Wind turbine generator converter temperature dynamic control method and system based on sandstorm climate

Abstract

The invention discloses a wind turbine generator converter temperature dynamic control method and system based on the sandstorm climate, and relates to the technical field of wind power plant control, and the technical scheme is characterized in that a wind speed time sequence model capable of representing the sandstorm climate characteristics is established; establishing a correlation model of the converter temperature and the output power of the wind turbine generator; and combining the wind speed time sequence model and the correlation model of the converter temperature and the output power of the wind turbine generator, establishing an objective function at the same time, performing real-time optimization solution to obtain an optimal active power reference value and an optimal reactive power reference value, and realizing accurate regulation and control on the wind turbine generator according to the optimal active power reference value and the optimal reactive power reference value. According to the method, the thermal safety margin and the operation reliability of the wind turbine generator under the extreme working condition of the sand storm can be remarkably improved, the adaptability of a wind power plant to atrocious weathers such as the sand storm is enhanced, and an efficient landing control strategy is provided for reducing the high-temperature fault rate of a converter and prolonging the service life of power electronic devices.

Description

Technical Field

[0001] This invention relates to the field of wind farm control technology, and more specifically, to a method and system for dynamic temperature control of wind turbine converters based on sandstorm climate. Background Technology

[0002] Wind energy, as a clean and renewable energy source, has become an important component of the power system. However, in areas prone to sandstorms, wind turbines have long faced the severe challenges of extreme sandstorm weather. During sandstorms, a single strong sandstorm can cause fluctuations in the overall output of a wind farm exceeding 70%, seriously threatening grid connection stability.

[0003] More importantly, the drastic power fluctuations caused by sandstorms are directly transmitted to the power electronic converters. As the core power conversion device of wind turbines, the IGBT module losses and junction temperatures of the converters are highly sensitive to the effective value of the current and the frequency of fluctuations. During sandstorms, the reduced equivalent aerodynamic efficiency of the blades leads to lower output of the unit under high wind speeds. The converters are in a light-load, high-frequency switching state for extended periods, or are suddenly subjected to heavy-load impacts when wind speeds increase rapidly, both of which significantly exacerbate the accumulation of thermal stress. At the same time, sandstorms are often accompanied by a sudden rise in ambient temperature (up to 40-50°C) and blockage of cooling ducts, which greatly reduces the efficiency of traditional air-cooled systems, further increasing the converter junction temperature and easily triggering over-temperature protection shutdowns, or even causing permanent damage to the modules.

[0004] Existing wind turbine control strategies are mostly designed for conventional wind conditions, failing to incorporate the coupling effects of high turbulence and high wind speeds unique to sandstorms on power characteristics and converter thermal load into a unified framework. While existing converter temperature management technologies have developed loss calculation and thermal model estimation methods, they are largely based on steady-state or mild fluctuation assumptions, making it difficult to cope with the transient thermal shocks caused by the drastic power fluctuations occurring on a second- to minute-level scale during sandstorms. Wind farm-level dispatch strategies also primarily focus on power point tracking and voltage support, lacking proactive protection mechanisms against high-temperature risks to converters.

[0005] In view of this, the present invention proposes a method and system for dynamic temperature control of wind turbine converters based on sandstorm climate. Summary of the Invention

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: The first aspect of this invention provides a method for dynamic temperature control of wind turbine converters based on sandstorm climate, comprising the following steps: Establish a time series model of wind speed that can characterize the climate characteristics of sandstorms; Establish a correlation model between converter temperature and wind turbine output power; By combining a wind speed time series model and a correlation model between converter temperature and wind turbine output power, a real-time mathematical mapping relationship between wind turbine terminal voltage, converter temperature, and output power under sandstorm conditions is established. At the same time, an objective function is established, and the optimal active power reference value and reactive power reference value are obtained in real time by using the operational stability of the wind turbine under sandstorm climate as a constraint. Based on the optimal active power reference value and reactive power reference value, the wind turbine can be precisely controlled.

