A method for calculating wind power low-temperature starting heating time

Abstract

The application discloses a kind of wind power low-temperature starting heating time calculation method, belong to wind power technical field.The method includes: obtaining the historical operation data and real-time state data of wind turbine unit under low-temperature environment, construct heating response delay evaluation model based on time series decomposition;The time difference sequence between heating starting time and the actual time when unit reaches starting permission condition is input into model, and the heating response reference delay value is calculated by extracting trend component and periodic component;According to the reference delay value, the current cabin temperature, gear box oil temperature and environmental wind speed are used as boundary conditions to generate heating power dynamic distribution queue;Each heating component is allocated initial heating time window, and iterative correction is carried out according to the dynamic matching relationship of real-time temperature change rate and initial time window until the real-time temperature of all components synchronously reaches the preset starting temperature threshold, and the final heating starting time is output.

Description

Technical Field

[0001] This invention relates to the field of wind power technology, specifically a method for calculating the low-temperature start-up heating time of wind power. Background Technology

[0002] In low-temperature environments, wind turbine generators require preheating of critical components before startup to prevent damage caused by excessively high lubricant viscosity or structural embrittlement. Existing methods for calculating heating time typically rely on fixed empirical temperature rise curves or simple temperature difference proportional control. These methods fail to consider the changes in the heating system's response characteristics over years of operation and due to periodic environmental fluctuations. Aging of heaters, contactors, and heat conduction paths can cause deviations in electrothermal conversion efficiency, and long-term changes in lubricant performance can affect heat diffusion rates. Static heating time settings can easily lead to insufficient or excessive actual heating time, causing the generator to deviate from the expected startup time. Furthermore, existing methods often employ a fixed heating power distribution sequence, resulting in weak coordinated control of the heating process for each component. When multiple heating components operate in parallel or sequentially, their heating processes are difficult to converge naturally to the same target time due to differences in their thermal inertia, environmental heat dissipation conditions, and initial temperatures. If a component reaches its startup temperature too early while other components are still at a low temperature, the component that has finished heating early will be in an ineffective waiting state, resulting in wasted heating energy and a longer startup cycle. If the heating time is forcibly uniform in pursuit of synchronization, some components may be allowed to start without sufficient preheating, increasing mechanical wear and the risk of false alarms in the control system.

[0003] The problem to be solved by this invention is how to accurately calculate the heating time required for low-temperature start-up of a wind turbine generator set under the conditions of heating system performance changing over time and environmental disturbances, and to achieve time synchronization of the heating process of each component near a preset temperature threshold. Summary of the Invention

[0004] A method for calculating the low-temperature start-up heating time of wind power is provided. By performing trend and periodic decomposition on the historical heating response delay time series, a benchmark delay reflecting the aging of equipment and the periodic impact of the environment is obtained. Based on this, dynamic power allocation and iterative correction are performed so that multiple heating components can reach the start-up temperature threshold synchronously under changing operating conditions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a method for calculating the heating time for wind power startup at low temperatures, comprising: acquiring historical operating data and real-time status data of a wind turbine generator set in a low-temperature environment, and constructing a heating response delay evaluation model based on time series decomposition; inputting the time difference sequence between the heating startup time in the historical operating data and the actual time when the generator set reaches the start-up allowable conditions into the heating response delay evaluation model, and calculating the heating response baseline delay value by extracting the trend component and periodic component in the time difference sequence; generating a heating power dynamic allocation queue based on the heating response baseline delay value, using the current nacelle temperature, current gearbox oil temperature, and current ambient wind speed in the real-time status data as boundary conditions; pre-allocating the heating time of multiple heating components of the wind turbine generator set using the heating power dynamic allocation queue to obtain the initial heating time window for each heating component; iteratively correcting the heating time window of each heating component according to the dynamic matching relationship between the real-time monitored component temperature change rate and the initial heating time window, until the real-time temperature of all heating components synchronously reaches the preset startup temperature threshold, and outputting the final heating startup time. This method eliminates the interference of environmental periodic fluctuations and long-term component changes on heating delay estimation by using time series decomposition, and achieves dynamic coordination of heating duration and power of each component through priority queue and real-time feedback correction, thereby improving the accuracy of heating time prediction and reducing self-consumption during low-temperature standby.

[0006] As a preferred technical solution of the present invention, the specific process of constructing the heating response delay evaluation model is as follows: Heating start-up records of the wind turbine generator set during multiple historical low-temperature start-up events are collected. Each heating start-up record includes the time when the heating command is issued, the time when the temperature rise of the first heating component reaches an inflection point, and the time when the start-up permission flag of the generator set's control system flips. The difference between the time when the heating command is issued and the time when the temperature rise of the first heating component reaches an inflection point is defined as the electrothermal conversion delay time, and the difference between the time when the temperature rise of the first heating component reaches an inflection point and the time when the start-up permission flag flips is defined as the heat diffusion equalization time. The sum of the electrothermal conversion delay time and the heat diffusion equalization time is used as a single heating response delay sample value. Multiple single heating response delay sample values ​​corresponding to multiple historical low-temperature start-up events are arranged in chronological order to form a heating response delay time sequence. This constructed sequence fully reflects the time lag characteristics of the entire process of electrical energy injection into the generator set's start-up.

[0007] Preferably, the method for calculating the heating response baseline delay value is as follows: Local weighted regression smoothing is performed on the heating response delay time series to separate the trend component reflecting the aging of the heating component or the long-term change in lubricant viscosity; from the remaining series after deducting the trend component, a periodic component is extracted according to the daily variation cycle of ambient temperature, which reflects the regular fluctuation of the heating response delay under the same ambient temperature conditions; the trend component and the periodic component are superimposed and reconstructed using an additive model to obtain the predicted baseline of the heating response delay, and the extrapolated value of this predicted baseline at the current moment is determined as the heating response baseline delay value. By separating the trend and periodic factors, the baseline delay value can adaptively follow equipment state drift and seasonal temperature changes, ensuring computational stability during long-term use.

