Wind power generation capacity prediction method and system considering operation state of fan equipment
By constructing wind turbine operating state characteristics and quantifying them into operating state coefficients, the theoretical power generation of wind turbines is corrected, thus solving the prediction deviation problem caused by changes in wind turbine operating state and improving the accuracy and applicability of wind power generation prediction.
Patent Information
- Application Number
- CN202511994556.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-03-20
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing wind power generation prediction methods fail to fully consider changes in the operating status of wind turbines due to factors such as mechanical wear, control deviations, or local faults during long-term operation, resulting in discrepancies between the prediction results and the actual power generation.
By collecting wind turbine operating status data, operating status characteristics are constructed and quantified into operating status coefficients, and the theoretical power generation of the wind turbine is corrected to reflect the impact of equipment operating status.
It improves the accuracy of wind power generation forecasting, enabling the differentiation of actual power generation capacity under different operating conditions under the same wind conditions, reducing forecast deviations, and enhancing the flexibility and stability of forecasts.
Smart Images
Figure CN121701413A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wind power generation prediction technology, and more specifically, to a method and system for predicting wind power generation that takes into account the operating status of wind turbine equipment. Background Technology
[0002] Wind power is an important form of renewable energy, and predicting the power generation of wind power plants is an important part of routine maintenance.
[0003] Existing wind power generation forecasting methods are typically based on meteorological data such as wind speed and direction. These methods generally assume that wind turbines are operating under ideal conditions, failing to adequately account for changes in operating status caused by factors such as mechanical wear, control deviations, or localized faults during long-term operation. When wind turbines are operating under non-ideal conditions, even with identical wind conditions, their actual power generation capacity may be significantly lower than the theoretical value, leading to discrepancies between the predicted and actual power generation.
[0004] Therefore, it is urgent to improve the wind turbine power generation prediction method so that it can take into account the operating status of wind turbine equipment on the basis of wind conditions prediction, so as to improve the accuracy of the prediction results. Summary of the Invention
[0005] The purpose of this invention is to provide a method and system for predicting wind power generation that takes into account the operating status of wind turbine equipment, which can improve the accuracy of the prediction results.
[0006] This invention is achieved through the following technical solution:
[0007] A method for predicting wind power generation considering the operating status of wind turbine equipment includes the following steps:
[0008] Multiple wind force data and multiple wind turbine operating status data are periodically collected within a time period;
[0009] Construct wind turbine operating status characteristics based on wind turbine operating status data;
[0010] The operating state characteristics of the fan are quantified to obtain the operating state coefficient of the fan, which is used to describe the degree to which the fan deviates from the ideal operating state;
[0011] Calculate the theoretical power generation of the wind turbines for the next time period based on wind data;
[0012] By correcting the theoretical power generation of the wind turbine using the operating state coefficient, the predicted power generation considering the influence of equipment operating state is obtained.
[0013] Preferably, the wind turbine operating status data includes vibration amplitude, vibration frequency, wind turbine bearing temperature, and yaw error;
[0014] The wind data includes wind speed and air density.
[0015] Preferably, the method for constructing wind turbine operating status characteristics based on wind turbine operating status data is as follows:
[0016] A time series sequence of wind turbine operating status data is constructed for each type of wind turbine operating status data.
[0017] Preferably, the method for quantifying the operating state characteristics of the wind turbine to obtain the operating state coefficient of the wind turbine is as follows:
[0018] Calculate the operating status data of each type of wind turbine in time. The mean and standard deviation within the range;
[0019] Based on the wind turbine operating status data described in the i-th type, in time... The operating state coefficient of the i-th type of wind turbine operating state data is calculated by the average value and standard deviation within the range, where the value of i is the number of all the wind turbine operating state data.
[0020] The wind turbine operating status coefficient is calculated based on the operating status coefficient of all the wind turbine operating status data.