[0007] In conjunction with the first aspect, the present invention is further configured such that the objective function is: ; in, Obj This represents the value of the objective function; N p The total number of steps taken within the prediction time range is represented as: ,in T p To predict the time range, T s The sampling period; N W This indicates the number of wind turbine units in the wind farm; λ V , λ P and λ T Indicates the weighting coefficient; T n,i Indicates wind turbine i Temperature; T e Indicates ambient temperature; V i (k) represents a wind turbine generator. i The k The terminal voltage of each sampling step; Indicates wind turbine i The k The reference voltage for each sampling step; P i (k) represents a wind turbine generator. i The k Active power of each sampling step; Indicates wind turbine i The k The reference voltage for each sampling step.

[0008] In conjunction with the first aspect, the present invention is further configured such that the constraint condition of the objective function is: ; ; in, and These represent the power reference value and maximum available power of wind turbine i, respectively. and These are the power reference value and the maximum available power of the wind farm, respectively.

[0009] In conjunction with the first aspect, the present invention is further configured such that: the wind speed time series model takes the average wind speed value during the sandstorm process as input and outputs the effective inflow wind speed time series at the center of each wind turbine hub.

[0010] In conjunction with the first aspect, the present invention is further configured such that: the establishment of a wind speed time series model capable of characterizing the climate characteristics of sandstorms includes the following steps: The dust storm process was divided into five macroscopic stages, and piecewise linear interpolation was used to generate a time-varying average wind speed sequence at a reference height. The average wind speed is extrapolated to the hub height based on the IEC 61400-1 power law model; Time-varying average wind speed V at hub center height h hub (t) is calculated in real time using the following formula: Wheel hub center height h Time-varying average wind speed V at the location hub (t) is calculated in real time using the following formula: In the formula: V ref ( t ) represents the average wind speed time series at a reference height (usually 10 m above the weather tower); h The height of the wheel hub center (typical value is 80-140 m); h ref The reference height is α; the wind shear index is α. Using time-varying Kaimal spectrum and IIR filtering, the time-varying average wind speed at the height h of the hub center is input to generate turbulent components that dynamically increase with the sandstorm stage; The wake loss is obtained by calculating the velocity loss of multiple upstream wind turbines using the Jensen wake model and the sum of square roots superposition method. The average wind speed, turbulence component, and wake deficit are superimposed to form the effective inflow wind speed time series at the center of each wind turbine hub.

[0011] In conjunction with the first aspect, the present invention is further configured such that the five macroscopic stages are: Calm period before a dust storm (0–0.5 h): This corresponds to the background atmospheric conditions before the arrival of the dust storm front. The wind speed remains at a low level (usually 30%–50% of the rated wind speed), indicating that the near-surface layer is still in a relatively stable and normal wind condition. Rapid frontal passage and wind speed surge phase (0.5–1.5 h): Simulates the rapid advance of a dust storm front triggered by a cold front or downburst. During this phase, the wind speed increases from the baseline value to 80%–100% of the peak value within 1 h, reflecting the typical dynamic characteristics of downward momentum transport triggered by strong convection and sudden wind surge at the surface. The main stage of the dust storm is characterized by sustained high wind speeds (1.5–3.5 h): During the peak of the dust storm, the average wind speed remains stable in the high range of 18–28 m / s (which may reach the level of strong dust storm II–III depending on the region). This stage is accompanied by the highest average aerodynamic load and is the main period contributing to the heat load and structural fatigue accumulation of the converter. The dust storm dissipates and wind speed gradually weakens (3.5–4.5 h and beyond): The descending airflow behind the front gradually becomes dominant, the momentum exchange in the near-surface layer weakens, and the average wind speed shows a linear decreasing trend until it returns to the normal background wind speed level. Calm period after a sandstorm (4.5–5 hours): This corresponds to the normal wind conditions after the sandstorm ends.

