Modeling method of wind power output uncertainty based on typhoon-rainstorm coupling mechanism

Abstract

The present invention discloses a wind power generation output uncertainty modeling method based on the typhoon-storm coupling mechanism, comprising the following steps: S1, constructing a first model and a second model; the first model includes a typhoon wind speed model based on dynamic feedback iteration; the second model includes a rainstorm rainfall rate model considering the change of typhoon center pressure; S2, constructing a third model based on the first model and the second model in combination with equivalent theory; the third model includes a wind-driven rain equivalent wind speed model based on equivalent theory; S3, constructing a fourth model based on the third model in combination with micro-topography factors; the fourth model includes a wind speed correction model; S4, constructing a wind power generation output uncertainty model based on the fourth model in combination with the first curve; the first curve includes a wind speed and power characteristic curve of a wind power generation device. The method of the present invention helps to accurately predict and respond to the impact of extreme weather on wind power equipment, and lays a solid foundation for ensuring the safe and stable operation of the distribution system under typhoons and rainstorms.

Description

Technical Field

[0001] The present invention relates to the field of power emergency technology, and in particular to a wind power generation output uncertainty modeling method based on a typhoon-rainstorm coupling mechanism. Background Art

[0002] The strong winds and heavy rains caused by typhoons are extremely destructive to wind turbines, not only causing damage to equipment, but also significantly reducing the output power of renewable energy, thus causing huge economic losses. In order to improve the survivability of wind power and other renewable energy units under typhoon and rainstorm conditions, it is crucial to conduct in-depth research on the output uncertainty characteristics of wind turbines under typhoon and rainstorm conditions.

[0003] Typhoons are often accompanied by heavy rains and are typical natural disasters active in the northwest Pacific. Typhoon wind speed models are usually characterized by time-varying gradient wind speed. Common typhoon wind speed modeling methods include numerical weather forecast modeling methods, statistical regression-based modeling methods, and typhoon simulation modeling methods. The statistical regression-based modeling method predicts the change of wind speed with typhoons through historical typhoon paths and intensity data. The numerical weather forecast modeling method estimates the typhoon path and wind speed by simulating atmospheric dynamics. The calculations of these two modeling methods are relatively complex, especially under the micro-topography conditions where new energy units are located. The prediction accuracy is insufficient. The typhoon simulation modeling method usually uses empirical models such as the Batts model and the Kongmo model to calculate the typhoon wind field through the maximum wind speed, the distance from the typhoon center, and the maximum wind speed radius. The typhoon simulation modeling method is relatively simple to calculate, but the constructed typhoon wind speed has low accuracy and ignores the influence of micro-topography (such as mountains and hills) on wind speed. The rainstorm modeling method mainly uses precipitation to represent. Existing modeling methods include radar-based precipitation modeling methods and meteorological satellite-based rainfall modeling methods. Although the existing rainstorm modeling methods can better reflect local rainfall conditions, they often focus on the rainstorm itself and ignore the complex impact of wind speed changes during typhoons on the precipitation process. At the same time, it is difficult to effectively combine with the output model of new energy units. In summary, in the existing technology, on the one hand, the accuracy of typhoon modeling is not high, especially in the output scenario of new energy components, it fails to provide sufficiently detailed local wind speed predictions, and does not consider micro-topography factors; on the other hand, the coupling factors of typhoons and rainstorms are not considered, and the uncertainty of the interaction of multiple factors on the output of new energy in extreme weather is ignored. The above two problems lead to low modeling accuracy of wind power output uncertainty.

[0004] Therefore, a new technical solution is urgently needed to solve the technical problem of how to improve the accuracy of uncertainty modeling of wind power output under typhoon and rainstorm conditions. Summary of the invention

[0005] The present invention provides a wind power generation output uncertainty modeling method based on the typhoon-storm coupling mechanism, which is used to solve the technical problem of how to improve the accuracy of wind power generation output uncertainty modeling under typhoon-storm conditions.

