A method and system for operation analysis based on a doubly-fed motor model

Abstract

The application provides a method and system for operation analysis based on a double-fed motor model. The method comprises the following steps: obtaining first characteristic frequency value information of a reference double-fed motor and first acquisition time information corresponding to the first characteristic frequency value information; obtaining second characteristic frequency value information corresponding to a plurality of motor models to be evaluated; obtaining second acquisition time information corresponding to each second characteristic frequency value information; generating characteristic frequency mean value information based on the same second acquisition time information and the plurality of second characteristic frequency value information; and determining target correction time information based on the first characteristic frequency value information and the characteristic frequency mean value information. The application can accurately identify the key time node of the motor model that needs manual correction, realize the transition from fixed cycle operation and maintenance to state-driven on-demand intervention, effectively guarantee the accuracy of the simulation results, and significantly improve the long-term reliability of the model operation.

Description

Technical Field

[0001] This application relates to the technical field of motor analysis, specifically to an operation analysis method and system based on a doubly fed motor model. Background Technology

[0002] The core function of doubly-fed induction generators (DFIGs) in hydropower plants is to achieve variable-speed, constant-frequency power generation, thereby significantly improving the operating efficiency of the turbine under different head and flow conditions. Traditional hydropower units must operate at a constant speed, and their efficiency drops significantly when hydraulic conditions change. DFIGs, however, allow for flexible speed adjustments through rotor excitation regulation to adapt to optimal operating conditions, while ensuring a constant output frequency. This improves water resource utilization and reduces unit vibration and wear, making them particularly suitable for run-of-river power plants and pumped-storage power plants where head changes frequently.

[0003] The main purpose of building a doubly-fed motor model is to provide an efficient and safe verification platform for system design and control strategies. Through accurate mathematical models and simulation environments, maintenance personnel can test the doubly-fed motor in a virtual environment.

[0004] Currently, the application of doubly fed motor models still lacks a systematic simulation reliability analysis process. The fitting accuracy of the doubly fed motor model under actual working conditions has not been fully verified, making it difficult to detect and correct modeling deviations in a timely manner, resulting in low reliability. Further improvements are needed. Summary of the Invention

[0005] The embodiments of this application aim to at least solve one of the technical problems existing in the prior art, and provide an operation analysis method and system based on a doubly fed motor model.

[0006] On the one hand, embodiments of this application provide an operation analysis method based on a doubly-fed motor model, the method comprising: Acquire the first characteristic frequency value information of the reference doubly fed motor and the first acquisition time information corresponding to the first characteristic frequency value information; acquire the second characteristic frequency value information corresponding to multiple motor models to be evaluated; and acquire the second acquisition time information corresponding to each of the second characteristic frequency value information. Based on the same second acquisition time information, the average feature frequency information is generated according to multiple second feature frequency value information. Based on the first characteristic frequency value information and the characteristic frequency mean information, the target correction time information is determined; wherein, the target correction time information is used to describe the time when the motor model to be evaluated needs to be corrected.

[0007] On the other hand, embodiments of this application provide an operation analysis system based on a doubly-fed motor model, the system comprising: Feature frequency value information acquisition module: used to acquire the first feature frequency value information of the reference doubly fed motor and the first acquisition time information corresponding to the first feature frequency value information, acquire the second feature frequency value information corresponding to multiple motor models to be evaluated, and acquire the second acquisition time information corresponding to each of the second feature frequency value information; Feature frequency mean information generation module: used to generate feature frequency mean information based on the same second acquisition time information and multiple second feature frequency value information; Target correction time information determination module: used to determine target correction time information based on the first characteristic frequency value information and the characteristic frequency mean information; wherein, the target correction time information is used to describe the time when the motor model to be evaluated needs to be corrected.

[0008] On the other hand, embodiments of this application also provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0009] On the other hand, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described above.