[0012] The correlation model between converter temperature and wind turbine output power is described as follows: ; ; ; ; In the formula: Indicates the lead resistance of the IGBT. V IGBT This is the voltage across the collector and emitter of the IGBT; E ON and E OFF These are the turn-on loss and turn-off loss of the IGBT, respectively. E rr This represents the turn-off loss of the reverse diode. This indicates the q-axis current of the machine-side converter at the current moment; This represents the q-axis current of the grid-side converter at the current moment; This represents the effective value of the q-axis current of the grid-side converter; I C,nom This is the rated collector current of the IGBT; f sw Switching frequency; T n For converter temperature, T e For ambient temperature, , and These represent the thermal resistances from the device junction to the case, from the case to the heat sink, and from the heat sink to the external environment, respectively.

[0013] A second aspect of the present invention also provides a device / equipment / system for dynamic temperature control of wind turbine converters based on sandstorm climate, comprising a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the computer program to implement the steps of the above method.

[0014] A third aspect of the present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0015] A fourth aspect of the present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0016] In summary, the present invention has the following beneficial effects: This invention constructs a wind speed time series model capable of characterizing the climate features of dust storms, describing the macroscopic evolution of average wind speed under different dust storm intensities and the corresponding wind speed field with time-varying turbulence intensities; simultaneously, it establishes a coupled correlation model between converter temperature and wind turbine output power; furthermore, by combining the above wind speed time series model and temperature-power correlation model, a multi-objective optimization function is constructed with converter temperature deviation, terminal voltage deviation, and output power deviation as the main indicators, and operational stability constraints under dust storm conditions are introduced. A real-time optimization algorithm is used to solve the problem and obtain the optimal active power reference value and reactive power reference value, thereby achieving precise dynamic control of converter temperature under the premise of meeting grid dispatch instructions.

[0017] When wind turbines operate during sandstorms, their output power fluctuates significantly and randomly, which in turn exacerbates the thermal load on the converter and drives up the junction temperature. This invention addresses this by real-time sensing of sandstorm intensity changes and dynamically adjusting the turbine output, ensuring the converter always operates within a safe temperature window. This method significantly improves the thermal safety margin and operational reliability of wind turbines under extreme sandstorm conditions, enhances the adaptability of wind farms to severe weather conditions such as sandstorms, and provides an efficient and practical control strategy for reducing the high-temperature failure rate of converters and extending the lifespan of power electronic devices. Attached Figure Description

[0018] Figure 1 This is a flowchart of a method for dynamic temperature control of wind turbine converters based on sandstorm climate in Embodiment 1 of the present invention; Figure 2 These are turbulence intensity curves of the normal turbulence model under different average wind speeds and different turbulence intensity levels in the embodiments of the present invention. Figure 3This is a schematic diagram of wind speed time series under normal conditions in an embodiment of the present invention; Figure 4 This is a schematic diagram of wind speed time series of sandstorm climate characteristics in an embodiment of the present invention; Figure 5 This is a block diagram of wind power cluster control considering the influence of converter temperature under extreme weather cold waves in this embodiment of the invention; Figure 6 This refers to the active power output of a wind turbine under normal conditions in this embodiment of the invention. Figure 7 This refers to the active power output of a wind turbine under a sandstorm environment in this embodiment of the invention. Figure 8 This describes the temperature change of a wind turbine converter under normal conditions in an embodiment of the present invention. Figure 9 This is an example of the temperature change of a wind turbine converter using the control method of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: like Figure 1 As shown, the present invention provides a method and system for dynamic temperature control of wind turbine converters based on sandstorm climate, which includes the following steps.

[0021] Step 1: Establish a wind speed time series model that can characterize the climate characteristics of sandstorms.

[0022] Step 2: Establish a correlation model between converter temperature and wind turbine output power.

[0023] Step 3: Combining the wind speed time series model and the correlation model, establish an objective function based on the converter temperature deviation, wind turbine terminal voltage deviation, and wind turbine output power deviation, with the operational stability of the wind turbine under sandstorm climate as the constraint condition.

[0024] Step 4: Adjust the operation of the wind turbine under extreme weather sandstorms based on the active power reference value and the reactive power reference value.