[0021] Preferably, the method for calculating the operating state coefficient of the i-th type of wind turbine operating state data is as follows:
[0022] The deviation of the average value of the i-th type of wind turbine operating status data :
[0023] ;
[0024] Where max is the function for finding the maximum value. The average value of the operating status data of the i-th type of wind turbine is... This is a standard reference value for the average value of the i-th type of wind turbine operating status data, set based on experience. The preset deviation threshold for the average value of the i-th type of wind turbine operating state data from the standard reference value;
[0025] The degree of deviation of the standard deviation of the i-th type of wind turbine operating status data :
[0026] ;
[0027] Where max is the function for finding the maximum value. Let be the standard deviation of the i-th type of wind turbine operating status data. The standard reference value is the standard deviation of the i-th type of wind turbine operating status data, set based on experience. The standard deviation threshold of the i-th type of wind turbine operating status data from the standard reference value is set.
[0028] Calculate the operating state coefficient of the i-th type of wind turbine operating state data. :
[0029] .
[0030] Preferably, the method for calculating the wind turbine operating state coefficient based on the operating state coefficients of all the wind turbine operating state data is as follows:
[0031] ;
[0032] in, The operating state coefficient is N, where N is the total number of types of wind turbine operating state data.
[0033] Preferably, the method for calculating the theoretical power generation of the wind turbine in the next time period based on wind power data is as follows:
[0034] Predict time-series wind speed data for the next time period based on wind speed data from wind power data;
[0035] Calculate the average value of time-series wind speed data for the next time period. ;
[0036] Calculate the theoretical power generation of the wind turbine in the next time period. :
[0037] ;
[0038] in, Where is the air density, and A is the swept area of the wind turbine. This represents the wind energy utilization coefficient.
[0039] Preferably, the method for predicting the time-series wind speed data for the next time period is as follows:
[0040] Obtain time-series wind speed data up to at least one time period up to the current time, and form a time-series historical wind speed sequence;
[0041] The time-series wind speed data for the next time period is output through a long short-term memory network.
[0042] Preferably, the method for correcting the theoretical power generation of the wind turbine using the operating state coefficient is as follows:
[0043] ;
[0044] in, The corrected predicted power generation considering the influence of equipment operating conditions is given, where e is a natural constant. The operating state coefficient, The theoretical power generation of the wind turbine in the next time period is given. These are the coefficients obtained through fitting.
[0045] This invention also provides a wind power generation prediction system that considers the operating status of wind turbine equipment, applied to the aforementioned wind power generation prediction method that considers the operating status of wind turbine equipment, comprising:
[0046] The data acquisition module is used to periodically collect various wind force data and various wind turbine operating status data within a time period;
[0047] The operating state coefficient acquisition module is used to construct the operating state characteristics of the wind turbine based on the wind turbine operating state data, and to quantify the wind turbine operating state characteristics to obtain the operating state coefficient of the wind turbine. The operating state coefficient is used to describe the degree to which the wind turbine deviates from the ideal operating state.
[0048] The wind turbine theoretical power generation acquisition module is used to calculate the wind turbine theoretical power generation for the next time period based on wind data.
[0049] The correction module is used to correct the theoretical power generation of the wind turbine by means of the operating state coefficient, so as to obtain the predicted power generation considering the influence of the equipment operating state.
[0050] The technical solution of the present invention has at least the following advantages and beneficial effects:
[0051] This invention constructs and quantifies the features of wind turbine operating status data to form an operating status coefficient, which is used to describe the degree to which the wind turbine deviates from the ideal operating state, so that the prediction model can accurately reflect the actual power generation capacity of the wind turbine under different operating states.
[0052] This invention presents a structured representation of wind turbine operation monitoring data and provides a unified quantitative characterization of wind turbine operating status, enabling different wind turbines and operating statuses at different times to have descriptive data on the same scale, thus providing a consistent numerical basis for power correction.
[0053] This invention can distinguish the actual power generation capacity under different operating conditions under the same wind conditions, reduce prediction deviations caused by changes in wind turbine operating conditions, and improve the accuracy of power generation prediction.