[0010] The embodiments of this application provide a method and system for operation analysis based on a doubly-fed motor model. The terminal device can first acquire the first characteristic frequency value information of the benchmark doubly-fed motor and the first acquisition time information corresponding to the first characteristic frequency value information, acquire the second characteristic frequency value information corresponding to multiple motor models to be evaluated, and acquire the second acquisition time information corresponding to each second characteristic frequency value information. Then, based on the same second acquisition time information, the average characteristic frequency information is accurately generated according to multiple second characteristic frequency values. Finally, the target correction time information is effectively determined based on the first characteristic frequency value information and the average characteristic frequency information. This enables accurate identification of key time nodes where the motor model needs manual correction, realizing the transformation from fixed-cycle operation and maintenance to state-driven on-demand intervention. While effectively ensuring the accuracy of simulation results, it significantly improves the long-term reliability of model operation and solves the problem of low reliability to a certain extent. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating an embodiment of the operation analysis method based on a doubly fed motor model according to this application. Figure 2 This is a flowchart illustrating step S300 in an operation analysis method based on a doubly fed motor model according to an embodiment of this application. Figure 3This is a flowchart illustrating the process after step S300 in an operation analysis method based on a doubly fed motor model according to an embodiment of this application. Figure 4 This is a flowchart illustrating the process after step S460 in an operation analysis method based on a doubly fed motor model according to an embodiment of this application. Figure 5 This is a flowchart illustrating the process after step S493 in an operation analysis method based on a doubly fed motor model according to an embodiment of this application. Figure 6 This is a block diagram of a system for analyzing operation based on a doubly fed motor model according to an embodiment of this application; Figure 7 This is a schematic diagram of a terminal device according to an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0014] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0015] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0016] Please see Figure 1 , Figure 1This is a flowchart illustrating the operation analysis method based on a doubly-fed motor model provided in this application embodiment. In this embodiment, the execution subject of the operation analysis method is a terminal device. It is understood that the types of terminal devices include, but are not limited to, tablet computers, laptops, Ultra-Mobile Personal Computers (UMPCs), netbooks, Personal Digital Assistants (PDAs), etc., and this application embodiment does not impose any restrictions on the specific type of terminal device.

[0017] Please see Figure 1 The operational analysis method provided in this application includes, but is not limited to, the following steps: In S100, the first characteristic frequency value information of the reference doubly fed motor and the first acquisition time information corresponding to the first characteristic frequency value information are obtained, the second characteristic frequency value information corresponding to multiple motor models to be evaluated is obtained, and the second acquisition time information corresponding to each second characteristic frequency value information is obtained.

[0018] Specifically, the terminal device can first acquire the first characteristic frequency value information of the reference doubly-fed motor and the first acquisition time information corresponding to the first characteristic frequency value information. Then, it can acquire the second characteristic frequency value information corresponding to multiple motor models to be evaluated, and acquire the second acquisition time information corresponding to each second characteristic frequency value information. Here, the reference doubly-fed motor is used to describe the real doubly-fed speed-regulating motor as a reference; the first characteristic frequency value information is used to describe the peak frequency of the real doubly-fed speed-regulating motor under no-load conditions; the first acquisition time information is used to describe the time when the first characteristic frequency value information is acquired; the motor model to be evaluated is used to describe the doubly-fed motor model constructed for the real doubly-fed speed-regulating motor; the second characteristic frequency value information is used to describe the peak frequency of the doubly-fed motor model under no-load conditions; and the second acquisition time information is used to describe the time when the second characteristic frequency value information is acquired.

[0019] In S200, based on the same second acquisition time information, the average value of the characteristic frequency is generated according to multiple second characteristic frequency values.

[0020] Specifically, after the terminal device acquires the first characteristic frequency value information, the first acquisition time information, multiple second characteristic frequency value information and multiple second acquisition time information, the terminal device can generate characteristic frequency average information based on the same second acquisition time information and the average value of the sum of multiple second characteristic frequency value information. The second characteristic frequency value information involved in calculating the average value is all from the same acquisition time.

[0021] In S300, the target correction time information is determined based on the first characteristic frequency value information and the characteristic frequency mean information.

