Wind turbine generator converter thermal load optimization control method and system

Abstract

The embodiment of the application relates to the technical field of wind turbine optimization control, and provides a wind turbine converter thermal load optimization control method and system, which quantifies the degree of thermal shock about to occur by real-time estimation and prediction of the change rate of the junction temperature of a wind turbine power device, and when the change rate exceeds a preset safety threshold, uses the huge rotor inertia of the fan itself as a cost-free short-term energy storage buffer, actively adjusts the output power instruction, temporarily stores or releases part of the sharply changing power in the form of kinetic energy into or out of the rotor, thereby actively shaving the peak and filling the valley of the power flowing through the converter. This mode decouples the rigid following relationship between the converter power and the instantaneous wind power, significantly smooths the sharp fluctuations of the internal loss of the device, and finally effectively suppresses the amplitude and frequency of the junction temperature cycle, while guaranteeing the power generation efficiency, improves the long-term operation reliability and service life of the converter.

Description

Technical Field

[0001] This invention relates to the field of wind turbine optimization control technology, and in particular to a method and system for optimizing the thermal load control of a wind turbine converter. Background Technology

[0002] Wind power, as a clean and renewable energy source, occupies an increasingly important position in the global energy structure. Wind turbines are the core equipment for converting wind energy into electricity. Among them, the power electronic converter, as a key link connecting the generator and the power grid, is responsible for converting the unstable wind energy captured by the wind turbine into high-quality electrical energy with constant frequency and voltage. Its operational reliability directly affects the stability and economic benefits of the entire wind turbine and even the wind farm. However, current field operation data for wind turbines shows that the power electronic converter, especially the power semiconductor devices, which are core components, is one of the weakest links in wind turbines with a relatively high failure rate.

[0003] To maximize energy capture efficiency, existing wind turbine control systems generally employ a maximum power point tracking (MPPT) strategy. The core objective of this strategy is to track the optimal tip speed ratio in real time, rapidly adjusting the generator's electromagnetic torque to ensure the turbine's output electromagnetic power follows the rapidly changing input wind power. However, under conditions of drastically fluctuating wind speeds, such as gusts or strong turbulence, this control logic, solely focused on maximizing power generation, introduces serious technical flaws. The drastic fluctuations in wind power are directly translated by the MPPT control system into drastic fluctuations in the electromagnetic power that the converter must handle, leading to a sharp change in the power loss of the power semiconductor devices within the converter. Limited by the devices' inherent physical and thermal characteristics, these drastic power loss fluctuations ultimately manifest as large-scale, high-frequency cycles of junction temperature, which are highly destructive to device lifespan. In other words, while pursuing maximum power generation, the traditional MPPT control strategy inadvertently exacerbates the thermal fatigue accumulation of the converter, sacrificing the long-term reliability of critical components for short-term power generation gains. This contradicts the wind power industry's long-term development goals of high reliability, long lifespan, and low cost of electricity. Summary of the Invention

[0004] The present invention aims to solve at least one of the problems existing in the prior art, and provides a method and system for optimizing the thermal load control of wind turbine converters.

[0005] In one aspect, the present invention provides a method for optimizing the thermal load control of a wind turbine converter, the method comprising: Obtain electromagnetic power and coolant temperature measurements from the wind turbine generator set; Thermal state prediction is performed based on electromagnetic power measurement and coolant temperature measurement to obtain junction temperature estimate and junction temperature prediction for the next control cycle; Thermal cycling stress assessment is performed on the estimated junction temperature and the predicted junction temperature for the next control cycle to obtain the junction temperature change rate. Based on the junction temperature change rate and rotor angular velocity measurements, a buffer power command is generated. Based on the buffer power command and junction temperature change rate, the original MPPT power command is modified to obtain the final converter power command.

[0006] Optionally, thermal state prediction is performed based on electromagnetic power measurements and coolant temperature measurements to obtain junction temperature estimates and predicted junction temperatures for the next control cycle, including: Obtain the measured effective value of stator phase current, the current switching frequency setting, and the junction temperature and case temperature state vectors of the previous control cycle; The measured effective value of the stator phase current is calculated to obtain the characteristic value of the collector current, which includes the average collector current and the effective collector current. Based on the collector current characteristic value and the current switching frequency setting, calculate the conduction loss, switching loss and total power loss. The junction temperature state is updated based on the total power loss, the measured coolant temperature, and the junction temperature and case temperature state vectors from the previous control cycle to obtain the estimated junction temperature and case temperature. Based on the estimated junction temperature, estimated case temperature, measured electromagnetic power, measured electromagnetic power from the previous control cycle, and measured coolant temperature, the thermal state trend for the next control cycle is predicted to obtain the predicted junction temperature for the next control cycle.

[0007] Optionally, based on the collector current characteristic value and the current switching frequency setting, the conduction loss, switching loss, and total power loss are calculated, including: Calculate conduction loss, switching loss, and total power loss using the following formulas: ; ; ; in, For conduction loss, This is the average collector current. collector effective current, For saturation pressure drop, The collector-emitter equivalent resistance, For switching losses, This is the measured value of the effective value of the stator phase current. To enable the energy loss function, For the power consumption function to be turned off, Set the current switching frequency value. This represents the total power loss during the current control cycle.

[0008] Optionally, the junction temperature state is updated based on the total power loss, the measured coolant temperature, and the junction temperature and case temperature state vectors from the previous control cycle to obtain estimated junction temperature and case temperature values, including: Based on the total power loss, the measured coolant temperature, and the junction temperature and case temperature state vectors from the previous control cycle, the junction temperature state is updated using the following formula: ; in, This is the junction temperature and case temperature state vector for the current control cycle, composed of the junction temperature estimate and the case temperature estimate. This is the estimated junction temperature for the current control cycle. This is the estimated shell temperature for the current control cycle. This represents the junction temperature and case temperature state vectors from the previous control cycle. This is the estimated junction temperature from the previous control cycle. This is the estimated shell temperature value from the previous control cycle. This represents the total power loss during the current control cycle. This is the measured coolant temperature value for the current control cycle. and These are fixed coefficient matrices calculated offline based on the thermal resistance and thermal capacity parameters of the devices.