[0054] This invention is reasonably designed and simple to calculate. It can dynamically adjust the prediction results according to the changes in the operating status of the wind turbine, and is suitable for complex working conditions such as wind turbine aging and sub-health operation, thus enhancing the flexibility, applicability and stability of the prediction. Attached Figure Description
[0055] Figure 1 This is a flowchart illustrating a wind power generation prediction method that considers the operating status of wind turbine equipment, as provided in Embodiment 1 of the present invention. Detailed Implementation
[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0057] Example 1
[0058] This embodiment provides a method for predicting wind power generation considering the operating status of wind turbine equipment. (See reference...) Figure 1 This includes the following steps:
[0059] Step S1: Periodically collect various wind force data and various wind turbine operating status data within a time period. Specifically, the wind turbine operating status data includes vibration amplitude, vibration frequency, wind turbine bearing temperature, and yaw error; the wind force data includes wind speed and air density.
[0060] Step S2: Construct wind turbine operating status features based on wind turbine operating status data. That is, a time series of wind turbine operating status data can be constructed for each type of wind turbine operating status data to facilitate subsequent data processing.
[0061] Step S3: Quantify the operating state characteristics of the wind turbine to obtain the operating state coefficient of the wind turbine. The operating state coefficient is used to describe the degree to which the wind turbine deviates from the ideal operating state. The method for obtaining the coefficient can be as follows:
[0062] Step S301: Calculate the operating status data of each type of wind turbine in time. The average value and standard within the period can reflect the overall level and fluctuation of the wind turbine's operating status data over a time period.
[0063] Step S302: Based on the i-th type of wind turbine operating status data in time... The operating state coefficient of the i-th type of wind turbine operating state data is calculated by taking the average value and standard deviation within the range, where i is the number of all the wind turbine operating state data.
[0064] In this embodiment, the method for calculating the operating state coefficient of the i-th type of wind turbine operating state data is as follows:
[0065] The deviation of the average value of the i-th type of wind turbine operating status data :
[0066] ;
[0067] Where max is the function for finding the maximum value. The average value of the operating status data of the i-th type of wind turbine is... This is a standard reference value for the average value of the i-th type of wind turbine operating status data, set based on experience. The preset deviation threshold for the average value of the i-th type of wind turbine operating state data from the standard reference value;
[0068] The degree of deviation of the standard deviation of the i-th type of wind turbine operating status data :
[0069] ;
[0070] Where max is the function for finding the maximum value. Let be the standard deviation of the i-th type of wind turbine operating status data. The standard reference value is the standard deviation of the i-th type of wind turbine operating status data, set based on experience. The standard deviation threshold of the i-th type of wind turbine operating status data from the standard reference value is set.
[0071] Calculate the operating state coefficient of the i-th type of wind turbine operating state data. :
[0072] .
[0073] Step S303: Calculate the wind turbine operating state coefficient based on the operating state coefficients of all the wind turbine operating state data, using the following method:
[0074] ;
[0075] in, The operating state coefficient is N, where N is the total number of types of wind turbine operating state data.
[0076] This embodiment maps the deviation of operational status data from standard reference values to a range of 0 to 1, facilitating unified calculation and processing using a consistent dimension and method. When the average value or fluctuation of operational status data exceeds a preset threshold, the corresponding deviation decreases rapidly, which helps to promptly reflect the deterioration trend of wind turbine operational status. Then, by averaging and fusing the operational status coefficients of various operational status data, the excessive influence of a single status indicator on the overall evaluation result is avoided, improving the stability of the overall operational status evaluation. Based on the above calculations, the operational status coefficients... The value range is from 0 to 1, where, The closer the value is to 1, the closer the fan's operating condition is to the ideal condition; The smaller the value, the greater the deviation of the wind turbine's operating state from the ideal state.