[0022] Specifically, after the terminal device generates the characteristic frequency mean information, the terminal device can effectively determine the target correction time information based on the first characteristic frequency value information and the characteristic frequency mean information. This effectively determines the time when the motor model to be evaluated deviates from the benchmark doubly fed motor by the greatest probability, allowing maintenance personnel to intervene in a timely manner to correct and adjust the motor model to be evaluated. This is beneficial for ultimately optimizing the motor model that best fits the actual operating conditions of the benchmark doubly fed motor. The target correction time information is used to describe the time when the motor model to be evaluated needs to be corrected.

[0023] In some possible implementations, for determining the target correction time information, please refer to [link / reference needed]. Figure 2 Step S300 includes, but is not limited to, the following steps: In S310, characteristic frequency deviation information is generated based on the first characteristic frequency value information and the characteristic frequency mean information.

[0024] Specifically, after the terminal device generates the characteristic frequency mean information, the terminal device can generate the characteristic frequency deviation information based on the difference between the first characteristic frequency value information and the characteristic frequency mean information.

[0025] In S320, the characteristic frequency deviation information is compared with the preset compliance deviation information.

[0026] Specifically, after the terminal device generates the characteristic frequency deviation value information, the terminal device can compare the characteristic frequency deviation value information with the preset compliance deviation value information, where the compliance deviation value information is a value predefined by the operation and maintenance personnel.

[0027] In S330, if the characteristic frequency deviation information is greater than the compliance deviation information, simulation deviation result information is generated.

[0028] Specifically, if the characteristic frequency deviation information is greater than the compliance deviation information, the terminal device can generate simulation deviation result information, which describes that the simulation capability of the motor model to be evaluated has deviated from expectations.

[0029] In S340, based on the simulation deviation result information, the second acquisition time information of the second characteristic frequency value information corresponding to the characteristic frequency mean information is determined as the target correction time information.

[0030] Specifically, after the terminal device generates simulation deviation result information, the terminal device can determine the second acquisition time information of the second characteristic frequency value information corresponding to the average characteristic frequency information as the target correction time information based on the simulation deviation result information, thereby effectively determining the time when intervention correction is needed.

[0031] In some possible implementations, to help operations and maintenance personnel understand the degree of deviation and make targeted corrections, please refer to [link to relevant documentation]. Figure 3 After step S300, the method further includes, but is not limited to, the following steps: In S400, based on the preset sampling time interval value and the target correction time information, the first sampling time information and the second sampling time information are generated.

[0032] Specifically, after the terminal device determines the target correction time information, the terminal device can quickly generate first sampling time information and second sampling time information based on the preset sampling time interval value information and the target correction time information. The first sampling time information is earlier than the target correction time information, and the time interval between the first sampling time information and the target correction time information is the sampling time interval value information; the second sampling time information is later than the target correction time information, and the time interval between the second sampling time information and the target correction time information is the sampling time interval value information.

[0033] In S410, based on the first sampling time information, the second feature frequency value information and the first feature frequency value information are compared sequentially.

[0034] Specifically, after the terminal device generates the first sampling time information and the second sampling time information, the terminal device can compare the second feature frequency value information and the first feature frequency value information at the same sampling time in turn based on the first sampling time information.

[0035] In S420, if the difference between the second characteristic frequency value information and the first characteristic frequency value information is greater than the compliance deviation value information, then the motor model to be evaluated corresponding to the second characteristic frequency value information is determined to be the first deviation motor model.

[0036] Specifically, if the difference between the second characteristic frequency value information and the first characteristic frequency value information is greater than the compliance deviation value information, the terminal device can determine that the motor model to be evaluated corresponding to the second characteristic frequency value information is the first deviation motor model, wherein the first deviation motor model is used to describe the motor model that exhibits simulation deviation in the information at the first sampling time.

[0037] In S430, first deviation rate information is generated based on the number of first deviation motor models and the total number of motor models to be evaluated.

[0038] Specifically, after the terminal device determines the first deviation motor model, it can effectively generate the first deviation rate information by dividing the number of the first deviation motor models by the total number of motor models to be evaluated.

[0039] In S440, based on the second sampling time information, the first feature frequency value information and the second feature frequency value information are compared sequentially.