[0025] The process of establishing the wind speed time series model characterizing the climate characteristics of sandstorms in step 1 includes: Step 101: Divide the sandstorm process into five macroscopic stages: normal, climbing, peak duration, decay, and return to normal. Use piecewise linear interpolation to generate a time-varying average wind speed sequence at the reference height.

[0026] To reconstruct the macroscopic time-varying characteristics of average wind speed under dust storm climatic conditions, this invention, based on measured meteorological statistical laws of typical dust storm events, divides a complete dust storm process (total duration approximately 5 hours) into the following five evolutionary stages with clear physical significance: (1) Calm phase before a dust storm (0-0.5 h): This corresponds to the background atmospheric conditions before the dust storm front arrives. The wind speed remains at a low level (usually 30%-50% of the rated wind speed), indicating that the near-surface layer is still in a relatively stable normal wind condition. (2) Rapid passage of front and wind speed surge stage (0.5-1.5 h): Simulates the rapid advance of the dust storm front caused by cold front or downburst. During this stage, the wind speed increases from the baseline value to 80%-100% of the peak value within 1 h, reflecting the typical dynamic characteristics of downward momentum transport triggered by strong convection and sudden wind increase at the ground. (3) The main stage of the sandstorm with high wind speed (1.5 to 3.5 h): During the strongest period of the sandstorm, the average wind speed is maintained in the high range of 18 to 28 m / s (which may reach the level of strong sandstorm of level II to III depending on the region). This stage is accompanied by the highest average aerodynamic load and is the main period of contribution to the heat load and structural fatigue accumulation of the converter. (4) The dust storm dissipates and the wind speed gradually weakens (3.5 to 4.5 h and beyond): The descending airflow behind the front gradually becomes dominant, the momentum exchange in the near-surface layer weakens, and the average wind speed shows a linear decay trend until it returns to the normal background wind speed level. (5) Calm period after sandstorm (4.5-5 h): corresponding to normal wind conditions after the sandstorm ends.

[0027] After determining the start and end times of the above five stages and their corresponding representative average wind speed values, a continuous piecewise linear interpolation algorithm is used to smoothly connect the wind speeds between adjacent nodes, preserving the physical change characteristics of each stage of the dust storm. Finally, a deterministic average wind speed sequence Vref(t) is formed that can reflect the complete macroscopic evolution process of the dust storm from its inception, outbreak, duration to its dissipation with high fidelity, providing accurate low-frequency background wind speed input for subsequent turbulence superposition and wake calculation.

[0028] Step 102: Extrapolate the average wind speed to the hub height according to the IEC 61400-1 power law model.

[0029] To accurately reflect the distribution characteristics of the near-surface vertical wind speed gradient under extreme atmospheric conditions of dust storms, this invention uses the power-law wind profile model recommended in the International Electrotechnical Commission (IEC) 61400-1 design standard to characterize the wind shear effect. Hub center height h Time-varying average wind speed at the location V hub ( t The following formula can be used to calculate in real time: In the formula: V ref (t) is the time series of average wind speed at the reference height (usually 10 m high from the weather tower) generated in step 101; h The height of the wheel hub center (typical value is 80-140 m); h ref For reference height; α The wind shear index is used in this invention. α = 0.14.

[0030] Step 103: Use time-varying Kaimal spectra and IIR filtering to generate turbulent components that dynamically increase with the dust storm stage.

[0031] To generate high-frequency random turbulent fluctuations with characteristics specific to sandstorms, the key to this invention lies in designing the turbulence intensity as a time-varying parameter that dynamically changes with the progress of the sandstorm, thereby realistically reproducing the turbulence levels during sandstorms that are far higher than those under normal wind conditions. The specific construction process is as follows: (1) The Kaimal spectrum recommended by the IEC 61400-1 standard is selected as the target power spectral density function S(f) for the turbulence spectrum model. This spectrum can accurately describe the distribution characteristics of typical turbulent energy in the frequency domain of onshore wind farms.