[0077] Step S4: Calculate the theoretical power generation of the wind turbine for the next time period based on the wind data. The preferred method is:
[0078] Step S401: Predict the time-series wind speed data for the next time period based on the wind speed data in the wind force data. This can be achieved by: acquiring time-series wind speed data up to at least one time period to form a time-series historical wind speed sequence; and outputting the time-series wind speed data for the next time period through a Long Short-Term Memory (LSTM) network. This embodiment introduces an LSM network to achieve continuous prediction data output for a time window, improving the stability and continuity of wind speed prediction.
[0079] Step S402: Calculate the average value of the time-series wind speed data for the next time period. This step can effectively reduce the impact of instantaneous wind speed fluctuations on power results, making the theoretical power generation smoother and more stable.
[0080] Step S403: Calculate the theoretical power generation of the wind turbine for the next time period according to the basic physical model of wind power generation. :
[0081] ;
[0082] in, Where is the air density, and A is the swept area of the wind turbine. This represents the wind energy utilization coefficient.
[0083] The theoretical power generation of the wind turbine obtained from the above steps can be used as a reference benchmark under ideal operating conditions, which can be easily corrected by combining factors such as wind turbine operating status, yaw error or equipment efficiency, thereby improving the accuracy of actual power generation prediction.
[0084] Step S5: Correct the theoretical power generation of the wind turbine using the operating state coefficient to obtain the predicted power generation considering the influence of equipment operating state:
[0085] ;
[0086] in, The corrected predicted power generation considering the influence of equipment operating conditions is given, where e is a natural constant. The operating state coefficient, The theoretical power generation of the wind turbine in the next time period is given. These are the coefficients obtained through fitting.
[0087] Based on the above scheme, when the wind turbine is in good operating condition and the operating condition coefficient is good... As it approaches 1, the exponentially decaying term When the value approaches 1, the predicted power generation is basically consistent with the theoretical power generation. However, when the wind turbine experiences mechanical wear, control deviations, or operational abnormalities, the operating state coefficient may increase. When it decreases, the exponential decay term The theoretical power generation capacity will be lowered.
[0088] Example 2
[0089] This embodiment provides a wind power generation prediction system that considers the operating status of wind turbine equipment, applied to a wind power generation prediction method considering the operating status of wind turbine equipment in the above embodiment, including:
[0090] The data acquisition module is used to periodically collect various wind force data and various wind turbine operating status data within a time period;
[0091] The operating state coefficient acquisition module is used to construct the operating state characteristics of the wind turbine based on the wind turbine operating state data, and to quantify the wind turbine operating state characteristics to obtain the operating state coefficient of the wind turbine. The operating state coefficient is used to describe the degree to which the wind turbine deviates from the ideal operating state.
[0092] The wind turbine theoretical power generation acquisition module is used to calculate the wind turbine theoretical power generation for the next time period based on wind data.
[0093] The correction module is used to correct the theoretical power generation of the wind turbine by means of the operating state coefficient, so as to obtain the predicted power generation considering the influence of the equipment operating state.
[0094] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for predicting wind power generation considering the operating status of wind turbine equipment, characterized in that, Includes the following steps: Multiple wind force data and multiple wind turbine operating status data are periodically collected within a time period; Construct wind turbine operating status characteristics based on wind turbine operating status data; The operating state characteristics of the fan are quantified to obtain the operating state coefficient of the fan, which is used to describe the degree to which the fan deviates from the ideal operating state; Calculate the theoretical power generation of the wind turbines for the next time period based on wind data; By correcting the theoretical power generation of the wind turbine using the operating state coefficient, the predicted power generation considering the influence of equipment operating state is obtained.
2. The wind power generation prediction method considering the operating status of wind turbine equipment according to claim 1, characterized in that, The wind turbine operating status data includes vibration amplitude, vibration frequency, wind turbine bearing temperature, and yaw error; The wind data includes wind speed and air density.
3. The wind power generation prediction method considering the operating status of wind turbine equipment according to claim 1, characterized in that, The method for constructing wind turbine operating status features based on wind turbine operating status data is as follows: A time series sequence of wind turbine operating status data is constructed for each type of wind turbine operating status data.