[0040] Specifically, after the terminal device generates the first deviation rate information, the terminal device can compare the first characteristic frequency value information and the second characteristic frequency value information at the same sampling time in turn based on the second sampling time information.

[0041] In S450, if the difference between the first characteristic frequency value information and the second characteristic frequency value information is greater than the compliance deviation value information, then the motor model to be evaluated corresponding to the second characteristic frequency value information is determined to be the second deviation motor model.

[0042] Specifically, if the difference between the first characteristic frequency value information and the second characteristic frequency value information is greater than the compliance deviation value information, the terminal device can determine that the motor model to be evaluated corresponding to the second characteristic frequency value information is the second deviation motor model. The second deviation motor model is used to describe the motor model that exhibits simulation deviation in the second sampling time information.

[0043] In S460, second deviation rate information is generated based on the number of second deviation motor models and the total number of motor models to be evaluated.

[0044] Specifically, after the terminal device determines the second deviation motor model, it can generate the second deviation rate information by dividing the number of second deviation motor models by the total number of motor models to be evaluated. This allows maintenance personnel to use more in-depth analysis results to understand the degree of deviation of the motor model and make targeted corrections to the motor model.

[0045] For further analysis of the motor model, please refer to the following for some possible implementations. Figure 4 After step S460, the method further includes, but is not limited to, the following steps: In S470, the first deviation growth rate is generated based on the difference between the second deviation rate information and the first deviation rate information.

[0046] Specifically, after the terminal device generates the second deviation rate information, the terminal device can generate the first deviation rate growth rate based on the difference between the second deviation rate information and the first deviation rate information.

[0047] In S480, the first deviation growth rate is compared with the preset growth rate threshold information.

[0048] Specifically, after the terminal device generates the first deviation growth rate, the terminal device can compare the first deviation growth rate with the preset growth rate threshold information, where the growth rate threshold information is a value predefined by the operation and maintenance personnel.

[0049] In S490, if the first deviation growth rate is greater than the growth rate threshold information, abnormal growth information is generated.

[0050] Specifically, if the first deviation growth rate is greater than the growth rate threshold information, the terminal device can generate abnormal growth information to determine that the deviation has become more severe.

[0051] In S491, if the first deviation growth rate is less than or equal to the growth rate threshold information, then multiple third sampling time information are generated based on multiple target correction time information and second sampling time information with different multiples.

[0052] Specifically, if the first deviation growth rate is less than or equal to the growth rate threshold information, the terminal device can generate multiple third sampling time information based on multiple target correction time information and second sampling time information of different multiples. For example, the terminal device can first generate the first third sampling time information based on the target correction time information and the second sampling time information of one multiple, with the time interval between the first third sampling time information and the second sampling time information being the target correction time information of one multiple. Then, it can generate the second third sampling time information based on the target correction time information and the second sampling time information of two multiples, with the time interval between the second third sampling time information and the second sampling time information being the target correction time information of two multiples. Then, it can generate the second third sampling time information based on the target correction time information and the second sampling time information of three multiples, with the time interval between the second third sampling time information and the second sampling time information being the target correction time information of three multiples. It should be noted that all the third sampling time information is later than the second sampling time information.

[0053] In S492, based on the information at each third sampling time, the third deviation rate information corresponding to each third sampling time is determined.

[0054] Specifically, after the terminal device generates multiple third sampling time information, the terminal device can determine the third deviation rate information corresponding to each third sampling time information based on each third sampling time information. The specific process of calculating the third deviation rate information can be referred to steps S400 to S460 above, so it will not be described in detail.

[0055] In S493, for each third deviation rate information: the second deviation growth rate is generated based on the difference between the third deviation rate information and the second deviation rate information.

[0056] Specifically, after the terminal device determines the third deviation rate information, the terminal device can perform the following processing for each third deviation rate information: generate the second deviation rate growth rate based on the difference between the third deviation rate information and the second deviation rate information.

[0057] In some possible implementations, to help operations and maintenance personnel identify the time points when the deviation becomes significantly more severe, please refer to [link / reference needed]. Figure 5 After step S493, the method further includes, but is not limited to, the following steps: In S494, the target abnormal growth rate is determined based on multiple second deviation growth rates.

