Fan blade air-thermal deicing adaptive temperature control method

Abstract

The application relates to the technical field of wind turbine control, and discloses a wind turbine blade air-thermal deicing adaptive temperature control method, which acquires real-time icing thickness, cabin external environment wind speed, environment temperature and blade internal temperature, calculates an icing growth rate, compares the icing data with a set threshold value, controls the running state to be converted between standby, anti-icing and deicing modes, utilizes a progressive interpolation algorithm to control the real-time set temperature to be smoothly transitioned to a new target temperature set value when state conversion occurs, calculates feedforward compensation and closed-loop feedback control amount based on environment and state parameters, superimposes the two on a basic output value to generate a total control instruction, and finally maps the output to a blower and a heating module. The application avoids frequent mode jumping under critical ice thickness, reduces transient thermal shock during state switching, and improves the stability of deicing control and equipment operation safety.

Description

Technical Field

[0001] This invention relates to the field of wind turbine generator control technology, specifically to an adaptive temperature control method for air-thermal de-icing of wind turbine blades. Background Technology

[0002] Wind turbines operating in cold and humid climates frequently face blade icing issues. Icing disrupts the aerodynamic shape of the blades and reduces the turbine's power generation efficiency, making air-thermal de-icing systems crucial for ensuring safe turbine operation. However, existing air-thermal de-icing systems still have significant limitations in their operational logic and temperature control. Conventional systems typically trigger mode switching based solely on the absolute value of ice thickness, leading to frequent jumps between anti-icing, de-icing, and standby modes when ice thickness is critical. During mode transitions, target temperature setpoints are often issued and assigned in a step-like manner. This abrupt change in command can subject high-power electric heating modules to instantaneous thermal shock, impacting equipment lifespan. At the temperature control algorithm level, existing solutions generally rely excessively on a single closed-loop feedback regulation logic, exhibiting significant lag in response to disturbances such as changes in external wind speed and ambient temperature fluctuations, making it difficult to accurately balance the actual de-icing heat load with dynamically changing environmental heat dissipation.

[0003] Furthermore, current control strategies often fail to take into account the real-time heating status of core electrical hardware such as engine room converters when continuously outputting high-power heating commands. This can easily lead to potential thermal overload of underlying equipment under long-term high-load operation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an adaptive temperature control method for air-thermal de-icing of wind turbine blades. This method solves the problems commonly found in existing air-thermal de-icing control systems, such as frequent changes in operating modes under critical ice thickness, transient thermal shocks caused by sudden temperature command jumps, delayed response to complex environmental disturbances, and lack of underlying hardware thermal overload protection.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for adaptive temperature control of air-thermal de-icing of wind turbine blades, comprising the following steps: Acquire real-time icing thickness data, external wind speed, ambient temperature, ambient humidity parameters monitored by the weather station, and real-time internal temperature of the blades from sensors installed in the airfoil mid-section and tip region of the wind turbine blades. The ice growth rate is calculated based on the real-time ice thickness data from multiple cycles. The real-time ice thickness data, the ice growth rate, and the environmental humidity parameters are compared with a preset state switching threshold to control the current operating state to switch between standby mode, anti-icing mode, and de-icing mode. When in the anti-icing mode or the de-icing mode, a corresponding target temperature setting value is assigned based on atmospheric pressure and altitude correction factor. When a state transition occurs, a progressive interpolation algorithm with second-order smoothing characteristics is used within a preset transition time window to control the real-time set temperature to approach the new target temperature set value after the transition from the original target temperature set value before the transition. Based on ambient wind speed, ambient temperature, real-time icing thickness, current operating status, the real-time set temperature, and real-time internal temperature of the blade, calculate the nonlinear feedforward compensation control quantity and the closed-loop feedback control quantity. The nonlinear feedforward compensation control quantity and the closed-loop feedback control quantity are fused to generate a total control command, and the total control command is mapped to output a blower operating frequency command and a heating module power command.

[0006] As one embodiment of the present invention, in the step of calculating the ice growth rate, a composite filtering process combining bandpass filtering and moving average is applied to the time series of the continuously input real-time ice thickness data. The thickness change difference is obtained by performing a difference operation between the real-time ice thickness data collected at the current moment and the real-time ice thickness data collected at the previous adjacent sampling moment. The thickness change difference is then divided by a preset discrete data sampling time period to obtain the ice growth rate.

[0007] As one embodiment of the present invention, the preset state switching thresholds include anti-icing start threshold, anti-icing exit threshold, de-icing start threshold and de-icing exit threshold. Under the same mode, the exit threshold is strictly less than the corresponding start threshold, and the difference between the two constitutes the hysteresis interval of state switching. When the current state is the standby mode and the real-time ice thickness data is greater than or equal to the anti-icing activation threshold and less than the de-icing activation threshold, a state transition command is triggered to switch to the anti-icing mode. In the anti-icing mode, when the real-time ice thickness data is less than or equal to the anti-icing exit threshold, the system reverts to the standby mode. When the real-time ice thickness data is greater than or equal to the de-icing activation threshold, the de-icing mode is entered; In the de-icing mode, when the real-time ice thickness data is less than or equal to the de-icing exit threshold, the system will switch back to the anti-icing mode.

[0008] As one embodiment of the present invention, when the ambient temperature outside the cabin is higher than a preset safe temperature threshold, the wind speed outside the cabin is lower than a preset calm wind threshold, and the ambient humidity parameter is lower than a preset dryness threshold, the current operating state is forcibly locked in the standby mode through a hardware interlocking circuit. In non-forced standby mode, when the ice growth rate exceeds a preset positive rate threshold based on the statistical average of historical meteorological data within multiple consecutive judgment cycles, the system will switch to the anti-icing mode or control the current operating state in the anti-icing mode to switch to the de-icing mode.

