An intelligent coordinated control system and method for energy storage for wind power generation

Abstract

The present invention discloses an intelligent coordinated control system and method for energy storage for wind power generation, relates to the field of wind power generation, solves the problem of low wind power generation prediction accuracy, comprises a data acquisition module, a model construction module, a wind power analysis module, and an energy storage control module; the data acquisition module is used to collect structural data and historical power generation data of the wind generator, and send the structural data to the model construction module and the wind power analysis module, and send the historical power generation data to the wind power analysis module; the model construction module is used to build a generator power model and send it to the wind power analysis module; the wind power analysis module is used to calculate the predicted power generation of the wind generator based on the generator power model and send it to the energy storage control module; the energy storage control module is used to perform intelligent control based on the future predicted power generation to obtain the charging and discharging control results of the target power grid. The present invention realizes the accurate prediction of the power generation capacity of the wind generator.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and in particular relates to an intelligent coordinated energy storage control system and method for wind power generation. Background Art

[0002] Wind power generation is the process of converting wind energy into mechanical energy and then further into electrical energy. It uses wind power to rotate windmill blades, which are then accelerated by a speed increaser to drive a generator, generating electricity and ultimately outputting alternating current for use. As a key component of clean energy, wind power generation will play a key role in the global energy transition. With continuous technological innovation, cost reductions, and an optimized regulatory environment, wind power generation is expected to achieve wider application and make greater contributions to addressing climate change and promoting sustainable development.

[0003] However, at present, when storing wind power, the accuracy of wind power generation prediction is not high enough, and thus it is impossible to accurately obtain the predicted power generation in the future. Therefore, it is impossible to make corresponding adjustments to the energy storage plan in time, resulting in a waste of power resources.

[0004] To this end, the present invention proposes an intelligent coordinated energy storage control system and method for wind power generation. Summary of the Invention

[0005] The purpose of the present invention is to propose an intelligent coordinated control system and method for energy storage for wind power generation, so as to solve the problems raised in the above background technology.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] An energy storage intelligent coordination control system for wind power generation, including a data acquisition module, a model building module, a wind power analysis module, an energy storage control module and a display terminal;

[0008] The data acquisition module is used to collect structural data and historical power generation data corresponding to the wind turbine, and send the structural data of the wind turbine to the model building module and the wind power analysis module, and send the historical power generation data of the wind turbine to the wind power analysis module;

[0009] The model building module is used to build a corresponding generator power model for the wind turbine and send it to the wind power analysis module;

[0010] The wind power analysis module is used to calculate the predicted power generation of the wind turbine according to the generator power model corresponding to the wind turbine and send it to the energy storage control module;

[0011] The energy storage control module is used to perform intelligent control based on the predicted future power generation of the wind turbine, obtain the charge and discharge control results of the target power grid and send them to the display terminal; the display terminal is used to receive and display the charge and discharge control results of the power grid.

[0012] Furthermore, the structural data includes the number of motor pole pairs, impeller radius, and impeller swept area of ​​the wind turbine; and the historical power generation data includes the historical ambient wind speed, historical air density, and historical power generation at the location of the wind turbine.

[0013] Furthermore, the generator power model is constructed as follows:

[0014] Define A, B, and C as the axes of the three-phase stator winding; define a, b, and c as the axes of the rotor winding; the axes of the stator winding are stationary in space, while the axes of the rotor winding rotate with the movement of the motor rotor;

[0015] Select the A axis as the reference coordinate axis, define the electrical angle between the a axis and the A axis as the spatial angle θ, and obtain the voltage equations of the three-phase stator winding voltage and the three-phase rotor winding voltage of the wind turbine generator:

[0016] Three-phase stator winding voltage: ;

[0017] Three-phase rotor winding voltage: ;

[0018] The voltage equation of the wind turbine is summarized as follows:

[0019] ; (1)

[0020] Where U A 、U B and U C are the instantaneous values ​​of the stator phase voltages corresponding to the A, B and C axes respectively; U a 、U b and U c are the instantaneous values ​​of the rotor phase voltages corresponding to axes a, b, and c respectively;

[0021] i A 、i B and i C are the instantaneous values ​​of the stator phase current corresponding to the A, B and C axes respectively; i a 、i b and i c are the instantaneous values ​​of the rotor phase current corresponding to axes a, b and c respectively;

[0022] φ A 、φ B and φC are the full magnetic flux of the stator winding corresponding to the A, B and C axes respectively; φ a 、φ b and φ c are the total magnetic flux of the rotor winding corresponding to the a, b and c axes respectively; rd is the resistance of the stator winding, rz is the resistance of the rotor winding, and wf is the differential operator .