[0006] To achieve the above object, the present invention provides a wind power generation output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism, comprising the following steps:

[0007] S1, constructing a first model and a second model; the first model includes a typhoon wind speed model based on dynamic feedback iteration; the typhoon wind speed model based on dynamic feedback iteration includes a moving wind speed part and a circulation wind speed part obtained by dynamic feedback iteration; the second model includes a rainstorm rainfall rate model considering the change of typhoon center pressure;

[0008] S2. Constructing a third model based on the first model and the second model in combination with the equivalent theory; the third model includes a wind-driven rain equivalent wind speed model based on the equivalent theory;

[0009] S3, constructing a fourth model based on the third model combined with micro-topography factors; the fourth model includes a wind speed correction model;

[0010] S4. Constructing a wind power generation output uncertainty model based on the fourth model and the first curve; the first curve includes a wind speed and power characteristic curve of the wind power generation equipment.

[0011] Preferably, S1 comprises:

[0012] Constructing the first model, that is, constructing a typhoon wind speed model based on dynamic feedback iteration, including:

[0013] Typhoon gradient wind speed It is composed of circulation wind speed and moving wind speed:

[0014] ;

[0015] in, and Respectively The gradient wind speed at the time and the distance from the typhoon center Circulation wind speed at and They represent the maximum wind speed radius and the moving wind speed of the corresponding typhoon center respectively; Indicates the duration of the typhoon disaster; Indicates the moment; Indicates the distance between a point in a typhoon and the center of the typhoon;

[0016] The circulation wind speed in the gradient wind speed As a dynamic feedback iteration object, the typical circulation wind speed model set is used as the original model set for constructing the circulation wind speed part of the first model, including sub-models and , which can be expressed by the following relationship:

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] in, and Respectively The maximum circulation wind speed at the time and the distance from the typhoon center; and is the shape parameter;

[0024] Get typhoon data, including the center coordinates and center pressure of the typhoon, the radius of the seventh-level wind circle and the radius of the tenth-level wind circle, the center moving wind speed and the maximum wind speed; get the minimum value of the seventh-level wind speed and the minimum value of the tenth-level wind speed according to the typhoon data, which can be expressed by the following relationship:

[0025] ;

[0026] in, Indicates the minimum wind speed of level 7; Indicates the minimum wind speed of level 10; express The actual value of the circulation wind speed at the level 7 wind circle at the moment; express The actual value of the circulating wind speed at the level 10 wind circle at that moment; Indicates the radius of the seventh-level wind circle; Indicates the radius of the tenth-level wind circle;

[0027] According to the minimum wind speed of level 7 and level 10 and the original model set, the original model set is obtained. , and The initial value of

[0028] according to , and The initial value of is adjusted by joint dynamic feedback iteration to obtain , and The optimized value of

[0029] Joint dynamic feedback iterative adjustment includes The dynamic feedback iterative adjustment part and and The dynamic feedback iterative adjustment part; In the dynamic feedback iterative adjustment part, from and Dynamic feedback iterative adjustment part of the acquisition and The real-time iteration value is and The value of and In the dynamic feedback iterative adjustment part, from Dynamic feedback iterative adjustment part of the acquisition The real-time iteration value is The value of

[0030] The dynamic feedback iterative adjustment part includes the weight coefficients of each sub-model in the original model set , represents the index of the sub-model in the original model set, ;right Perform dynamic feedback iterative adjustment to obtain the weight coefficient of each sub-model in the original model set The optimized value of

[0031] Will , , and Substitute the optimized value of into the original model set to obtain the circulation wind speed part, and substitute the circulation wind speed part into the gradient wind speed In , get the first model .

[0032] Preferably, the combined dynamic feedback iterative adjustment includes:

[0033] according to The initial value of The dynamic feedback iterative adjustment part includes:

[0034] ;

[0035] in, express Moment original model set The circulation wind speed error of each sub-model; represents the original model set; and Respectively Moment original model set The weighted square error and weight coefficient of the circulation wind speed of each sub-model; express At the radius of the seventh wind speed Calculated values ​​of circulation wind speed for each model; express At the moment, the radius of the tenth wind speed Calculated values ​​of circulation wind speed for each model; and They represent the weight coefficients of the square error of the circulation wind speed in the seventh and tenth wind circles respectively.