[0008] Furthermore, the specific method for generating the dynamic heating power allocation queue is as follows: Obtain the current nacelle temperature, current gearbox oil temperature, and current ambient wind speed from real-time status data; compare the current gearbox oil temperature with a preset minimum pumping oil temperature threshold to calculate the priority coefficient required for the gearbox heater; calculate the priority coefficient required for the nacelle heater based on the difference between the current nacelle temperature and a preset minimum allowable battery charging temperature; calculate the priority coefficient for the anti-freezing heating belt based on the ratio of the current ambient wind speed to a preset fan self-consumption power balance wind speed; and sort the gearbox heater, nacelle heater, and anti-freezing heating belt according to their respective priority coefficients from high to low to form a dynamic heating power allocation queue. This allocation mechanism ensures that limited heating power is preferentially supplied to components most significantly impacting low-temperature start-up safety.

[0009] The process of obtaining the initial heating time window for each heating component is as follows: Obtain the upper limit of the total heating power available to the wind turbine generator during low-temperature startup, and the rated power of the heating component ranked first in the dynamic heating power allocation queue; calculate the initial heating duration of the heating component ranked first based on the ratio of the heating response baseline delay value to the sum of the priority coefficients of the first heating component and the total priority coefficients; sequentially calculate the initial heating duration of each subsequent heating component in the queue. The initial heating duration of each heating component is calculated by proportionally allocating its own priority coefficient to the remaining total heating power after the allocated time of all heating components preceding it, thus obtaining the initial heating time window for each heating component. This process achieves a quantitative mapping from priority coefficients to heating duration under physical power constraints, avoiding over-allocation or under-allocation.

[0010] The iterative correction of the heating time window for each heating component is implemented as follows: After starting the first heating component according to the initial heating time window, the rate of temperature change in the corresponding heated area is monitored in real time, and the slope of this rate of temperature change is compared with the slope of the pre-stored standard heating curve of the heating component under the same environmental conditions. If the real-time monitored rate of temperature change is lower than the corresponding slope of the standard heating curve, the heating time window of the current heating component is extended by a duration equal to the remaining heating time of the current heating component multiplied by the rate deviation ratio. If the real-time monitored rate of temperature change is higher than the corresponding slope of the standard heating curve, the heating time window of the current heating component is shortened by a duration equal to the remaining heating time of the current heating component multiplied by the rate deviation ratio, and the power quota saved by the shortening is transferred to the next heating component in the dynamic heating power allocation queue. This closed-loop correction based on the actual heating response allows the heating time to automatically adapt to the instantaneous changes in the field conditions, improving the estimation robustness.

[0011] The specific implementation method for ensuring that the real-time temperatures of all heating components synchronously reach the preset start-up temperature threshold includes: during the iterative correction process, recording the expected target time for each heating component to reach the temperature threshold, and arranging all target times in chronological order to form a target time sequence; calculating the time span between the earliest and latest target times in the target time sequence, and determining whether the time span is greater than a preset synchronization allowable deviation value; if the time span is greater than the preset synchronization allowable deviation value, selecting the heating component with the latest target time as the reference component, and progressively advancing the heating start-up time of other heating components or increasing their heating power allocation ratio, so that all target times converge within the synchronization allowable deviation value range. Forced synchronization control reduces the ineffective waiting energy consumption caused by inconsistent component heating paces.

[0012] The specific method for outputting the final heating start-up time is as follows: When the absolute value of the difference between the real-time temperature of all heating components and their respective start-up temperature thresholds is less than the preset temperature dead zone value, the current system time is recorded as the candidate start-up time; historical temperature data within a complete ambient temperature fluctuation cycle is acquired from the current time, and the same time series decomposition method used to extract trend and periodic components is employed to predict the time when the lowest cabin temperature occurs in the next complete cycle; the candidate start-up time is compared with the time when the lowest cabin temperature occurs. If the candidate start-up time falls before the time when the lowest cabin temperature occurs, the heating start-up time is output as the candidate start-up time; otherwise, the heating start-up time is delayed to the same phase time of the next temperature fluctuation cycle. This output strategy follows the natural trend of ambient temperature, avoids starting during periods of high temperature, and further reduces the power consumption required for heating.

[0013] As a technical solution of the present invention, the real-time monitored component temperature change rate is obtained by the following method: multiple temperature sensors are arranged on each heating component to collect the temperature at multiple points on the surface of the heating component and the temperature at multiple points on the housing of the key component served by the heating component; the difference between the average temperature at multiple points on the surface of the heating component and the average temperature at multiple points on the housing of the key component is divided by the preset heat conduction path length between the surface of the heating component and the housing of the key component to obtain the average temperature gradient along the heat conduction path; the difference between the average temperature gradient at the current moment and the average temperature gradient at the previous moment is divided by the sampling time interval to obtain the temperature change rate. By using multi-point averaging and heat conduction path normalization, single-point temperature measurement noise is effectively suppressed, and the heat transfer intensity of heating to the component body is truly reflected.

