An artificial intelligence-based wind power intelligent protection control system and method

Abstract

The application discloses an intelligent protection and control system and method for wind power based on artificial intelligence, and the method comprises the following steps: collecting the operation record, rated parameters and stress record of a wind driven generator, wherein the operation record comprises the main shaft vibration amplitude, environmental wind speed and actual output power of the wind driven generator; calculating the vibration characteristic value of the wind driven generator based on the main shaft vibration amplitude; obtaining the theoretical output value of the wind driven generator based on the rated parameters and environmental wind speed; calculating the output deviation value of the wind driven generator based on the actual output power and the theoretical output value; obtaining the damage value of the wind driven generator based on the stress record, summing up the damage value of the wind driven generator to obtain the cumulative damage value of the wind driven generator, and generating the maintenance decision of the wind driven generator; calculating the real-time comprehensive health value of the wind driven generator based on the vibration characteristic value, output deviation value and cumulative damage value, and generating the control decision of the wind driven generator; and dynamically adjusting the operation state to avoid unnecessary shutdown.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and in particular to an intelligent protection control system and method for wind power based on artificial intelligence. Background Technology

[0002] A wind farm is a site that generates electricity using wind energy. It typically consists of multiple wind turbines. Wind turbines utilize the kinetic energy of the wind, driving the turbine blades to rotate and transferring mechanical energy to the generator. This mechanical energy is then converted into electrical energy through electromagnetic induction. However, wind energy is a natural energy source that is intermittent and fluctuates. Changes in wind speed and direction can lead to unstable output power from wind turbines. As the scale of wind farms continues to expand, this instability can significantly impact the frequency and voltage stability of the power system. Therefore, the safety and stability of wind power generation systems are crucial to the reliable operation of the overall power system.

[0003] Due to the instability of wind conditions, the generator speed cannot be kept constant. Excessive wind speed can damage the generator by causing it to rotate too fast. Existing wind power protection systems use simple protection logic: when excessive wind speed or mechanical problems such as bearing overheating or blade damage occur, the wind turbine is directly shut down. This simple protection logic lacks the ability to make intelligent judgments and make dynamic adjustments, resulting in reduced production efficiency or unnecessary system shutdowns.

[0004] Therefore, there is an urgent need for an artificial intelligence-based intelligent protection and control method and system for wind power to solve the above problems. Summary of the Invention

[0005] In view of the above-mentioned prior art, the present invention provides a wind power intelligent protection and control method and system based on artificial intelligence, which mainly solves the technical problems existing in the background art.

[0006] To achieve the above objectives, the technical solution of this invention is implemented as follows:

[0007] In a first aspect, the present invention provides a wind power intelligent protection and control method based on artificial intelligence, comprising the following steps:

[0008] The operation records, rated parameters, and stress records corresponding to the time periods of the wind turbine are collected. The operation records include the main shaft vibration amplitude, ambient wind speed, and actual output power of the wind turbine.

[0009] The vibration characteristic value of the wind turbine is calculated based on the vibration amplitude of the main shaft;

[0010] Based on the rated parameters and the ambient wind speed, the theoretical output value of the wind turbine is obtained; based on the actual output power and the theoretical output value, the output deviation value of the wind turbine is calculated.

[0011] Based on the stress record, the damage value of the wind turbine is obtained. The damage values ​​of the wind turbine are summed to obtain the cumulative damage value of the wind turbine, and a maintenance decision for the wind turbine is generated.

[0012] Based on the vibration characteristic value, the output deviation value, and the cumulative damage value, the real-time comprehensive health value of the wind turbine is calculated, and the control decision of the wind turbine is generated.

[0013] Optionally, calculating the vibration characteristic value of the wind turbine based on the vibration amplitude of the main shaft includes:

[0014] Obtain a first dataset of main shaft vibration amplitude values, calculate the mean and standard deviation of the first dataset of main shaft vibration amplitude values, set a filtering range based on the mean and standard deviation to obtain a second dataset of main shaft vibration amplitude values, calculate the mean of the second dataset of main shaft vibration amplitude values, and obtain the vibration characteristic value of the wind turbine.

[0015] The expression for the filtering range is:

[0016] [A1-α×A2, A1+α×A2]

[0017] Where A1 represents the average value of the spindle vibration amplitude of all running records in the first dataset, A2 represents the standard deviation of the spindle vibration amplitude of all running records in the first dataset, and α represents the range parameter.

[0018] Optionally, the rated parameters include the wind speed at which the wind turbine starts, the wind speed at which it is rated, the wind speed at which it stops, and the rated power.