[0023] Furthermore, the generator power model construction process also includes:

[0024] Construct the magnetic flux equation of the wind turbine. The magnetic flux equation is:

[0025] ; (2)

[0026] Where, φ z is the rotor flux vector, φ d is the stator flux vector; i z is the rotor current vector, i d is the stator current vector; G dd is the stator self-inductance matrix, G zz is the rotor self-inductance matrix, G dz and G zd is the stator-rotor mutual inductance matrix;

[0027] φ d =[φ A φ B φ C ] T ;φ z =[φ a φ b φ c ] T ;Where, T represents the transpose of the matrix;

[0028] i d =[i A i B i C ] T ;i z =[i a i b i c ] T ;

[0029] ;

[0030] ;

[0031] ;

[0032] Where Gld is the stator leakage inductance, Glz is the rotor leakage inductance, the stator leakage inductance of all stator windings is the same, and the rotor leakage inductance of all rotor windings is the same;

[0033] Ghd is the stator mutual inductance, Ghz is the rotor mutual inductance, the stator mutual inductance of all stator windings is the same, and the rotor mutual inductance is the same.

[0034] Furthermore, the generator power model construction process also includes:

[0035] Construct the torque equation for the wind turbine:

[0036] ; (3)

[0037] Where DZ is the electromagnetic torque, JDS is the number of motor pole pairs, i z T is the rotor current vector i z The transpose of is the partial derivative of the stator-rotor mutual inductance matrix with respect to the electrical angle θ;

[0038] Construct the motion equation corresponding to the wind turbine, the motion equation is:

[0039] ; (4)

[0040] Where JZ is the mechanical torque output by the wind turbine in the motion equation, ZG is the moment of inertia of the wind turbine in the motion equation, and ω represents the angular velocity of the impeller of the wind turbine in the motion equation, specifically the angular velocity of the impeller during rotation;

[0041] Combining equations (1), (2), (3) and (4) we can obtain the generator dynamic model corresponding to the wind turbine.

[0042] Furthermore, the analysis process of the wind power analysis module is as follows:

[0043] Obtain the historical power generation data corresponding to the wind turbine, and obtain the historical ambient wind speed FS, historical air density ρ, and historical power generation corresponding to the wind turbine;

[0044] Then, the structural data of the wind turbine is obtained to obtain the impeller radius R and the swept area MJ of the wind turbine;

[0045] The historical tip speed ratio YJ of the wind turbine is calculated by the formula, which is as follows:

[0046] YJ=ω×R / FS, ω is the angular velocity of the wind turbine blade;

[0047] The relationship equation between the wind turbine's power generation GL and wind speed under the corresponding historical environment is constructed by formula:

[0048] GL=BH×ω 3 ; ; BH is the wind energy capture coefficient, LY is the wind energy utilization coefficient;

[0049] Since the power generation = mechanical torque × angular velocity of the wind turbine;

[0050] A generator power model of the wind turbine is obtained, and an estimated historical power generation corresponding to the wind turbine is obtained based on the generator power model and a relationship equation between power generation and wind speed.

[0051] Furthermore, the analysis process of the wind power analysis module further includes:

[0052] Subtract the estimated historical power generation from the historical power generation, take the absolute value, and then divide it by the historical power generation to obtain the estimated power generation deviation rate; compare the estimated power generation deviation rate with the deviation rate threshold;

[0053] If the estimated power generation deviation rate is greater than or equal to the deviation rate threshold, the value of the wind energy utilization coefficient is adjusted;

[0054] If the estimated power generation deviation rate is less than the deviation rate threshold, the generator dynamic model of the wind turbine and the relationship equation between the generated power and the wind speed are recorded as the wind power generation model of the corresponding wind turbine;

[0055] Obtain weather forecast data for the location of the wind turbine, and then read the predicted real-time wind speed of the wind turbine and the duration of the corresponding predicted real-time wind speed;

[0056] The predicted real-time wind speed is imported into the wind power generation model to obtain the predicted power generation of the wind turbine at the time corresponding to the predicted real-time wind speed. The predicted power generation is obtained by multiplying the predicted power generation by the corresponding duration.