[0036] Preferably, the joint dynamic feedback iterative adjustment further includes:

[0037] according to The initial value of The dynamic feedback iterative adjustment part includes:

[0038] ;

[0039] in, express Moment Model Circulation wind speed error; Representation Model The weighted square error of the circulation wind speed;

[0040] according to The initial value of The dynamic feedback iterative adjustment part includes:

[0041] ;

[0042] in, express Moment Model Circulation wind speed error; Representation Model The weighted square error of the circulation wind speed.

[0043] Preferably, Dynamic feedback iterative adjustments include:

[0044] Dynamic feedback iterative adjustment based on the average Sum weighted squared error ,include:

[0045] ;

[0046] in, Indicates the maximum allowable change value of the preset sub-model weight coefficient.

[0047] Preferably, , , and Substituting the optimized values ​​into the original model set includes:

[0048] Will , , and Substitute the optimized value into the original model set, assign weights to the models in the original model set, and obtain the first model for calculating typhoon wind speed , which can be expressed by the following relationship:

[0049] ;

[0050] ;

[0051] in, express Circulation wind speed at any moment; and Respectively represent the first model Circulation wind speed and central moving wind speed of each sub-model; express The optimized value of express The optimized value of .

[0052] Preferably, S1 further comprises:

[0053] Construct the second model, that is, construct a rainstorm rainfall rate model that takes into account the changes in the typhoon center air pressure, including:

[0054] The rainfall rate is determined by the central pressure difference, the central pressure change rate and the typhoon moving speed. The second model can be expressed by the following relationship:

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] in, express time The rainfall rate at Indicates the central air pressure difference; Indicates the rate of change of central air pressure; and Respectively and Correction factor of express Typhoon moving wind speed at the moment; Represents auxiliary variables; is the radius of the Earth; Indicates the distance from the typhoon center; and Represents the longitude and latitude coordinates of any two points in the typhoon.

[0061] Preferably, the third model includes:

[0062] The third model includes the wind-driven rain equivalent wind speed model based on the equivalent theory , which can be expressed by the following relationship:

[0063] ;

[0064] ;

[0065] ;

[0066] in, and Respectively and correction factor.

[0067] Preferably, the fourth model includes:

[0068] ;

[0069] in, Indicates the height of a mountain; Indicates the slope of the windward side; is the correction constant; It is the total height of a hilltop or slope; is a constant, its value is 1.5; Represents the wind speed correction model.

[0070] Preferably, S4 includes:

[0071] The wind speed and power characteristic curve, i.e. the first curve, includes:

[0072] ;

[0073] The uncertainty model of wind power output can be expressed by the following relationship:

[0074] ;

[0075] in, and are the cut-in wind speed and cut-out wind speed of the wind turbine respectively; is the rated wind speed; and They are the actual output power and rated output power of the wind power generation equipment respectively.

[0076] The present invention has the following beneficial effects:

[0077] The wind power generation output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism of the present invention comprehensively considers the coupling effect of typhoon and rainstorm and micro-topography factors, firstly establishes a typhoon wind speed model based on dynamic feedback iteration and a rainstorm rainfall rate model considering the change of typhoon center air pressure, wherein the typhoon wind speed model based on dynamic feedback iteration has higher accuracy than the conventional typhoon wind speed model, so that the typhoon wind speed model of the present invention is more accurate; based on the typhoon wind speed model and the rainstorm rainfall rate model, a wind-driven rain equivalent wind speed model is constructed based on equivalent theory, so that the present invention comprehensively considers the coupling effect of typhoon and rainstorm and improves the modeling accuracy; based on the wind-driven rain equivalent wind speed model and micro-topography factors, a wind speed correction model is constructed, so that the present invention comprehensively considers the micro-topography factors and further improves the modeling accuracy; finally, a wind power generation output uncertainty model is constructed according to the wind speed correction model combined with the wind speed and power characteristic curve of the wind power generation equipment; the method of the present invention can effectively improve the accuracy of wind power generation output uncertainty modeling under typhoon and rainstorm conditions. The method of the present invention helps to more accurately predict and respond to the impact of extreme weather on new energy equipment, and also lays a solid foundation for ensuring the safe and stable operation of new energy distribution systems under typhoons and rainstorms.

[0078] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0080] Figure 1 It is a flow chart of a preferred embodiment of the present invention.