[0019] The process of obtaining the theoretical output value of the wind turbine based on the rated parameters and the ambient wind speed includes:

[0020] If the ambient wind speed is less than or equal to the starting wind speed, then the theoretical output value is zero;

[0021] If the ambient wind speed is greater than the starting wind speed and the ambient wind speed is less than or equal to the rated wind speed, then the theoretical output value of the wind turbine is calculated using the following formula:

[0022] P theo =P r ×{(V real -V in ) / (V r -V out )} 3

[0023] Among them, P theo P represents the theoretical output value of the wind turbine generator. r This indicates the rated power of the wind turbine generator, V. real V represents the ambient wind speed of the wind turbine generator. in V represents the starting wind speed of a wind turbine generator. r V represents the rated wind speed of a wind turbine generator. out This indicates the wind speed at which the wind turbine is shut down.

[0024] If the ambient wind speed is greater than the rated wind speed and the ambient wind speed is less than or equal to the shutdown wind speed, then the theoretical output value is the rated power.

[0025] If the ambient wind speed is greater than the shutdown wind speed, then the theoretical output value is zero;

[0026] The output deviation of the wind turbine is calculated based on the actual output power and the theoretical output value, and the calculation formula is as follows:

[0027] ΔP=P real -P theo

[0028] Where ΔP represents the output deviation value of the wind turbine, P real P represents the actual output power. theo This represents the theoretical output value of the wind turbine.

[0029] Optionally, the stress record includes stress amplitude and number of cycles;

[0030] The process of obtaining damage values ​​for the wind turbine based on the stress records, summing these damage values ​​to obtain the cumulative damage value, and generating maintenance decisions for the wind turbine includes:

[0031] The damage value of the wind turbine is calculated based on the stress amplitude and the number of cycles, using the following formula:

[0032] B1 = B2 / {β1 × (B3)} β 2}

[0033] Where B1 represents the damage value of the wind turbine, B2 represents the number of cycles, B3 represents the stress amplitude, β1 represents the damage constant, and β2 represents the damage index.

[0034] Obtain the stress record at the current moment and all stress records before the current moment to obtain the third stress record dataset; sum the damage values ​​of the wind turbine at each moment in the third stress record dataset to obtain the cumulative damage value;

[0035] Obtain all cumulative damage values, arrange all cumulative damage values ​​in chronological order to obtain a time series, construct a neural network model, train the neural network model, and obtain a trained neural network model.

[0036] The trained neural network model is used to predict the cumulative damage value of the wind turbine, and the predicted cumulative damage value of the wind turbine is output. A cumulative damage threshold is set, and the predicted cumulative damage value is compared with the cumulative damage threshold. If the predicted cumulative damage value of the wind turbine is less than or equal to the cumulative damage threshold, the maintenance decision of the wind turbine is to not trigger the maintenance warning.

[0037] If the predicted cumulative damage value is greater than or equal to the cumulative damage threshold, the generated maintenance decision for the wind turbine is to trigger a maintenance warning and inspect and maintain the wind turbine.

[0038] Optionally, generating control decisions for the wind turbine based on the vibration characteristic value, the output deviation value, and the cumulative damage value includes:

[0039] The real-time comprehensive health value of the wind turbine at the current moment is calculated using the following formula:

[0040] C t =γ1×A+γ2×B+γ3×ΔP

[0041] Among them, C t γ1 represents the overall health value at time t, A represents the vibration characteristic value, B represents the cumulative damage value, γ1 represents the vibration weighting coefficient, γ2 represents the damage weighting coefficient, γ3 represents the deviation weighting coefficient, and ΔP represents the output deviation value.

[0042] Set a first threshold and a second threshold, if C t If the value is less than or equal to the first threshold, the control decision for the generated wind turbine is to allow the wind turbine to operate normally without any control measures.

[0043] If C t Greater than the first threshold and C t If the value is less than or equal to the second threshold, the resulting control decision for the wind turbine is to reduce the load on the wind turbine, lowering its actual output power so that the actual output power is less than or equal to the rated power. The expression is:

[0044] P real ≤P r ×(1-δ)

[0045] Among them, P real P represents the actual output power of the wind turbine. rThis represents the rated power of the wind turbine, and δ represents the derating factor.

[0046] If C t If the value exceeds the second threshold, the control decision for the generated wind turbine will be shutdown protection.

[0047] Secondly, the present invention also provides an intelligent protection and control system for wind power based on artificial intelligence, the system comprising: a data acquisition module, a calculation module, a health assessment module and a decision-making module;

[0048] The acquisition module is used to collect the operating data of the wind turbine and the stress data for the corresponding time period, and store the data in the database;

[0049] The calculation module is used to calculate the vibration characteristic value and output deviation value of the wind turbine based on the collected operating data;

[0050] The health assessment module is used to assess the health status of the wind turbine. By calculating the cumulative damage value and comprehensive health value of the wind turbine, the overall health status of the wind turbine is determined.