[0057] Furthermore, the control process of the energy storage control module is as follows:

[0058] Obtain the predicted power generation of the wind turbine in the future, and divide the predicted power generation into multiple power generation intervals according to fixed time intervals;

[0059] For any sub-interval of power generation, the power generation power of the sub-interval of the corresponding sub-interval is obtained by dividing the predicted power generation corresponding to the sub-interval of power generation by the time interval;

[0060] Comparing the real-time state of charge of the target power grid with the state of charge constraint interval; if the real-time state of charge is less than or equal to a first state of charge constraint value, charging all electric energy corresponding to the sub-interval of the power generation into the target power grid based on the sub-interval power generation, and updating the state of charge in real time using a charge update function until the real-time state of charge of the target power grid reaches a second state of charge constraint value; wherein the first state of charge constraint value is less than the second state of charge constraint value;

[0061] The charge update function is as follows:

[0062] SOC （t） =SOC （t-1） +(GL cd -GL fd )×XL×△t; where t is the number of different electron-emitting intervals, SOC （t） is the current state of charge of the target grid, SOC （t-1） is the charge state of the target grid in the last electron generation interval, GL cd Indicates the total charging power of the target grid, GL fd It represents the total discharge power of the target grid, XL is the charging efficiency, and △t is the time interval.

[0063] Furthermore, the control process of the energy storage control module also includes:

[0064] If the real-time state of charge is greater than the first state of charge constraint value and less than or equal to the second state of charge constraint value, obtaining a grid connection limit value of the target power grid; wherein the grid connection limit value is the maximum charging power that the target power grid can withstand;

[0065] The sub-interval power generation is compared with the grid connection limit. When the sub-interval power generation is greater than the grid connection limit, the expected charging power is obtained by subtracting the grid connection limit from the sub-interval power generation. The target grid is charged with the power corresponding to the grid connection limit, and the backup battery pack is charged with the expected charging power.

[0066] When the sub-interval generated power is less than or equal to the grid connection limit, the target grid is charged with the sub-interval generated power until the real-time state of charge reaches the second state of charge constraint value;

[0067] When the real-time state of charge is greater than or equal to the second state of charge constraint value, power is directly supplied to the corresponding city of the target power grid using the sub-interval power generation efficiency;

[0068] Furthermore, the real-time electricity price of the city corresponding to the target power grid is obtained. If the real-time electricity price of the city corresponding to the target power grid is greater than or equal to a preset threshold, and the real-time state of charge of the target power grid is greater than a first state of charge constraint value, a discharge operation is performed to control the target power grid to discharge to the corresponding city.

[0069] An intelligent coordinated control method for energy storage for wind power generation, the method comprising:

[0070] Step S101, collecting structural data and historical power generation data corresponding to the wind turbine;

[0071] Step S102, constructing a generator power model corresponding to the wind turbine in combination with the structural data;

[0072] Step S103: constructing a wind power generation model based on the generator dynamic model of the wind turbine and historical power generation data.

[0073] Step S104, predicting future power generation based on the wind power generation model and weather forecast data;

[0074] Step S105 , intelligently regulating the target power grid according to the predicted future power generation of the wind turbines.

[0075] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0076] 1. The present invention first collects structural data and historical power generation data corresponding to the wind turbine through the data acquisition module, and sends the structural data of the wind turbine to the model construction module and the wind power analysis module, and sends the historical power generation data of the wind turbine to the wind power analysis module; then, the model construction module constructs a corresponding generator dynamic model for the wind turbine and sends it to the wind power analysis module; the present invention realizes the construction of the generator dynamic model corresponding to the wind turbine;

[0077] 2. The present invention utilizes a wind power analysis module to calculate the predicted power generation of a wind turbine based on the generator dynamic model corresponding to the wind turbine and sends the calculation to an energy storage control module. Finally, the energy storage control module performs intelligent control based on the predicted future power generation of the wind turbine, obtains the charge and discharge control results of the target power grid and sends them to a display terminal. The present invention realizes accurate prediction of the power generation capacity of a wind turbine. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0079] Figure 1 is a block diagram of the overall system of the present invention;