[0081] Figure 2 It is a schematic diagram of dimensionless circulation wind speed curves of different typhoon wind speed models in a preferred embodiment of the present invention.

[0082] Figure 3 It is a partial schematic diagram of dimensionless circulation wind speed curves of different typhoon wind speed models in the preferred embodiment of the present invention.

[0083] Figure 4 It is a schematic diagram of the temporal and spatial variation of the equivalent wind speed under the coupling of typhoon and rainstorm according to a preferred embodiment of the present invention.

[0084] Figure 5 It is a comparison diagram of different wind speed distribution curves considering only typhoons, considering typhoon rainstorms, and considering typhoon rainstorms under micro-topography in a preferred embodiment of the present invention.

[0085] Figure 6 It is a comparison diagram of the time-varying uncertainty output distribution curves of wind turbines considering only typhoons, considering typhoon rainstorms, and considering typhoon rainstorms under micro-topography according to the preferred embodiment of the present invention.

[0086] Figure 7 It is a schematic diagram of a curve comparison between the calculated values ​​of the circulation wind speed at the seventh-level wind circle and the tenth-level wind circle calculated by the first model of the present invention and the classical model in a preferred embodiment of the present invention and the actual values ​​of the circulation wind speed. DETAILED DESCRIPTION

[0087] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0088] See also Figure 1 In a preferred embodiment of the present invention, a method for modeling uncertainty of wind power output based on typhoon-rainstorm coupling mechanism is provided, comprising the following steps:

[0089] S1, construct the first model and the second model; the first model includes a typhoon wind speed model based on dynamic feedback iteration; the typhoon wind speed model based on dynamic feedback iteration includes a moving wind speed part and a circulation wind speed part obtained through dynamic feedback iteration; the second model includes a rainstorm rainfall rate model that takes into account the changes in the typhoon center pressure. S1 specifically includes:

[0090] (1) Construct the first model, that is, construct a typhoon wind speed model based on dynamic feedback iteration, including:

[0091] Typhoon gradient wind speed It is composed of circulation wind speed and moving wind speed:

[0092] ;

[0093] in, and Respectively The gradient wind speed at the time and the distance from the typhoon center Circulation wind speed at and They represent the maximum wind speed radius and the moving wind speed of the corresponding typhoon center respectively; Indicates the duration of the typhoon disaster; Indicates the moment; Indicates the distance between a point in a typhoon and the center of the typhoon; It is a natural constant, an irrational number, approximately equal to 2.71828.

[0094] Since the mobile wind speed is usually much smaller than the circulation wind speed, the focus is on the study of different circulation wind speed models. As the dynamic feedback iteration object, a typical circulation wind speed model set commonly used to describe the dimensionless circulation wind speed curve is adopted as the original model set for constructing the circulation wind speed part of the first model, including Batts model, Rankine model, Jelesn65 model, Jelesn66 model, Kongmo model and Demaria model.

[0095] The original model set includes sub-models and , which can be expressed by the following relationship:

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] in, and Respectively The maximum circulation wind speed at the time and the distance from the typhoon center; and is the shape parameter;

[0103] The dimensionless circulation wind speed curves of each model can be found in Figures 2 to 3 ,from Figure 2 and Figure 3 It can be seen that the parameters , and The changes in wind speed will lead to significant deviations. The research results show that relying on a single common wind speed model to model typhoon wind speed may result in large errors. Therefore, it is necessary to establish a more accurate typhoon wind speed model.

[0104] Get typhoon data, which comes from the typhoon database of the China Meteorological Administration and is updated every hour. Typhoon data includes the center coordinates and center pressure of the typhoon, as well as the radius of the seventh-level wind circle and the radius of the tenth-level wind circle, the center moving wind speed and the maximum wind speed; the minimum value of the seventh-level wind speed and the minimum value of the tenth-level wind speed are obtained according to the typhoon data, which can be expressed by the following relationship:

[0105] ;

[0106] in, Indicates the minimum wind speed of level 7, which is 13.9m / s; Indicates the minimum value of level 10 wind speed, which is 24.5m / s; express The actual value of the circulation wind speed at the level 7 wind circle at the moment; express The actual value of the circulating wind speed at the level 10 wind circle at that moment; Indicates the radius of the seventh-level wind circle; Indicates the radius of the tenth-level wind circle; and It can be calculated by combining the minimum value of level 7 wind speed and the minimum value of level 10 wind speed with the original model set.