[0058] Specifically, after the terminal device generates the second deviation growth rate, the terminal device can determine the target abnormal growth rate based on multiple second deviation growth rates, where the target abnormal growth rate is used to describe the maximum value among multiple second deviation growth rates; In S495, the third sampling time information corresponding to the target abnormal growth rate is determined as the important correction time information.

[0059] Specifically, after the terminal device determines the target abnormal growth rate, the terminal device can determine the third sampling time information corresponding to the target abnormal growth rate as the important correction time information. The important correction time information serves as the time node when the deviation of the motor model simulation worsens.

[0060] The implementation principle of the operation analysis method based on the doubly fed motor model in this application embodiment is as follows: The terminal device can first obtain the first characteristic frequency value information of the reference doubly fed motor and the first acquisition time information corresponding to the first characteristic frequency value information, obtain the second characteristic frequency value information corresponding to multiple motor models to be evaluated, and obtain the second acquisition time information corresponding to each second characteristic frequency value information. Then, based on the same second acquisition time information, according to multiple second characteristic frequency value information, the average characteristic frequency information is accurately generated. Finally, based on the first characteristic frequency value information and the average characteristic frequency information, the target correction time information is effectively determined, thereby effectively determining the time node when the operation and maintenance personnel need to intervene to correct the motor model, greatly improving the simulation accuracy and reliability.

[0061] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0062] Embodiments of this application also provide an operation analysis system based on a doubly-fed motor model. For ease of explanation, only the parts relevant to this application are shown, such as... Figure 6 As shown, the system 60 includes: Feature frequency value information acquisition module 61: used to acquire the first feature frequency value information of the reference doubly fed motor and the first acquisition time information corresponding to the first feature frequency value information, acquire the second feature frequency value information corresponding to multiple motor models to be evaluated, and acquire the second acquisition time information corresponding to each second feature frequency value information; Feature frequency mean information generation module 62: used to generate feature frequency mean information based on the same second acquisition time information and multiple second feature frequency value information; Target correction time information determination module 63: used to determine target correction time information based on the first characteristic frequency value information and the characteristic frequency mean information; wherein, the target correction time information is used to describe the time when the motor model to be evaluated needs to be corrected.

[0063] Optionally, the target correction time information determination module 63 mentioned above includes: Feature frequency deviation information generation submodule: used to generate feature frequency deviation information based on the first feature frequency value information and the feature frequency mean information; Feature frequency deviation information comparison submodule: used to compare feature frequency deviation information with preset compliance deviation information; Simulation deviation result information generation submodule: used to generate simulation deviation result information if the characteristic frequency deviation value is greater than the compliance deviation value. The target correction time information determination submodule is used to determine the second acquisition time information of the second characteristic frequency value information corresponding to the characteristic frequency mean information as the target correction time information based on the simulation deviation result information.

[0064] Optionally, the system 60 also includes: The sampling time information generation module is used to generate first sampling time information and second sampling time information based on preset sampling time interval value information and target correction time information; wherein, the first sampling time information is earlier than the target correction time information, and the time interval between the first sampling time information and the target correction time information is the sampling time interval value information; the second sampling time information is later than the target correction time information, and the time interval between the second sampling time information and the target correction time information is the sampling time interval value information. First comparison module for feature frequency value information: used to compare each second feature frequency value information with the first feature frequency value information sequentially based on the first sampling time information; First Deviation Motor Model Determination Module: If the difference between the second characteristic frequency value information and the first characteristic frequency value information is greater than the compliance deviation value information, then the motor model to be evaluated corresponding to the second characteristic frequency value information is determined as the first deviation motor model. First Deviation Rate Information Generation Module: Used to generate first deviation rate information based on the number of first deviation motor models and the total number of motor models to be evaluated; The second comparison module for feature frequency value information is used to compare each first feature frequency value information and the second feature frequency value information sequentially based on the second sampling time information. The second deviation motor model determination module is used to determine the motor model to be evaluated corresponding to the second characteristic frequency value information as the second deviation motor model if the difference between the first characteristic frequency value information and the second characteristic frequency value information is greater than the compliance deviation value information. Second Deviation Rate Information Generation Module: Used to generate second deviation rate information based on the number of second deviation motor models and the total number of motor models to be evaluated.

