A wind power prediction method and system

Abstract

This application discloses a wind power prediction method, which relates to the field of wind power technology. By analyzing the acquired regional weather forecast information from the Internet, a predictive air flow factor is determined. Air flow information is collected through an on-site air flow device to determine the on-site air flow factor. Based on the composite analysis of the predicted air flow factor and the on-site air flow factor, the past wind power generation curve is corrected, thereby generating a predicted power generation curve. This method enables the determination of the power generation of wind power equipment based on weather forecast information, which is more accurate than manual judgment.

Description

Technical Field

[0001] This invention relates to the field of wind power technology, and in particular to a wind power prediction method and system. Background Technology

[0002] my country possesses abundant wind energy resources, with exploitable wind energy reserves of approximately 1 billion kW. Of this, approximately 253 million kW is onshore (calculated from data at a height of 10m above ground) and approximately 750 million kW is offshore, totaling 1 billion kW. At the end of 2003, the nation's total installed power capacity was approximately 567 million kW. Wind is one of the pollution-free energy sources. Moreover, it is inexhaustible. For coastal islands, grassland pastoral areas, mountainous regions, and plateau areas lacking water, fuel, and with inconvenient transportation, utilizing wind power in accordance with local conditions is highly suitable and has great potential. Offshore wind power is an important area of ​​renewable energy development, a vital force driving technological progress and industrial upgrading in wind power, and an important measure to promote energy structure adjustment.

[0003] For wind farms, in order to better coordinate the construction between the power grid and power sources including wind power, photovoltaics and other forms of power generation, and to guide power generation planning, grid coordination and dispatch, it is necessary to predict the wind power output of wind power generation equipment in wind farms. Conventional prediction methods mostly involve managers making predictions based on power generation data collected in the background, which is relatively inaccurate. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for predicting the wind power generation capacity of wind power generation equipment in power plants.

[0005] Therefore, this invention discloses a wind power prediction method, including:

[0006] Obtain regional weather forecast information from the Internet, and determine the predicted air flow factor based on the Internet weather forecast information;

[0007] Acquire airflow information collected by the on-site airflow detection device and generate on-site airflow factor;

[0008] Based on past wind power generation logs, a past wind power generation curve is established, where the horizontal axis of the past wind power generation curve represents time and the vertical axis represents the power generation value.

[0009] A predictive power generation curve model is established. The power generation adjustment model is used to scan and analyze the past wind power generation curves and set several adjustment nodes on the past wind power generation curves. The adjustment nodes are adjusted up and down according to the difference between the predicted air flow factor and the on-site air flow factor to generate the predictive power generation curve.

[0010] In some embodiments of this application, in order to determine the adjustment method of the adjustment node to the past power generation curve, a method for generating a predicted power generation curve is further disclosed. The method for generating a predicted power generation curve includes:

[0011] A power influence expression relationship is established for the aforementioned difference characteristics, and the power influence expression relationship determines the predicted power generation difference based on the difference values ​​of flow direction angle and flow intensity.

[0012] Based on the predicted power generation difference, determine the upper and lower adjustment difference of the adjustment node relative to the corresponding node of the past wind power generation curve;

[0013] The difference in flow direction angle is defined as the difference in the angle of airflow direction expressed by the predicted airflow factor and the on-site flow factor.

[0014] In some embodiments of this application, the method for applying the power influence expression relationship is further disclosed, and the method for applying the power influence expression relationship to the direction angle difference value includes:

[0015] A first power influence weight is configured for the difference in flow direction angle between the predicted air flow factor and the on-site air flow factor. The first power influence weight is used to express the degree of influence on power generation under different flow direction angle differences.

[0016] Based on the first power influence weight and the difference value of the flow direction angle, a flow direction angle influence operator is constructed, and the flow direction angle influence operator is applied to the power influence expression relationship.

[0017] In some embodiments of this application, the method for applying the power influence expression relationship to the flow intensity difference value is further disclosed, and the method for applying the power influence expression relationship to the flow intensity difference value includes:

[0018] A second power influence weight is configured for the difference in flow intensity between the predicted air flow factor and the on-site air flow factor. The second power influence weight is used to express the degree of influence on power generation under different flow intensity difference values.