[0009] As one embodiment of the present invention, the process of calculating the real-time set temperature using a progressive interpolation algorithm with second-order smoothing characteristics within the preset transition time window includes: Record the moment of state transition as the initial switching moment; The temperature increment is obtained by multiplying the difference between the new target temperature setting and the original target temperature setting by the time difference between the current time and the initial switching time, and then dividing by the preset transition time window. The real-time set temperature is obtained by adding the original target temperature set value to the temperature increment; If a mode switching command is received again during the active period of the preset transition time window, the real-time set temperature calculated at the current moment will be overwritten with the new original target temperature set value, the preset transition time window will be reset and the interpolation calculation will be restarted. When the time difference between the current time and the initial switching time is greater than or equal to the preset transition time window, the real-time set temperature is assigned the new target temperature set value.

[0010] As one embodiment of the present invention, the nonlinear feedforward compensation control quantity includes environmental heat dissipation feedforward compensation quantity and heat load feedforward compensation quantity; Multiply the wind speed outside the cabin by the wind speed convection heat dissipation compensation coefficient, and add the product of the temperature difference between the real-time set temperature and the cabin outside temperature and the temperature difference conduction heat dissipation compensation coefficient to obtain the environmental heat dissipation feedforward compensation amount. In the anti-icing mode, the heat load feedforward compensation is calculated as the product of the anti-icing heat load coefficient and the real-time icing thickness data; In the de-icing mode, the heat load feedforward compensation is calculated as the product of the de-icing heat load coefficient and the real-time icing thickness data.

[0011] As one embodiment of the present invention, the process of calculating the closed-loop feedback control quantity includes: The temperature deviation value is obtained by subtracting the real-time set temperature from the real-time internal temperature of the blade. The temperature deviation value is input into a proportional-integral-derivative algorithm unit that adaptively adjusts the weight based on the deviation change rate to obtain the closed-loop feedback control quantity. When the closed-loop feedback control quantity reaches the preset maximum physical output power limit and the temperature deviation value is in the same direction as the change of the closed-loop feedback control quantity, the integral accumulation term is frozen and the mathematical operation of the branch corresponding to the integral term is stopped. A first-order low-pass filtering algorithm is used to filter out high-frequency noise from the input of the differential operation branch.

[0012] As one embodiment of the present invention, the process of generating the general control command includes: A weighted summation fusion algorithm based on multidimensional disturbance observer gain allocation is adopted to perform weight allocation and fusion calculation on the environmental heat dissipation feedforward compensation, the heat load feedforward compensation, and the closed-loop feedback control quantity to obtain the total control command for the current control cycle.

[0013] As one embodiment of the present invention, before performing the fusion operation, the zero-order hold is called to perform the multi-source data timestamp alignment logic, so as to smoothly discretize the asynchronous variables to the current control cycle; The maximum available power threshold is obtained by dynamically derating the current real-time heating temperature of the nacelle converter, the residual temperature of the converter and the ambient convection intensity. The minimum output power threshold is preset. When the calculated total control command exceeds the maximum available power threshold, it is forcibly truncated to the maximum available power threshold. When the total control command is lower than the minimum output power threshold, it is limited to the minimum output power threshold or directly set to zero.

[0014] As one embodiment of the present invention, the process of mapping the general control command into a blower operating frequency command and a heating module power command output includes: Set the blower operating frequency command to a fixed speed mode or a single-variable lookup table speed regulation mode based on the ambient temperature outside the cabin. The total control command is divided by the maximum available power threshold to obtain a percentage quotient. The percentage-form quotient is sent as a pulse width modulation duty cycle command for execution, thereby controlling the power command of the heating module.

[0015] This invention provides an adaptive temperature control method for air-thermal de-icing of wind turbine blades. It has the following beneficial effects: 1. This invention avoids frequent changes in operating mode caused by slight data fluctuations under critical ice thickness conditions by setting a state switching hysteresis interval where the exit threshold is less than the start threshold, and by combining it with a skip-level switching mechanism based on the multi-cycle ice growth rate. At the same time, it enables the control system to directly skip-level switching to the corresponding de-icing mode when the ice layer accumulates rapidly, thus shortening the physical response time to severe icing conditions.

[0016] 2. When the working state changes, the present invention uses a progressive interpolation algorithm based on a preset transition time window to calculate the real-time set temperature, and transforms the step temperature target difference into a continuous transition command that changes smoothly over time. This eliminates the large power jump caused by the sudden change in the set value during the mode switch and reduces the transient thermal shock that the high-power electric heating module suffers during the state switch.

[0017] 3. This invention adopts an algorithm architecture that integrates feedforward compensation and closed-loop feedback to generate control commands, and adds dynamic derating calculation logic based on the heating temperature of the nacelle converter at the output. The combined feedforward and closed-loop mechanism independently calculates the environmental heat dissipation and ice layer heat load, improving the system's temperature regulation stability under complex wind conditions. Dynamic derating control ensures that the underlying hardware will not be overloaded and damaged under long-term high-load de-icing conditions, thus guaranteeing the safe operation of the entire equipment. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] The technical solutions in 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] Please see the appendix Figure 1 This invention provides an adaptive temperature control method for air-thermal de-icing of wind turbine blades, which is implemented based on the air-thermal de-icing system of the wind turbine blades.

[0021] The wind turbine blade air-heat de-icing system includes an icing sensor, a weather station, a temperature sensor, a blower, a heating module, and a main control module.

[0022] An icing sensor is installed on the surface of the wind turbine blades to collect data on the icing thickness on the blades in real time.

[0023] The weather station is located outside the wind turbine nacelle to collect ambient wind speed and ambient temperature data.