[0051] The decision-making module is used to predict the cumulative damage value of the wind turbine based on the processing results of historical and real-time data, and generate maintenance decisions based on the prediction results. At the same time, it generates control decisions based on the real-time comprehensive health value of the wind turbine.

[0052] Optionally, the acquisition module includes a running data acquisition unit and a stress data acquisition unit;

[0053] The operation data acquisition unit is used to collect the operation data of the wind turbine and store it in the database; the operation data includes the main shaft vibration amplitude, ambient wind speed and actual output power;

[0054] The stress data acquisition unit is used to collect stress data of the wind turbine and store it in the database; the stress data includes stress amplitude and number of cycles.

[0055] Optionally, the calculation module includes a vibration characteristic calculation unit and an output deviation calculation unit.

[0056] The vibration characteristic calculation unit is used to calculate the mean and standard deviation of the spindle vibration amplitude based on the selected operating records, and obtain the vibration characteristic value.

[0057] The output deviation calculation is used to calculate the output deviation value based on the difference between the actual output power and the theoretical output power of the wind turbine.

[0058] Optionally, the health assessment module includes a damage calculation unit and a health calculation unit;

[0059] The damage calculation unit is used to calculate the damage value of each stress record, and sum the damage values ​​corresponding to all stress records to obtain the cumulative damage value of the wind turbine.

[0060] The health calculation unit is used to calculate the comprehensive health value of the wind turbine based on the vibration characteristic value, output deviation value, and cumulative damage value, and to assess the overall health status of the wind turbine.

[0061] Optionally, the decision-making module includes a maintenance decision-making unit and a control decision-making unit;

[0062] The maintenance decision unit is used to train a neural network model to predict the cumulative damage value based on the cumulative damage value of the wind turbine, and generate maintenance decisions.

[0063] The control decision unit is used to generate control decisions based on the comprehensive health value calculated in real time, and to provide intelligent protection for the wind turbine.

[0064] The beneficial effects of this invention are as follows: This invention provides an intelligent protection and control method for wind power based on artificial intelligence. By collecting real-time operating data and stress records of wind turbines and combining this with a neural network model to predict the cumulative damage value of the wind turbines, it monitors their operating status in real time, generates maintenance decisions promptly, reduces over-maintenance or delayed maintenance, and effectively extends the service life of the equipment. Furthermore, by calculating the comprehensive health value of the wind turbine units, it can intelligently judge and dynamically adjust the operating status, thereby avoiding unnecessary shutdowns and improving the operating efficiency of the wind power system. Additionally, by optimizing the protection strategy of the wind turbine units, it ensures that appropriate control measures are taken during high wind speeds or equipment failures, avoiding losses caused by excessive shutdowns or unstable operation, improving the stability, safety, and economy of the wind farm, and enhancing the overall operating efficiency of the wind power system. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating an artificial intelligence-based intelligent protection and control method for wind power provided in an embodiment of the present invention.

[0066] Figure 2 This is a schematic diagram of an artificial intelligence-based intelligent protection and control system for wind power provided in an embodiment of the present invention. Detailed Implementation

[0067] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. In the following description, the expression "some embodiments" refers to a subset of all possible embodiments; however, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0068] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0069] It should be understood that the present invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Furthermore, the terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0070] It should also be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0071] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0072] Example 1

[0073] Please refer to the attached document. Figure 1 This invention provides an intelligent protection and control method for wind power based on artificial intelligence, comprising the following steps:

[0074] The operation records, rated parameters, and stress records corresponding to the time periods of the wind turbine are collected. The operation records include the main shaft vibration amplitude, ambient wind speed, and actual output power of the wind turbine.

[0075] The vibration characteristic value of the wind turbine is calculated based on the vibration amplitude of the main shaft;

[0076] Based on the rated parameters and the ambient wind speed, the theoretical output value of the wind turbine is obtained; based on the actual output power and the theoretical output value, the output deviation value of the wind turbine is calculated.

[0077] Based on the stress record, the damage value of the wind turbine is obtained. The damage values ​​of the wind turbine are summed to obtain the cumulative damage value of the wind turbine, and a maintenance decision for the wind turbine is generated.