[0019] Based on the second power influence weight and the flow intensity difference value, a flow intensity influence operator is constructed, and the flow intensity influence operator is applied to the power influence expression relationship.

[0020] In some embodiments of this application, the relational expression for the power influence relationship is disclosed, and the expression for the power influence relationship is as follows:

[0021] Y n =(k n1 ·Δx n1 )·(k n2 ·Δcosx n2 )

[0022] Among them, Y n Let (k) be the difference in predicted power generation. n1 ·Δx n1 ) is the flow intensity influence operator, (k n2 ·Δcosx n2 ) represents the operator affecting the flow direction angle, k n1 As the first power influence weight, x n1 k represents the difference in flow intensity. n2 The second power influences the weight, x n2 This represents the difference in the direction and angle of the flow.

[0023] In some embodiments of this application, in order to improve the accuracy of the method for generating predicted power generation curves, the wind power prediction method has been improved, and further includes:

[0024] Establish an actual power generation curve and compare and analyze the actual power generation curve with the corresponding predicted power generation curve to determine the distortion characteristics of the predicted power generation curve relative to the actual power generation curve;

[0025] Based on the distortion characteristics, the first power influence weight and the second power influence weight corresponding to different adjustment nodes are corrected.

[0026] In some embodiments of this application, the specific content of the distortion features is disclosed, and the distortion features include:

[0027] The area between the actual power generation curve and the predicted power generation curve within a preset range near the adjustment node.

[0028] In some embodiments of this application, in order to accurately correct the first power influence weight and the second power influence weight, a specific correction method is disclosed. The method for correcting the first power influence weight and the second power influence weight includes:

[0029] A first-stage adjustment range is set for the first power influence weight, and a second-stage adjustment range is set for the second power influence weight;

[0030] The adjustment process for the first power influence weight and the second power influence weight is divided into several steps, and each adjustment adjusts the first power influence weight and the second power influence weight, with the adjustment range corresponding to the adjustment range of the first stage or the adjustment range of the second stage, respectively.

[0031] For each adjustment of the weight of the first power influence, a first virtual predicted power generation curve is generated, and the area of ​​the first region between the first virtual predicted power generation curve and the actual power generation curve is calculated within a preset range near the adjustment node.

[0032] For each adjustment of the weighting of the second power influence, a second virtual predicted power generation curve is generated, and the area of ​​the second region between the second virtual predicted power generation curve and the actual power generation curve is calculated within a preset range near the adjustment node.

[0033] Comparative analysis of the areas of the first and second regions:

[0034] If the area of ​​the first region is less than or equal to the area of ​​the second region, then the adjustment of the first power influence weight will be adjusted according to the adjustment range of the first stage for this adjustment.

[0035] If the area of ​​the first region is larger than the area of ​​the second region, then the adjustment of the second power influence weight will be adjusted according to the adjustment range of the second stage.

[0036] In some embodiments of this application, a wind power prediction system is also disclosed, comprising:

[0037] The Internet weather forecast analysis module is used to acquire regional weather forecast information from the Internet and determine the predicted air flow factor based on the Internet weather forecast information.

[0038] The on-site airflow analysis module is used to acquire airflow information collected by the on-site airflow detection device and generate on-site airflow factors.

[0039] The power generation curve generation module is used to establish a past wind power generation curve based on past wind power generation logs, and to set several adjustment nodes on the past wind power generation curve. The adjustment nodes are adjusted up and down according to the difference between the predicted air flow factor and the on-site air flow factor to generate the predicted power generation curve.

[0040] In some embodiments of this application, the system has been improved to facilitate managers' understanding and prediction of the power generation capacity of each wind turbine in the wind farm, and a display module has also been added.

[0041] The display module is used to display the past wind power generation curve and the predicted power generation curve, and sets several adjustment nodes on the predicted power generation curve.