[0107] According to the minimum wind speed of level 7 and level 10 and the original model set, the original model set is obtained. , and The initial value of

[0108] according to , and The initial value of is adjusted by joint dynamic feedback iteration to obtain , and The optimized value of

[0109] Joint dynamic feedback iterative adjustment includes The dynamic feedback iterative adjustment part and and The dynamic feedback iterative adjustment part; In the dynamic feedback iterative adjustment part, from and Dynamic feedback iterative adjustment part of the acquisition and The real-time iteration value is and The value of and In the dynamic feedback iterative adjustment part, from Dynamic feedback iterative adjustment part of the acquisition The real-time iteration value is The value of

[0110] In a preferred embodiment of the present invention, the joint dynamic feedback iterative adjustment includes:

[0111] according to The initial value of The dynamic feedback iterative adjustment part includes:

[0112] ;

[0113] in, express Moment original model set The circulation wind speed error of each sub-model; represents the original model set; and Respectively Moment original model set The weighted square error and weight coefficient of the circulation wind speed of each sub-model; express At the radius of the seventh wind speed Calculated values ​​of circulation wind speed for each model; express At the moment, the radius of the tenth wind speed Calculated values ​​of circulation wind speed for each model; and They represent the weight coefficients of the square error of the circulation wind speed in the seventh and tenth wind circles respectively.

[0114] according to The initial value of The dynamic feedback iterative adjustment part includes:

[0115] ;

[0116] in, express Moment Model Circulation wind speed error; Representation Model The weighted square error of the circulation wind speed;

[0117] according to The initial value of The dynamic feedback iterative adjustment part includes:

[0118] ;

[0119] in, express Moment Model Circulation wind speed error; Representation Model The weighted square error of the circulation wind speed.

[0120] The dynamic feedback iterative adjustment part includes the weight coefficients of each sub-model in the original model set , represents the index of the sub-model in the original model set, ;right Perform dynamic feedback iterative adjustment to obtain the weight coefficient of each sub-model in the original model set The optimized value of

[0121] In a preferred embodiment of the present invention, Dynamic feedback iterative adjustments include:

[0122] Dynamic feedback iterative adjustment based on the average Sum weighted squared error ,include:

[0123] ;

[0124] in, Indicates the maximum allowable change value of the preset sub-model weight coefficient.

[0125] Will and Substitute the optimized value of into the original model set to obtain the circulation wind speed part, and substitute the circulation wind speed part into the gradient wind speed In , get the first model ,include:

[0126] Will and Substitute the optimized value into the original model set, assign weights to the models in the original model set, and obtain the first model for calculating typhoon wind speed , which can be expressed by the following relationship:

[0127] ;

[0128] ;

[0129] in, express Circulation wind speed at any moment; and Respectively represent the first model Circulation wind speed and central moving wind speed of each sub-model; express The optimized value of express The optimized value of .

[0130] (2) Construct the second model, that is, construct a rainstorm rainfall rate model that takes into account the changes in the typhoon center air pressure, including:

[0131] The rainfall rate is determined by the central pressure difference, the central pressure change rate and the typhoon moving speed. The second model can be expressed by the following relationship:

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] in, express time The rainfall rate at Indicates the central air pressure difference; Indicates the rate of change of central air pressure; and Respectively and Correction factor of express Typhoon moving wind speed at the moment; Represents auxiliary variables; is the radius of the Earth; Indicates the distance from the typhoon center; and Represents the longitude and latitude coordinates of any two points in the typhoon.

[0138] S2. Construct a third model based on the first model and the second model in combination with the equivalent theory; the third model includes a wind-driven rain equivalent wind speed model based on the equivalent theory.

[0139] In a preferred embodiment of the present invention, the third model includes a wind-driven rain equivalent wind speed model based on the equivalent theory. , which can be expressed by the following relationship:

[0140] ;

[0141] ;

[0142] ;

[0143] in, and Respectively and correction factor.

[0144] In the preferred embodiment of the present invention, the temporal and spatial variation diagram of the equivalent wind speed under the coupling of typhoon and rainstorm is shown in Figure 4 , Figure 4 middle , and Indicates time.