[0009] Optionally, based on the estimated junction temperature, estimated case temperature, measured electromagnetic power, measured electromagnetic power from the previous control cycle, and measured coolant temperature, a thermal state trend prediction for the next control cycle is made to obtain a predicted junction temperature for the next control cycle, including: The following formula is used to predict the thermal state trend for the next control cycle: ; ; in, This is the predicted junction temperature for the next control cycle. This is the predicted shell temperature value for the next control cycle. This represents the total power loss in the next control cycle. , These are the electromagnetic power measurement values ​​for the current control cycle and the previous control cycle, respectively. This is the predicted electromagnetic power value for the next control cycle. To control the cycle duration.

[0010] Optionally, a thermal cycling stress assessment is performed on the estimated junction temperature and the predicted junction temperature for the next control cycle to obtain the junction temperature change rate, including: The thermal cycling stress is assessed using the following formula: (Estimated junction temperature and predicted junction temperature for the next control cycle) ; in, and These are the predicted junction temperature for the next control cycle and the estimated junction temperature for the next control cycle, respectively. To control the cycle duration, This represents the rate of change of junction temperature.

[0011] Optionally, based on the junction temperature change rate and rotor angular velocity measurements, a buffer power command is generated, including: Calculate the temperature change error rate between the junction temperature change rate and the upper limit of the expected junction temperature change rate; The temperature change error rate is input into the PID controller to obtain the initial buffer power command; Based on the rotor angular velocity measurement, the initial buffer power command is constrained to obtain the buffer power command.

[0012] Optionally, based on the rotor angular velocity measurement, the initial buffer power command is constrained to obtain the buffer power command, including: In response to a junction temperature change greater than zero, if the measured rotor angular velocity is less than the maximum value of the rotor angular velocity buffer, the speed constraint gain is set to 1. In response to a junction temperature change greater than zero, if the measured rotor angular velocity is between the maximum and minimum values ​​of the rotor angular velocity buffer, the speed constraint gain is calculated using the following formula: ; in, This is the measured value of the rotor angular velocity. The maximum value of the safe operating range, This represents the maximum value of the rotor angular velocity buffer zone. For speed-constrained gain; The speed constraint gain is multiplied by the initial buffer power command to obtain the buffer power command.

[0013] Optionally, based on the rotor angular velocity measurement, the initial buffer power command is constrained to obtain the buffer power command, further comprising: In response to a junction temperature change of less than zero, if the measured rotor angular velocity is greater than the minimum value of the rotor angular velocity buffer, the speed constraint gain is set to 1. In response to a junction temperature change of less than zero, if the measured rotor angular velocity is between the maximum and minimum values ​​of the rotor angular velocity buffer, the speed constraint gain is calculated using the following formula: ; in, This is the minimum value of the rotor angular velocity buffer zone. To be the minimum value of the safe operating range, This is the measured value of the rotor angular velocity. For speed-constrained gain; The speed constraint gain is multiplied by the initial buffer power command to obtain the buffer power command.

[0014] In another aspect, the present invention provides a wind turbine converter thermal load optimization control system, the wind turbine converter thermal load optimization control system comprising: The measurement acquisition module is used to acquire the electromagnetic power measurement value and coolant temperature measurement value of the wind turbine. The thermal state prediction module is used to predict the thermal state based on the electromagnetic power measurement value and the coolant temperature measurement value, so as to obtain the junction temperature estimate and the junction temperature prediction value for the next control cycle. The thermal cycling stress assessment module is used to assess the thermal cycling stress of the estimated junction temperature and the predicted junction temperature for the next control cycle in order to obtain the junction temperature change rate. The buffer power command generation module is used to generate buffer power commands based on the junction temperature change rate and rotor angular velocity measurements. The final power command correction module is used to correct the original MPPT power command based on the buffered power command and the junction temperature change rate to obtain the final converter power command.

[0015] Compared with existing technologies, the wind turbine converter thermal load optimization control method and system proposed in this invention establishes a closed-loop collaborative control mechanism between the internal thermal state of the converter and the mechanical system of the wind turbine, aiming to solve the problem of thermal cycle fatigue accumulation in the converter caused by traditional maximum power point tracking control. Specifically, this invention quantifies the degree of impending thermal shock by estimating and predicting the rate of change of junction temperature of the wind turbine power devices in real time. When the rate of change exceeds a preset safety threshold, the huge rotor inertia of the wind turbine itself is used as a cost-free short-term energy storage buffer. By actively adjusting the output power command, it temporarily stores or releases some of the drastically changing power in the form of kinetic energy into or out of the rotor, thereby actively smoothing the peak and valley of the power flowing through the converter. This method decouples the rigid following relationship between the converter power and the instantaneous wind power, significantly suppresses the drastic fluctuations in internal losses of the devices, and ultimately effectively suppresses the amplitude and frequency of junction temperature cycling. While ensuring power generation efficiency, it improves the long-term operational reliability and service life of the converter. Attached Figure Description

[0016] One or more embodiments are illustrated by way of example with the corresponding pictures in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0017] Figure 1 A flowchart of a wind turbine converter heat load optimization control method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the data flow in the wind turbine converter heat load optimization control method according to an embodiment of the present invention; Figure 3 A flowchart illustrating the method for optimizing the thermal load control of a wind turbine converter according to an embodiment of the present invention, which predicts the thermal state based on electromagnetic power measurement and coolant temperature measurement to obtain the junction temperature estimate and the junction temperature prediction for the next cycle. Figure 4 A flowchart for generating buffer power commands based on junction temperature change rate and rotor angular velocity measurements in the wind turbine converter thermal load optimization control method according to an embodiment of the present invention; Figure 5 This is a block diagram of a wind turbine converter thermal load optimization control system according to an embodiment of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] Unless otherwise specifically stated, the technical or scientific terms used in the embodiments of this invention should be understood in their ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains. The terms "comprising" or "including," as used in the embodiments of this invention, do not limit the shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof mentioned, nor do they exclude the appearance or addition of one or more other different shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof, or the inclusion of these.

[0020] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale, and techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail; however, where appropriate, the illustrated techniques, methods, and apparatus should be considered part of the specification. In all the examples shown and discussed herein, any other specific example may have different values. It should be noted that similar symbols and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0021] In the description of the embodiments of the present invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In the embodiments of the present invention, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in the embodiments of the present invention, as well as the features of the different embodiments or examples.