4. The wind power generation prediction method considering the operating status of wind turbine equipment according to claim 1, characterized in that, The method for quantifying the operating state characteristics of the fan to obtain the operating state coefficient of the fan is as follows: Calculate the operating status data of each type of wind turbine in time. The mean and standard deviation within the range; Based on the wind turbine operating status data described in the i-th type, in time... The operating state coefficient of the i-th type of wind turbine operating state data is calculated by the average value and standard deviation within the range, where the value of i is the number of all the wind turbine operating state data. The wind turbine operating status coefficient is calculated based on the operating status coefficient of all the wind turbine operating status data.
5. The wind power generation prediction method considering the operating status of wind turbine equipment according to claim 4, characterized in that, The calculation method for the operating state coefficient of the i-th type of wind turbine operating state data is as follows: The deviation of the average value of the i-th type of wind turbine operating status data : ； Where max is the function for finding the maximum value. The average value of the operating status data of the i-th type of wind turbine is... This is a standard reference value for the average value of the i-th type of wind turbine operating status data, set based on experience. The preset deviation threshold for the average value of the i-th type of wind turbine operating state data from the standard reference value; The degree of deviation of the standard deviation of the i-th type of wind turbine operating status data : ； Where max is the function for finding the maximum value. Let be the standard deviation of the i-th type of wind turbine operating status data. The standard reference value is the standard deviation of the i-th type of wind turbine operating status data, set based on experience. The standard deviation threshold of the i-th type of wind turbine operating status data from the standard reference value is set. Calculate the operating state coefficient of the i-th type of wind turbine operating state data. : 。 6. The wind power generation prediction method considering the operating status of wind turbine equipment according to claim 5, characterized in that, The method for calculating the operating state coefficient of the wind turbine based on the operating state coefficient of all the aforementioned wind turbine operating state data is as follows: ； in, The operating state coefficient is N, where N is the total number of types of wind turbine operating state data.
7. The wind power generation prediction method considering the operating status of wind turbine equipment according to claim 1, characterized in that, The method for calculating the theoretical power generation of wind turbines in the next time period based on wind data is as follows: Predict the time-series wind speed data for the next time period based on the wind speed data in the wind data; Calculate the average value of time-series wind speed data for the next time period. ; Calculate the theoretical power generation of the wind turbine in the next time period. : ； in, Where A is the air density in the wind data, and A is the swept area of the wind turbine. This represents the wind energy utilization coefficient.
8. The wind power generation prediction method considering the operating status of wind turbine equipment according to claim 7, characterized in that, The method for predicting time-series wind speed data for the next time period is as follows: Obtain time-series wind speed data up to at least one time period up to the current time, and form a time-series historical wind speed sequence; The time-series wind speed data for the next time period is output through a long short-term memory network.
9. The wind power generation prediction method considering the operating status of wind turbine equipment according to claim 1, characterized in that, The method for correcting the theoretical power generation of wind turbines using operating state coefficients is as follows: ； in, The corrected predicted power generation considering the influence of equipment operating conditions is given, where e is a natural constant. The operating state coefficient, The theoretical power generation of the wind turbine in the next time period is given. These are the coefficients obtained through fitting.
10. A wind power generation prediction system considering the operating status of wind turbine equipment, applied to the wind power generation prediction method considering the operating status of wind turbine equipment as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to periodically collect various wind force data and various wind turbine operating status data within a time period; The operating state coefficient acquisition module is used to construct the operating state characteristics of the wind turbine based on the wind turbine operating state data, and to quantify the wind turbine operating state characteristics to obtain the operating state coefficient of the wind turbine. The operating state coefficient is used to describe the degree to which the wind turbine deviates from the ideal operating state. The wind turbine theoretical power generation acquisition module is used to calculate the wind turbine theoretical power generation for the next time period based on wind data. The correction module is used to correct the theoretical power generation of the wind turbine by means of the operating state coefficient, so as to obtain the predicted power generation considering the influence of the equipment operating state.