[0145] S3. Construct a fourth model based on the third model and micro-topography factors; the fourth model includes a wind speed correction model.

[0146] Micro-topography such as mountains, hills, and plains will affect the equivalent wind speed of typhoons and rainstorms. Therefore, the equivalent wind speed needs to be corrected based on the micro-topography factor.

[0147] In a preferred embodiment of the present invention, the fourth model includes:

[0148] ;

[0149] in, Indicates the height of a mountain; Indicates the slope of the windward side; is the correction constant; It is the total height of a hilltop or slope; is a constant, its value is 1.5; Represents the wind speed correction model.

[0150] S4, constructing a wind power output uncertainty model based on the fourth model combined with the first curve; the first curve includes a wind speed and power characteristic curve of the wind power generation equipment. S4 specifically includes:

[0151] The wind speed and power characteristic curve, i.e. the first curve, includes:

[0152] ;

[0153] The uncertainty model of wind power output can be expressed by the following relationship:

[0154] ;

[0155] in, and are the cut-in wind speed and cut-out wind speed of the wind turbine respectively; is the rated wind speed; and They are the actual output power and rated output power of the wind power generation equipment respectively.

[0156] In order to evaluate the effectiveness of the method of the present invention, simulation verification is carried out:

[0157] A super typhoon is used as a simulation example. The typhoon data is provided by the meteorological department, see Table 1. In order to maintain generality, the time period (10:00-21:00) when the typhoon has the greatest impact on a wind power plant in a certain place is selected for analysis.

[0158] See also Figure 5 The method of the present invention is used to obtain different wind speed distribution curves of a wind turbine at a certain location, including different wind speed distribution curves considering only typhoons, considering typhoon rainstorms, and considering typhoon rainstorms under micro-topography.

[0159] See also Figure 6 The method of the present invention is used to obtain a time-varying uncertainty output distribution curve of a wind turbine at a certain location, including a time-varying uncertainty output distribution curve of a wind turbine considering only typhoons, considering typhoon rainstorms, and considering typhoon rainstorms under micro-topography.

[0160] See also Figure 7 The calculated value of the circulation wind speed at the level 7 wind circle is calculated by the first model in the method of the present invention (i.e., the typhoon wind speed model based on dynamic feedback iteration). and the calculated value of the circulation wind speed at the level 10 wind circle , the calculated value of the circulation wind speed at the seventh wind circle is calculated by the classic model in the original model set (Rankine, Kongmo, Demaria, Batts, Jelesn65 and Jelesn66) and the calculated values ​​of the circulation wind speed at the tenth wind circle (Rankine, Kongmo, Demaria, Batts, Jelesn65 and Jelesn66) and compared with actual data. Figure 7 In the method of the present invention, data refers to the output value calculated by the first model in the method of the present invention.

[0161] from Figure 5 It can be concluded that: ① The equivalent wind speed distribution obtained by considering the wind-driven rain coupling equivalent model under typhoon and rainstorm is significantly increased compared with that of considering only the typhoon; ② The micro-topography has a certain influence on the actual equivalent wind speed distribution. For example, if the wind turbine is located in a hilly area, its equivalent wind speed is somewhat increased compared with the wind speed distribution considering the micro-topography, which is mainly caused by the terrain lift effect and smaller friction in the hilly area.

[0162] from Figure 6 It can be concluded that the method of the present invention can better track the actual value of the wind power output model, while the wind power output model that only considers typhoons and does not consider micro-topography more optimistically predicts the wind power output, which has a large deviation from the actual wind power output and is not conducive to the subsequent effective evaluation and prevention and control of the system. This proves the effectiveness of the method of the present invention.

[0163] from Figure 7 It can be obtained that the calculated values ​​of the circulating wind speed at the seventh wind circle and the tenth wind circle calculated by the first model in the method of the present invention are closer to the actual values ​​than the calculation results of the classical model, which proves that the first model of the method of the present invention is more accurate than the existing classical model.