[0024] Temperature sensors are placed inside the cavity of the wind turbine blades to obtain the actual temperature data inside the wind turbine blades.

[0025] The blower and heating module together form a pneumatic-thermal actuator, which is responsible for delivering hot air into the blower blades.

[0026] The main control module establishes communication connections with the icing sensor, weather station, temperature sensor, blower, and heating module, respectively.

[0027] Please see the appendix Figure 2 This invention provides an adaptive temperature control method for air-thermal de-icing of wind turbine blades, comprising the following steps: S10, obtain real-time monitoring parameters; S20, determine the switching conditions between anti-icing mode and de-icing mode; S30 sets the temperature control targets for anti-icing mode and de-icing mode respectively and performs transition control; S40, calculate the feedforward compensation control quantity and the closed-loop feedback control quantity; S50, the final control command is output.

[0028] The processing logic for each of the above steps is as follows: In step S10, the main control module reads the icing thickness on the blade surface from the icing sensor in real time. Simultaneously, the main control module acquires the ambient wind speed and ambient temperature output from the weather station, and reads the real-time internal temperature of the blade from the temperature sensor. Based on the icing thickness data from multiple consecutive sampling periods, the main control module calculates the icing growth rate.

[0029] In step S20, the main control module internally presets the anti-icing mode activation thickness threshold, the anti-icing mode deactivation thickness threshold, the de-icing mode activation thickness threshold, and the de-icing mode deactivation thickness threshold. The main control module compares the currently read blade surface ice thickness and the calculated ice growth rate with the corresponding preset thresholds. Based on the comparison results, the main control module controls the wind turbine blade air-thermal de-icing system to switch between standby mode, anti-icing mode, and de-icing mode.

[0030] In step S30, when the wind turbine blade air-thermal de-icing system is in anti-icing mode or de-icing mode, the main control module assigns a corresponding target temperature setpoint according to the current mode. The target temperature setpoint for anti-icing mode is set to a base value higher than the freezing point. The target temperature setpoint for de-icing mode is set to the high-temperature value corresponding to heating and ice melting. When the system switches modes, the main control module starts a progressive interpolation algorithm within a preset transition time window. The main control module controls the actual setpoint temperature to gradually approach the new target temperature from the original target temperature, limiting the step change range of the temperature command.

[0031] In step S40, the main control module calculates the feedforward compensation control quantity based on the current ambient wind speed, ambient temperature, and blade surface icing thickness. Ambient wind speed and ambient temperature constitute the heat dissipation compensation term, while blade surface icing thickness, combined with the current system mode, constitutes the heat load compensation term. Based on the numerical deviation between the current set temperature and the real-time internal temperature of the blade, the main control module calculates the closed-loop feedback control quantity using a proportional-integral-derivative algorithm.

[0032] In step S50, the main control module superimposes the preset basic output value, the calculated feedforward compensation control quantity, and the feedback control quantity to generate a total control command. The main control module maps the total control command to the blower's operating frequency command and the heating module's power command, and sends the command to the blower and heating module for execution, completing the closed-loop regulation of the internal temperature of the blower blades.

[0033] In step S10, the specific process of obtaining real-time monitoring parameters includes the following sub-steps: S101, acquire real-time icing thickness data on the surface of the wind turbine blades. The main control module reads the thickness data collected by the icing sensor installed on the surface of the wind turbine blades via the communication bus. During this detection process, the system, based on the general physical principle of the significant difference in relative permittivity between water, ice, and air, captures the characteristic reflections and frequency shifts generated when microwave signals penetrate the interface of different media, and then maps the changes in radio frequency signals to the physical thickness of the solid ice layer. In this embodiment, to meet the system's requirement for identifying the slight ice crystal state before icing occurs, the microwave icing monitoring technology specifically employs the microwave resonant cavity method or the microwave pulse radar detection method to extract the parameters of the lower-level physical characteristics. For the hardware deployment of the microwave signal transceiver circuit and the specific medium conversion logic, those skilled in the art can use conventional microwave detection hardware circuits, which are well-known technologies in the field and will not be described in detail here.

[0034] The real-time ice thickness data on the blade surface output by the icing sensor is recorded as follows: ,in This is the current data sampling time. The detection equipment based on microwave icing monitoring technology has a hardware measurement accuracy better than 0.1 mm, a data resolution of 0.05 mm, and an effective measurement range of 0–50 mm. The determination of the above range and accuracy parameters is based on balancing extreme operating conditions at both ends: the low-range segment needs to meet the microscopic detection requirements of the initial extremely thin water film and initial frost, supporting preventative actions; the high-range segment needs to ensure that the macroscopic thickness does not exceed limits under extreme severe freezing rain conditions.

[0035] S102 acquires the external wind speed, external temperature, and real-time internal temperature of the wind turbine nacelle. Considering the uncontrollable communication delays during data transmission from the spatially distributed sensors, the main control module performs timestamp-based alignment logic when receiving multi-source data to ensure that the multi-dimensional parameters remain time-synchronized across the same control cycle. After time alignment, the main control module synchronously reads the external wind speed collected by the weather station. and the ambient temperature outside the wind turbine nacelle The physical cause and effect of introducing the above external meteorological parameters lies in the wind speed outside the wind turbine nacelle. Directly determines the intensity of convective heat dissipation on the surface of the wind turbine blades and the ambient temperature outside the wind turbine nacelle. This reflects the baseline temperature of the external cold source, and the two together constitute the nonlinear boundary condition for heat loss from the blade surface. The main control module simultaneously reads the real-time internal temperature of the blade from temperature sensors located within the internal cavity of the wind turbine blade. This value is used as the tracking object for subsequent closed-loop feedback control algorithms.