[0022] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention; it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0023] Existing wind turbine control technologies generally prioritize maximizing energy capture. However, when dealing with conditions involving severe wind speed fluctuations such as turbulence and gusts, their control strategies cause the converter to faithfully follow power fluctuations. This results in the internal power semiconductor devices being subjected to severe and frequent temperature cycling shocks, seriously threatening the fatigue life of the devices and the long-term reliability of the unit. Therefore, the technical solution of this invention proposes a method and system for optimizing the thermal load control of wind turbine converters. Specifically, the technical solution of this invention first establishes and applies an electro-thermal model of the device by acquiring measured values ​​such as electromagnetic power and coolant temperature. This model not only estimates the current junction temperature state in real time but also performs feedforward prediction of the junction temperature change trend in the next control cycle. Next, based on the estimated and predicted junction temperature values, the impending thermal cycle stress is assessed, i.e., the junction temperature change rate is calculated. When this junction temperature change rate exceeds a preset safety threshold, the controller inputs this temperature error rate into the PID controller to generate an initial buffer power command. To ensure the safe operation of the wind turbine, the initial buffer power command is further constrained based on the current rotor angular velocity. By calculating the speed constraint gain, the command is dynamically adjusted to obtain the final buffer power command. Finally, based on the sign of the junction temperature change rate and the magnitude of the buffer power command, the original MPPT power command is corrected, ultimately generating a power command that has undergone thermal load smoothing and is sent to the converter for execution. In this way, the huge rotor inertia of the wind turbine is cleverly used as a short-term energy buffer, achieving peak shaving and valley filling of the power impact on the converter and suppressing drastic fluctuations in junction temperature.

[0024] Figure 1 This is a flowchart of a wind turbine converter thermal load optimization control method according to an embodiment of the present invention. Figure 2 This is a schematic diagram of data flow in a wind turbine converter thermal load optimization control method according to an embodiment of the present invention. (In conjunction with...) Figure 1 and Figure 2 According to an embodiment of the present invention, a method for optimizing the thermal load control of a wind turbine converter includes the following steps: S100, acquiring electromagnetic power measurement values ​​and coolant temperature measurement values ​​of the wind turbine; S200, performing thermal state prediction based on the electromagnetic power measurement values ​​and coolant temperature measurement values ​​to obtain an estimated junction temperature value and a predicted junction temperature value for the next control cycle; S300, performing thermal cycle stress assessment on the estimated junction temperature value and the predicted junction temperature value for the next control cycle to obtain a junction temperature change rate; S400, generating a buffer power command based on the junction temperature change rate and rotor angular velocity measurement values; S500, performing a final power command correction on the original MPPT power command based on the buffer power command and the junction temperature change rate to obtain a final converter power command.

[0025] Specifically, in step S100, the electromagnetic power measurement value and coolant temperature measurement value of the wind turbine are obtained. It should be understood that the internal power loss of the wind turbine converter is directly related to the current flowing through it, and electromagnetic power is the core electrical quantity that determines the current magnitude; simultaneously, the coolant temperature defines the thermodynamic boundary reference of the heat dissipation path and determines the heat dissipation efficiency. Therefore, in the technical solution of this invention, the electromagnetic power measurement value and coolant temperature measurement value of the wind turbine are obtained to provide real-time and accurate physical quantity inputs for subsequent junction temperature estimation and thermal state prediction. This ensures that all subsequent calculations for the entire thermal load optimization control are based on the current actual operating conditions of the wind turbine, providing a foundation for the accuracy and effectiveness of the control.

[0026] More specifically, in a specific example of the present invention, the process of obtaining the electromagnetic power measurement value and coolant temperature measurement value of the wind turbine is as follows. First, the electromagnetic power is not directly measured, but obtained through calculation. Using a three-phase voltage sensor and a Hall effect current sensor configured at the output terminal of the generator stator of the wind turbine, the instantaneous values ​​of the generator's output voltage and current are continuously collected. These analog signals are sent to the digital signal processor of the converter. After analog-to-digital conversion, the current electromagnetic power value is calculated in each control cycle using an active power calculation algorithm, which serves as the electromagnetic power measurement value. Simultaneously, a PT100 platinum resistance temperature sensor is installed at the inlet of the coolant main pipe of the converter's liquid cooling system. This sensor senses the coolant temperature flowing into the radiator in real time. Its output resistance signal is processed by a signal conditioning circuit and an analog-to-digital converter to form a digital quantity characterizing the coolant temperature, which serves as the coolant temperature measurement value. These two key operating parameters, namely the electromagnetic power measurement value and the coolant temperature measurement value, are placed in a shared data area for use in the next stage of the heating state prediction process.

[0027] Specifically, in step S200, thermal state prediction is performed based on electromagnetic power measurements and coolant temperature measurements to obtain an estimated junction temperature and a predicted junction temperature for the next control cycle. It should be understood that the junction temperature of a power semiconductor device is a core physical quantity characterizing its thermal stress level and leading to fatigue failure, but it cannot be directly measured in engineering, and any effective thermal control requires a forward-looking rather than a hysteretic response. Therefore, in the technical solution of this invention, thermal state prediction is further performed based on electromagnetic power measurements and coolant temperature measurements to obtain an estimated junction temperature and a predicted junction temperature for the next control cycle. This allows for real-time calculation of the currently unmeasurable internal junction temperature of the device and prediction of its changing trend in the next control cycle. This provides two key state quantities for subsequent thermal cycling stress assessment: the current temperature state benchmark and the future temperature state forecast, transforming the entire control strategy from passive hysteretic protection to proactive predictive intervention, providing a decision-making basis for accurately suppressing thermal shock.

[0028] Figure 3 This is a flowchart illustrating the thermal state prediction based on electromagnetic power measurements and coolant temperature measurements in a wind turbine converter thermal load optimization control method according to an embodiment of the present invention, to obtain estimated junction temperature and predicted junction temperature for the next control cycle. Figure 3 As shown, step S200 includes: S210, acquiring the measured value of the stator phase current RMS, the current switching frequency setting, and the junction temperature and case temperature state vectors of the previous control cycle; S220, calculating the measured value of the stator phase current RMS to obtain the collector current characteristic value, which includes the average collector current and the effective collector current; S230, calculating the conduction loss, switching loss, and total power loss based on the collector current characteristic value and the current switching frequency setting; S240, updating the junction temperature state based on the total power loss, the measured value of the coolant temperature, and the junction temperature and case temperature state vectors of the previous control cycle to obtain the estimated value of the junction temperature and the estimated value of the case temperature; S250, predicting the thermal state trend of the next control cycle based on the estimated value of the junction temperature, the estimated value of the case temperature, the measured value of the electromagnetic power, the measured value of the electromagnetic power of the previous control cycle, and the measured value of the coolant temperature to obtain the predicted value of the junction temperature of the next control cycle.