[0078] Specifically, vibration characteristic values ​​are important indicators of the operating status of wind turbines. Changes in vibration characteristic values ​​can reflect abnormal conditions of mechanical components, such as bearing wear, blade imbalance, or gearbox failure. By analyzing the characteristic values ​​of vibration signals, fault diagnosis and health monitoring of wind turbines can be achieved. Output deviation values ​​reflect the difference between the actual output power of a wind turbine and its theoretical or design power, and are an important indicator for evaluating the performance and control effectiveness of wind turbine units. By analyzing output deviation values, the control strategy of the wind turbine can be adjusted, such as pitch angle control or speed regulation, to achieve maximum power capture. Damage values ​​are indicators of the fatigue degree of wind turbine components. By calculating cumulative damage values, the health status of wind power generation equipment in a wind farm can be assessed. By predicting the degree of damage in advance, sudden failures can be avoided, and corresponding strategies can be developed to reduce maintenance costs and downtime.

[0079] Therefore, based on vibration characteristic values, output deviation values, and cumulative damage values, the real-time comprehensive health value of the wind turbine is calculated, and control decisions for the wind turbine are generated to achieve comprehensive monitoring and optimization of the wind power generation system and improve the overall operating efficiency of the wind farm.

[0080] As an optional implementation, the calculation of the vibration characteristic value of the wind turbine based on the main shaft vibration amplitude includes:

[0081] Obtain a first dataset of main shaft vibration amplitude values, calculate the mean and standard deviation of the first dataset of main shaft vibration amplitude values, set a filtering range based on the mean and standard deviation to obtain a second dataset of main shaft vibration amplitude values, calculate the mean of the second dataset of main shaft vibration amplitude values, and obtain the vibration characteristic value of the wind turbine.

[0082] The expression for the filtering range is:

[0083] [A1-α×A2, A1+α×A2]

[0084] Where A1 represents the average value of the spindle vibration amplitude of all running records in the first dataset, A2 represents the standard deviation of the spindle vibration amplitude of all running records in the first dataset, and α represents the range parameter;

[0085] Specifically, by filtering the first dataset of acquired main shaft vibration amplitude values, outliers or noisy data can be removed, which helps to improve the accuracy and reliability of the data and enables basic monitoring of the wind turbine's operating status. This ensures that subsequent analysis can be based on accurate data, avoids misjudgments caused by abnormal data, and provides basic data for subsequent wind turbine health assessments.

[0086] As an optional implementation, the rated parameters include the wind turbine's starting wind speed, rated wind speed, stopping wind speed, and rated power.

[0087] The process of obtaining the theoretical output value of the wind turbine based on the rated parameters and the ambient wind speed includes:

[0088] If the ambient wind speed is less than or equal to the starting wind speed, then the theoretical output value is zero;

[0089] If the ambient wind speed is greater than the starting wind speed and the ambient wind speed is less than or equal to the rated wind speed, then the theoretical output value of the wind turbine is calculated using the following formula:

[0090] P theo =P r ×{(V real -V in ) / (V r -V out )} 3

[0091] Among them, P theo P represents the theoretical output value of the wind turbine generator. r This indicates the rated power of the wind turbine generator, V. real V represents the ambient wind speed of the wind turbine generator. in V represents the starting wind speed of a wind turbine generator. r V represents the rated wind speed of a wind turbine generator.out This indicates the wind speed at which the wind turbine is shut down.

[0092] If the ambient wind speed is greater than the rated wind speed and the ambient wind speed is less than or equal to the shutdown wind speed, then the theoretical output value is the rated power.

[0093] If the ambient wind speed is greater than the shutdown wind speed, then the theoretical output value is zero;

[0094] The output deviation of the wind turbine is calculated based on the actual output power and the theoretical output value, and the calculation formula is as follows:

[0095] ΔP=P real -P theo

[0096] Where ΔP represents the output deviation value of the wind turbine, P real P represents the actual output power. theo This represents the theoretical output value of the wind turbine.

[0097] Specifically, based on the relationship between wind speed and output power, the theoretical output value is determined, and then the output deviation value is determined. The output deviation value can reflect the difference between the actual output and the theoretical output of the wind turbine, thus providing a basis for the subsequent health assessment of the wind turbine, and providing an accurate benchmark for whether the wind turbine is within the normal operating range, providing a basis for decision-making for the control and maintenance of the wind farm.

[0098] As an optional implementation, the stress record includes stress amplitude and number of cycles;

[0099] The process of obtaining damage values ​​for the wind turbine based on the stress records, summing these damage values ​​to obtain the cumulative damage value, and generating maintenance decisions for the wind turbine includes:

[0100] The damage value of the wind turbine is calculated based on the stress amplitude and the number of cycles, using the following formula:

[0101] B1 = B2 / {β1 × (B3)} β 2}

[0102] Where B1 represents the damage value of the wind turbine, B2 represents the number of cycles, B3 represents the stress amplitude, β1 represents the damage constant, and β2 represents the damage index.