[0014] The heating priority of each heating component in the dynamic heating power allocation queue can be dynamically adjusted during the heating process. The adjustment method is as follows: The gearbox oil temperature change rate and the engine compartment air temperature change rate are collected in real time, and the ratio of the gearbox oil temperature change rate to the preset expected gearbox oil temperature rise slope is calculated to obtain the gearbox heating efficiency coefficient; the ratio of the engine compartment air temperature change rate to the preset expected engine compartment air temperature rise slope is calculated to obtain the engine compartment heating efficiency coefficient; the gearbox heating efficiency coefficient and the engine compartment heating efficiency coefficient are compared. If the gearbox heating efficiency coefficient is less than the engine compartment heating efficiency coefficient, the gearbox heater is moved forward one position in the dynamic heating power allocation queue; if the gearbox heating efficiency coefficient is greater than the engine compartment heating efficiency coefficient, the engine compartment heater is moved forward one position in the dynamic heating power allocation queue. This dynamic priority adjustment ensures that heating resources are continuously tilted towards components with slower actual heating, maintaining the balance of the overall heating process.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: Heating response delay time series are extracted from historical low-temperature startup events. Local weighted regression smoothing is used to decompose the series into long-term trend components and diurnal variation periodic components. The trend component captures the monotonic changes in heating response delay caused by factors such as heating component aging, contact resistance changes, and long-term lubricant viscosity shifts. The periodic component reflects the regular influence of diurnal ambient temperature variations on electrothermal conversion and heat diffusion efficiency. These two components are reconstructed using an additive model and extrapolated to obtain the current heating response baseline delay value. This processing method avoids the tracking distortion of non-stationary, multi-timescale processes by conventional fixed-parameter or simple moving average methods, allowing the heating duration calculation baseline to be dynamically adjusted according to equipment status and external environment, maintaining a reasonable prediction of duration during long-term operation. Based on the obtained heating response baseline delay value, a dynamic heating power allocation queue is generated using real-time engine compartment temperature, gearbox oil temperature, and ambient wind speed as boundary conditions, and initial heating time windows are allocated to each component. During the heating process, the matching relationship between the component temperature change rate and the initial time window is continuously monitored. If the actual temperature rise slope deviates from the standard temperature rise curve under the same environmental conditions, the heating time window of the current component is extended or shortened proportionally to the rate deviation, and the saved power quota is transferred to subsequent components. Simultaneously, the target time when each component is expected to reach its temperature threshold is recorded. When the time span between the earliest and latest target times exceeds the allowable synchronization deviation, the heating start time of other components is advanced or their power allocation ratio is increased, using the component with the latest time as the benchmark, so that all target times converge within the deviation range. This iterative correction mechanism does not rely on a pre-set fixed power allocation template and can automatically adjust the heating duration and power share of each component when the thermal response characteristics of multiple components are inconsistent and change with the environment. This enables multiple heating components to reach their respective start-up temperature thresholds synchronously in real time, reducing ineffective waiting and energy waste caused by inconsistent component heating paces. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a flowchart of the method for calculating the heating time for low-temperature start-up of wind power. Figure 2 This is a flowchart of the method for determining the baseline delay value of the heating response during low-temperature start-up of wind turbine generator sets; Figure 3 This is a flowchart of the dynamic allocation method for heating power during low-temperature startup; Figure 4 This is a flowchart of the dynamic allocation queue adjustment of heating power based on the comparison of heating efficiency coefficients; Figure 5 This is a schematic diagram of the ambient temperature prediction baseline and the heating start-up time. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0019] See Figure 1 This invention provides a method for calculating the heating time for wind power startup in low temperatures, comprising: acquiring historical operating data and real-time status data of a wind turbine generator set in a low-temperature environment, and constructing a heating response delay evaluation model based on time series decomposition; inputting the time difference sequence between the heating startup time in the historical operating data and the actual time when the generator set reaches the start-up allowable conditions into the heating response delay evaluation model, and calculating the heating response baseline delay value by extracting the trend component and periodic component in the time difference sequence; generating a dynamic heating power allocation queue based on the heating response baseline delay value, using the current nacelle temperature, current gearbox oil temperature, and current ambient wind speed in the real-time status data as boundary conditions; pre-allocating the heating time of multiple heating components of the wind turbine generator set using the dynamic heating power allocation queue to obtain the initial heating time window for each heating component; iteratively correcting the heating time window of each heating component according to the dynamic matching relationship between the real-time monitored component temperature change rate and the initial heating time window, until the real-time temperature of all heating components synchronously reaches the preset startup temperature threshold, and outputting the final heating startup time.

[0020] Example 1:

[0021] In specific implementation, please refer to Figure 2This method acquires historical operating data of wind turbine generators in low-temperature environments and extracts heating start-up records corresponding to multiple historical low-temperature start-up events from the historical operating data. Each heating start-up record includes the time when the heating command is issued, the time when the temperature rise of the first heating component reaches an inflection point, and the time when the start-allowed flag bit in the unit control system flips. The time when the heating command is issued refers to the point in time when the unit control system sends a heating start command to the heating component. The time when the temperature rise of the first heating component reaches an inflection point is determined as follows: temperature data collected by temperature sensors installed on each heating component is continuously sampled, the rate of temperature change over time is calculated, and when the rate of temperature change of a certain heating component first exceeds a preset inflection point detection threshold, this moment is recorded as the time when the temperature rise of the first heating component reaches an inflection point. The time when the start-allowed flag bit flips refers to the point in time when the flag bit in the unit control system indicating that the unit has met the start-allowed conditions flips from an invalid state to an valid state.

[0022] The difference between the time the heating command was issued and the time of the first inflection point of the heating component's temperature rise in each heating start-up record is calculated as the electrothermal conversion delay time. The difference between the time of the first inflection point of the heating component's temperature rise and the time when the start-up flag is allowed to flip is calculated as the heat diffusion equalization time. For each historical low-temperature start-up event, the corresponding electrothermal conversion delay time and heat diffusion equalization time are added together to obtain a single heating response delay sample value. Multiple single heating response delay sample values ​​corresponding to several historical low-temperature start-up events are arranged in chronological order of the events to form a heating response delay time sequence. Each element in the heating response delay time sequence corresponds to a timestamp and a single heating response delay sample value.

[0023] A locally weighted regression smoothing operation is performed on the heating response delay time series to separate the trend component. The locally weighted regression smoothing operation uses a sliding window, the width of which is set to one-third of the length of the heating response delay time series and no less than 5 data points. For each data point within the sliding window, a weight is assigned, calculated using a cubic weighting function centered at the current smoothing point; the weight decreases as the data point is farther from the center. A local straight line or a local quadratic curve is fitted using weighted least squares to obtain the estimated value of the trend component at the smoothing point. After traversing the entire heating response delay time series, a trend component sequence reflecting the aging of the heating component or long-term changes in lubricant viscosity is obtained.

[0024] The trend component at each time point is subtracted from the original heating response delay time series to obtain the residual series after removing the trend component. Periodicity extraction is then performed on the residual series, based on the daily variation cycle of ambient temperature, which is set to 24 hours. A periodogram analysis method based on Fourier transform is used to calculate the power spectral density of the residual series, identify the frequencies corresponding to the peak power spectral density, extract the periodic components that match the daily variation cycle of ambient temperature, and reconstruct the periodic component series. The periodic component series reflects the regular fluctuations in heating response delay under the same daily variation pattern of ambient temperature.