[0036] S103, calculate the icing growth rate on the wind turbine blade surface. Since differential calculations over a single time step are highly susceptible to interference from high-frequency sensor noise, potentially causing erroneous control commands, this embodiment pre-applies a moving average filter to the continuously input time-series data before performing the rate calculation to smooth out abrupt thickness changes. The main control module inputs and stores real-time icing thickness data from multiple consecutive sampling periods in its internal register, extracting the thickness variation difference over the time series. This embodiment sets the calculation logic as follows: the icing growth rate... Calculated as real-time icing thickness data at the current moment. Real-time ice thickness data collected at the previous adjacent sampling time. The difference, divided by the system's preset discrete data sampling time period. During this calculation, the formula uses real-time ice thickness data at the current moment. Real-time ice thickness data at the previous adjacent sampling time These all refer to the smoothed thickness values ​​after the moving average filtering process in the preliminary steps. This logic ensures that the difference calculation of the numerator is not interfered with by spurious abrupt changes caused by high-frequency measurement noise, thus guaranteeing the authenticity and reliability of the calculated icing growth rate; among which, The value range is typically set to 1–5 seconds, with the specific value determined based on a combination of the main control module's logical operation processing power and the field communication bus's transmission bandwidth. To ensure the stability of the underlying operations, the main control module checks the denominator term before executing the division instruction. Check if it is greater than a preset minimum positive real number to avoid division by zero anomalies caused by communication blockage.

[0037] The main control module calculates the icing growth rate continuously. Numerical dynamic assessment of the icing deterioration trend of wind turbine blades. When When the calculated result exceeds the system's preset positive rate threshold, the main control module sets it as one of the conditions for triggering a skip-level mode switch. To improve the robustness of the judgment logic, the system abandons the one-sided judgment method that relies on a single extreme point, and instead requires... Required in continuous The system must maintain a positive velocity threshold above the threshold throughout each judgment cycle before finally outputting a valid judgment. Under typical implementation conditions, the positive velocity threshold is set to 0.2 mm / min. This empirical threshold is calibrated from the statistical average of severe freezing rain icing processes in the historical meteorological data of the target wind field over the years.

[0038] In step S20, the specific process of determining the switching conditions between anti-icing mode and de-icing mode includes the following sub-steps: S201 defines the system's operating states and core judgment thresholds. To address complex environmental and meteorological changes, the system constructs a three-state machine model based on the hysteresis comparison principle at the logic layer. The main control module has multiple pre-set control modes to handle icing conditions at different physical stages. In this embodiment, the system's operating states are divided into standby mode, anti-icing mode, and de-icing mode. In standby mode, the system stops active heating and maintains environmental monitoring; in anti-icing mode, it outputs low-energy preheating to prevent water droplets on the blade surface from undergoing phase change and freezing; and in de-icing mode, it outputs high power to quickly melt the formed solid ice layer. To provide a quantitative benchmark for the main control module's state judgment, the system presets four thickness boundary parameters, specifically including the anti-icing activation threshold. Anti-icing exit threshold De-icing start threshold and the de-icing exit threshold . The typical value is set at 2 mm, and the effective value range is 0.05 to 5 mm; The typical value is set at 0.3 mm, with an effective range of 0–0.5 mm. Furthermore, to define the boundary for entering severe icing conditions, The effective value range is set to 2–10 mm, and the upper limit of this threshold is determined based on the maximum ice load redundancy allowed by the aerodynamic performance of the wind turbine blade airfoil; correspondingly The value range is set to 0.5–1.5 mm. The exit threshold in the same mode is strictly lower than its corresponding start threshold; this difference constitutes the hysteresis interval for state switching. The purpose of introducing the hysteresis interval is to prevent the main control module from frequently issuing contradictory commands when real-time icing thickness data fluctuates slightly near the critical point due to measurement noise or local ice shedding, thereby avoiding abnormal start-up and shutdown of the pneumatic actuator.

[0039] S202 performs multi-mode adaptive flow under normal operating conditions. The main control module is based on real-time icing thickness data. State routing is performed within the given numerical range. To ensure the rigor of the state transition logic, the system traverses all available transition branches of the current state in each control cycle. When the system is in standby mode and the real-time thickness meets the requirements... When this occurs, the main control module triggers a state transition command, and the control system switches to anti-icing mode to implement preventative heating. During anti-icing mode operation, if the ice melts or sublimates, causing... Continued decline and met The system returns to standby mode; if the thickness remains within the hysteresis range, the main control module maintains the current state machine flag unchanged. When weather conditions continue to deteriorate... At this time, the main control module forces the system into de-icing mode, calling in a high-power heat source for the de-icing operation. Later in the de-icing process, once the ice thickness has decreased to a certain level... Instead of immediately cutting off the air and heat output, the main control module reverts the system state to anti-icing mode to consolidate the blade surface condition. Based on the above cascaded state machine model, the main control module achieves differentiated unlocking for anti-icing and de-icing conditions.

