model.

[0019] According to one embodiment of the present application, the anomaly recognition model is generated based on a machine learning algorithm.

[0020] To achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the fault detection method for an offshore wind turbine converter as described in the first aspect embodiment of the present application.

[0021] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the fault detection method of the offshore wind turbine converter as described in the first aspect embodiment of the present application.

[0022] To achieve the above-mentioned purpose, the fifth aspect embodiment of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the fault detection method for an offshore wind turbine converter as described in the first aspect embodiment of the present application.

[0023] The present application achieves at least the following beneficial effects: by using an abnormality recognition model, the present application can accurately predict the faults of wind turbine converters based on historical data and real-time data, thereby avoiding the occurrence of sudden faults; traditional wind turbine converter maintenance often relies on regular inspections or human judgment, while data-driven solutions can improve the accuracy of fault warnings, thereby reducing unplanned downtime; through real-time monitoring and fault prediction of offshore wind turbine converters, the operating risks of the equipment can be effectively reduced to ensure the long-term stable operation of the equipment; by predicting faults in advance, the cost of emergency repairs and equipment replacement is reduced, and at the same time, production interruptions caused by equipment failures can be avoided, thereby increasing the availability of wind turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 It is an exemplary schematic diagram of a fault detection method for an offshore wind turbine converter shown in one embodiment of the present application.

[0026] Figure 2 It is a schematic diagram of a fault detection device for an offshore wind turbine converter shown in one embodiment of the present application.

[0027] Figure 3 It is a schematic diagram of an electronic device shown in one embodiment of the present application. DETAILED DESCRIPTION

[0028] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] Figure 1 is an exemplary schematic diagram of a fault detection method for an offshore wind turbine converter shown in the present application, such as Figure 1 As shown, the fault detection method of the offshore wind turbine converter comprises the following steps:

[0030] S101, obtaining operating data of an offshore wind turbine converter, where the operating data at least includes temperature data, current data, vibration data and voltage data.

[0031] Preferably, the operating data of the offshore wind turbine converter is acquired based on wireless communication, and the operating data is collected by relevant sensors installed on the offshore wind turbine converter.

[0032] Optionally, the operating data of the offshore wind turbine converter is obtained through relevant data transmission cables.

[0033] S102, inputting the operating data into the trained abnormality recognition model, performing time series analysis through the abnormality recognition model, and obtaining the fault prediction parameters output by the abnormality recognition model, wherein the fault prediction parameters include the predicted fault type and the predicted fault time.

[0034] The predicted fault type refers to what type of fault the predicted fault may be (for example, overheating, short circuit, abnormal vibration, etc.).

[0035] The predicted failure time refers to the time period or critical point when the failure is expected to occur.

[0036] Among them, the fault prediction parameters output by the abnormal recognition model can identify potential faults in advance, avoiding sudden shutdown of equipment or large-scale equipment damage.

[0037] The training method of the abnormality recognition model includes: obtaining a training sample set, each training sample in the training sample set includes historical operation data of an offshore wind turbine converter in a preset time window and historical fault information of the offshore wind turbine converter, and the historical fault information includes historical fault types and historical fault times; training the abnormality recognition model to be trained based on the training sample set to obtain a trained abnormality recognition model.

[0038] The anomaly recognition model is built based on machine learning algorithms (such as neural networks, support vector machines, decision trees, etc.).

[0039] S103: Generate a maintenance plan for the offshore wind turbine converter based on the fault prediction parameters.

[0040] Among them, the maintenance plan can be understood as: based on the predicted fault type and predicted fault time, the required maintenance resources, personnel scheduling and spare parts preparation can be arranged in advance, thereby optimizing the maintenance work.

[0041] The embodiment of the present application proposes a fault detection method for an offshore wind turbine converter, comprising: obtaining the operating data of the offshore wind turbine converter, the operating data including at least temperature data, current data, vibration data and voltage data; inputting the operating data into a trained abnormal recognition model, performing time series analysis through the abnormal recognition model, obtaining the fault prediction parameters output by the abnormal recognition model, the fault prediction parameters including the predicted fault type and the predicted fault time; generating a maintenance plan for the offshore wind turbine converter based on the fault prediction parameters. The present application uses an abnormal recognition model to accurately predict the fault of the wind turbine converter based on historical data and real-time data, thereby avoiding the occurrence of sudden faults; traditional wind turbine converter maintenance often relies on regular inspections or human judgment, while data-driven solutions can improve the accuracy of fault warnings, thereby reducing unplanned downtime; through real-time monitoring and fault prediction of offshore wind turbine converters, the operating risk of the equipment can be effectively reduced to ensure long-term stable operation of the equipment; by predicting faults in advance, the cost of emergency repairs and equipment replacement is reduced, and production interruptions caused by equipment failures can be avoided, thereby increasing the availability of wind turbines.

[0042] Furthermore, fault warning information is generated based on fault prediction parameters; the fault warning information and maintenance plan are sent to the corresponding terminal device of the maintenance personnel (such as a mobile terminal, tablet, smart phone or dedicated terminal). By generating fault alarms in real time and sending them to the terminal devices of maintenance personnel, the operation and maintenance team can be notified at the first time. In this way, when a potential fault occurs in the wind turbine converter, the maintenance personnel can respond immediately, avoiding the fault from developing into a more serious condition, which can effectively reduce equipment downtime and reduce production capacity loss caused by equipment failure.

[0043] Among them, the alarm information can also be conveyed to the maintenance personnel through instant messages (such as text messages, APP notifications, emails, etc.), sound reminders or visual interfaces.