[0103] Specifically, each time stress data is collected, a stress record of the wind turbine is generated and stored in the database. That is, each stress data collection corresponds to the stress data collection at each moment. The collection of stress data and the collection of operational data are carried out simultaneously, so that the collected stress data corresponds to the time period of the operational data.

[0104] For example, the damage constant β1 of the material used in a certain wind turbine is 1 × 10⁻⁶. 8 Given a damage index β2 = -3, and a stress amplitude B3 = 100 and cycle count B2 = 1000 for a certain stress record of the wind turbine, calculate the damage value of the wind turbine corresponding to this stress record using the formula B1 = 1 × 10⁻⁶. -11 ;

[0105] The damage constant is directly related to the static strength and damage of a material. It reflects the service life of the material under high stress amplitude. The longer the service life of the material, the larger the value of β1. The damage index is usually a negative value, indicating that the fatigue life of the material decreases rapidly with the increase of stress amplitude. By calculating the damage value, we can understand the overall performance and component life of the wind turbine, which can provide a basis for subsequent control decisions of the wind turbine.

[0106] Obtain the stress record at the current moment and all stress records before the current moment to obtain the third stress record dataset; sum the damage values ​​of the wind turbine at each moment in the third stress record dataset to obtain the cumulative damage value;

[0107] For example, the third dataset of stress records contains three stress records, and the damage value of the wind turbine corresponding to each stress record is 1×10. -11 2.37×10 -12 and 6.25×10 -13 The damage value of the wind turbine corresponding to each stress record in the third stress record dataset is summed to obtain 1×10. -11 +2.37×10 -12 +6.25×10 -13 =1.61×10 -11 That is: 1.61 × 10 -11 This represents the cumulative damage value of the wind turbine.

[0108] Obtain all cumulative damage values, arrange all cumulative damage values ​​in chronological order to obtain a time series, construct a neural network model, train the neural network model, and obtain a trained neural network model.

[0109] The trained neural network model is used to predict the cumulative damage value of the wind turbine, and the predicted cumulative damage value of the wind turbine is output. A cumulative damage threshold is set, and the predicted cumulative damage value is compared with the cumulative damage threshold. If the predicted cumulative damage value of the wind turbine is less than or equal to the cumulative damage threshold, the maintenance decision of the wind turbine is to not trigger the maintenance warning.

[0110] If the predicted cumulative damage value is greater than or equal to the cumulative damage threshold, the generated maintenance decision for the wind turbine is to trigger a maintenance warning and inspect and maintain the wind turbine.

[0111] Specifically, after acquiring all cumulative damage values, they are arranged chronologically to obtain a time series. A neural network model is then constructed, using the cumulative damage value corresponding to the last time point in the sorted time series as the output and the cumulative damage values ​​corresponding to other time points in the time series as the input to train the neural network model. By arranging the cumulative damage values ​​chronologically and using them as input, the neural network model can better capture the time dependence in the data and learn the pattern of damage value changes over time, thereby improving the accuracy of predicting future damage values. For example, the neural network model can use an RNN model or a Transformer model. The predicted cumulative damage value is then compared with a set cumulative damage threshold. If the predicted cumulative damage value exceeds the threshold, a maintenance warning is triggered, reminding maintenance personnel to take appropriate measures in a timely manner to avoid accidents. This enables the prediction of potential structural damage to wind turbines, helps to identify potential failure risks in advance, and provides data support for maintenance decisions.

[0112] As an optional implementation, generating control decisions for the wind turbine based on the vibration characteristic value, the output deviation value, and the cumulative damage value includes:

[0113] The real-time comprehensive health value of the wind turbine at the current moment is calculated using the following formula:

[0114] C t =γ1×A+γ2×B+γ3×ΔP

[0115] Among them, C t γ1 represents the overall health value at time t, A represents the vibration characteristic value, B represents the cumulative damage value, γ1 represents the vibration weighting coefficient, γ2 represents the damage weighting coefficient, γ3 represents the deviation weighting coefficient, and ΔP represents the output deviation value.

[0116] Set a first threshold and a second threshold, where the first threshold is less than the second threshold. If C tIf the value is less than or equal to the first threshold, the control decision for the generated wind turbine is to allow the wind turbine to operate normally without any control measures.

[0117] If C t Greater than the first threshold and C t If the value is less than or equal to the second threshold, the resulting control decision for the wind turbine is to reduce the load on the wind turbine, lowering its actual output power so that the actual output power is less than or equal to the rated power. The expression is:

[0118] P real ≤P r ×(1-δ)

[0119] Among them, P real P represents the actual output power of the wind turbine. r This represents the rated power of the wind turbine, and δ represents the derating factor.