[0080] Figure 2 Equivalent model diagram of the wind turbine generator in the present invention;

[0081] Figure 3 A schematic diagram of a scenario in which the present invention is applied;

[0082] Figure 4 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0083] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0084] Example 1, please refer to Figure 1-Figure 3 As shown, the technical solution provided by the present invention is: an intelligent coordinated control system for energy storage for wind power generation, which constructs a generator dynamic model based on the structural data of the wind turbine, obtains a wind power generation model based on the historical power generation data of the wind turbine, obtains the expected power generation of the wind turbine based on the wind power generation model and weather forecast, and performs multi-mode regulation of the energy storage system based on the expected power generation, such as wind curtailment absorption and high-price discharge;

[0085] In this embodiment, the energy storage intelligent coordination system includes a data acquisition module, a model building module, a wind power analysis module, an Internet of Things, an energy storage control module, and a display terminal;

[0086] The data acquisition module is used to collect structural data and historical power generation data corresponding to the wind turbine, wherein the structural data includes the number of motor pole pairs, impeller radius, and impeller swept area of ​​the wind turbine; and the historical power generation data includes the historical ambient wind speed, historical air density, and historical power generation at the location of the wind turbine.

[0087] The data acquisition module sends the structural data of the wind turbine to the model building module and the wind power analysis module, and sends the historical power generation data of the wind turbine to the wind power analysis module;

[0088] Among them, the wind turbine is the current mainstream model of wind power generation - the doubly fed induction motor. Its core feature is that the rotor winding is connected to the power grid through a converter, and the decoupling of the speed and the grid frequency is achieved by controlling the frequency and phase of the rotor current.

[0089] In the present invention, the model building module is used to build a corresponding generator power model for the wind turbine. The construction process is as follows:

[0090] like Figure 2 As shown in the figure, the equivalent model diagram of the wind turbine generator is shown. A, B, and C are the axes of the three-phase stator winding, and a, b, and c are the axes of the rotor winding. The axis of the stator winding is stationary in space, while the axis of the rotor winding rotates with the movement of the motor rotor.

[0091] Select the A axis as the reference coordinate axis, define the electrical angle between the a axis and the A axis as the spatial angle θ, and obtain the voltage equations of the three-phase stator winding voltage and the three-phase rotor winding voltage of the wind turbine generator:

[0092] Three-phase stator winding voltage: ;

[0093] Three-phase rotor winding voltage: ;

[0094] The voltage equation of the wind turbine is summarized as follows:

[0095] ; (1)

[0096] Where U A 、U B and U C are the instantaneous values ​​of the stator phase voltages corresponding to the A, B and C axes respectively; U a 、U b and U c are the instantaneous values ​​of the rotor phase voltages corresponding to axes a, b, and c respectively;

[0097] i A 、i B and i C are the instantaneous values ​​of the stator phase current corresponding to the A, B and C axes respectively; i a 、i b and i c are the instantaneous values ​​of the rotor phase current corresponding to axes a, b and c respectively;

[0098] φ A 、φ B and φ C are the full magnetic flux of the stator winding corresponding to the A, B and C axes respectively; φ a 、φ b and φ c are the total magnetic flux of the rotor winding corresponding to the a, b and c axes respectively; rd is the resistance of the stator winding, rz is the resistance of the rotor winding, and wf is the differential operator ;

[0099] Construct the magnetic flux equation of the wind turbine. The magnetic flux equation is:

[0100] ; (2)

[0101] Where, φ z is the rotor flux vector, φ d is the stator flux vector; i z is the rotor current vector, i d is the stator current vector; Gdd is the stator self-inductance matrix, G zz is the rotor self-inductance matrix, G dz and G zd is the stator-rotor mutual inductance matrix;

[0102] φ d =[φ A φ B φ C ] T ;φ z =[φ a φ b φ c ] T ;Where, T represents the transpose of the matrix;

[0103] i d =[i A i B i C ] T ;i z =[i a i b i c ] T ;

[0104] ;

[0105] ;

[0106] ;

[0107] Where Gld is the stator leakage inductance, and Glz is the rotor leakage inductance. Since the stator winding and the rotor winding are symmetrical, the stator leakage inductance of all stator windings is the same, and the rotor leakage inductance of all rotor windings is the same.