[0029] In step S210, the measured value of the stator phase current RMS, the current switching frequency setting, and the junction temperature and case temperature state vectors from the previous control cycle are obtained. It should be understood that the total power loss of the converter is composed of both conduction losses and switching losses. Conduction losses mainly depend on the current magnitude, while switching losses depend on both the current magnitude and the switching frequency. Furthermore, the dynamic change of the junction temperature is a time-series process with thermal inertia; the current thermal state is a continuation of the state at the previous moment. Therefore, in the technical solution of this invention, the measured value of the stator phase current RMS, the current switching frequency setting, and the junction temperature and case temperature state vectors from the previous control cycle are further obtained to provide complete boundary conditions and initial state values ​​for the power loss calculation and thermal state update of the current control cycle. This allows for precise calculation of losses down to specific physical causes and ensures that the state solution of the thermal model is continuous and iterative, thereby improving the accuracy of the junction temperature estimate.

[0030] More specifically, in a specific example of the present invention, the specific process of step S210 is executed inside the digital signal processor of the converter. The measured value of the stator phase current RMS is obtained by processing continuous sampling values ​​from the current sensor. A root mean square (RMS) calculation program calculates the stator phase current sampling data within the most recent grid cycle, thereby outputting a stable and updated RMS value as the measured value of the stator phase current RMS. The current switching frequency setting is a value directly read from the controller parameter register. This value is preset by the wind turbine's main control system according to the current operating conditions and sent to the converter controller. The junction temperature and case temperature state vectors of the previous control cycle are not external inputs, but rather a two-dimensional vector composed of the estimated junction temperature and case temperature values ​​after the thermal state update is completed in the previous calculation cycle. This vector is stored in a dedicated state buffer of the processor. At the beginning of the current control cycle, the thermal state prediction process first reads the junction temperature and case temperature state vectors of the previous control cycle from the buffer as the initial conditions for this iteration calculation.

[0031] In step S220, the measured effective value of the stator phase current is calculated to obtain the collector current characteristic value, which includes the average collector current and the effective collector current. It should be understood that the loss calculation formula for power semiconductor devices is based on the waveform characteristics of the current flowing through a single device. However, the externally measured stator phase current is the macroscopic AC current output from the entire converter bridge arm. The current of a single device and the macroscopic AC current output from the entire converter bridge arm differ significantly in waveform and amplitude, and cannot be directly substituted into the loss calculation formula. Therefore, in the technical solution of this invention, the measured effective value of the stator phase current is further calculated to obtain the collector current characteristic value, which includes the average collector current and the effective collector current. This obtains an equivalent value that accurately characterizes the current stress borne by a single IGBT chip. This establishes a correct mapping relationship between system-level electrical measurements and device-level physical loss models, providing a prerequisite for subsequent accurate calculations of conduction and switching losses.

[0032] More specifically, in a concrete example of this invention, the calculation of the effective value of the stator phase current is achieved using a multidimensional lookup table pre-stored in the controller's memory. In the offline phase, a functional relationship is established beforehand, through simulation analysis or experimental calibration, between the effective value of the stator phase current, power factor angle, and modulation ratio output by the converter under a specific space vector pulse width modulation strategy, and the average and effective currents borne by a single IGBT and its anti-parallel diode within one fundamental cycle. This functional relationship is solidified into a lookup table and burned into the digital signal processor. During real-time operation, the thermal state prediction process not only acquires the effective value of the stator phase current but also obtains the current power factor angle and modulation ratio from the control loop. Using these three real-time operating parameters as address indices, the lookup table is searched, and a multilinear interpolation algorithm is used to accurately calculate the corresponding average collector current and effective collector current under the current operating condition. These two calculated characteristic values, namely the average collector current and effective collector current, are then passed as the final output to the power loss calculation stage.

[0033] In step S230, based on the collector current characteristic value and the current switching frequency setting, the conduction loss, switching loss, and total power loss are calculated, including: calculating the conduction loss, switching loss, and total power loss using the following formulas: ; ; ; in, For conduction loss, This is the average collector current. collector effective current, For saturation pressure drop, The collector-emitter equivalent resistance, For switching losses, This is the measured value of the effective value of the stator phase current. To enable the energy loss function, For the power consumption function to be turned off, Set the current switching frequency value. This represents the total power loss during the current control cycle.

[0034] It should be understood that the heat generated by power semiconductor devices during operation is the direct cause of their junction temperature rise, and the total power loss is determined by two physical mechanisms: conduction loss during device conduction and switching loss during switching transients. Therefore, in the technical solution of this invention, based on the collector current characteristic value and the current switching frequency setting, conduction loss, switching loss, and total power loss are calculated. This allows for the quantification of these two core loss components, conduction loss and switching loss, according to the device's physical characteristics, and their summation yields the total power loss under the current operating condition. This provides an accurate input source term representing the total heat generation power of the device for subsequent thermal network models, thus forming a crucial bridge from electrical operating conditions to thermal state evolution calculations.

[0035] In step S240, the junction temperature state is updated based on the total power loss, the measured coolant temperature, and the junction temperature and case temperature state vectors from the previous control cycle to obtain estimated junction and case temperatures. This includes updating the junction temperature state using the following formula: ; in, This is the junction temperature and case temperature state vector for the current control cycle, composed of the junction temperature estimate and the case temperature estimate. This is the estimated junction temperature for the current control cycle. This is the estimated shell temperature for the current control cycle. This represents the junction temperature and case temperature state vectors from the previous control cycle. This is the estimated junction temperature from the previous control cycle. This is the estimated shell temperature value from the previous control cycle. This represents the total power loss during the current control cycle. This is the measured coolant temperature value for the current control cycle. and These are fixed coefficient matrices calculated offline based on the thermal resistance and thermal capacity parameters of the devices. Indicates the current control cycle. This indicates the previous control cycle.

[0036] In step S250, based on the estimated junction temperature, estimated case temperature, measured electromagnetic power, measured electromagnetic power from the previous control cycle, and measured coolant temperature, a thermal state trend prediction for the next control cycle is performed to obtain the predicted junction temperature for the next control cycle, including: The following formula is used to predict the thermal state trend for the next control cycle: ; ; in, This is the predicted junction temperature for the next control cycle. This is the predicted shell temperature value for the next control cycle. This represents the total power loss in the next control cycle. , These are the electromagnetic power measurement values ​​for the current control cycle and the previous control cycle, respectively. This is the predicted electromagnetic power value for the next control cycle. To control the cycle duration.