[0032] (2) The turbulence intensity is designed to be time-varying, and the standard deviation of turbulence is defined as: σ(t)=I ref (t)·V hub (t) Wherein, turbulence intensity coefficient I ref (t) corresponds one-to-one with the macroscopic stages of the dust storm described in step 101. During the peak duration stage, Iref(t)≥0.16 (which can be further increased to 0.20-0.28 based on the measured dust storm intensity), which is significantly higher than the reference value under the normal turbulence model (NTM), thus reflecting the extreme turbulence characteristics of the dust storm.

[0033] (3) The turbulence time series is generated using an efficient time-domain filtering method, and the steps include: a. First, generate a standard Gaussian white noise sequence with a mean of 0 and a variance of 1; b. Design a first-order or second-order IIR digital filter so that its amplitude-frequency response approximates the low-pass characteristics of the Kaimal spectrum; c. Input the white noise sequence into the IIR filter to obtain a colored noise sequence whose spectral shape conforms to the Kaimal spectrum; d. After normalizing the colored noise sequence, multiply it by the target turbulence standard deviation σ(t) at the current time to finally obtain the turbulence component sequence V, which meets the requirements of the time-varying Kaimal spectrum of dust storms in both spectral shape and statistical intensity. turb,i (t), at this point, the spatial correlation differences of the wakes of multiple aircraft are not considered.

[0034] Step 104: Calculate the velocity loss of multiple upstream wind turbines using the Jensen wake model and the sum of square roots method.

[0035] In multi-turbine coupled simulation scenarios of wind farms, the significant disturbance of upstream turbine wakes to the inflow conditions of downstream turbines must be considered, mainly manifested as mean wind speed deficit and additional turbulence enhancement. This invention uses the Jensen wake model to calculate the wind speed attenuation characteristics under operating conditions. The wind speed deficit generated by a single upstream turbine j at a downstream axial distance x is: In the formula: C T denoted as the real-time thrust coefficient of the upstream wind turbine; D is the rotor diameter; and k is the wake expansion coefficient.

[0036] When the i-th wind turbine is simultaneously affected by the wake of multiple upstream wind turbines, the total wind speed loss is: Step 105: Superimpose the average wind speed, turbulence component, and wake loss to form the effective inflow wind speed time series at the center of each wind turbine hub, which will be used for the simulation and real-time application of the subsequent converter temperature dynamic control strategy.

[0037] The spatiotemporal synthesis of the effective wind speed at the hub center of the i-th wind turbine ultimately affects the instantaneous effective wind speed V of the downstream i-th wind turbine. i (t) is the superposition of the aforementioned component vectors: (1) Figure 2 The turbulence intensity curves for different turbulence intensity levels under different average wind speeds are shown for the normal turbulence model established in step 1. Figure 3 This is a schematic diagram of the wind speed time series under normal conditions generated in step 1 in this invention. Figure 4 This is a schematic diagram of the wind speed time series generated based on the wind speed time series model that characterizes the sandstorm climate characteristics in step 1 of this invention.

[0038] Step 2 establishes a correlation model between converter temperature and wind turbine output power. This invention decomposes the process of establishing the correlation model into the following four sub-steps: Step 201: Establish a linear model between the output power of the wind turbine and the terminal voltage.

[0039] The voltage-power sensitivity matrix is ​​used to quantitatively describe the impact of node-injected power on bus voltage, and to construct a linearized model of wind farm voltage and active and reactive power: in, and These are the increments of voltage and phase angle, respectively. and The increments of active and reactive power, respectively, are defined as... and Define the active and reactive power requirements of the wind turbine as and The measured value at the current moment is P 0 and Q 0 ; , , and This is the sensitivity coefficient, and the sensitivity coefficient is calculated as follows: in, express The conjugate of the reaction, the influence of active and reactive power on voltage; wind turbine terminal voltage V i Wind turbine i Output power ( P i , Q i The impact of ).

[0040] Considering the control cycle is on the order of seconds, the power flow distribution near the system operating point can be assumed to remain basically unchanged in a short period of time. Therefore, the sensitivity matrix method is used to perform a first-order Taylor expansion of the power flow equations: in, V i (k) represents a wind turbine generator. i No. k Terminal voltage of each sampling step V i,0 Indicates wind turbine i The terminal voltage at the current moment, V i Indicates the wind turbine unit at the next momenti Predicted value of terminal voltage.