[0040] S203 executes forced standby and over-level trigger judgments under special operating conditions. To cover extreme weather scenarios and improve the overall energy efficiency of the system, the main control module sets up boundary constraint logic based on multi-source environmental parameters in parallel with the conventional thickness judgment logic. In engineering implementation, to avoid logic deadlock caused by concurrent multi-dimensional judgment conditions, the system sets the forced standby mechanism based on environmental parameters to have the highest judgment priority. The main control module compares environmental meteorological data in real time, and when the ambient temperature outside the wind turbine nacelle... The temperature exceeds the preset safety threshold and the wind speed outside the fan nacelle is higher than the preset safety threshold. When the wind speed is below a preset calm wind threshold, the main control module blocks the thickness-based state trigger signal and locks the system in standby mode. In this embodiment, the safe temperature threshold is set to 3°C, and the calm wind threshold is set to 3 m / s. When the external environment exhibits positive temperature and low aerodynamic dissipation characteristics, the liquid water on the blade surface does not possess the thermodynamic conditions for significant freezing, and the system's active locking can avoid unnecessary energy waste. On the other hand, in the non-forced standby state, the system constructs a step-by-step acceleration mechanism based on the icing growth rate. The main control module reads the icing growth rate in real time. When judging When the forward velocity exceeds the preset threshold, it indicates that the wind turbine is in severe airflow such as freezing rain. To avoid misjudgment due to extreme values ​​caused by sudden meteorological winds or single sensor noise, the main control module requires that the velocity exceeding the limit be maintained for a continuous preset time window (e.g., 3 control cycles) before outputting a skip-level command. After the judgment takes effect, the main control module skips the time delay of thickness accumulation and directly controls the system to switch from standby mode to anti-icing mode, or controls the system in anti-icing mode to accelerate to de-icing mode. This ensures that the system can seize the thermal time window for anti-icing and de-icing operations when encountering sudden extreme freezing weather, preventing mechanical damage to the blades caused by excessive ice load.

[0041] In step S30, the specific process of setting the temperature control targets for anti-icing mode and de-icing mode and performing transition control includes the following sub-steps: S301, Set differentiated target temperature setting reference values. After the system establishes the current operating mode through the state machine, the main control module assigns independent temperature control targets to different modes based on aerodynamic and thermodynamic constraints. In this embodiment, when the system switches to anti-icing mode, the main control module records the anti-icing target temperature setting value as... The core technical objective of anti-icing operation is to maintain the liquid phase state of water droplets by providing a base heat flux slightly above the freezing point to the blade surface, thus offsetting the latent heat of phase change released during the impact of supercooled water droplets. Based on this thermodynamic equilibrium requirement, The effective temperature range is set to 2℃~5℃, which ensures anti-icing performance while avoiding energy waste caused by ineffective heating. When the system switches to de-icing mode, the main control module records the de-icing target temperature setpoint as... De-icing operations require establishing a significant temperature difference between the inside and outside of the structure within a short time to break the ice adhesion; however, the upper limit of heating in this process is strictly limited by the glass transition temperature of the fiberglass composite material of the wind turbine blades. Considering both the thermal stress release safety threshold and the gas-thermal de-icing efficiency, The valid value range is set to 20℃~35℃.

[0042] S302 monitors the mode switching flag and initializes the transition time window. Because there is a difference of tens of degrees Celsius between the anti-icing target temperature setpoint and the de-icing target temperature setpoint, a step change in the target temperature command during mode switching would cause a sharp surge in the output power of the underlying heating module. This transient change not only causes unexpected surge current impacts on the DC bus of the wind turbine converter, but the resulting high-frequency thermal expansion of the internal cavity can also induce fatigue cracking of the adhesive lines between the blade web and the shell. To smooth out this step oscillation, a smooth transition mechanism is embedded in the underlying logic of the system. The main control module polls the mode switching flag of the state machine in each control cycle. Once a change in operating mode is detected, the main control module records the current system time as the initial switching moment. And simultaneously activate the preset transition time window. In actual control processes, the transition time window The specific value is not a fixed constant, but is dynamically calculated and output by the main control module based on the current ambient temperature and the internal and external temperature difference to be crossed. Its typical value range is set to 30s to 120s; when the target temperature difference is large or in an extremely cold environment, the system automatically adjusts the time value to be biased towards the upper limit of this range, providing a gentler heat injection slope.

[0043] S303 executes a progressive temperature interpolation algorithm to calculate the real-time setpoint temperature. During the active period of the transition time window, the main control module suspends the static temperature direct assignment logic and instead calls the time-variable-based progressive interpolation algorithm to control the actual setpoint temperature to gradually approach the new target temperature setpoint from the original target temperature setpoint. To ensure that the hardware execution layer can obtain continuous and smooth temperature reference commands, this embodiment constructs a linear ramp generator as an interpolation model. The specific mathematical expression of this, which is one of the core protection points, is set as follows: ; In the formula, For the present The real-time set temperature is actually output by the system and used for subsequent feedback closed-loop calculations; This is the original target temperature setpoint of the system before the mode switch; The new target temperature setpoint that the system is expected to achieve after the switch. During the above calculation, the main control module will adjust the transition time window of the denominator term in the underlying driver. By forcibly assigning a lower limit protection zone (such as locking the minimum threshold to 1 second), the risk of controller crashing due to division-by-zero overflow is avoided at the software level.

[0044] Furthermore, considering the complex and ever-changing wind field weather conditions, the state machine may experience extreme conditions with frequent jumps within a short period of time. Therefore, the main control module has specifically added transition period interrupt reset logic. During the transition, the system received another mode switching command, and the main control module forcibly switched to the real-time set temperature calculated at the current moment. Directly overwrite the original target temperature setting value Then, using this moment as a new starting point, the transition time window is reset, and the interpolation calculation is restarted.

[0045] As the control cycle progresses, the main control module continuously performs timing comparisons. When it is determined that the current system time meets the requirements... When this time is reached, it indicates that the gradual transition process has ended. The main control module then closes the transition time window status flag and directly assigns a value. The system performs steady-state limiting and solidification of the target command to restore the system's steady-state temperature tracking. Through the above-mentioned servo approximation calculation, the system forcibly transforms the discrete temperature step signal of the state machine into a continuous ramp signal with a fixed slope, achieving an engineering balance between electrical dynamic response and thermodynamic protection of composite materials.