[0037] It is understood that thermodynamic systems have an inherent time delay, and reactive control based solely on the current temperature is insufficient to effectively suppress rapid thermal shocks. Therefore, a feedforward control mechanism with predictive capabilities is needed to address these challenges in advance. Thus, in the technical solution of this invention, the thermal state trend of the next control cycle is further predicted based on the estimated junction temperature, estimated shell temperature, measured electromagnetic power, measured electromagnetic power from the previous control cycle, and measured coolant temperature. This yields a predicted junction temperature for the next control cycle. By interpolating the current electromagnetic power change trend, the power loss in the next control cycle is predicted. This predicted power loss, along with the current thermal state vector, is then substituted into the thermal network state equation for single-step derivation. This provides a predicted value for the future junction temperature state. Combining this predicted value with the current estimated junction temperature allows for the calculation of the junction temperature change rate, providing direct data support for subsequent quantification of thermal cycle stress.

[0038] Specifically, in step S300, a thermal cycling stress assessment is performed on the estimated junction temperature and the predicted junction temperature for the next control cycle to obtain the junction temperature change rate, including: performing a thermal cycling stress assessment on the estimated junction temperature and the predicted junction temperature for the next control cycle using the following formula: ; in, and These are the predicted junction temperature for the next control cycle and the estimated junction temperature for the next control cycle, respectively. To control the cycle duration, This represents the rate of change of junction temperature.

[0039] It should be understood that the main threat to the fatigue life of power semiconductor devices is the drastic fluctuation of junction temperature rather than its absolute value. Therefore, an indicator that can directly quantify the severity of this dynamic change is needed. Thus, in the technical solution of this invention, a thermal cycling stress assessment is further performed on the estimated junction temperature and the predicted junction temperature for the next control cycle to obtain the junction temperature change rate. This rate is then used to calculate the difference between the predicted junction temperature for the next control cycle and the current estimated junction temperature, and divided by the control cycle duration to obtain the slope value characterizing the instantaneous rate of junction temperature change. In this way, the complex temperature dynamic process can be converged into a key scalar quantity that can be used for closed-loop control. The junction temperature change rate serves as the core feedback control quantity, providing a direct and quantitative decision basis for the subsequent generation of buffer power commands.

[0040] In step S400, a buffer power command is generated based on the junction temperature change rate and rotor angular velocity measurements. It should be understood that after quantifying the degree of thermal shock represented by the junction temperature change rate, a control law is needed to transform the passive state assessment into an active control command, and the execution of this control command must not jeopardize the mechanical safety of the wind turbine. Therefore, in the technical solution of this invention, a buffer power command is further generated based on the junction temperature change rate and rotor angular velocity measurements. This first inputs the error between the junction temperature change rate and the desired safety threshold into the PID controller to calculate the initial buffer power command required to suppress the thermal shock. Then, the initial buffer power command is constrained based on whether the current rotor angular velocity is close to the upper or lower limit of its safe operating range. In this way, a final buffer power command can be generated that effectively responds to changes in thermal load while ensuring that the wind turbine speed remains within a safe range. This command quantifies the power value that needs to be stored or released from the rotor kinetic energy, providing an execution basis for the final correction of the converter power command.

[0041] Figure 4 This is a flowchart illustrating the generation of buffer power commands based on junction temperature change rate and rotor angular velocity measurements in a wind turbine converter thermal load optimization control method according to an embodiment of the present invention. Figure 4 As shown, step S400 includes: S410, calculating the temperature change error rate between the junction temperature change rate and the desired upper limit of the junction temperature change rate; S420, inputting the temperature change error rate into the PID controller to obtain the initial buffer power command; S430, constraining the initial buffer power command based on the rotor angular velocity measurement value to obtain the buffer power command.

[0042] In step S410, the temperature error rate between the junction temperature change rate and the desired upper limit of the junction temperature change rate is calculated. It should be understood that not all junction temperature changes threaten device lifespan; control intervention should be limited to drastic fluctuations exceeding safe limits to avoid unnecessary power adjustments and ensure the stability of the wind turbine system. Therefore, in the technical solution of this invention, the temperature error rate between the junction temperature change rate and the desired upper limit of the junction temperature change rate is further calculated. This compares the previously obtained absolute value of the junction temperature change rate with a safety threshold preset according to the device lifespan model, thereby generating a non-negative error signal. This provides a clear drive input for the subsequent PID controller, which is activated only when the severity of the thermal shock exceeds an acceptable range, forming an effective control dead zone and ensuring the accuracy and necessity of active thermal load control.

[0043] More specifically, in a specific example of the present invention, the calculation process of the temperature change error rate is executed periodically in a digital signal processor. First, the upper limit of the desired junction temperature change rate is determined offline and fixed in a parameter of the controller based on the fatigue life characteristic curve of the power semiconductor device, for example, set to 2 degrees Celsius per second. In each control cycle, the junction temperature change rate calculated in the previous step is retrieved first, and its absolute value is taken. Then, this absolute value is subtracted from the preset upper limit value, i.e., the upper limit of the desired junction temperature change rate. Finally, the result of the subtraction is processed by a maximum value function. If the result of the subtraction is negative or zero, the final output temperature change error rate is zero, indicating that the thermal shock is within the safe range; if the result of the subtraction is positive, the result of the subtraction is the final output temperature change error rate, indicating that the thermal shock has exceeded the safe threshold, and its magnitude represents the degree to which the safe threshold has been exceeded. This temperature change error rate is then transmitted to the input of the PID controller.

[0044] In step S420, the temperature variation error rate is input to the PID controller to obtain the initial buffer power command. It should be understood that the quantized temperature variation error rate needs to be converted into a dynamic power regulation command capable of stably eliminating the error. Simple proportional control alone is insufficient to guarantee the dynamic performance and steady-state accuracy of the wind turbine system. Therefore, in the technical solution of this invention, the temperature variation error rate is further input to the PID controller to obtain the initial buffer power command. This utilizes a proportional-integral-derivative control law to comprehensively consider the magnitude of the current error, the accumulation of historical errors, and the future trend of error changes, thereby calculating a dynamic and forward-looking power command. This generates an ideal buffer power value that can quickly respond and stably suppress thermal shock. This ideal buffer power value serves as the basis for subsequent constraint processing, ensuring the robustness and optimal dynamic response characteristics of the control system.