[0120] Specifically, wind turbines operate in complex natural environments and are affected by many uncontrollable factors such as random winds, wind shear, and external power grid disturbances, inevitably bearing enormous loads. In the control system of wind turbine generator sets, the speed of the wind turbine is closely related to the output power. Controlling the generator speed is one of the common ways to adjust the power. Most wind turbines use constant speed or variable speed control technology. Under load reduction, reducing the generator speed can reduce the output power. Power control can also be achieved by adjusting the blade's angle of attack, such as using the method of wind turbine pitch control. Wind turbine pitch control changes the angle between the wind turbine blades and the wind direction, thereby reducing the impact of wind on the rotor and thus reducing the power output of the wind turbine generator.

[0121] It should be noted that the main purpose of wind turbine load reduction is to reduce mechanical load and protect the wind power generation equipment from damage; while reducing the output power of the wind turbine is more to respond to grid demand or protect the equipment from overload. In some cases, the purpose of load reduction can be achieved by reducing the output power.

[0122] If C t If the value exceeds the second threshold, the control decision for the generated wind turbine will be shutdown protection.

[0123] Specifically, shutdown protection refers to the automatic or manual shutdown of the wind turbine when it encounters certain abnormal conditions or reaches preset protection conditions. Shutdown protection can prevent the wind turbine from continuing to operate under abnormal conditions, thereby avoiding equipment damage due to overload, overheating or excessive vibration. If the comprehensive health value exceeds the second threshold, shutdown protection can maintain the safety of the power grid.

[0124] By comprehensively assessing the health status of wind turbines and automatically generating control decisions, the operating mode can be adjusted in real time to ensure the safety and efficiency of wind turbines, avoid excessive wear and failure, and achieve intelligent protection for wind turbines.

[0125] Example 2

[0126] Please refer to the attached document. Figure 2 This invention provides an intelligent protection and control system for wind power based on artificial intelligence, comprising: a data acquisition module, a calculation module, a health assessment module, and a decision-making module;

[0127] The acquisition module is used to collect the operating data of the wind turbine and the stress data for the corresponding time period, and store the data in the database;

[0128] The calculation module is used to calculate the vibration characteristic value and output deviation value of the wind turbine based on the collected operating data;

[0129] The health assessment module is used to assess the health status of the wind turbine. By calculating the cumulative damage value and comprehensive health value of the wind turbine, the overall health status of the wind turbine is determined.

[0130] The decision-making module is used to predict the cumulative damage value of the wind turbine based on the processing results of historical and real-time data, and generate maintenance decisions based on the prediction results. At the same time, it generates control decisions based on the real-time comprehensive health value of the wind turbine.

[0131] As an optional implementation, the acquisition module includes a running data acquisition unit and a stress data acquisition unit;

[0132] The operation data acquisition unit is used to collect the operation data of the wind turbine and store it in the database; the operation data includes the main shaft vibration amplitude, ambient wind speed and actual output power;

[0133] The stress data acquisition unit is used to collect stress data of the wind turbine and store it in the database; the stress data includes stress amplitude and number of cycles.

[0134] As an optional implementation, the calculation module includes a vibration characteristic calculation unit and an output deviation calculation unit.

[0135] The vibration characteristic calculation unit is used to calculate the mean and standard deviation of the spindle vibration amplitude based on the selected operating records, and obtain the vibration characteristic value.

[0136] The output deviation calculation is used to calculate the output deviation value based on the difference between the actual output power and the theoretical output power of the wind turbine.

[0137] As an optional implementation, the health assessment module includes a damage calculation unit and a health calculation unit;

[0138] The damage calculation unit is used to calculate the damage value of each stress record, and sum the damage values ​​corresponding to all stress records to obtain the cumulative damage value of the wind turbine.

[0139] The health calculation unit is used to calculate the comprehensive health value of the wind turbine based on the vibration characteristic value, output deviation value, and cumulative damage value, and to assess the overall health status of the wind turbine.

[0140] As an optional implementation, the decision-making module includes a maintenance decision-making unit and a control decision-making unit;

[0141] The maintenance decision unit is used to train a neural network model to predict the cumulative damage value based on the cumulative damage value of the wind turbine, and generate maintenance decisions.

[0142] The control decision unit is used to generate control decisions based on the comprehensive health value calculated in real time, and to provide intelligent protection for the wind turbine.