[0046] In step S40, the specific process of calculating the feedforward compensation control quantity and the closed-loop feedback control quantity includes the following sub-steps: S401 calculates the feedforward compensation for environmental heat dissipation based on meteorological disturbances. Wind turbine blades operate under complex climatic conditions, and their surface heat loss is constrained by nonlinear physical boundary conditions, primarily consisting of forced convection heat dissipation caused by ambient wind speed and conduction heat loss due to internal and external temperature differences. Based on fundamental laws of heat transfer, to compensate for significant internal temperature fluctuations caused by external meteorological disturbances, the system constructs an environmental disturbance feedforward channel in its underlying algorithm. The main control module calculates the feedforward compensation based on the currently acquired ambient wind speed outside the wind turbine nacelle. Ambient temperature outside the fan nacelle and the system's real-time temperature setting Calculate the feedforward compensation amount for environmental heat dissipation. To broaden the protection boundary of the scheme, the calculation logic for this compensation amount is set as follows: the ambient wind speed outside the wind turbine nacelle is taken into account. Compensation coefficient for convection heat dissipation with wind speed Multiply and add the system's real-time set temperature. With the ambient temperature outside the wind turbine nacelle Temperature difference and heat dissipation compensation coefficient The product of.

[0047] Its specific mathematical expression is set as follows: Through this linear superposition model, the main control module can directly and quantitatively map multi-source meteorological data into offset commands for the underlying energy output; in this embodiment, and The specific values ​​are not fixed constants, but rather empirical coefficients dynamically matched by the main control module using a built-in three-dimensional lookup table based on the aerodynamic parameters of the target wind turbine blade airfoil and the equivalent heat transfer area of ​​the surface. Before actual engineering deployment, the specific basic value ranges of the above two coefficients are obtained through offline calibration using wind tunnel thermal balance experimental data. The physical significance of introducing this feedforward channel lies in directly and proactively converting measurable external meteorological disturbances into a countermeasure for the control output, significantly reducing the system hysteresis effect caused by the limitation of material conduction delay in a single closed loop.

[0048] S402 calculates the heat load feedforward compensation based on icing status and operating mode. Besides external meteorological heat dissipation, the solid ice layer adhering to the blade surface absorbs a large amount of latent heat of phase change during melting or sublimation, and this heat consumption constitutes the system's dynamic internal heat load. The main control module calculates the feedforward compensation based on the current control mode of the state machine and real-time icing thickness data. Calculate the heat load feedforward compensation amount To address the differences in thermodynamic requirements under different modes, the main control module employs piecewise gain logic for calculation. In this embodiment, this logic is configured as follows: When the system is in anti-icing mode, only a small amount of condensate or initial frost exists on the blade surface, resulting in a low latent heat demand. The main control module uses a small anti-icing coefficient for linear feedforward compensation, calculating this compensation as the product of the anti-icing heat load coefficient and the real-time ice thickness data. When the system is in de-icing mode, the solid ice layer is thicker, requiring rapid input of sensible and latent heat to break the ice adhesion. The main control module forcibly switches to a large de-icing coefficient for feedforward compensation, calculating this compensation as the product of the de-icing heat load coefficient and the real-time ice thickness data. The values ​​of the aforementioned anti-icing and de-icing heat load coefficients are primarily determined based on the conversion of physical constants such as the specific heat capacity and latent heat of fusion of water and ice. Furthermore, the numerical relationship strictly satisfies that the de-icing heat load coefficient is greater than the anti-icing heat load coefficient to match the transient high heat demand under de-icing conditions.

[0049] S403 calculates the closed-loop feedback control quantity based on temperature deviation. While feedforward control can proactively offset most known disturbances, the unmodeled dynamic characteristics of the heat transfer path in the composite material within the wind turbine blades and the inherent measurement errors of the temperature sensors necessitate closed-loop feedback to eliminate the final steady-state error. The main control module uses the current system temperature set in real time. With the acquired real-time internal temperature of the blade Perform the difference operation to obtain the temperature deviation value. , is defined as its mathematical relation as The main control module then inputs the calculated temperature deviation value into the proportional-integral-derivative (PID) algorithm unit to obtain the closed-loop feedback control quantity. Considering the discretized operation characteristics of digital main control chips, the discrete feedback operation model is set as follows: ; In the formula, The proportional coefficient is used to output a control quantity that is proportional to the current deviation in order to speed up the response. This is the integral coefficient, used to accumulate historical deviations until the steady-state error approaches zero; e is a differential coefficient used to assess the trend of deviation change in order to provide advance damping; This represents the temperature deviation value at the historical sampling time. This represents the temperature deviation value at the previous adjacent sampling time. Since the discrete time period has already been defined above, it can be referenced here. For the above three core feedback coefficients, those skilled in the art can perform online tuning using conventional engineering tuning methods such as the critical proportionality method or relay feedback method, combined with the field-measured thermal response curve. Specifically, when performing differential term calculations, the underlying driver forces the system to adhere to the preset discrete data sampling time period. Zero-value verification is performed to ensure that the value is greater than the preset minimum time constant, thereby avoiding the risk of division by zero overflow caused by communication anomalies.

[0050] S404 performs comprehensive processing for anti-saturation constraint and high-frequency filtering. In actual industrial engineering implementation, when the actuator (such as a heating module) reaches its maximum physical output power limit, if the temperature deviation is caused by an extremely cold environment... If this condition persists for an extended period, continuous integral calculations can lead to saturation of the integral term within the controller. Once this state is established, it can cause system hysteresis and overshoot oscillations when the temperature deviation reverses. To compensate for the logical flaws in the standard algorithm, the system incorporates an integral anti-saturation mechanism in the feedback channel. The main control module monitors the limiting flag at the underlying physical output port in real time. Once the actuator reaches its full-load output limit, and the current temperature deviation value... In line with the direction of the control command output, the main control module immediately freezes the integral accumulation term and stops. The mathematical operations correspond to the branch. Meanwhile, considering that electromagnetic interference in the field can easily introduce high-frequency noise into the measurement cables, causing pulses in the differential term output, the main control module connects a first-order low-pass filter in series before the differential operation branch, ensuring closed-loop feedback control from the underlying software architecture. Output smoothness and anti-interference ability.