[0065] Optionally, the system 60 also includes: First Deviation Growth Rate Generation Module: Used to generate the first deviation growth rate based on the difference between the second deviation rate information and the first deviation rate information; First Deviation Growth Rate Comparison Module: Used to compare the first deviation growth rate with the preset growth rate threshold information; Abnormal growth information generation module: used to generate abnormal growth information if the first deviation growth rate is greater than the growth rate threshold information; The third sampling time information generation module is used to generate multiple third sampling time information based on multiple target correction time information and second sampling time information with different multiples if the first deviation growth rate is less than or equal to the growth rate threshold information. The multiple third sampling time information are all later than the second sampling time information. The third deviation rate information determination module is used to determine the third deviation rate information corresponding to each third sampling time based on the information at each third sampling time. The second deviation growth rate generation module is used to generate a second deviation growth rate based on the difference between the third deviation rate information and the second deviation rate information for each third deviation rate information.

[0066] Optionally, the system 60 also includes: Target abnormal growth rate determination module: used to determine the target abnormal growth rate based on multiple second deviation growth rates; wherein, the target abnormal growth rate is used to describe the maximum value among multiple second deviation growth rates; Important Correction Time Information Determination Module: Used to determine the third sampling time information corresponding to the abnormal growth rate of the target as important correction time information.

[0067] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0068] This application also provides a terminal device, such as... Figure 7 As shown, the terminal device 70 of this embodiment includes: a processor 71, a memory 72, and a computer program 73 stored in the memory 72 and executable on the processor 71. When the processor 71 executes the computer program 73, it implements the steps in the above-described execution analysis method embodiment, for example... Figure 1 Steps S100 to S300 are shown; or, when processor 71 executes computer program 73, it implements the functions of each module in the above-described device, for example... Figure 6 The functions of modules 61 to 63 are shown.

[0069] The terminal device 70 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device, and includes, but is not limited to, a processor 71 and a memory 72. Those skilled in the art will understand that... Figure 7 This is merely an example of terminal device 70 and does not constitute a limitation on terminal device 70. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal device 70 may also include input / output devices, network access devices, buses, etc.

[0070] The processor 71 can be a central processing unit (CPU), or 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, etc.; the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0071] The memory 72 can be an internal storage unit of the terminal device 70, such as the hard disk or memory of the terminal device 70. The memory 72 can also be an external storage device of the terminal device 70, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device 70. Furthermore, the memory 72 can include both internal storage units and external storage devices of the terminal device 70. The memory 72 can also store computer program 73 and other programs and data required by the terminal device 70. The memory 72 can also be used to temporarily store data that has been output or will be output.

[0072] One embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0073] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the methods, principles and structures of this application should be covered within the scope of protection of this application.

Claims

1. A method for operational analysis based on a doubly-fed induction generator (DFIG) model, characterized in that, The method includes: Acquire the first characteristic frequency value information of the reference doubly fed motor and the first acquisition time information corresponding to the first characteristic frequency value information; acquire the second characteristic frequency value information corresponding to multiple motor models to be evaluated; and acquire the second acquisition time information corresponding to each of the second characteristic frequency value information. Based on the same second acquisition time information, the average feature frequency information is generated according to multiple second feature frequency value information. Based on the first characteristic frequency value information and the characteristic frequency mean information, the target correction time information is determined; wherein, the target correction time information is used to describe the time when the motor model to be evaluated needs to be corrected.

2. The method according to claim 1, characterized in that, The step of determining the target correction time information based on the first characteristic frequency value information and the characteristic frequency mean information includes: Based on the first feature frequency value information and the feature frequency mean information, feature frequency deviation information is generated; Compare the characteristic frequency deviation information with the preset compliance deviation information; If the characteristic frequency deviation information is greater than the compliance deviation information, then simulation deviation result information is generated; Based on the simulation deviation result information, the second acquisition time information of the second characteristic frequency value information corresponding to the characteristic frequency mean information is determined as the target correction time information.