[0025] An additive model is used to align and superimpose the trend component series and periodic component series on the time axis to obtain the predicted baseline series for heating response delay. The additive model is as follows:

[0026] in, The predicted baseline representing the heating response delay at time [time] The value, This represents the trend component separated from the heating response delay time series at time 1. The value of , This represents the periodic component separated from the heating response delay time series at time t. The value of . It is a time variable, measured in hours, and its value range covers the time range of historical data and future extrapolated time points. It was obtained by performing a locally weighted regression smoothing operation on the heating response delay time series. It is obtained by performing periodic extraction and reconstruction on the remaining sequence after deducting the trend component using Fourier transform. For the current time... The corresponding time value is searched in the predicted baseline sequence of heating response delay. If there is a corresponding predicted baseline value of heating response delay at the current time, it is read directly. If the current time is between two sequence points, the extrapolated value is obtained by linear interpolation, and the extrapolated value is determined as the heating response baseline delay value.

[0027] In some embodiments, the sliding window width of the local weighted regression smoothing operation is adjusted according to the sampling density of the heating response delay time series, but the sliding window width does not exceed half the total length of the heating response delay time series. The inflection point detection threshold is set to 0.1 degrees Celsius per minute, and the inflection point detection threshold is determined based on the typical initial temperature rise characteristics of the heating component in a low-temperature environment, in order to distinguish between small fluctuations in ambient temperature and effective heating response.

[0028] Example 2:

[0029] In specific implementation, please refer to Figure 3The system acquires real-time status data of the wind turbine generator set in low-temperature environments. This data includes the current nacelle temperature, current gearbox oil temperature, and current ambient wind speed. The current nacelle temperature is collected by a temperature sensor installed inside the nacelle; the current gearbox oil temperature is collected by a temperature sensor installed on the gearbox oil pan; and the current ambient wind speed is collected by an anemometer installed outside the nacelle. A preset minimum pumping oil temperature threshold is determined based on pour point data and a safety pumping margin provided by the gearbox lubricant supplier and is pre-stored in the generator control system. The system compares the acquired current gearbox oil temperature with the preset minimum pumping oil temperature threshold. When the current gearbox oil temperature is lower than the minimum pumping oil temperature threshold, the priority coefficient required by the gearbox heater is set to the first absolute value, which is determined based on the proportion of the gearbox heater's rated power to the total heating power. When the current gearbox oil temperature is higher than or equal to the minimum pumping oil temperature threshold, the priority coefficient required by the gearbox heater is set to zero. A preset minimum allowable battery charging temperature is the minimum charging temperature value required by the battery management system and is pre-stored in the generator control system. The priority coefficient required for the nacelle heater is calculated based on the difference between the current nacelle temperature and the preset minimum allowable battery charging temperature. When the difference is less than zero, the priority coefficient is set to the product of the absolute value of the difference and a scaling factor of 0.5. This scaling factor is set to 0.5 to linearly map the temperature difference effect to power allocation requirements. When the difference is greater than or equal to zero, the priority coefficient is set to zero. The preset wind turbine self-consumption power balance wind speed is the critical wind speed at which the wind turbine generator's self-consumption power equals the grid power supply in standby mode. The wind turbine self-consumption power balance wind speed is obtained in advance through wind tunnel testing or on-site calibration. The priority coefficient of the anti-freezing heating belt is calculated based on the ratio of the current ambient wind speed to the preset wind speed for the fan's self-consumption power balance. When the ratio is less than 1, the priority coefficient of the anti-freezing heating belt is set as the product of the ratio and a second constant, which is set to 10. The basis for setting the second constant to 10 is to map the wind speed ratio to a numerical range equivalent to the priority coefficients of the other two heating components. When the ratio is greater than or equal to 1, the priority coefficient of the anti-freezing heating belt is set to zero. The gearbox heater, nacelle heater, and anti-freezing heating belt are sorted from high to low according to their respective priority coefficients. If two or three priority coefficients are equal, their positions are determined according to a fixed order: gearbox heater takes precedence over nacelle heater, and nacelle heater takes precedence over anti-freezing heating belt, forming a dynamic heating power allocation queue.

[0030] The upper limit of the total available heating power for the wind turbine generator during low-temperature startup is obtained. This upper limit is determined by the auxiliary winding capacity of the generator transformer and the current-carrying capacity of the cables, and is pre-configured in the control system. The rated power of the heating component at the top of the dynamic heating power allocation queue is obtained; this rated power is the nominal power value on the heating component's nameplate. The initial heating duration of the top-ranked heating component is calculated using the heating response baseline delay value and the ratio of the priority coefficient of the top-ranked heating component to the sum of the total priority coefficients. The sum of the total priority coefficients is the sum of the priority coefficients of the gearbox heater, the nacelle heater, and the anti-freezing heating belt. The initial heating duration of the top-ranked heating component is determined by the following formula:

[0031] in, This indicates the initial heating time of the first heating element, in minutes. This represents the baseline delay value for the heating response, in minutes, and is obtained by extrapolating the predicted baseline for the heating response delay at the current moment. This represents the priority coefficient of the heating component that is ranked first; it is dimensionless. This represents the sum of the priority coefficients for the gearbox heater, engine room heater, and anti-freezing heating zone, and is dimensionless. The initial heating time window for the first-priority heating component starts from the current moment and continues... A time interval of minutes.

[0032] After determining the initial heating duration of the first heating element, the power consumed by the first heating element within the initial heating time window is its rated power. The remaining total heating power is obtained by subtracting the rated power of the first heating element from the upper limit of the available total heating power. For the second heating element in the dynamic heating power allocation queue, its own priority coefficient is proportionally allocated to the heating capacity corresponding to the remaining total heating power. The initial heating duration of the second heating element is calculated as the product of the heating response baseline delay value and the ratio of the priority coefficient of the second heating element to the sum of the total priority coefficients, multiplied by the ratio of the remaining total heating power to the upper limit of the total heating power. Following the same method, the initial heating duration of each subsequent heating element in the queue is calculated sequentially. The initial heating duration of each heating element is calculated by proportionally allocating its own priority coefficient to the remaining total heating power after the allocated time of all heating elements preceding it, thus obtaining the initial heating time window for each heating element. Each initial heating time window starts at the end of the initial heating time window of the previous heating component and continues for the corresponding initial heating duration.

[0033] In some embodiments, the priority coefficients required for the gearbox heater, the nacelle heater, and the anti-freezing heating belt are normalized before being sorted in the dynamic heating power allocation queue. Normalization involves dividing each priority coefficient by the maximum value among all priority coefficients, limiting the priority coefficient values ​​to between 0 and 1. Sorting is performed from highest to lowest based on the normalized priority coefficients. The available total heating power upper limit is dynamically updated based on the actual grid connection status of the unit and the auxiliary transformer load; the latest total heating power upper limit is read before each initial heating time window calculation.