[0143] Specifically, the acquisition module is equipped with corresponding sensors and includes an operating data unit and a stress data unit. These units collect real-time operating and stress data from the wind turbine and store the collected data in a central dataset. The calculation module includes a vibration characteristic calculation unit and an output deviation calculation unit. Based on the data collected by the acquisition module, these units calculate vibration characteristic values ​​and output deviation values. The health assessment module includes a damage calculation unit and a health calculation unit. These units calculate cumulative damage values ​​and comprehensive health values, respectively. The decision-making module includes a maintenance decision unit and a control decision unit. These units generate maintenance and control decisions for the wind turbine, thereby achieving intelligent protection of the wind turbine.

[0144] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An artificial intelligence-based intelligent protection control method for wind power, characterized in that, The method comprises the following steps: collecting operation records, rated parameters, and stress records corresponding to a time period of the operation records of a wind turbine, the operation records comprising a main shaft vibration amplitude, an ambient wind speed, and an actual output power of the wind turbine; calculating a vibration characteristic value of the wind turbine based on the main shaft vibration amplitude; obtaining a theoretical output value of the wind turbine based on the rated parameters and the ambient wind speed; calculating an output deviation value of the wind turbine based on the actual output power and the theoretical output value; obtaining a damage value of the wind turbine based on the stress records, summing up the damage values of the wind turbine to obtain a cumulative damage value of the wind turbine, and generating a maintenance decision for the wind turbine, wherein the stress records comprise a stress amplitude and a cycle number; calculating a real-time comprehensive health value of the wind turbine based on the vibration characteristic value, the output deviation value, and the cumulative damage value, and generating a control decision for the wind turbine; wherein the step of obtaining the damage value of the wind turbine based on the stress records, summing up the damage values of the wind turbine to obtain the cumulative damage value of the wind turbine, and generating the maintenance decision for the wind turbine comprises: calculating the damage value of the wind turbine according to the stress amplitude and the cycle number, and the calculation formula is: B1 = B2 / {β1 x (B3) β 2} wherein B1 represents the damage value of the wind turbine, B2 represents the cycle number, B3 represents the stress amplitude, β1 represents a damage constant, and β2 represents a damage index; obtaining stress records at a current time and all stress records before the current time to obtain a third data set of stress records; summing up the damage values of the wind turbine at each time in the third data set of stress records to obtain the cumulative damage value; arranging all the cumulative damage values in chronological order to obtain a time sequence, constructing a neural network model, training the neural network model, and obtaining a trained neural network model; using the trained neural network model to predict the cumulative damage value of the wind turbine, outputting a predicted cumulative damage value of the wind turbine, setting a cumulative damage threshold, comparing the predicted cumulative damage value with the cumulative damage threshold, and if the predicted cumulative damage value of the wind turbine is less than or equal to the cumulative damage threshold, generating a maintenance decision for the wind turbine that does not trigger a maintenance warning; if the predicted cumulative damage value is greater than or equal to the cumulative damage threshold, generating a maintenance decision for the wind turbine that triggers a maintenance warning, and checking and maintaining the wind turbine.

2. The wind power intelligent protection control method based on artificial intelligence according to claim 1, characterized in that, The step of calculating the vibration characteristic value of the wind turbine based on the main shaft vibration amplitude comprises: obtaining a first data set of main shaft vibration amplitudes, calculating the mean and standard deviation of the first data set of main shaft vibration amplitudes, setting a screening range according to the mean and standard deviation to obtain a second data set of main shaft vibration amplitudes, calculating the mean of the second data set of main shaft vibration amplitudes, and obtaining the vibration characteristic value of the wind turbine; the expression of the screening range is: [A1-α×A2, A1+α×A2] Wherein, A1 represents the average value of the main shaft vibration amplitude of all running records in the first data set, A2 represents the standard deviation of the main shaft vibration amplitude of all running records in the first data set, and a represents a range parameter.

3. The wind power intelligent protection control method based on artificial intelligence according to claim 1, characterized in that, The rated parameters include a start-up wind speed, a rated wind speed, a shutdown wind speed and a rated power of the wind turbine. The theoretical output value of the wind turbine is obtained based on the rated parameters and the environmental wind speed, including: If the environmental wind speed is less than or equal to the start-up wind speed, the theoretical output value is zero; If the environmental wind speed is greater than the start-up wind speed and less than or equal to the rated wind speed, the theoretical output value of the wind turbine is calculated, and the calculation formula is: P theo =P r ×{(V real -V in ) / (V r -V out )} 3 wherein P theo represents the theoretical output value of the wind power generator, P r represents the rated power of the wind power generator, V real represents the ambient wind speed of the wind power generator, V in represents the cut-in wind speed of the wind power generator, V r represents the rated wind speed of the wind power generator, V out represents the cut-out wind speed of the wind power generator; If the environmental wind speed is greater than the rated wind speed and less than or equal to the shutdown wind speed, the theoretical output value is the rated power; If the environmental wind speed is greater than the shutdown wind speed, the theoretical output value is zero; The output deviation value of the wind turbine is calculated based on the actual output power and the theoretical output value, and the calculation formula is: ΔP = P real - P theo wherein ΔP represents an output deviation value of the wind power generator, P real represents an actual output power, P theo represents a theoretical output value of the wind power generator.