[0041] Step 202: Establish a mathematical model between the wind turbine output current and the converter temperature.

[0042] The thermal state of the converter is a key indicator reflecting the health of the IGBT module. To achieve temperature control, this invention constructs a junction temperature-power mapping model with IGBTs and diodes as the primary heat sources, considering only conduction and switching losses and ignoring secondary losses, to establish a power loss model for the converter: in, Indicates converter losses; Indicates the lead resistance of the IGBT; V IGBT This is the voltage across the collector and emitter of the IGBT; E ON and E OFF These are the turn-on loss and turn-off loss of the IGBT, respectively. E rr This represents the turn-off loss of the reverse diode. This indicates the q-axis current of the machine-side converter at the current moment; This represents the q-axis current of the grid-side converter at the current moment; This represents the effective value of the q-axis current of the grid-side converter; I C,nom This is the rated collector current of the IGBT; f sw This refers to the switching frequency.

[0043] Based on the power loss model of the converter, the temperature model of the converter is established as follows: in, T n For converter temperature, T e For ambient temperature, , and These represent the thermal resistances from the device junction to the case, from the case to the heat sink, and from the heat sink to the external environment, respectively.

[0044] Based on the converter temperature model, the state-space equation for converter temperature is established as follows: in, Step 203: Establish a correlation model between the output power of the wind turbine and the current of the turbine-side and grid-side converters.

[0045] Mathematical model of machine-side converter: Mathematical model of grid-side converter: in, express and P Integral of the error between; express and Q Integral of the error between; This indicates the increment of the reference value for active power in a wind farm; This indicates the increment of the reactive power reference value of the wind farm; U sq Indicates the voltage of the machine-side converter; U m Indicates the voltage of the grid-side converter; , , and This represents the proportional gain and integral gain of the PI controller; T ip and T iq Represents the time constant of the current loop; T op and T oq It is expressed as the time constant of the filter circuit.

[0046] Based on the mathematical models of the turbine-side and grid-side converters, the matrix form of the state-space equations of the wind turbine converters is derived: in, Step 204: Derive the mathematical model of the output power of the wind turbine and the temperature of the converter under control.

[0047] Based on the mathematical models of converter temperature and current and fan output power and current, it can be deduced that: in, in, In step 3, based on the wind speed time series model and wind turbine correlation model characterizing the sandstorm climate characteristics, an objective function is established: Where Obj represents the value of the objective function; N p The total number of steps taken within the prediction time range can be expressed as: ,in T p To predict the time range, T s The sampling period; N W This indicates the number of wind turbine units in the wind farm; λ V , λ P and λ T Indicates the weighting coefficient; T n,i Indicates wind turbine i Temperature; T e Indicates ambient temperature; V i (k) represents a wind turbine generator. i The k The terminal voltage of each sampling step; Indicates wind turbine i The k The reference voltage for each sampling step; P i (k) represents a wind turbine generator. i The k Active power of each sampling step; Indicates wind turbine i The k The reference voltage for each sampling step.

[0048] The objective function described above minimizes the deviation between the converter temperature and the ambient temperature, the deviation of the wind turbine terminal voltage, and the deviation between the wind farm output power and the power demand.

[0049] The constraints of the objective function are as follows: in, in, and These represent wind turbine units. i The power reference value and maximum available power. and These are the power reference value and the maximum available power of the wind farm, respectively.

[0050] Steps 3 and 4, based on the above model optimization, obtain the optimal active power reference value of the wind turbine under blade icing conditions during a cold wave. P WT and reactive power Q WT Reference values ​​are used to achieve dynamic control of wind turbine units.

[0051] Figure 5 This is a control block diagram for wind power clusters that takes into account the impact of converter temperature on extreme weather and cold waves.