[0034] Example 3:

[0035] In practice, after the first heating component is activated according to the initial heating time window, the temperature of the heated area corresponding to the first heating component is continuously monitored to obtain the real-time temperature change rate. The temperature of the heated area is collected by a temperature sensor located on the housing of the key component served by the first heating component. The temperature sensor records a temperature value every preset sampling period. The real-time monitored component temperature change rate is defined as the difference between the temperature value at the current sampling moment and the temperature value at the previous sampling moment, divided by the preset sampling period. The pre-stored standard temperature rise curve is the temperature change curve over time recorded during a standard heating test of the heating component under the same environmental conditions. The standard temperature rise curve is stored in the storage unit of the unit control system. The slope of the real-time monitored component temperature change rate is compared with the pre-stored standard temperature rise curve of the heating component under the same environmental conditions. The same environmental conditions mean that the deviations of the ambient temperature and ambient wind speed from those during the standard test are within the preset allowable deviation range. The preset allowable deviation range is set as an absolute value of ambient temperature deviation not exceeding 2 degrees Celsius and an absolute value of ambient wind speed deviation not exceeding 1 meter per second. Slope comparison begins from the second sampling period after heating starts, as the temperature change rate in the first sampling period has not yet stabilized. At each sampling moment, the real-time monitored component temperature change rate is calculated, and the corresponding slope of the standard heating curve at the corresponding time point is found on the standard heating curve. The corresponding slope of the standard heating curve is obtained by dividing the temperature difference between two adjacent standard sampling points on the standard heating curve by the sampling interval. If the real-time monitored component temperature change rate is lower than the corresponding slope of the standard heating curve, the rate deviation ratio is calculated as the difference between the corresponding slope of the standard heating curve and the real-time monitored component temperature change rate, divided by the corresponding slope of the standard heating curve. The rate deviation ratio is used to characterize the degree to which the real-time heating rate deviates from the standard heating curve. Under this condition, the heating time window of the current heating component is extended, and the extension time is equal to the product of the remaining heating time of the current heating component and the rate deviation ratio. The remaining heating time of the current heating component is the initial heating time of the current heating component minus the heating time already experienced by the current heating component. The heating time already experienced by the current heating component is the time interval from the heating start time of the current heating component to the current sampling time. If the real-time monitored rate of temperature change of a component is higher than the corresponding slope of the standard heating curve, the rate deviation ratio is calculated as the difference between the real-time monitored rate of temperature change and the corresponding slope of the standard heating curve, divided by the corresponding slope of the standard heating curve. Under this condition, the heating time window of the current heating component is shortened, and the shortened time is equal to the product of the remaining heating time of the current heating component and the rate deviation ratio. The power quota saved by the shortening is transferred to the next heating component in the dynamic heating power allocation queue.The power quota transferred to the next heating element is the energy corresponding to the rated power of the current heating element within the shortened time period. This energy is calculated by multiplying the rated power of the current heating element by the shortened time period. The heating time window of the next heating element is correspondingly increased, and the increased time period is obtained by dividing the transferred energy by the rated power of the next heating element.

[0036] In some embodiments, the rate deviation ratio is limited to a range of 0 to 1. When the calculated rate deviation ratio exceeds 1, it is truncated to 1; when the calculated rate deviation ratio is less than 0.1, it is set to 0.1. The truncation of the rate deviation ratio to 1 is to avoid the heating time window correction range exceeding the remaining heating time, while the setting of the rate deviation ratio to 0.1 is to ensure the minimum correction step size. The truncation operation is performed before the calculation of extending or shortening the duration.

[0037] Optionally, slope comparison and heating time window correction are performed in each sampling period, with the sampling period set from 10 seconds to 60 seconds.

[0038] During the iterative correction process, for each heating component, the target time for reaching the expected temperature threshold is calculated based on its heating time window and real-time temperature change trend. The target time for reaching the expected temperature threshold is the current time plus the time required for the current real-time temperature to rise to the corresponding start-up temperature threshold. The required time is the difference between the start-up temperature threshold and the current real-time temperature divided by the real-time monitored component temperature change rate. The start-up temperature threshold is the allowable start-up temperature value of the components served by each heating component, pre-set according to the wind turbine generator start-up conditions. When the real-time monitored component temperature change rate is lower than the preset minimum effective heating rate, the target time for reaching the expected temperature threshold is deemed invalid and not included in subsequent sorting. The preset minimum effective heating rate is set to 0.05 degrees Celsius per minute. The effective target times for reaching the expected temperature threshold for each heating component are arranged in chronological order to form a target time sequence. The absolute value of the difference between the earliest and latest target times in the target time sequence is calculated, and this absolute value of the difference is used as the time span. The time span is compared with a preset synchronization allowable deviation value, which is set at 5 minutes. This preset allowable deviation value is based on the time tolerance design index of the wind turbine generator control system for the temperature synchronization readiness of multiple components. If the time span exceeds the preset synchronization allowable deviation value, synchronization convergence correction is performed. The heating component with the latest target time in the target time sequence is selected as the reference component. For each other heating component in the target time sequence that is earlier than the reference component, the time difference between its target time and the target time of the reference component is calculated. For each other heating component that needs to be advanced, its target time is shortened by increasing its heating power allocation ratio. Increasing the heating power allocation ratio is achieved by adjusting power from subsequent heating components or from the currently unallocated power margin. The amount of power adjustment is determined by the additional energy demand corresponding to the time difference and the thermal capacity characteristics of the heating component. When power allocation is limited, the heating start time of other heating components is advanced one by one, and the advancement time is the time difference between the target time of the corresponding heating component and the target time of the reference component. Repeat the above convergence correction process until the maximum time span between all valid target times when the expected temperature threshold is reached falls within the preset synchronization allowable deviation range.

[0039] It is understandable that during the synchronous convergence correction process, only heating components whose target time is earlier than that of the reference component are adjusted each time, while the heating parameters of the reference component itself remain unchanged. When there are multiple heating components with the same target time, the earliest value of one of the target times is retained, and the other identical values ​​are not repeatedly included in the target time sequence.