[0045] More specifically, in a particular example of the invention, the initial buffer power command is acquired by executing an incremental PID algorithm in a digital signal processor. First, the proportional gain of the controller... Integral gain and differential gain The optimal temperature range is determined during the offline design phase through simulation and optimization of the wind turbine system's thermo-mechanical-electrical multiphysics model. This aims to rapidly suppress thermal shock without causing severe speed oscillations. In each control cycle, the current temperature error rate calculated in the previous step is retrieved first. The proportional term is then calculated by multiplying the current temperature error rate by the proportional gain. The integral term is obtained by multiplying the current temperature variation error rate by the integral gain. The integral term is calculated by adding the current temperature error rate to the cumulative value of the integral term from the previous control cycle. The integral term calculation includes anti-integral saturation logic, pausing integral accumulation when the final output is limited. The differential term is calculated by dividing the current temperature error rate by the control cycle and then multiplying by the differential gain. Finally, the results of these three terms—the proportional term, the integral term, and the differential term—are added together, and the sum is used as the initial buffer power command output for the current cycle.

[0046] In step S430, the initial buffer power command is constrained based on the rotor angular velocity measurement to obtain the buffer power command. It should be understood that the essence of power buffering using rotor inertia is adjusting the rotor speed. However, the rotor of a wind turbine must operate within a safe speed range determined by mechanical structural safety and aerodynamic characteristics. Therefore, the buffer command calculated from the heat load demand cannot be executed unconditionally and must be constrained by mechanical safety boundaries. Thus, in the technical solution of this invention, the initial buffer power command is further constrained based on the rotor angular velocity measurement to obtain the buffer power command. This allows for the determination of the degree to which the current rotor speed approaches the high and low speed limits, and the calculation of a dynamic speed constraint gain to adjust the initial buffer power command output by the PID controller. This generates a final, safe buffer power command that achieves a balance between meeting heat load mitigation requirements and ensuring the mechanical safety of the wind turbine, ensuring that the heat load optimization function will not cause overspeed or stall hazards in the wind turbine under any operating condition.

[0047] More specifically, in a specific example of the present invention, the initial buffer power command is constrained based on the rotor angular velocity measurement value to obtain the buffer power command, including: in response to a junction temperature change greater than zero, if the rotor angular velocity measurement value is less than the maximum value of the rotor angular velocity buffer, then setting the speed constraint gain to 1; in response to a junction temperature change greater than zero, if the rotor angular velocity measurement value is between the maximum value and the minimum value of the rotor angular velocity buffer, calculating the speed constraint gain using the following formula: ; in, This is the measured value of the rotor angular velocity. The maximum safe operating range is defined as the maximum safe operating range for rotor angular velocity. This represents the maximum value of the rotor angular velocity buffer zone. The speed constraint gain is multiplied by the initial buffer power command to obtain the buffer power command.

[0048] It is understandable that when the junction temperature rises rapidly, the wind turbine system needs to store energy through rotor acceleration to mitigate thermal shock. However, this acceleration must be performed without exceeding the turbine's maximum safe operating speed. Therefore, as the speed increases, the margin available for acceleration decreases. In this invention, in response to a junction temperature change greater than zero, if the measured rotor angular velocity is less than the maximum value of the rotor angular velocity buffer zone, the speed constraint gain is set to 1. If the measured rotor angular velocity is between the maximum value of the rotor angular velocity buffer zone and the maximum value of the safe operating range, the speed constraint gain is calculated using a specific formula and multiplied by the initial buffer power command to obtain the buffer power command. This allows the full execution of the buffer power command calculated to suppress thermal shock when the speed is still within a range with a large safety margin. After the speed enters the buffer zone near the upper limit, the gain is linearly reduced according to the degree of proximity to the upper limit, thereby proportionally reducing the power command used for rotor acceleration. This ensures that while maximizing the use of rotor inertia for thermal load optimization, the risk of overspeed in the wind turbine is strictly prevented, achieving seamless coordination between thermal load control and mechanical safety constraints.

[0049] In a specific example of the present invention, the initial buffer power command is constrained based on the rotor angular velocity measurement value to obtain the buffer power command. The method further includes: in response to a junction temperature change being less than zero, if the rotor angular velocity measurement value is greater than the minimum value of the rotor angular velocity buffer, then setting the speed constraint gain to 1; in response to a junction temperature change being less than zero, if the rotor angular velocity measurement value is between the maximum and minimum values ​​of the rotor angular velocity buffer, calculating the speed constraint gain using the following formula: ; in, This is the minimum value of the rotor angular velocity buffer zone. This refers to the minimum safe operating range, i.e., the minimum safe operating range for rotor angular velocity. This is the measured value of the rotor angular velocity. The speed constraint gain is multiplied by the initial buffer power command to obtain the buffer power command.

[0050] It is understandable that when the junction temperature drops rapidly, the wind turbine system needs to release energy through rotor deceleration to slow down the cooling rate. However, this deceleration must ensure that the wind turbine does not fall below its minimum stable operating speed to prevent unstable conditions such as stall. Therefore, as the speed decreases, the margin of kinetic energy available for release will also decrease. Therefore, in the technical solution of this invention, in response to the junction temperature change being less than zero, if the measured rotor angular velocity is greater than the minimum value of the rotor angular velocity buffer zone, the speed constraint gain is set to 1. If the measured rotor angular velocity is between the minimum value of the rotor angular velocity buffer zone and the minimum value of the safe operating range, the speed constraint gain is calculated using a specific formula, and this speed constraint gain is multiplied by the initial buffer power command to obtain the final buffer power command. This allows the full execution of the buffer power command calculated to mitigate the cooling impact when the speed is still far from the minimum limit. After the speed enters the buffer region close to the lower limit, the gain is linearly reduced according to the degree of proximity to the lower limit, thereby proportionally reducing the power command released from the rotor. This ensures that while effectively utilizing rotor kinetic energy to mitigate thermal cycle stress, it also strictly avoids the risk of the unit entering an unstable operating state, achieving a reliable synergy between thermal load optimization and unit operating stability.