[0042] The wind power prediction method disclosed in this application has the following advantages compared with manual wind power prediction methods:

[0043] By analyzing the regional weather forecast information obtained from the Internet, the predicted air flow factor is determined. Air flow information is collected through on-site air flow devices to determine the on-site air flow factor. Based on the composite analysis of the predicted air flow factor and the on-site air flow factor, the past wind power generation curve is corrected, and then the predicted power generation curve is generated. This enables the determination of the power generation of wind power equipment through weather forecast information, which is more accurate than manual judgment.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the steps of a wind power prediction method in an embodiment of this application. Detailed Implementation

[0046] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0047] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. It should be understood that the preferred embodiments described herein are only for illustration and explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Those skilled in the art can make some non-essential improvements and adjustments based on the following content of the present invention. In the present invention, unless otherwise expressly specified and limited, the technical terms used in this application should have the ordinary meaning understood by those skilled in the art. The terms "connected," "linked," "fixed," "set," etc., should be interpreted broadly, and can refer to fixed connection, detachable connection, or integral connection; can refer to direct connection or indirect connection through an intermediate medium; can refer to mechanical connection or electrical connection, unless otherwise expressly limited. For those skilled in the art, the specific meaning of the above terms in the present invention can be understood according to the specific circumstances. Unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Moreover, "above," "on top of," or "on the second feature" can mean that the first feature is directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. The phrase "below," "under," or "beneath" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature. Relational terms such as "first," "second," etc., are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. It should be noted that similar labels and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0048] Example:

[0049] The purpose of this invention is to provide a method and system for predicting the wind power generation capacity of wind power generation equipment in power plants.

[0050] Therefore, this invention discloses a wind power prediction method, see [reference]. Figure 1 ,include:

[0051] Step S100: Obtain regional weather forecast information from the Internet and determine the predicted air flow factor based on the Internet weather forecast information.

[0052] It should be understood that the predicted airflow factor is an element variable for predicting airflow, including predicting the direction of airflow and predicting the intensity of airflow.

[0053] Step S200: Obtain airflow information collected by the on-site airflow detection device and generate on-site airflow factor.

[0054] It should be understood that the on-site airflow factor is an element variable of on-site airflow, including the direction of on-site airflow and the intensity of on-site airflow.

[0055] Step S300: Based on the past wind power generation log, establish a past wind power generation curve, where the horizontal axis of the past wind power generation curve is time and the vertical axis is the power generation value.

[0056] Step S400: Establish a predicted power generation curve model. The power generation adjustment model is used to scan and analyze the past wind power generation curve and set several adjustment nodes on the past wind power generation curve. The adjustment nodes are adjusted up and down according to the difference between the predicted air flow factor and the on-site air flow factor to generate the predicted power generation curve.

[0057] In some embodiments of this application, in order to determine the adjustment method of the adjustment node to the past power generation curve, a method for generating a predicted power generation curve is further disclosed. The method for generating a predicted power generation curve includes:

[0058] The first step is to establish a power impact expression relationship for the aforementioned difference characteristics. This power impact expression relationship determines the predicted power generation difference based on the difference in flow direction angle and the difference in flow intensity.

[0059] It should be understood that the flow direction angle difference value is a set value for the difference in the air flow direction angle, specifically based on the difference in angle between the predicted air flow direction and the actual air flow direction, and the flow intensity difference value is a set value for the difference in air flow intensity, specifically based on the difference in intensity between the predicted air flow direction and the actual air flow direction.

[0060] The second step is to determine the upper and lower adjustment difference of the adjustment node relative to the corresponding node of the past wind power generation curve based on the predicted power generation difference.

[0061] The difference in flow direction angle is defined as the difference in the angle of airflow direction expressed by the predicted airflow factor and the on-site flow factor.

[0062] In some embodiments of this application, the method for applying the power influence expression relationship is further disclosed, and the method for applying the power influence expression relationship to the direction angle difference value includes:

[0063] The first step is to configure a first power influence weight for the difference in flow direction angle between the predicted air flow factor and the on-site air flow factor. The first power influence weight is used to express the degree of influence on power generation under different flow direction angle differences.