[0108] Ghd is the stator mutual inductance, and Ghz is the rotor mutual inductance. Since the stator windings have the same number of turns and equal magnetic resistance, the stator mutual inductance of all stator windings is the same, and the rotor mutual inductance is the same.

[0109] Build into the torque equation of the wind turbine:

[0110] ; (3)

[0111] Where DZ is the electromagnetic torque, JDS is the number of motor pole pairs, i z T is the rotor current vector i z The transpose of is the partial derivative of the stator-rotor mutual inductance matrix with respect to the electrical angle θ. Its physical meaning is: the rate of change of mutual inductance caused by a unit electrical angle change, which reflects the degree of tangential distortion of the magnetic field;

[0112] Construct the motion equation corresponding to the wind turbine, the motion equation is:

[0113] ; (4)

[0114] Where JZ is the mechanical torque output by the wind turbine in the motion equation, ZG is the moment of inertia of the wind turbine in the motion equation, and ω represents the angular velocity of the impeller of the wind turbine in the motion equation, specifically the angular velocity of the impeller during rotation;

[0115] Combining equations (1), (2), (3) and (4) we can get the generator dynamic model corresponding to the wind turbine.

[0116] The model building module sends the generator dynamic model of the wind turbine to the wind power analysis module.

[0117] Furthermore, the wind power analysis module is used to calculate the predicted power generation of the wind turbine based on the generator power model corresponding to the wind turbine. The calculation process is as follows:

[0118] Obtain the historical power generation data corresponding to the wind turbine, and obtain the historical ambient wind speed FS, historical air density ρ, and historical power generation corresponding to the wind turbine;

[0119] Then, the structural data of the wind turbine is obtained to obtain the impeller radius R and the swept area MJ of the wind turbine;

[0120] The historical tip speed ratio YJ of the wind turbine is calculated by the formula, which is as follows:

[0121] YJ=ω×R / FS, ω is the angular velocity of the wind turbine blade;

[0122] The relationship equation between the wind turbine's power generation GL and wind speed under the corresponding historical environment is constructed by formula:

[0123] GL=BH×ω 3 ; ; BH is the wind energy capture coefficient, LY is the wind energy utilization coefficient;

[0124] And since the power generation = mechanical torque × angular velocity of the wind turbine;

[0125] Therefore, a generator power model of the wind turbine is obtained, and an estimated historical power generation corresponding to the wind turbine is obtained based on the generator power model and a relationship equation between power generation and wind speed;

[0126] Subtract the estimated historical power generation from the historical power generation, take the absolute value, and then divide it by the historical power generation to obtain the estimated power generation deviation rate; compare the estimated power generation deviation rate with the deviation rate threshold;

[0127] If the estimated power generation deviation rate is greater than or equal to the deviation rate threshold, the value of the wind energy utilization coefficient is adjusted;

[0128] If the estimated power generation deviation rate is less than the deviation rate threshold, the generator dynamic model of the wind turbine and the relationship equation between the generated power and the wind speed are recorded as the wind power generation model of the corresponding wind turbine;

[0129] Obtain weather forecast data at the location of the wind turbine through the Internet of Things, and then read the predicted real-time wind speed of the wind turbine and the duration of the corresponding predicted real-time wind speed;

[0130] Import the predicted real-time wind speed into the wind power generation model to obtain the predicted power generation of the wind turbine at the time corresponding to the predicted real-time wind speed. The predicted power generation is obtained by multiplying the predicted power generation by the corresponding duration.

[0131] The wind power analysis module sends the predicted future power generation of the wind turbine to the energy storage control module.

[0132] In this embodiment, the energy storage control module performs intelligent control based on the predicted future power generation of the wind turbine. The control process is as follows:

[0133] Obtain the predicted power generation of the wind turbine in the future, and divide the predicted power generation into multiple power generation sub-intervals according to fixed time intervals; in the present invention, the time interval is preferably one hour;

[0134] For any sub-interval of power generation, the power generation power of the sub-interval of the corresponding sub-interval is obtained by dividing the predicted power generation corresponding to the sub-interval of power generation by the time interval;

[0135] The real-time state of charge of the target power grid is compared with the state of charge constraint interval. If the real-time state of charge is less than or equal to the first state of charge constraint value, all the electric energy corresponding to the sub-interval of the power generation is charged into the target power grid according to the power generation of the sub-interval. The state of charge is updated in real time through the charge update function until the real-time state of charge of the target power grid reaches the second state of charge constraint value. The first state of charge constraint value is less than the second state of charge constraint value. Specifically, the first state of charge constraint value is 20% and the second state of charge constraint value is 90%.