[0051] Specifically, in step S500, the MPPT original power command is modified based on the buffer power command and the junction temperature change rate to obtain the final converter power command. It should be understood that the wind turbine control system uses the maximum power point tracking command as a reference during normal operation. The buffer power command generated in the aforementioned steps only represents the power adjustment required to suppress thermal load. Therefore, this power adjustment must be effectively superimposed on the reference command to form a final execution command that considers both power generation and thermal protection. Therefore, in the technical solution of this invention, the MPPT original power command is further modified based on the buffer power command and the junction temperature change rate to obtain the final converter power command. This determines whether the buffer power command should be arithmetically synthesized with the MPPT original power command in a compensatory or suppressive manner based on the sign of the junction temperature change rate. This generates a unique final active power reference value that integrates both power generation efficiency and thermal health management objectives. This reference value is directly sent to the converter's underlying controller as the final converter power, thereby achieving real-time and smooth adjustment of the converter's output power.

[0052] More specifically, in a concrete example of this invention, two logics run in parallel: one is the standard maximum power point tracking (MPPT) logic, which generates the initial MPPT power command by looking up a table based on the current rotational speed; the other is the heat load optimization logic proposed in this invention, which generates the final buffered power command and junction temperature change rate. At an adder node, the sign of the junction temperature change rate is first determined. If the sign is positive, it indicates that the junction temperature is rising, and the converter electromagnetic power needs to be reduced; therefore, the buffered power command is subtracted from the initial MPPT power command. If the sign is negative, it indicates that the junction temperature is falling, and the converter electromagnetic power needs to be increased; therefore, the buffered power command is added to the initial MPPT power command. The result of the adder's operation forms the final converter power command, which is then sent to the converter's inner-loop current controller as the setpoint for the active power component, thereby driving the converter to operate according to this corrected power target.

[0053] In summary, the wind turbine converter thermal load optimization control method according to embodiments of the present invention has been clarified. It establishes a closed-loop collaborative control mechanism between the internal thermal state of the converter and the mechanical system of the wind turbine, aiming to solve the problem of thermal cycle fatigue accumulation in the converter caused by traditional maximum power point tracking control. Specifically, the wind turbine converter thermal load optimization control method of the present invention quantifies the degree of impending thermal shock by estimating and predicting the rate of change of junction temperature of the power devices of the wind turbine in real time. When the rate of change exceeds a preset safety threshold, the control system uses the huge rotor inertia of the wind turbine itself as a cost-free short-term energy storage buffer. By actively adjusting the output power command, it temporarily stores or releases some of the drastically changing power in the form of kinetic energy into or out of the rotor, thereby actively smoothing the peak and valley of the power flowing through the converter. This method decouples the rigid following relationship between the converter power and the instantaneous wind power, significantly suppresses the drastic fluctuations in internal losses of the devices, and ultimately effectively suppresses the amplitude and frequency of junction temperature cycling. While ensuring power generation efficiency, it improves the long-term operational reliability and service life of the converter.

[0054] This invention also provides a wind turbine converter thermal load optimization control system.

[0055] Figure 5 This is a block diagram of a wind turbine converter thermal load optimization control system according to an embodiment of the present invention. Figure 5 As shown, the wind turbine converter thermal load optimization control system 100 according to an embodiment of the present invention includes: a measurement value acquisition module 110, used to acquire electromagnetic power measurement values ​​and coolant temperature measurement values ​​of the wind turbine; a thermal state prediction module 120, used to perform thermal state prediction based on electromagnetic power measurement values ​​and coolant temperature measurement values ​​to obtain junction temperature estimate and junction temperature prediction value for the next control cycle; a thermal cycle stress assessment module 130, used to perform thermal cycle stress assessment on junction temperature estimate and junction temperature prediction value for the next control cycle to obtain junction temperature change rate; a buffer power command generation module 140, used to generate a buffer power command based on junction temperature change rate and rotor angular velocity measurement values; and a final power command correction module 150, used to perform final power command correction on the original MPPT power command based on the buffer power command and junction temperature change rate to obtain the final converter power command.

[0056] The specific implementation method of the wind turbine converter thermal load optimization control system provided in this embodiment of the invention can be found in the wind turbine converter thermal load optimization control method provided in this embodiment of the invention, and will not be repeated here.

[0057] The wind turbine converter thermal load optimization control system 100 according to embodiments of the present invention can be deployed in the edge computing unit at the wind turbine site, such as in a dedicated industrial control computer inside the wind turbine nacelle or tower base, or in the controller of the converter itself, and interact with the current and voltage sensors of the converter, the temperature sensors of the cooling system, the speed sensors of the generator, and the main control system of the wind turbine in real time. In one possible implementation, the wind turbine converter thermal load optimization control system 100 according to embodiments of the present invention can be integrated into the main control system of the wind turbine as an independent software module or hardware module. For example, the core parameters used for thermal state prediction and control in the wind turbine converter thermal load optimization control system 100, including the thermal network model matrix, device loss model parameters, and PID controller gain, can be calculated, tuned, and optimized offline on the back-end server of the wind farm control center using device datasheets and multiphysics simulation models. The optimized control parameter package is then sent to the front-end control unit. Of course, the complete control process used to perform real-time online optimization in the wind turbine converter thermal load optimization control system 100, including multi-source data acquisition, loss power calculation, thermal state estimation and prediction, buffered power command generation, and final power command correction, can also be embedded in dedicated edge computing hardware, such as a digital signal processor or field-programmable gate array module inside the converter, to accelerate the processing of real-time data streams and the iteration process of control algorithms, ensuring low-latency correction and output of the final power command.

[0058] Those skilled in the art will understand that the above embodiments are specific implementations of the present invention, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A method for optimizing the thermal load control of a wind turbine converter, characterized in that, The wind turbine converter heat load optimization control method includes: Obtain electromagnetic power and coolant temperature measurements from the wind turbine generator set; Thermal state prediction is performed based on electromagnetic power measurement and coolant temperature measurement to obtain junction temperature estimate and junction temperature prediction for the next control cycle; Thermal cycling stress assessment is performed on the estimated junction temperature and the predicted junction temperature for the next control cycle to obtain the junction temperature change rate. Based on the junction temperature change rate and rotor angular velocity measurements, a buffer power command is generated. Based on the buffer power command and junction temperature change rate, the original MPPT power command is modified to obtain the final converter power command.