[0064] The second step is to construct a flow direction angle influence operator based on the first power influence weight and the flow direction angle difference value, and then apply the flow direction angle influence operator to the power influence expression relationship.

[0065] In some embodiments of this application, the method for applying the power influence expression relationship to the flow intensity difference value is further disclosed, and the method for applying the power influence expression relationship to the flow intensity difference value includes:

[0066] The first step is to configure a second power influence weight for the difference in flow intensity between the predicted air flow factor and the on-site air flow factor. The second power influence weight is used to express the degree of influence on power generation under different flow intensity differences.

[0067] The second step involves constructing a flow intensity influence operator based on the second power influence weight and the flow intensity difference value, and then applying the flow intensity influence operator to the power influence expression relationship.

[0068] In some embodiments of this application, the relational expression for the power influence relationship is disclosed, and the expression for the power influence relationship is as follows:

[0069] Y n =(k n1 ·Δx n1 )·(k n2 ·Δcosx n2 )

[0070] Among them, Y n Let (k) be the difference in predicted power generation. n1 ·Δx n1 ) is the flow intensity influence operator, (k n2 ·Δcosx n2 ) represents the operator affecting the flow direction angle, k n1 As the first power influence weight, x n1 k represents the difference in flow intensity. n2 The second power influences the weight, x n2 This represents the difference in the direction and angle of the flow.

[0071] In some embodiments of this application, in order to improve the accuracy of the method for generating predicted power generation curves, the wind power prediction method has been improved, and further includes:

[0072] The first step is to establish the actual power generation curve and compare and analyze it with the corresponding predicted power generation curve to determine the distortion characteristics of the predicted power generation curve relative to the actual power generation curve.

[0073] It should be understood that the distortion feature is used to express the difference between the predicted power generation curve and the actual power generation curve.

[0074] The second step is to correct the first power influence weight and the second power influence weight corresponding to different adjustment nodes based on the distortion characteristics.

[0075] In some embodiments of this application, the specific content of the distortion feature is disclosed, which includes: the area between the actual power generation curve and the predicted power generation curve within a preset range near the adjustment node.

[0076] It should be understood that the larger the area, the greater the difference between the predicted power generation curve and the actual power generation curve, and the higher the degree of distortion.

[0077] In some embodiments of this application, in order to accurately correct the first power influence weight and the second power influence weight, a specific correction method is disclosed. The method for correcting the first power influence weight and the second power influence weight includes:

[0078] The first step is to set a first-stage adjustment range for the first power influence weight and a second-stage adjustment range for the second power influence weight.

[0079] The second step involves adjusting the first power influence weight and the second power influence weight in several steps, with each adjustment adjusting the first power influence weight and the second power influence weight, and the adjustment range corresponding to the adjustment range of the first stage or the adjustment range of the second stage, respectively.

[0080] The third step involves generating a first virtual predicted power generation curve for each adjustment of the weighting of the first power influence, and calculating the area of ​​the first region between the first virtual predicted power generation curve and the actual power generation curve within a preset range near the adjustment node.

[0081] The fourth step involves generating a second virtual predicted power generation curve for each adjustment of the weighting of the second power influence, and calculating the area of ​​the second region between the second virtual predicted power generation curve and the actual power generation curve within a preset range near the adjustment node.

[0082] Fifth step: Compare and analyze the areas of the first region and the second region:

[0083] If the area of ​​the first region is less than or equal to the area of ​​the second region, then the adjustment of the first power influence weight will be adjusted according to the adjustment range of the first stage for this adjustment.

[0084] If the area of ​​the first region is larger than the area of ​​the second region, then the adjustment of the second power influence weight will be adjusted according to the adjustment range of the second stage.

[0085] In some embodiments of this application, a wind power prediction system is also disclosed, including: an Internet weather forecast analysis module, an on-site air flow analysis module, and a power generation curve generation module.