[0164] ;

[0165] In summary, the wind power generation output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism of the present invention comprehensively considers the coupling effect of typhoon and rainstorm and micro-topography factors, and firstly establishes a typhoon wind speed model based on dynamic feedback iteration and a rainstorm rainfall rate model considering the change of typhoon center air pressure, wherein the typhoon wind speed model based on dynamic feedback iteration has higher accuracy than the conventional typhoon wind speed model, so that the typhoon wind speed model of the present invention is more accurate; according to the typhoon wind speed model and the rainstorm rainfall rate model, a wind-driven rain equivalent wind speed model is constructed based on the equivalent theory, so that the present invention comprehensively considers the coupling effect of typhoon and rainstorm and improves the modeling accuracy; according to the wind-driven rain equivalent wind speed model combined with micro-topography factors, a wind speed correction model is constructed, so that the present invention comprehensively considers the micro-topography factors and further improves the modeling accuracy; finally, a wind power generation output uncertainty model is constructed according to the wind speed correction model combined with the wind speed and power characteristic curve of the wind power generation equipment; the method of the present invention can effectively improve the accuracy of wind power generation output uncertainty modeling under typhoon and rainstorm conditions. The method of the present invention helps to more accurately predict and respond to the impact of extreme weather on new energy equipment, and also lays a solid foundation for ensuring the safe and stable operation of new energy distribution systems under typhoons and rainstorms.

[0166] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wind power output uncertainty modeling method based on typhoon-rainstorm coupling mechanism, characterized in that: The following steps are involved: S1, constructing a first model and a second model; the first model includes a typhoon wind speed model based on dynamic feedback iteration; the typhoon wind speed model based on dynamic feedback iteration includes a moving wind speed part and a circulation wind speed part obtained by dynamic feedback iteration; the second model includes a rainstorm rainfall rate model considering the change of typhoon center pressure; S2. Constructing a third model based on the first model and the second model in combination with equivalent theory; the third model includes a wind-driven rain equivalent wind speed model based on equivalent theory; S3, constructing a fourth model based on the third model combined with micro-topography factors; the fourth model includes a wind speed correction model; S4. Constructing a wind power output uncertainty model based on the fourth model and the first curve; the first curve includes a wind speed and power characteristic curve of the wind power generation equipment; The wind power output uncertainty model can be expressed by the following relationship: ， in, and Respectively represent the cut-in wind speed and cut-out wind speed of the wind turbine; Indicates rated wind speed; and Respectively represent the actual output power and rated output power of wind power generation equipment; represents the wind speed correction model; Represents the wind speed and power characteristic curve.

2. The wind power output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism according to claim 1 is characterized in that: The S1 includes: Constructing the first model, that is, constructing a typhoon wind speed model based on dynamic feedback iteration, including: Typhoon gradient wind speed It is composed of circulation wind speed and moving wind speed: ， in, and Respectively The gradient wind speed at the time and the distance from the typhoon center Circulation wind speed at and They represent the maximum wind speed radius and the moving wind speed of the corresponding typhoon center respectively; Indicates the duration of the typhoon disaster; Indicates the moment; Indicates the distance between a certain point in a typhoon and the center of the typhoon; The circulation wind speed in the gradient wind speed As a dynamic feedback iteration object, the typical circulation wind speed model set is used as the original model set for constructing the circulation wind speed part of the first model, including sub-models and , which can be expressed by the following relationship: ， ， ， ， ， ， in, and Respectively The maximum circulation wind speed at the time and the distance from the typhoon center; and is the shape parameter; Acquire typhoon data, the typhoon data including the center coordinates and center pressure of the typhoon, as well as the radius of the seventh-level wind circle and the radius of the tenth-level wind circle, the center moving wind speed and the maximum wind speed; obtain the minimum value of the seventh-level wind speed and the minimum value of the tenth-level wind speed according to the typhoon data, which can be expressed by the following relationship: ， in, Indicates the minimum wind speed of level 7; Indicates the minimum wind speed of level 10; express The actual value of the circulation wind speed at the level 7 wind circle at the moment; express The actual value of the circulating wind speed at the level 10 wind circle at that moment; Indicates the radius of the seventh-level wind circle; Indicates the radius of the tenth-level wind circle; The calculation is performed based on the minimum wind speed of level 7 and the minimum wind speed of level 10 and the original model set to obtain the value of the original model set. and The initial value of according to and The initial value of is adjusted by joint dynamic feedback iteration to obtain and The optimized value of The combined dynamic feedback iterative adjustment includes The dynamic feedback iterative adjustment part and and The dynamic feedback iterative adjustment part; In the dynamic feedback iterative adjustment part, from and Dynamic feedback iterative adjustment part of the acquisition and The real-time iteration value is and The value of and In the dynamic feedback iterative adjustment part, from Dynamic feedback iterative adjustment part of the acquisition The real-time iteration value is The value of Said The dynamic feedback iterative adjustment part includes the weight coefficients of each sub-model in the original model set , represents the index of the sub-model in the original model set, ;right Perform dynamic feedback iterative adjustment to obtain the weight coefficient of each sub-model in the original model set The optimized value of Will and Substitute the optimized value of into the original model set to obtain the circulation wind speed part, and substitute the circulation wind speed part into the gradient wind speed In , and obtain the first model .