[0040] Example 4:

[0041] In practice, the real-time temperature of all heating components is continuously monitored, and the real-time temperature of each component is compared with its corresponding start-up temperature threshold. The start-up temperature threshold for each heating component is a pre-set fixed value. The start-up temperature threshold for the gearbox heater is the minimum operating oil temperature of the gearbox; the start-up temperature threshold for the nacelle heater is the minimum ambient temperature at which the battery in the nacelle can be charged; and the start-up temperature threshold for the anti-freezing heating belt is the safe temperature at which condensation does not occur on the blade surface. The preset temperature dead zone is limited to 2 degrees Celsius. This setting is to avoid frequent state reversals caused by small temperature fluctuations. When the absolute value of the difference between the real-time temperature of all heating components and their respective start-up temperature thresholds is less than the preset temperature dead zone, the system is determined to have entered the temperature-ready state. At the point when the temperature-ready state is determined, the current system time is recorded as a candidate start-up time, obtained by reading the real-time clock of the unit control system.

[0042] Historical temperature data for a complete environmental temperature fluctuation cycle, traced back from the current moment, is acquired. This complete cycle is defined as 24 hours, based on the 24-hour periodicity of diurnal temperature variations caused by the Earth's rotation. Historical temperature data is obtained from external ambient temperature sensors stored in the engine control system's storage unit. The time series of historical temperature data includes temperature values ​​at fixed sampling intervals, set to 10 minutes. The historical temperature data for the complete environmental temperature fluctuation cycle is processed using the same time series decomposition method used to extract the trend and periodic components from the time difference sequence. Specifically, a local weighted regression smoothing operation is performed on the historical environmental temperature time series to separate the temperature trend component reflecting slow seasonal changes. The sliding window width for the local weighted regression smoothing operation is set to 48 data points, covering an 8-hour time period. This window width is chosen to filter out high-frequency fluctuations below the daily cycle while retaining daily cycle characteristics. After subtracting the temperature trend component from the historical environmental temperature time series, the remaining temperature series is obtained. A periodogram analysis method based on Fourier transform was used to analyze the residual temperature sequence, extracting periodic components that match the diurnal variation period of the ambient temperature. The diurnal variation period of the ambient temperature is 24 hours, corresponding to a frequency of [missing information]. Hour The reconstructed temperature periodic component sequence describes the periodic fluctuation pattern of ambient temperature over 24 hours.

[0043] The temperature trend component sequence and the temperature periodic component sequence are superimposed using an additive model to obtain the ambient temperature prediction baseline sequence. From the ambient temperature prediction baseline sequence, the time of occurrence of the lowest cabin temperature within the next complete cycle after the current moment is identified. The identification method is to traverse all predicted temperature values ​​in the ambient temperature prediction baseline sequence from the current moment to the current moment plus 24 hours, find the minimum value, and determine the time index corresponding to the minimum value as the time of occurrence of the lowest cabin temperature.

[0044] The candidate start-up time is compared with the time when the lowest cabin temperature occurs. The comparison method involves converting both the candidate start-up time's timestamp and the timestamp of the lowest cabin temperature occurrence into values ​​in minutes and directly comparing their magnitudes. If the timestamp of the candidate start-up time is less than the timestamp of the lowest cabin temperature occurrence, the candidate start-up time is determined to occur before the lowest cabin temperature occurrence. Under this condition, the heating start-up time is output as the candidate start-up time, written into the heating start-up command time register of the unit control system. If the timestamp of the candidate start-up time is greater than or equal to the timestamp of the lowest cabin temperature occurrence, the candidate start-up time is determined to occur before the lowest cabin temperature occurrence. Under this condition, the same phase time of the next temperature fluctuation cycle is calculated, determined by the following formula:

[0045] in, This indicates the output heating start-up time, in minutes; Indicates the candidate start time, in minutes; This indicates the ambient temperature fluctuation period, which is fixed at 1440 minutes, or 24 hours. This fixed value of 1440 minutes is based on the duration of day-night temperature fluctuations corresponding to the Earth's rotation period. The heating start-up time will be delayed until... The heating start time is output as a delayed heating start time, and the output method is to write the delayed heating start time into the heating start command time register of the unit control system.

[0046] In some embodiments, if the absolute value of the time difference between the candidate start-up time and the time when the lowest nacelle temperature occurs is less than 30 minutes, the delay processing is no longer performed, and the candidate start-up time is directly used as the output heating start-up time. The 30-minute threshold is based on the fact that a time difference of less than 30 minutes has a lower impact on the economic efficiency of unit start-up than the power generation loss caused by a delay of a full cycle. During the comparison process, the timestamp values ​​of both times are rounded to the minute level, discarding digits at the second level and below. Optionally, the sampling interval of historical temperature data is adjusted between 5 and 30 minutes according to the storage capacity of the unit control system. When the sampling interval changes, the number of data points included in the sliding window of the local weighted regression smoothing operation is adjusted accordingly, maintaining the time length covered by the sliding window at no less than 6 hours and no more than 12 hours.

[0047] It can be understood that the next complete cycle is a period of 24 hours starting from the current moment. If multiple identical minimum temperatures are detected in the ambient temperature prediction baseline sequence, the moment when the first minimum occurs is taken as the moment when the cabin temperature reaches its lowest point. If the system clock jumps or synchronizes during the calculation process, the candidate start time is reread based on the system time after the jump or synchronization.

[0048] See Figure 5 In the graph, the horizontal axis represents time in hours, and the vertical axis represents ambient temperature in degrees Celsius. The solid curve represents the baseline sequence for ambient temperature prediction, reflecting the trend and periodic fluctuations of ambient temperature changes after local weighted regression smoothing and Fourier transform period extraction based on historical ambient temperature data. The curve generally shows a typical diurnal temperature variation pattern over 24 hours, with a slow rise from the negative temperature zone to the positive temperature zone, reaching a peak of about 7 degrees Celsius, followed by a gradual decrease in temperature.