[0052] First, based on steps 1-4, a time series model of sandstorm wind speed and a converter temperature-power correlation model are established to create a real-time mathematical mapping relationship between wind turbine terminal voltage, converter temperature, and output power under sandstorm conditions. Based on operational data from the wind farm, including voltage, current, and power, key information such as current terminal voltage, converter junction temperature, and power tracking status are acquired in real time. Second, an objective function is constructed based on the current terminal voltage deviation, converter temperature deviation, and the deviation between output power and dispatch commands. Minimizing this objective function is the optimization goal, while also considering constraints on the wind farm's output power, including ensuring the wind farm's output power meets dispatch requirements and that the wind turbine's output power does not exceed its maximum available power. The optimal active power reference value and reactive power reference value are obtained in real time, enabling precise control of the wind turbine. Ultimately, while fully responding to the power demand of the external power grid and keeping the terminal voltage within the specified range, the converter temperature is reduced as much as possible. This optimizes the overall power generation performance of the wind farm and minimizes the thermal load of the converter under extreme weather conditions such as sandstorms, effectively improving the reliability and health of the unit operation and significantly reducing the high-temperature failure rate.

[0053] Figure 6 This refers to the active power output of a wind turbine under normal conditions in this invention. Figure 7 This refers to the active power output of a wind turbine under a sandstorm environment in this invention. Figure 8 This invention describes the temperature variation of a wind turbine converter under normal conditions. Figure 9 This invention describes the temperature change of a wind turbine in a sandstorm environment using the control method of this invention.

[0054] This invention establishes a mathematical model of wind turbine terminal voltage, converter temperature, and output power, and incorporates the direct impact of sandstorm climate characteristics on power output and converter temperature. Under different sandstorm intensities, drastic changes and surges in turbulence can lead to violent fluctuations in turbine output power, thus significantly altering the converter's thermal load characteristics. Therefore, the model incorporates a time-series-driven influence factor of sandstorm wind speed to achieve dynamic and accurate prediction of converter temperature. Under the premise of meeting operational performance requirements (such as stable bus voltage, meeting external grid power demands, and stability constraints under sandstorm conditions), through real-time optimization, the optimal active and reactive power reference values ​​are obtained while reducing converter temperature. This achieves effective control of converter temperature and optimal coordination of power output under extreme sandstorm weather conditions, thereby significantly improving the operational health and thermal safety margin of the wind turbine.

[0055] Example 2: The present invention also provides a device / equipment / system for dynamic temperature control of wind turbine converter based on sandstorm climate, including a memory, a processor and a computer program stored in the memory, characterized in that the processor executes the computer program to implement the steps of the above method.

[0056] The present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0057] The present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0058] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic temperature control method for wind turbine converters based on sandstorm climate, characterized by: Includes the following steps: Establish a time series model of wind speed that can characterize the climate characteristics of sandstorms; Establish a correlation model between converter temperature and wind turbine output power; By combining a wind speed time series model and a correlation model between converter temperature and wind turbine output power, a real-time mathematical mapping relationship between wind turbine terminal voltage, converter temperature, and output power under sandstorm conditions is established. At the same time, an objective function is established, and the optimal active power reference value and reactive power reference value are obtained in real time by using the operational stability of the wind turbine under sandstorm climate as a constraint. Based on the optimal active power reference value and reactive power reference value, the wind turbine can be precisely controlled.

2. The method for dynamic temperature control of wind turbine converters according to claim 1, characterized in that: The objective function is: ； in, Obj This represents the value of the objective function; N p The total number of steps taken within the prediction time range is represented as: ,in T p To predict the time range, T s The sampling period; N W This indicates the number of wind turbine units in the wind farm; λ V , λ P and λ T Indicates the weighting coefficient; T n,i Indicates wind turbine i Temperature; T e Indicates ambient temperature; V i (k) represents a wind turbine generator. i The k The terminal voltage of each sampling step; Indicates wind turbine i The k The reference voltage for each sampling step; P i (k) represents a wind turbine generator. i The k Active power of each sampling step; Indicates wind turbine i The k The reference voltage for each sampling step.