4. The wind power intelligent protection control method based on artificial intelligence according to claim 3, characterized in that, The control decision of the wind turbine is generated based on the vibration characteristic value, the output deviation value and the cumulative damage value, including: The real-time comprehensive health value of the wind turbine at the current time is calculated, and the calculation formula is: C t = γ1x A + γ2x B + γ3x ΔP wherein C t represents the comprehensive health value at the current time t, A represents the vibration characteristic value, B represents the cumulative damage value, γ1 represents the vibration weight coefficient, γ2 represents the damage weight coefficient, γ3 represents the deviation weight coefficient, and ΔP represents the output deviation value. The first threshold and the second threshold are set, and if C t If C is less than or equal to the first threshold, the generated control decision of the wind turbine is normal operation of the wind turbine, and the wind turbine is not controlled. If C t greater than the first threshold value and C t less than or equal to the second threshold value, the generated control decision of the wind turbine is that the wind turbine is operated in a reduced load mode, and the actual output power of the wind turbine is reduced so as to be less than or equal to the rated power, and the expression is: P real ≤P r × (1 - δ) where P real represents the actual output power of the wind power generator, P r represents the rated power of the wind power generator, and δ represents a load reduction coefficient. If C t If the generated wind turbine control decision is greater than the second threshold value, the generated wind turbine control decision is a shutdown protection.

5. An artificial intelligence-based intelligent protection control system for wind power, which is used to implement an artificial intelligence-based intelligent protection control method for wind power according to claim 1, characterized in that, including: The acquisition module, the calculation module, the health evaluation module and the decision module; The acquisition module is used for acquiring the running data of the wind turbine, the stress data corresponding to the running data, and storing the data in the database; The calculation module is used for calculating the vibration characteristic value and the output deviation value of the wind turbine according to the acquired running data; The health evaluation module is used for evaluating the health condition of the wind turbine, calculating the cumulative damage value and the comprehensive health value of the wind turbine, and judging the overall health state of the wind turbine; The decision module is used for predicting the cumulative damage value of the wind turbine according to the processing results of the historical and real-time data, generating a maintenance decision according to the prediction result, and generating a control decision according to the real-time comprehensive health value of the wind turbine.

6. The wind power intelligent protection control system based on artificial intelligence according to claim 5, characterized in that, The acquisition module includes a running data acquisition unit and a stress data acquisition unit; The running data acquisition unit is used for acquiring the running data of the wind turbine and storing the data in the database; the running data includes the main shaft vibration amplitude, the environmental wind speed and the actual output power; The stress data acquisition unit is used for acquiring the stress data of the wind turbine and storing the data in the database; the stress data includes the stress amplitude and the cycle number.

7. The wind power intelligent protection control system based on artificial intelligence according to claim 5, characterized in that, The calculation module includes a vibration characteristic calculation unit and an output deviation calculation unit The vibration characteristic calculation unit is used for calculating the mean value and the standard deviation of the main shaft vibration amplitude according to the screened running records to obtain the vibration characteristic value; The output deviation calculation is used for calculating the output deviation value according to the difference between the actual output power and the theoretical output power of the wind turbine.

8. The wind power intelligent protection control system based on artificial intelligence according to claim 5, characterized in that, The health evaluation module includes a damage calculation unit and a health calculation unit; The damage calculation unit is used for calculating the damage value of each stress record, summing up the damage values corresponding to all stress records to obtain the cumulative damage value of the wind turbine; The health computing unit is configured to calculate a comprehensive health value of the wind turbine according to the vibration characteristic value, the output deviation value and the cumulative damage value, and evaluate the overall health state of the wind turbine.

9. The wind power intelligent protection control system based on artificial intelligence according to claim 5, characterized in that, The decision module comprises a maintenance decision unit and a control decision unit. The maintenance decision unit is configured to train a neural network model to predict the cumulative damage value according to the cumulative damage value of the wind turbine, and generate a maintenance decision. The control decision unit is configured to generate a control decision according to the comprehensive health value calculated in real time, and intelligently protect the wind turbine.

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