[0136] The charge update function is as follows:

[0137] SOC （t） =SOC （t-1） +(GL cd -GLfd )×XL×△t; where t is the number of different electron-emitting intervals, SOC （t） is the current state of charge of the target grid, SOC （t-1） is the charge state of the target grid in the last electron generation interval, GL cd Indicates the total charging power of the target grid, GL fd represents the total discharge power of the target grid, XL is the charging efficiency, and △t is the time interval;

[0138] If the real-time state of charge is greater than the first state of charge constraint value and less than or equal to the second state of charge constraint value, obtaining a grid connection limit value of the target power grid; wherein the grid connection limit value is the maximum charging power that the target power grid can withstand;

[0139] The sub-interval power generation is compared with the grid connection limit. When the sub-interval power generation is greater than the grid connection limit, the target grid is deemed to be at risk of wind curtailment. The estimated charging power is obtained by subtracting the grid connection limit from the sub-interval power generation. The target grid is charged at the power corresponding to the grid connection limit, and the backup battery pack is charged at the estimated charging power.

[0140] When the sub-interval generated power is less than or equal to the grid connection limit, the target grid is charged with the sub-interval generated power until the real-time state of charge reaches the second state of charge constraint value;

[0141] When the real-time state of charge is greater than or equal to the second state of charge constraint value, power is directly supplied to the corresponding city of the target power grid using the sub-interval power generation efficiency;

[0142] Furthermore, the real-time electricity price of the city corresponding to the target power grid is obtained. If the real-time electricity price of the city corresponding to the target power grid is greater than or equal to a preset threshold, and the real-time state of charge of the target power grid is greater than a first state of charge constraint value, a discharge operation is performed to control the target power grid to discharge to the corresponding city.

[0143] The energy storage control module sends the charge and discharge control results of the target power grid to the display terminal; the display terminal is used to receive and display the charge and discharge control results of the power grid.

[0144] In this application, if a corresponding calculation formula appears, the above calculation formula is dimensionless and its numerical calculation is performed. The weight coefficient, proportional coefficient and other coefficients in the formula are set to a result value obtained by quantifying each parameter. Regarding the size of the weight coefficient and the proportional coefficient, as long as it does not affect the proportional relationship between the parameter and the result value, it is acceptable.

[0145] Example 2, based on another concept of the same invention, as Figure 4 As shown, a method for intelligent coordinated control of energy storage for wind power generation is proposed, which includes the following steps:

[0146] Step S101, collecting structural data and historical power generation data corresponding to the wind turbine;

[0147] Step S102, constructing a generator power model corresponding to the wind turbine in combination with the structural data;

[0148] Step S103, constructing a wind power generation model based on the generator dynamic model of the wind turbine and historical power generation data;

[0149] Step S104, predicting future power generation based on the wind power generation model and weather forecast data;

[0150] Step S105 , intelligently regulating the target power grid according to the predicted future power generation of the wind turbines.