[0051] In step S50, the specific process of performing control instruction fusion and mapping it to underlying hardware drive signals includes the following sub-steps: S501, Energetics fusion of multivariable control commands. Based on multivariable decoupled control theory, in actual engineering operation, since the various sub-control quantities calculated in parallel within the aforementioned control cycle correspond to different disturbance sources and error feedbacks, the system needs to integrate them into a unified energy output dimension. In this embodiment, to expand the applicability of the scheme and avoid being limited by simple arithmetic addition, the main control module adopts a weighted summation fusion algorithm to incorporate the forward-calculated environmental heat dissipation feedforward compensation quantity. Heat load feedforward compensation and closed-loop feedback control quantity based on error adjustment By performing weight allocation and fusion calculations, the total thermal power control quantity for the current control cycle is obtained. The physical essence of introducing this fusion logic lies in the system-level coordination of the forward-looking energy to offset known environmental disturbances and the feedback correction energy to unknown heat transfer dynamics, ensuring that the control system achieves an engineering balance between dynamic response speed and steady-state tracking accuracy.

[0052] S502 performs multi-source data alignment and dynamic limiting protection based on hardware electrical boundaries. Before executing the aforementioned fusion command and sending it to the lower layer, considering the significant difference between the data update rate of the meteorological sensor (e.g., seconds) and the operation frequency of the internal control loop (e.g., milliseconds), the main control module forcibly executes the timestamp alignment logic of the multi-source data in memory. By calling the zero-order hold, the system smoothly discretizes all asynchronous variables to the current control cycle, thereby avoiding command jumps caused by timing misalignment. After timing alignment is completed, the system immediately executes power limiting. Due to the clear physical ceilings of the current carrying capacity of the wind turbine hub slip ring, the power supply capacity of the transformer inside the nacelle, and the heat dissipation bottleneck of the heating wire itself, the total thermal power control amount... It cannot indefinitely increase the power output based on theoretical calculations. The main control module sets a maximum available power threshold within its underlying software. With minimum output power threshold When the calculated total heat power control quantity exceeds... At that time, the main control module forcibly truncated it to When it is lower than When, then it is restricted to Or set it to zero directly. The above maximum available power threshold... These are not static parameters fixed at the factory. Their determination is based not only on the smaller of the redundancy of the anti-icing and de-icing power distribution issued by the wind turbine control system and the rated power on the heater nameplate, but also on the dynamic derating calculations performed by the main control module in conjunction with the real-time heating temperature of the nacelle converter during actual operation.

[0053] S503, performs a dimensionality reduction mapping of the total heat power to the gas-thermal execution device. In this embodiment, the underlying physical execution unit of the gas-thermal anti-icing system mainly consists of a high-power electric heating module and a centrifugal circulating fan. To avoid control oscillations caused by strong coupling of multiple variables, the system adopts a decoupled drive strategy during the mapping phase. The main control module sets the centrifugal circulating fan to a fixed speed or a single-variable lookup table speed regulation mode based on ambient temperature, thereby maintaining a relatively stable convective mass flow rate within the fan blade cavity. Based on this, the main control module converts the amplitude-limited total heat power control quantity into a pulse width modulation duty cycle command to control the high-power electric heating module. To avoid limiting the protection range with a single algorithm, the mapping algorithm is specifically set as follows: the total heat power control quantity... Divide by the maximum available power threshold The quotient is then converted into a percentage and used as the real-time duty cycle value output to the drive circuit of a solid-state relay or high-power switching device. Its value is strictly limited to between 0% and 100%. Before executing this critical division instruction, the kernel error prevention mechanism of the main control module will forcibly read and verify the maximum available power threshold of the denominator. The register states are monitored to ensure that their values ​​are significantly greater than a preset minimum power constant (e.g., 1kW). This mechanism prevents system crashes caused by divide-by-zero overflows in the controller due to parameter loss caused by bus communication packet loss or sensor disconnection at the code level.

[0054] S504 outputs the underlying hardware drive level signal. After calculating the duty cycle value, the microcontroller of the main control module directly generates a high-frequency PWM digital signal with the corresponding duty cycle through its internal timer hardware. This signal is sent to the power switching devices in the distribution cabinet via a fieldbus or high-speed isolated I / O port. Regarding the generation mechanism of the underlying PWM level signal, the dead time setting, and the electrical drive circuit of the power semiconductor switch, those skilled in the art can perform conventional selection and circuit construction based on the type of heater used. The specific hardware topology is well-known in the field and will not be elaborated here.

[0055] The present invention can also be implemented as a computer device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the various steps of the above-mentioned adaptive temperature control method for air-heat de-icing of wind turbine blades.

Claims

1. A method for adaptive temperature control of air-heat de-icing of wind turbine blades, characterized in that, Includes the following steps: Acquire real-time icing thickness data, external wind speed, ambient temperature, ambient humidity parameters monitored by the weather station, and real-time internal temperature of the blades from sensors installed in the airfoil mid-section and tip region of the wind turbine blades. Calculate the ice growth rate based on multi-cycle real-time ice thickness data; The real-time ice thickness data, ice growth rate and environmental humidity parameters are compared with the preset state switching threshold to control the current operating state to switch between standby, anti-icing and de-icing modes. When in anti-icing or de-icing mode, the target temperature setpoint is assigned based on atmospheric pressure and altitude correction factor. When a state transition occurs, a progressive interpolation algorithm with second-order smoothing characteristics is used within a preset transition time window to control the real-time set temperature to approach the new target temperature set value from the original target temperature set value. Based on ambient wind speed, ambient temperature, real-time icing thickness, current operating status, the real-time set temperature, and real-time internal temperature of the blade, calculate the nonlinear feedforward compensation control quantity and the closed-loop feedback control quantity. The nonlinear feedforward compensation control quantity and the closed-loop feedback control quantity are fused to generate a total control command, which is then mapped to the blower operating frequency command and the heating module power command output.