[0086] The Internet weather forecast analysis module is used to acquire regional weather forecast information from the Internet and determine the predicted air flow factor based on the Internet weather forecast information;

[0087] The on-site airflow analysis module is used to acquire airflow information collected by the on-site airflow detection device and generate on-site airflow factors.

[0088] The power generation curve generation module is used to establish a past wind power generation curve based on past wind power generation logs, and to set several adjustment nodes on the past wind power generation curve. The adjustment nodes are adjusted up and down according to the difference between the predicted air flow factor and the on-site air flow factor to generate the predicted power generation curve.

[0089] In some embodiments of this application, the system has been improved to facilitate managers' understanding and prediction of the power generation capacity of each wind turbine in the wind farm, and a display module has also been added.

[0090] The display module is used to display the past wind power generation curve and the predicted power generation curve, and sets several adjustment nodes on the predicted power generation curve.

[0091] The wind power prediction method disclosed in this application has the following advantages compared with manual wind power prediction methods:

[0092] By analyzing the regional weather forecast information obtained from the Internet, the predicted air flow factor is determined. Air flow information is collected through on-site air flow devices to determine the on-site air flow factor. Based on the composite analysis of the predicted air flow factor and the on-site air flow factor, the past wind power generation curve is corrected, and then the predicted power generation curve is generated. This enables the determination of the power generation of wind power equipment through weather forecast information, which is more accurate than manual judgment.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for predicting wind power output, characterized in that, include: Obtain regional weather forecast information from the Internet, and determine the predicted air flow factor based on the regional weather forecast information from the Internet; Acquire airflow information collected by the on-site airflow detection device and generate on-site airflow factor; Based on past wind power generation logs, a past wind power generation curve is established, where the horizontal axis of the past wind power generation curve represents time and the vertical axis represents the power generation value. A predictive power generation curve model is established. The power generation adjustment model is used to scan and analyze the past wind power generation curve and set several adjustment nodes on the past wind power generation curve. The adjustment nodes are adjusted up and down according to the difference between the predicted air flow factor and the on-site air flow factor to generate the predictive power generation curve. Methods for generating predicted power generation curves include: A power influence expression relationship is established for the aforementioned difference characteristics, and the power influence expression relationship determines the predicted power generation difference based on the difference values ​​of flow direction angle and flow intensity. Based on the predicted power generation difference, determine the upper and lower adjustment difference of the adjustment node relative to the corresponding node of the past wind power generation curve; Wherein, the difference in flow direction angle is the difference in angle between the predicted airflow factor and the on-site flow factor in the direction of airflow; The method for applying the power influence expression relationship to the direction angle difference value includes: A first power influence weight is configured for the difference in flow direction angle between the predicted air flow factor and the on-site air flow factor. The first power influence weight is used to express the degree of influence on power generation under different flow direction angle differences. Based on the first power influence weight and the difference value of the flow direction angle, a flow direction angle influence operator is constructed, and the flow direction angle influence operator is applied to the power influence expression relationship. The method for applying the power influence relationship to the flow intensity difference value includes: A second power influence weight is configured for the difference in flow intensity between the predicted air flow factor and the on-site air flow factor. The second power influence weight is used to express the degree of influence on power generation under different flow intensity difference values. Based on the second power influence weight and the flow intensity difference value, a flow intensity influence operator is constructed, and the flow intensity influence operator is applied to the power influence expression relationship. The expression for the relationship between power influence is as follows: in, The difference in predicted power generation is... For the flow intensity influence operator, The operator that influences the flow direction angle. The first power influence weight, This represents the difference in flow intensity. The second power influences the weight. This represents the difference in the direction and angle of the flow. Also includes: An actual power generation curve is established, and the actual power generation curve is compared and analyzed with the corresponding predicted power generation curve to determine the distortion characteristics of the predicted power generation curve relative to the actual power generation curve; based on the distortion characteristics, the first power influence weight and the second power influence weight corresponding to different adjustment nodes are corrected.