2. The wind turbine converter thermal load optimization control method according to claim 1, characterized in that, Thermal state prediction is performed based on electromagnetic power measurements and coolant temperature measurements to obtain estimated junction temperatures and predicted junction temperatures for the next control cycle, including: Obtain the measured effective value of stator phase current, the current switching frequency setting, and the junction temperature and case temperature state vectors of the previous control cycle; The measured effective value of the stator phase current is calculated to obtain the characteristic value of the collector current, which includes the average collector current and the effective collector current. Based on the collector current characteristic value and the current switching frequency setting, calculate the conduction loss, switching loss and total power loss. The junction temperature state is updated based on the total power loss, the measured coolant temperature, and the junction temperature and case temperature state vectors from the previous control cycle to obtain the estimated junction temperature and case temperature. Based on the estimated junction temperature, estimated case temperature, measured electromagnetic power, measured electromagnetic power from the previous control cycle, and measured coolant temperature, the thermal state trend for the next control cycle is predicted to obtain the predicted junction temperature for the next control cycle.

3. The wind turbine converter thermal load optimization control method according to claim 2, characterized in that, Based on the collector current characteristic value and the current switching frequency setting, calculate the conduction loss, switching loss, and total power loss, including: Calculate conduction loss, switching loss, and total power loss using the following formulas: ； ； ； in, For conduction loss, This is the average collector current. collector effective current, For saturation pressure drop, The collector-emitter equivalent resistance, For switching losses, This is the measured value of the effective value of the stator phase current. To enable the energy loss function, For the power consumption function to be turned off, Set the current switching frequency value. This represents the total power loss during the current control cycle.

4. The wind turbine converter thermal load optimization control method according to claim 2, characterized in that, The junction temperature state is updated based on the total power loss, measured coolant temperature, and the junction and case temperature state vectors from the previous control cycle to obtain estimated junction and case temperatures, including: Based on the total power loss, the measured coolant temperature, and the junction temperature and case temperature state vectors from the previous control cycle, the junction temperature state is updated using the following formula: ； in, This is the junction temperature and case temperature state vector for the current control cycle, composed of the junction temperature estimate and the case temperature estimate. This is the estimated junction temperature for the current control cycle. This is the estimated shell temperature for the current control cycle. This represents the junction temperature and case temperature state vectors from the previous control cycle. This is the estimated junction temperature from the previous control cycle. This is the estimated shell temperature value from the previous control cycle. This represents the total power loss during the current control cycle. This is the measured coolant temperature value for the current control cycle. and These are fixed coefficient matrices calculated offline based on the thermal resistance and thermal capacity parameters of the devices.

5. The wind turbine converter thermal load optimization control method according to claim 4, characterized in that, Based on the estimated junction temperature, estimated case temperature, measured electromagnetic power, measured electromagnetic power from the previous control cycle, and measured coolant temperature, the thermal state trend is predicted for the next control cycle to obtain the predicted junction temperature for the next control cycle, including: The following formula is used to predict the thermal state trend for the next control cycle: ； ； in, This is the predicted junction temperature for the next control cycle. This is the predicted shell temperature value for the next control cycle. This represents the total power loss in the next control cycle. , These are the electromagnetic power measurement values ​​for the current control cycle and the previous control cycle, respectively. This is the predicted electromagnetic power value for the next control cycle. To control the cycle duration.

6. The wind turbine converter thermal load optimization control method according to claim 1, characterized in that, Thermal cycling stress assessment is performed on the estimated junction temperature and the predicted junction temperature for the next control cycle to obtain the junction temperature change rate, including: The thermal cycling stress is assessed using the following formula: (Estimated junction temperature and predicted junction temperature for the next control cycle) ； in, and These are the predicted junction temperature for the next control cycle and the estimated junction temperature for the next control cycle, respectively. To control the cycle duration, This represents the rate of change of junction temperature.

7. The wind turbine converter thermal load optimization control method according to claim 1, characterized in that, Based on the junction temperature change rate and rotor angular velocity measurements, a buffer power command is generated, including: Calculate the temperature change error rate between the junction temperature change rate and the upper limit of the expected junction temperature change rate; The temperature change error rate is input into the PID controller to obtain the initial buffer power command; Based on the rotor angular velocity measurement, the initial buffer power command is constrained to obtain the buffer power command.

8. The wind turbine converter heat load optimization control method according to claim 7, characterized in that, Based on the rotor angular velocity measurement, the initial buffer power command is constrained to obtain the buffer power command, including: In response to a junction temperature change greater than zero, if the measured rotor angular velocity is less than the maximum value of the rotor angular velocity buffer, the speed constraint gain is set to 1. In response to a junction temperature change greater than zero, if the measured rotor angular velocity is between the maximum and minimum values ​​of the rotor angular velocity buffer, the speed constraint gain is calculated using the following formula: ； in, This is the measured value of the rotor angular velocity. The maximum value of the safe operating range, This represents the maximum value of the rotor angular velocity buffer zone. For speed-constrained gain; The speed constraint gain is multiplied by the initial buffer power command to obtain the buffer power command.

9. The wind turbine converter heat load optimization control method according to claim 7, characterized in that, Based on the rotor angular velocity measurement, the initial buffer power command is constrained to obtain the buffer power command, and the process further includes: In response to a junction temperature change of less than zero, if the measured rotor angular velocity is greater than the minimum value of the rotor angular velocity buffer, the speed constraint gain is set to 1. In response to a junction temperature change of less than zero, if the measured rotor angular velocity is between the maximum and minimum values ​​of the rotor angular velocity buffer, the speed constraint gain is calculated using the following formula: ； in, This is the minimum value of the rotor angular velocity buffer zone. To be the minimum value of the safe operating range, This is the measured value of the rotor angular velocity. For speed-constrained gain; The speed constraint gain is multiplied by the initial buffer power command to obtain the buffer power command.

10. A heat load optimization control system for a wind turbine converter, characterized in that, The wind turbine converter heat load optimization control system includes: The measurement acquisition module is used to acquire the electromagnetic power measurement value and coolant temperature measurement value of the wind turbine. The thermal state prediction module is used to predict the thermal state based on the electromagnetic power measurement value and the coolant temperature measurement value, so as to obtain the junction temperature estimate and the junction temperature prediction value for the next control cycle. The thermal cycling stress assessment module is used to assess the thermal cycling stress of the estimated junction temperature and the predicted junction temperature for the next control cycle in order to obtain the junction temperature change rate. The buffer power command generation module is used to generate buffer power commands based on the junction temperature change rate and rotor angular velocity measurements. The final power command correction module is used to correct the original MPPT power command based on the buffered power command and the junction temperature change rate to obtain the final converter power command.

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