[0044] The maintenance plan may include detailed inspection items, repair steps, spare parts requirements, technical requirements and scheduled maintenance time of the converter.

[0045] Figure 2 is a schematic diagram of a fault detection device for an offshore wind turbine converter shown in the present application, such as Figure 2 As shown, the fault detection device 200 for the offshore wind turbine converter includes: an acquisition module 201, a prediction module 202 and a generation module 203, wherein:

[0046] The acquisition module 201 is used to acquire the operating data of the converter of the offshore wind turbine group, and the operating data at least includes temperature data, current data, vibration data and voltage data.

[0047] The prediction module 202 is used to input the operation data into the trained abnormality recognition model, perform time series analysis through the abnormality recognition model, and obtain the fault prediction parameters output by the abnormality recognition model. The fault prediction parameters include the predicted fault type and the predicted fault time.

[0048] The generating module 203 is used to generate a maintenance plan for the offshore wind turbine converter based on the fault prediction parameters.

[0049] By using an abnormality recognition model, this device can accurately predict the faults of wind turbine converters based on historical data and real-time data, thereby avoiding the occurrence of sudden faults; traditional wind turbine converter maintenance often relies on regular inspections or human judgment, while data-driven solutions can improve the accuracy of fault warnings, thereby reducing unplanned downtime; through real-time monitoring and fault prediction of offshore wind turbine converters, the operating risks of equipment can be effectively reduced to ensure the long-term stable operation of equipment; by predicting faults in advance, the cost of emergency repairs and equipment replacement is reduced, and at the same time, production interruptions caused by equipment failures can be avoided, thereby increasing the availability of wind turbines.

[0050] Furthermore, the generating module 203 is further used to: generate fault warning information based on the fault prediction parameters; and send the fault warning information and the maintenance plan to the terminal device corresponding to the maintenance personnel.

[0051] Furthermore, the acquisition module 201 is also used to: acquire the operating data of the offshore wind turbine converter based on wireless communication, where the operating data is collected by relevant sensors installed on the offshore wind turbine converter.

[0052] Furthermore, the fault detection device 200 for the offshore wind turbine converter also includes a training module, which is used to obtain a training sample set, each training sample in the training sample set includes historical operating data of the offshore wind turbine converter in a preset time window and historical fault information of the offshore wind turbine converter, and the historical fault information includes historical fault types and historical fault times; based on the training sample set, the abnormality recognition model to be trained is trained to obtain a trained abnormality recognition model.

[0053] Furthermore, the anomaly recognition model is generated based on the machine learning algorithm.

[0054] In order to implement the above embodiment, the present application embodiment also proposes an electronic device 300, such as Figure 3 As shown, the electronic device 300 includes: a processor 301 and a memory 302 that is communicatively connected to the processor, the memory 302 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 301 to implement the fault detection method for the offshore wind turbine converter as shown in the above embodiment.

[0055] In order to implement the above embodiments, the embodiments of the present application further propose a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to implement the fault detection method for an offshore wind turbine converter as shown in the above embodiments.

[0056] In order to implement the above embodiments, the embodiments of the present application further provide a computer program product, including a computer program, which, when executed by a processor, implements the fault detection method for an offshore wind turbine converter as shown in the above embodiments.

[0057] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0058] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0059] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0060] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A fault detection method for an offshore wind turbine converter, characterized in that: include: Acquiring operating data of an offshore wind turbine converter, wherein the operating data includes at least temperature data, current data, vibration data and voltage data; Inputting the operating data into a trained abnormality recognition model, performing time series analysis through the abnormality recognition model, and obtaining fault prediction parameters output by the abnormality recognition model, wherein the fault prediction parameters include a predicted fault type and a predicted fault time; A maintenance plan for the offshore wind turbine converter is generated based on the fault prediction parameters.

2. The method according to claim 1, characterized in that The method further comprises: generating fault warning information based on the fault prediction parameters; The fault alarm information and the maintenance plan are sent to the terminal device corresponding to the maintenance personnel.

3. The method according to claim 2, characterized in that The obtaining of operating data of the offshore wind turbine converter comprises: The operating data of the offshore wind turbine converter is acquired based on wireless communication, and the operating data is collected by relevant sensors installed on the offshore wind turbine converter.

4. The method according to claim 3, characterized in that The training method of the anomaly recognition model comprises: Acquire a training sample set, each training sample in the training sample set includes historical operation data of the offshore wind turbine converter in a preset time window and historical fault information of the offshore wind turbine converter, wherein the historical fault information includes historical fault type and historical fault time; The anomaly recognition model to be trained is trained based on the training sample set to obtain a trained anomaly recognition model.

5. The method according to claim 4, characterized in that The anomaly recognition model is generated based on a machine learning algorithm.

6. A fault detection device for an offshore wind turbine converter, characterized in that: include: An acquisition module, used to acquire operating data of an offshore wind turbine converter, wherein the operating data at least includes temperature data, current data, vibration data and voltage data; A prediction module, used to input the operation data into a trained abnormality recognition model, perform time series analysis through the abnormality recognition model, and obtain fault prediction parameters output by the abnormality recognition model, wherein the fault prediction parameters include a predicted fault type and a predicted fault time; A generating module is used to generate a maintenance plan for the offshore wind turbine converter based on the fault prediction parameters.

7. The device according to claim 6, characterized in that The generating module is further used for: generating fault warning information based on the fault prediction parameters; The fault alarm information and the maintenance plan are sent to the terminal device corresponding to the maintenance personnel.

8. An electronic device comprising: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

10. A computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 5.

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