[0151] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent coordinated control system for energy storage for wind power generation, characterized in that: It includes a data acquisition module, a model building module, a wind power analysis module, an energy storage control module and a display terminal. The data acquisition module is used to collect the structural data and historical power generation data corresponding to the wind turbine, and send the structural data of the wind turbine to the model building module and the wind power analysis module, and send the historical power generation data of the wind turbine to the wind power analysis module; The model building module is used to build a corresponding generator power model for the wind turbine and send it to the wind power analysis module; the wind power analysis module is used to calculate the predicted power generation of the wind turbine based on the generator power model corresponding to the wind turbine and send it to the energy storage control module; The analysis process of the wind power analysis module is as follows: Obtain the historical power generation data corresponding to the wind turbine, and obtain the historical ambient wind speed FS, historical air density ρ, and historical power generation corresponding to the wind turbine; Then, the structural data of the wind turbine is obtained to obtain the impeller radius R and the swept area MJ of the wind turbine; The historical tip speed ratio YJ of the wind turbine is calculated by the formula, which is as follows: YJ=ω×R / FS, ω is the angular velocity of the wind turbine blade; The relationship equation between the wind turbine's power generation GL and wind speed under the corresponding historical environment is constructed by formula: GL=BH×ω 3 ; ; BH is the wind energy capture coefficient, LY is the wind energy utilization coefficient; Since the power generation = mechanical torque × angular velocity of the wind turbine; Obtain a generator power model of the wind turbine, and obtain an estimated historical power generation corresponding to the wind turbine based on the generator power model and a relationship equation between power generation and wind speed; Subtract the estimated historical power generation from the historical power generation, take the absolute value, and then divide it by the historical power generation to obtain the estimated power generation deviation rate; compare the estimated power generation deviation rate with the deviation rate threshold; If the estimated power generation deviation rate is greater than or equal to the deviation rate threshold, the value of the wind energy utilization coefficient is adjusted; If the estimated power generation deviation rate is less than the deviation rate threshold, the generator dynamic model of the wind turbine and the relationship equation between the generated power and the wind speed are recorded as the wind power generation model of the corresponding wind turbine; Obtain weather forecast data for the location of the wind turbine, and then read the predicted real-time wind speed of the wind turbine and the duration of the corresponding predicted real-time wind speed; Import the predicted real-time wind speed into the wind power generation model to obtain the predicted power generation of the wind turbine at the time corresponding to the predicted real-time wind speed. The predicted power generation is obtained by multiplying the predicted power generation by the corresponding duration. The energy storage control module is used to perform intelligent control based on the predicted future power generation of the wind turbine, obtain the charge and discharge control results of the target power grid and send them to the display terminal; the display terminal is used to receive and display the charge and discharge control results of the power grid.

2. The intelligent coordinated control system for energy storage for wind power generation according to claim 1, characterized in that: The structural data are the number of motor pole pairs, impeller radius and impeller swept area of ​​the wind turbine; The historical power generation data includes the historical ambient wind speed, historical air density, and historical power generation at the location of the wind turbine.

3. The intelligent coordinated control system for energy storage for wind power generation according to claim 2, characterized in that: The construction process of the generator power model is as follows: Define A, B, and C as the axes of the three-phase stator winding; define a, b, and c as the axes of the rotor winding; the axes of the stator winding are stationary in space, while the axes of the rotor winding rotate with the movement of the motor rotor; Select the A axis as the reference coordinate axis, define the electrical angle between the a axis and the A axis as the spatial angle θ, and obtain the voltage equations of the three-phase stator winding voltage and the three-phase rotor winding voltage of the wind turbine generator: Three-phase stator winding voltage: ; Three-phase rotor winding voltage: ; The voltage equation of the wind turbine is summarized as follows: ； （1） Where U A 、U B and U C are the instantaneous values ​​of the stator phase voltages corresponding to the A, B and C axes respectively; U a 、U b and U c are the instantaneous values ​​of the rotor phase voltages corresponding to axes a, b, and c respectively; i A 、i B and i C are the instantaneous values ​​of the stator phase current corresponding to the A, B and C axes respectively; i a 、i b and i c are the instantaneous values ​​of the rotor phase current corresponding to axes a, b and c respectively; φ A 、φ B and φ C are the full magnetic flux of the stator winding corresponding to the A, B and C axes respectively; φ a 、φ b and φ c are the total magnetic flux of the rotor winding corresponding to the a, b and c axes respectively; rd is the resistance of the stator winding, rz is the resistance of the rotor winding, and wf is the differential operator .

4. The intelligent coordinated control system for energy storage for wind power generation according to claim 3, characterized in that: The generator power model construction process also includes: Construct the magnetic flux equation of the wind turbine. The magnetic flux equation is: ； （2） Where, φ z is the rotor flux vector, φ d is the stator flux vector; i z is the rotor current vector, i d is the stator current vector; G dd is the stator self-inductance matrix, G zz is the rotor self-inductance matrix, G dz and G zd is the stator-rotor mutual inductance matrix; φ d =[φ A φ B φ C ] T ;φ z =[φ a φ b φ c ] T ;Where, T represents the transpose of the matrix; i d =[i A i B i C ] T ;i z =[i a i b i c ] T ; ； ； ； Where Gld is the stator leakage inductance, Glz is the rotor leakage inductance, the stator leakage inductance of all stator windings is the same, and the rotor leakage inductance of all rotor windings is the same; Ghd is the stator mutual inductance, Ghz is the rotor mutual inductance, the stator mutual inductance of all stator windings is the same, and the rotor mutual inductance is the same.