[0049] The vertical line marked with a black inverted triangle in the diagram corresponds to the current time, representing the candidate start-up time. This time is the point at which the absolute value of the difference between the real-time temperature of all heating components and their respective start-up temperature thresholds, as determined by the system, is less than the preset temperature dead zone value. The vertical dashed line corresponds to the moment when the lowest cabin temperature occurs. The lowest cabin temperature within the next 24 hours is identified in the ambient temperature prediction baseline sequence. This point is marked with a solid black dot at approximately 9 hours, and the ambient temperature is approximately 3 degrees Celsius.

[0050] The candidate start time in the figure is at the current time 0 hours, which is significantly earlier than the time when the cabin temperature reaches its lowest point (9 hours), meeting the condition that the candidate start time is earlier than the time when the cabin temperature reaches its lowest point. According to this embodiment, in this case, the candidate start time is output as the heating start time, triggering the heating system to start. The figure shows the changing trend of the ambient temperature prediction baseline sequence and the marking of key moments, demonstrating the process of extracting trend and periodic components of the ambient temperature using a time series decomposition method, and determining a reasonable heating start time accordingly. The data in this figure clearly supports the technical solution in Embodiment 4 regarding the comparison and judgment of the candidate start time with the time when the cabin temperature reaches its lowest point, and the determination of the heating start time.

[0051] Example 5:

[0052] In specific implementation, please refer to Figure 4 The real-time monitoring of component temperature change rates is achieved by arranging multiple temperature sensors on each heating component. For the gearbox heater, at least three surface temperature sensors are uniformly arranged on the surface of the gearbox heater, and at least three housing temperature sensors are uniformly arranged on the surface of the gearbox housing served by the gearbox heater. For the nacelle heater, at least three surface temperature sensors are uniformly arranged on the surface of the nacelle heater, and at least three housing temperature sensors are uniformly arranged on the surface of the battery compartment housing served by the nacelle heater. For the anti-freezing heating belt, at least three surface temperature sensors are uniformly arranged along the length of the anti-freezing heating belt, and at least three housing temperature sensors are uniformly arranged on the surface of the blade root housing served by the anti-freezing heating belt. All temperature sensors synchronously collect temperature data according to a preset sampling period, which is set to 10 seconds.

[0053] At each sampling moment, multiple temperature values ​​are acquired at various points on the surface of the heating component. These values ​​are then arithmetically averaged to obtain the average temperature of the heating component surface. Simultaneously, multiple temperature values ​​are acquired at various points on the housing of the critical component served by the heating component. These values ​​are then arithmetically averaged to obtain the average temperature of the critical component housing. The preset heat conduction path length between the heating component surface and the critical component housing is determined based on the geometric distance from the installation position of the heating component to the temperature measurement point on the critical component housing. This preset heat conduction path length is obtained through actual measurement during the system installation and commissioning phase and stored in the control system. The difference between the average temperature of the multiple points on the heating component surface and the average temperature of the multiple points on the critical component housing is calculated. This difference is then divided by the preset heat conduction path length to obtain the average temperature gradient along the heat conduction path. The average temperature gradient along the heat conduction path is expressed by the following formula:

[0054] in, This represents the average temperature gradient along the heat conduction path, expressed in degrees Celsius per meter. This represents the average temperature of multiple points on the surface of the heating element, expressed in degrees Celsius. This represents the average temperature of multiple points on the housing of the critical component served by the heating element, expressed in degrees Celsius. This indicates the length of the preset heat conduction path between the surface of the heating element and the housing of the key component, in meters. The path tortuosity coefficient is obtained by measuring the straight-line distance from the geometric center of the heating component to the installation point of the temperature sensor on the surface of the key component housing on-site and multiplying it by a path tortuosity coefficient. The path tortuosity coefficient ranges from 1.0 to 1.5. The specific path tortuosity coefficient is determined based on the structural layout between the heating component and the key component. For structures that are directly attached, the path tortuosity coefficient is 1.0. For structures with gaps or indirect heat transfer paths, the path tortuosity coefficient is the ratio of the measured heat conduction path length to the straight-line distance.

[0055] The difference between the average temperature gradient calculated at the current sampling time and the average temperature gradient calculated at the previous sampling time is calculated. The difference is then divided by the sampling time interval, which is the time difference between two adjacent sampling times. The sampling time interval is equal to the preset sampling period and is set to 10 seconds to obtain the temperature change rate. The temperature change rate reflects how quickly the heating component transfers heat to the housing of the critical component.

[0056] In some embodiments, when a sensor in the heating element surface temperature sensor or the critical component housing temperature sensor malfunctions, resulting in invalid data acquisition, the invalid sensor data acquisition is automatically discarded when calculating the average of the multi-point temperatures on the heating element surface or the critical component housing, and the arithmetic mean is calculated using the remaining valid sensor data acquisition. When the number of valid sensors is less than two, the temperature change rate at that sampling moment is marked as invalid, and the valid temperature change rate at the previous sampling moment is used.

[0057] Optionally, the sampling period of the surface temperature sensor of the heating component and the housing temperature sensor of the key component can be adjusted between 5 seconds and 30 seconds. When the sampling period is adjusted, the sampling time interval is adjusted accordingly.

[0058] Regarding the dynamic adjustment of heating priority for each heating component in the dynamic power allocation queue, the gearbox oil temperature change rate and the engine compartment air temperature change rate are collected in real time during the heating process. The gearbox oil temperature is collected in real time by a temperature sensor installed on the gearbox oil pan. The gearbox oil temperature change rate is calculated by subtracting the gearbox oil temperature value from the previous sampling time from the current sampling time, and then dividing by the sampling time interval. The engine compartment air temperature is collected in real time by an air temperature sensor installed inside the engine compartment and not directly affected by the heater's radiation. The engine compartment air temperature change rate is calculated by subtracting the engine compartment air temperature value from the previous sampling time from the current sampling time, and then dividing by the sampling time interval. The sampling time interval is uniformly set to 10 seconds. The preset expected gearbox oil temperature rise slope is determined based on the thermodynamic characteristics of the gearbox oil and the power of the gearbox heater. The preset expected gearbox oil temperature rise slope is obtained in advance through heating bench testing and stored in the control system. The gearbox heating efficiency coefficient is obtained by calculating the ratio of the gearbox oil temperature change rate to the preset expected gearbox oil temperature rise slope. The gearbox heating efficiency coefficient characterizes the degree of conformity between the actual and expected heating effects of the gearbox heater. The preset expected engine compartment air temperature rise slope is determined based on the engine compartment volume, engine compartment insulation performance, and engine compartment heater power. This preset expected engine compartment air temperature rise slope is obtained in advance through low-temperature environment chamber testing and stored in the control system. The engine compartment heating efficiency coefficient is obtained by calculating the ratio of the engine compartment air temperature change rate to the preset expected engine compartment air temperature rise slope. The engine compartment heating efficiency coefficient characterizes the degree of conformity between the actual and expected heating effects of the engine compartment heater.