2. The method according to claim 1, characterized in that, A composite filtering process combining bandpass filtering and moving average is applied to the time series of continuously input real-time icing thickness data; The thickness change difference is obtained by performing a difference operation between the real-time ice thickness data collected at the current moment and the real-time ice thickness data collected at the previous adjacent sampling moment. The thickness change difference is then divided by a preset discrete data sampling time period to obtain the ice growth rate.

3. The method according to claim 1, characterized in that, The preset state switching thresholds include anti-icing start threshold, anti-icing exit threshold, de-icing start threshold and de-icing exit threshold. In the same mode, the exit threshold is strictly less than the corresponding start threshold, and the difference between the two constitutes the hysteresis interval of state switching. When the current state is standby mode and the real-time ice thickness data is greater than or equal to the anti-icing activation threshold and less than the de-icing activation threshold, a state transition command is triggered to switch to anti-icing mode. In the anti-icing mode, when the real-time ice thickness data is less than or equal to the anti-icing exit threshold, the system reverts to the standby mode. When the real-time ice thickness data is greater than or equal to the de-icing activation threshold, the de-icing mode is entered; In the de-icing mode, when the real-time ice thickness data is less than or equal to the de-icing exit threshold, the system will switch back to the anti-icing mode.

4. The method according to claim 1, characterized in that, When the ambient temperature outside the cabin is higher than the preset safe temperature threshold and the wind speed outside the cabin is lower than the preset calm wind threshold, and the relative humidity is lower than the preset dryness threshold, the current operating state is forcibly locked in the standby mode through a hardware interlocking circuit. In non-forced standby mode, when the ice growth rate exceeds a preset positive rate threshold based on the statistical average of historical meteorological data within multiple consecutive judgment cycles, the system will switch to the anti-icing mode or control the current operating state in the anti-icing mode to switch to the de-icing mode.

5. The method according to claim 1, characterized in that, The real-time set temperature is calculated using a progressive interpolation algorithm with second-order smoothing characteristics within the preset transition time window, including: Record the moment of state transition as the initial switching moment; The temperature increment is obtained by multiplying the difference between the new target temperature setting and the original target temperature setting by the time difference between the current time and the initial switching time, and then dividing by the preset transition time window. The real-time set temperature is obtained by adding the original target temperature set value to the temperature increment; If a mode switching command is received again during the active period of the preset transition time window, the real-time set temperature calculated at the current moment will be overwritten with the new original target temperature set value, the preset transition time window will be reset and the interpolation calculation will be restarted. When the time difference between the current time and the initial switching time is greater than or equal to the preset transition time window, the real-time set temperature is assigned the new target temperature set value.

6. The method according to claim 1, characterized in that, The nonlinear feedforward compensation control quantity includes environmental heat dissipation feedforward compensation quantity and heat load feedforward compensation quantity; Multiply the wind speed outside the cabin by the wind speed convection heat dissipation compensation coefficient, and add the product of the temperature difference between the real-time set temperature and the cabin outside temperature and the temperature difference conduction heat dissipation compensation coefficient to obtain the environmental heat dissipation feedforward compensation amount. In the anti-icing mode, the heat load feedforward compensation is calculated as the product of the anti-icing heat load coefficient and the real-time icing thickness data; In the de-icing mode, the heat load feedforward compensation is calculated as the product of the de-icing heat load coefficient and the real-time icing thickness data.

7. The method according to claim 1, characterized in that, The process of calculating the closed-loop feedback control quantity includes: The temperature deviation value is obtained by subtracting the real-time set temperature from the real-time internal temperature of the blade. The temperature deviation value is input into the proportional-integral-derivative algorithm unit, which calculates the proportional coefficient, integral coefficient, and derivative coefficient based on the deviation change rate adaptive adjustment weight, to obtain the closed-loop feedback control quantity. When the closed-loop feedback control quantity reaches the preset maximum physical output power limit and the temperature deviation value is in the same direction as the change of the closed-loop feedback control quantity, the integral accumulation term is frozen and the mathematical operation of the branch corresponding to the integral coefficient is stopped. A first-order low-pass filtering algorithm is used to filter out high-frequency noise from the input of the differential operation branch.

8. The method according to claim 6, characterized in that, The process of generating the overall control command includes: A weighted summation fusion algorithm based on multidimensional disturbance observer gain allocation is adopted to perform weight allocation and fusion calculation on the environmental heat dissipation feedforward compensation, the heat load feedforward compensation, and the closed-loop feedback control quantity to obtain the total control command for the current control cycle.

9. The method according to claim 8, characterized in that, Before performing the fusion operation, the zero-order hold is called to execute the multi-source data timestamp alignment logic, so that the asynchronous variables are smoothly discretized to the current control cycle; The maximum available power threshold is obtained by dynamically derating the current real-time heating temperature of the nacelle converter, the residual temperature of the converter and the ambient convection intensity. The minimum output power threshold is preset. When the calculated total control command exceeds the maximum available power threshold, it is forcibly truncated to the maximum available power threshold. When the total control command is lower than the minimum output power threshold, it is limited to the minimum output power threshold or directly set to zero.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-9.

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