5. The intelligent coordinated control system for energy storage for wind power generation according to claim 4, characterized in that: The generator power model construction process also includes: Construct the torque equation for the wind turbine: ； （3） Where DZ is the electromagnetic torque, JDS is the number of motor pole pairs, i z T is the rotor current vector i z The transpose of is the partial derivative of the stator-rotor mutual inductance matrix with respect to the electrical angle θ; Construct the motion equation corresponding to the wind turbine, the motion equation is: ；（4） Where JZ is the mechanical torque output by the wind turbine in the motion equation, ZG is the moment of inertia of the wind turbine in the motion equation, and ω represents the angular velocity of the impeller of the wind turbine in the motion equation, specifically the angular velocity of the impeller during rotation; Combining equations (1), (2), (3) and (4) we can obtain the generator dynamic model corresponding to the wind turbine.

6. The intelligent coordinated control system for energy storage for wind power generation according to claim 1, characterized in that: The control process of the energy storage control module is as follows: Obtain the predicted power generation of the wind turbine in the future, and divide the predicted power generation into multiple power generation intervals according to fixed time intervals; For any sub-interval of power generation, the power generation power of the sub-interval of the corresponding sub-interval is obtained by dividing the predicted power generation corresponding to the sub-interval of power generation by the time interval; Comparing the real-time state of charge of the target power grid with the state of charge constraint interval; if the real-time state of charge is less than or equal to a first state of charge constraint value, charging all electric energy corresponding to the sub-interval of the power generation into the target power grid based on the sub-interval power generation, and updating the state of charge in real time using a charge update function until the real-time state of charge of the target power grid reaches a second state of charge constraint value; wherein the first state of charge constraint value is less than the second state of charge constraint value; The charge update function is as follows: SOC （t） =SOC （t-1） +(GL cd -GL fd )×XL×△t; where t is the number of different electron-emitting intervals, SOC （t） is the current state of charge of the target grid, SOC （t-1） is the charge state of the target grid in the last electron generation interval, GL cd Indicates the total charging power of the target grid, GL fd It represents the total discharge power of the target grid, XL is the charging efficiency, and △t is the time interval.

7. The intelligent coordinated control system for energy storage for wind power generation according to claim 6, characterized in that: The control process of the energy storage control module also includes: If the real-time state of charge is greater than the first state of charge constraint value and less than or equal to the second state of charge constraint value, obtaining a grid connection limit value of the target power grid; wherein the grid connection limit value is the maximum charging power that the target power grid can withstand; The sub-interval power generation is compared with the grid connection limit. When the sub-interval power generation is greater than the grid connection limit, the expected charging power is obtained by subtracting the grid connection limit from the sub-interval power generation. The target grid is charged with the power corresponding to the grid connection limit, and the backup battery pack is charged with the expected charging power. When the sub-interval generated power is less than or equal to the grid connection limit, the target grid is charged with the sub-interval generated power until the real-time state of charge reaches the second state of charge constraint value; When the real-time state of charge is greater than or equal to the second state of charge constraint value, power is directly supplied to the corresponding city of the target power grid using the sub-interval power generation efficiency; Furthermore, the real-time electricity price of the city corresponding to the target power grid is obtained. If the real-time electricity price of the city corresponding to the target power grid is greater than or equal to a preset threshold, and the real-time state of charge of the target power grid is greater than a first state of charge constraint value, a discharge operation is performed to control the target power grid to discharge to the corresponding city.

8. An intelligent coordinated control method for energy storage for wind power generation, characterized in that: In combination with the intelligent coordinated control method for energy storage for wind power generation according to any one of claims 1 to 7, the method comprises: Step S101, collecting structural data and historical power generation data corresponding to the wind turbine; Step S102, constructing a generator power model corresponding to the wind turbine in combination with the structural data; Step S103, constructing a wind power generation model based on the generator dynamic model of the wind turbine and historical power generation data; Step S104, predicting future power generation based on the wind power generation model and weather forecast data; Step S105 , intelligently regulating the target power grid according to the predicted future power generation of the wind turbines.

Images