2. The wind power prediction method according to claim 1, characterized in that, The distortion features include: The area between the actual power generation curve and the predicted power generation curve within a preset range near the adjustment node.

3. The wind power prediction method according to claim 2, characterized in that, The methods for correcting the first power influence weight and the second power influence weight include: A first-stage adjustment range is set for the first power influence weight, and a second-stage adjustment range is set for the second power influence weight; The adjustment process for the first power influence weight and the second power influence weight is divided into several steps, and each adjustment adjusts the first power influence weight and the second power influence weight, with the adjustment range corresponding to the adjustment range of the first stage or the adjustment range of the second stage, respectively. For each adjustment of the weight of the first power influence, a first virtual predicted power generation curve is generated, and the area of ​​the first region between the first virtual predicted power generation curve and the actual power generation curve is calculated within a preset range near the adjustment node. For each adjustment of the weighting of the second power influence, a second virtual predicted power generation curve is generated, and the area of ​​the second region between the second virtual predicted power generation curve and the actual power generation curve is calculated within a preset range near the adjustment node. Comparative analysis of the areas of the first and second regions: If the area of ​​the first region is less than or equal to the area of ​​the second region, then the adjustment of the first power influence weight will be adjusted according to the adjustment range of the first stage for this adjustment. If the area of ​​the first region is larger than the area of ​​the second region, then the adjustment of the second power influence weight will be adjusted according to the adjustment range of the second stage.

4. A wind power prediction system, characterized in that, include: The Internet weather forecast analysis module is used to acquire regional weather forecast information from the Internet and determine the predicted air flow factor based on the regional weather forecast information from the Internet. The on-site airflow analysis module is used to acquire airflow information collected by the on-site airflow detection device and generate on-site airflow factors. The power generation curve generation module is used to establish a past wind power generation curve based on past wind power generation logs, and set several adjustment nodes on the past wind power generation curve. The adjustment nodes are adjusted up and down according to the difference between the predicted air flow factor and the on-site air flow factor to generate the predicted power generation curve. Methods for generating predicted power generation curves include: A power influence expression relationship is established for the aforementioned difference characteristics, and the power influence expression relationship determines the predicted power generation difference based on the difference values ​​of flow direction angle and flow intensity. Based on the predicted power generation difference, determine the upper and lower adjustment difference of the adjustment node relative to the corresponding node of the past wind power generation curve; Wherein, the difference in flow direction angle is the difference in angle between the predicted airflow factor and the on-site flow factor in the direction of airflow; The method for applying the power influence expression relationship to the direction angle difference value includes: A first power influence weight is configured for the difference in flow direction angle between the predicted air flow factor and the on-site air flow factor. The first power influence weight is used to express the degree of influence on power generation under different flow direction angle differences. Based on the first power influence weight and the difference value of the flow direction angle, a flow direction angle influence operator is constructed, and the flow direction angle influence operator is applied to the power influence expression relationship. The method for applying the power influence relationship to the flow intensity difference value includes: A second power influence weight is configured for the difference in flow intensity between the predicted air flow factor and the on-site air flow factor. The second power influence weight is used to express the degree of influence on power generation under different flow intensity difference values. Based on the second power influence weight and the flow intensity difference value, a flow intensity influence operator is constructed, and the flow intensity influence operator is applied to the power influence expression relationship. The expression for the relationship between power influence is as follows: in, The difference in predicted power generation is... For the flow intensity influence operator, The operator that influences the flow direction angle. The first power influence weight, This represents the difference in flow intensity. The second power influences the weight. This represents the difference in the direction and angle of the flow. Also includes: An actual power generation curve is established, and the actual power generation curve is compared and analyzed with the corresponding predicted power generation curve to determine the distortion characteristics of the predicted power generation curve relative to the actual power generation curve; based on the distortion characteristics, the first power influence weight and the second power influence weight corresponding to different adjustment nodes are corrected.

5. A wind power prediction system according to claim 4, characterized in that, It also includes a display module; The display module is used to display the past wind power generation curve and the predicted power generation curve, and sets several adjustment nodes on the predicted power generation curve.

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