3. The wind power output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism according to claim 2 is characterized in that: The joint dynamic feedback iterative adjustment includes: according to The initial value of The dynamic feedback iterative adjustment part includes: ， in, express The original model set at the moment The circulation wind speed error of each sub-model; represents the original model set; and Respectively The original model set at the moment The weighted square error and weight coefficient of the circulation wind speed of each sub-model; express At the radius of the seventh wind speed Calculated values ​​of circulation wind speed for each model; express At the moment, the radius of the tenth wind speed Calculated values ​​of circulation wind speed for each model; and They represent the weight coefficients of the square error of the circulation wind speed in the seventh and tenth wind circles respectively.

4. The wind power output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism according to claim 3 is characterized in that: The joint dynamic feedback iterative adjustment also includes: according to The initial value of The dynamic feedback iterative adjustment part includes: ， in, express Moment Model Circulation wind speed error; Representation Model The weighted square error of the circulation wind speed; according to The initial value of The dynamic feedback iterative adjustment part includes: ， in, express Moment Model Circulation wind speed error; Representation Model The weighted square error of the circulation wind speed.

5. The wind power output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism according to claim 4 is characterized in that: The pair Dynamic feedback iterative adjustments include: Dynamic feedback iterative adjustment based on the average Sum weighted squared error ,include: ， in, Indicates the maximum allowable change value of the preset sub-model weight coefficient.

6. The wind power output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism according to claim 5 is characterized in that: The and The optimized values ​​​​substituted into the original model set include: Will and Substitute the optimized value of into the original model set, assign weights to the models in the original model set, and obtain the first model for calculating the typhoon wind speed , which can be expressed by the following relationship: ， ， in, express Circulation wind speed at any moment; and Respectively represent the first model Circulation wind speed and central moving wind speed of each sub-model; express The optimized value of express The optimized value of .

7. The wind power output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism according to claim 6 is characterized in that: The S1 further comprises: Construct the second model, that is, construct a rainstorm rainfall rate model that takes into account the changes in the typhoon center air pressure, including: The rainfall rate is determined by the central pressure difference, the central pressure change rate and the typhoon moving speed. The second model can be expressed by the following relationship: ， ， ， ， ， in, express time The rainfall rate at Indicates the central air pressure difference; Indicates the rate of change of central air pressure; and Respectively and Correction factor of express Typhoon moving wind speed at the moment; Represents auxiliary variables; is the radius of the Earth; Indicates the distance from the typhoon center; and Represents the longitude and latitude coordinates of any two points in the typhoon.

8. The wind power output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism according to claim 7 is characterized in that: The third model includes: The third model includes a wind-driven rain equivalent wind speed model based on equivalent theory , which can be expressed by the following relationship: ， ， ， in, and Respectively and correction factor.

9. The wind power output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism according to claim 8 is characterized in that: The fourth model includes: ， in, Indicates the height of a mountain; Indicates the slope of the windward side; is the correction constant; It is the total height of a hilltop or slope; is a constant, its value is 1.5; Represents the wind speed correction model.

10. The wind power output uncertainty modeling method based on the typhoon-rainstorm coupling mechanism according to claim 8 is characterized in that: The S4 includes: The wind speed and power characteristic curve, i.e., the first curve, includes: 。

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