3. The method according to claim 1, characterized in that, The method further includes, after determining the target correction time information based on the first feature frequency value information and the feature frequency mean information, generating first sampling time information and second sampling time information based on preset sampling time interval value information and the target correction time information; wherein, the first sampling time information is earlier than the target correction time information, and the time interval between the first sampling time information and the target correction time information is the sampling time interval value information; the second sampling time information is later than the target correction time information, and the time interval between the second sampling time information and the target correction time information is the sampling time interval value information; Based on the first sampling time information, the second feature frequency value information and the first feature frequency value information are compared sequentially; If the difference between the second characteristic frequency value information and the first characteristic frequency value information is greater than the compliance deviation value information, then the motor model to be evaluated corresponding to the second characteristic frequency value information is determined to be the first deviation motor model. Based on the number of the first deviation motor models and the total number of motor models to be evaluated, the first deviation rate information is generated; Based on the second sampling time information, the first feature frequency value information and the second feature frequency value information are compared sequentially. If the difference between the first characteristic frequency value information and the second characteristic frequency value information is greater than the compliance deviation value information, then the motor model to be evaluated corresponding to the second characteristic frequency value information is determined to be the second deviation motor model. The second deviation rate information is generated based on the number of second deviation motor models and the total number of motor models to be evaluated.

4. The method according to claim 3, characterized in that, The method further includes, after generating second deviation rate information based on the number of second deviation motor models and the total number of motor models to be evaluated, generating a first deviation growth rate based on the difference between the second deviation rate information and the first deviation rate information. Compare the first deviation growth rate with the preset growth rate threshold information; If the first deviation growth rate is greater than the growth rate threshold information, then abnormal growth information is generated; If the first deviation growth rate is less than or equal to the growth rate threshold information, then multiple third sampling time information are generated based on the target correction time information and the second sampling time information of multiple different multiples, wherein the multiple third sampling time information are all later than the second sampling time information; Based on the information at each of the third sampling times, the third deviation rate information corresponding to each of the third sampling times is determined; For each of the third deviation rate information, a second deviation growth rate is generated based on the difference between the third deviation rate information and the second deviation rate information.

5. The method according to claim 4, characterized in that, The method further includes, after generating a second deviation growth rate based on the difference between the third deviation rate information and the second deviation rate information for each of the third deviation rate information, determining a target abnormal growth rate based on a plurality of second deviation growth rates; wherein the target abnormal growth rate is used to describe the maximum value among the plurality of second deviation growth rates; The third sampling time information corresponding to the target abnormal growth rate is determined as the important correction time information.

6. An operation analysis system based on a doubly-fed induction generator (DFIG) model, characterized in that, The system includes: Feature frequency value information acquisition module: used to acquire the first feature frequency value information of the reference doubly fed motor and the first acquisition time information corresponding to the first feature frequency value information, acquire the second feature frequency value information corresponding to multiple motor models to be evaluated, and acquire the second acquisition time information corresponding to each of the second feature frequency value information; Feature frequency mean information generation module: used to generate feature frequency mean information based on the same second acquisition time information and multiple second feature frequency value information; Target correction time information determination module: used to determine target correction time information based on the first characteristic frequency value information and the characteristic frequency mean information; wherein, the target correction time information is used to describe the time when the motor model to be evaluated needs to be corrected.

7. The system according to claim 6, characterized in that, The target correction time information determination module also includes: Feature frequency deviation information generation submodule: used to generate feature frequency deviation information based on the first feature frequency value information and the feature frequency mean information; Feature frequency deviation information comparison submodule: used to compare the feature frequency deviation information with preset compliance deviation information; Simulation deviation result information generation submodule: used to generate simulation deviation result information if the characteristic frequency deviation value information is greater than the compliance deviation value information; The target correction time information determination submodule is used to determine the second acquisition time information of the second characteristic frequency value information corresponding to the average characteristic frequency information as the target correction time information based on the simulation deviation result information.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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