3. The method for dynamic temperature control of wind turbine converter according to claim 2, characterized in that: The constraints of the objective function are: ； ； in, and These represent the power reference value and maximum available power of wind turbine unit i, respectively. and These are the power reference value and the maximum available power of the wind farm, respectively.

4. The method for dynamic temperature control of wind turbine converter according to claim 1, characterized in that: The wind speed time series model takes the average wind speed during the sandstorm process as input and outputs the effective inflow wind speed time series at the center of each wind turbine hub.

5. The method for dynamic temperature control of wind turbine converter according to claim 4, characterized in that: The establishment of a wind speed time series model capable of characterizing the climate characteristics of sandstorms includes the following steps: The dust storm process was divided into five macroscopic stages, and piecewise linear interpolation was used to generate a time-varying average wind speed sequence at a reference height. The average wind speed is extrapolated to the hub height based on the IEC 61400-1 power law model; Wheel hub center height h Time-varying average wind speed V at the location hub (t) is calculated in real time using the following formula: ； In the formula: V ref ( t ) represents the average wind speed time series at a reference height (usually 10 m above the weather tower); h The height of the wheel hub center (typical value is 80-140 m); h ref The reference height is α; the wind shear index is α. Using time-varying Kaimal spectrum and IIR filtering, the time-varying average wind speed at the height h of the hub center is input to generate turbulent components that dynamically increase with the sandstorm stage; The wake loss is obtained by calculating the velocity loss of multiple upstream wind turbines using the Jensen wake model and the sum of square roots superposition method. The average wind speed, turbulence component, and wake deficit are superimposed to form the effective inflow wind speed time series at the center of each wind turbine hub.

6. The method for dynamic temperature control of wind turbine converter according to claim 5, characterized in that: The five macro stages are: Calm period before a dust storm (0–0.5 h): This corresponds to the background atmospheric conditions before the arrival of the dust storm front. The wind speed remains at a low level (usually 30%–50% of the rated wind speed), indicating that the near-surface layer is still in a relatively stable and normal wind condition. Rapid frontal passage and wind speed surge phase (0.5–1.5 h): Simulates the rapid advance of a dust storm front triggered by a cold front or downburst. During this phase, the wind speed increases from the baseline value to 80%–100% of the peak value within 1 h, reflecting the typical dynamic characteristics of downward momentum transport triggered by strong convection and sudden wind surge at the surface. The main stage of the dust storm is characterized by sustained high wind speeds (1.5–3.5 h): During the peak of the dust storm, the average wind speed remains stable in the high range of 18–28 m / s (which may reach the level of strong dust storm II–III depending on the region). This stage is accompanied by the highest average aerodynamic load and is the main period contributing to the heat load and structural fatigue accumulation of the converter. The dust storm dissipates and wind speed gradually weakens (3.5–4.5 h and beyond): The descending airflow behind the front gradually becomes dominant, the momentum exchange in the near-surface layer weakens, and the average wind speed shows a linear decreasing trend until it returns to the normal background wind speed level. Calm period after a sandstorm (4.5–5 hours): This corresponds to the normal wind conditions after the sandstorm ends.

7. The method for dynamic temperature control of wind turbine converter according to claim 1, characterized in that: The correlation model between converter temperature and wind turbine output power is described as follows: ； ； ； ； In the formula: Indicates the lead resistance of the IGBT. V IGBT This is the voltage across the collector and emitter of the IGBT; E ON and E OFF These are the turn-on loss and turn-off loss of the IGBT, respectively. E rr This represents the turn-off loss of the reverse diode. This indicates the q-axis current of the machine-side converter at the current moment; This represents the q-axis current of the grid-side converter at the current moment; This represents the effective value of the q-axis current of the grid-side converter; I C,nom This is the rated collector current of the IGBT; f sw The switching frequency; T n For converter temperature, T e For ambient temperature, , and These represent the thermal resistances from the device junction to the case, from the case to the heat sink, and from the heat sink to the external environment, respectively.

8. A device / equipment / system for dynamic temperature control of wind turbine converters based on sandstorm climate, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1-7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1-7.

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