[0059] The real-time calculated gearbox heating efficiency coefficient is compared with the engine room heating efficiency coefficient. If the gearbox heating efficiency coefficient is less than the engine room heating efficiency coefficient, it indicates that the actual heating efficiency of the gearbox heater is relatively low. In the dynamic heating power allocation queue, the gearbox heater is moved forward one position, and the heating components that were originally in front of the gearbox heater are moved backward one position. If the gearbox heating efficiency coefficient is greater than the engine room heating efficiency coefficient, it indicates that the actual heating efficiency of the engine room heater is relatively low. In the dynamic heating power allocation queue, the engine room heater is moved forward one position, and the heating components that were originally in front of the engine room heater are moved backward one position. After the ranking is adjusted, the power allocation order of each heating component in the dynamic heating power allocation queue changes accordingly, and subsequent heating duration allocation and heating time window calculations are re-executed according to the adjusted queue order.

[0060] In some embodiments, the comparison of the gearbox heating efficiency coefficient and the engine compartment heating efficiency coefficient is performed once after every 5 minutes of continuous cumulative heating. The 5-minute execution cycle is based on the fact that the heating system needs a certain amount of time to reach a thermally stable state after startup; too short an execution cycle may lead to frequent oscillations in the ranking. When the absolute value of the difference between the gearbox heating efficiency coefficient and the engine compartment heating efficiency coefficient is less than 0.05, no ranking adjustment is performed. The 0.05 threshold is set to avoid unnecessary queue reordering caused by small efficiency differences. When the anti-freezing heating belt is also in a heating state, the heating efficiency coefficient of the anti-freezing heating belt is calculated in the same way, and the heating efficiency coefficient of the anti-freezing heating belt is compared with the heating efficiency coefficients of the gearbox and the engine compartment. The ranking adjustment rule for the three is consistent with the comparison rule for the two components: the heating component with the lower heating efficiency coefficient is moved one position forward in the ranking.

[0061] It is understood that the forward and backward shifting operations of the sorting position are limited to the internal order adjustment of the dynamic allocation queue of heating power, and do not change the calculation method of the priority coefficient of each heating component or the rated power value. During the calculation of the heating efficiency coefficient, when the preset expected heating slope is zero or infinitely close to zero, the heating efficiency coefficient of the heating component is not included in the comparison, and the sorting position of the corresponding heating component remains unchanged after the last adjustment.

[0062] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for calculating the low-temperature start-up heating time of wind power, characterized in that, include: Historical operating data and real-time status data of wind turbine generators in low-temperature environments were acquired, and a heating response delay evaluation model based on time series decomposition was constructed. The time difference sequence between the heating start-up time in historical operating data and the actual time when the unit reaches the start-up allowable conditions is input into the heating response delay evaluation model. By extracting the trend component and periodic component in the time difference sequence, the heating response baseline delay value is calculated. Based on the heating response baseline delay value, the current cabin temperature, current gearbox oil temperature and current ambient wind speed in the real-time status data are used as boundary conditions to generate a dynamic heating power allocation queue. The heating power dynamic allocation queue is used to pre-allocate the heating time of multiple heating components of the wind turbine generator set to obtain the initial heating time window for each heating component; Based on the dynamic matching relationship between the real-time monitored component temperature change rate and the initial heating time window, the heating time window of each heating component is iteratively corrected until the real-time temperature of all heating components synchronously reaches the preset start-up temperature threshold, and the final heating start-up time is output.

2. The method for calculating the low-temperature start-up heating time of wind power according to claim 1, characterized in that, The specific steps for constructing a heating response delay evaluation model based on time series decomposition include: The heating start-up records of the wind turbine generator set during multiple historical low-temperature start-up events were collected. Each heating start-up record includes the time when the heating command was issued, the time when the temperature of the first heating component rose to an inflection point, and the time when the start-up permission flag of the generator set control system flipped. The difference between the time when the heating command is issued and the time when the temperature rises to the inflection point of the first heating component is defined as the electrothermal conversion delay time, and the difference between the time when the temperature rises to the inflection point of the first heating component and the time when the start-up flag is flipped is defined as the heat diffusion equalization time. The sum of the electrothermal conversion delay time and the heat diffusion equilibrium time is used as the single heating response delay sample value. The multiple single heating response delay sample values ​​corresponding to multiple historical low temperature start-up events are arranged in chronological order to form a heating response delay time sequence.

3. The method for calculating the low-temperature start-up heating time of wind power according to claim 2, characterized in that, The specific steps for calculating the heating response baseline delay value by extracting the trend and periodic components from the time difference sequence include: A local weighted regression smoothing operation is performed on the heating response delay time series to separate the trend component reflecting the aging of the heating component or the long-term change in the viscosity of the lubricant. From the remaining sequence after removing the trend component, the periodic component is extracted according to the daily variation cycle of ambient temperature. This periodic component reflects the regular fluctuation of heating response delay under the same ambient temperature conditions. An additive model is used to superimpose and reconstruct the trend component and the periodic component to obtain the predicted baseline of the heating response delay, and the extrapolated value of the predicted baseline at the current time is determined as the heating response benchmark delay value.

4. The method for calculating the low-temperature start-up heating time of wind power according to claim 3, characterized in that, The specific steps for generating a dynamic heating power allocation queue include: The system acquires the current cabin temperature, current gearbox oil temperature, and current ambient wind speed from real-time status data, compares the current gearbox oil temperature with the preset minimum pumping oil temperature threshold, and calculates the priority coefficient required for the gearbox heater. The priority coefficient of the computer cabin heater is calculated based on the difference between the current cabin temperature and the preset lower limit of the allowable battery charging temperature. At the same time, the priority coefficient of the anti-condensation heating belt is calculated based on the ratio of the current ambient wind speed to the preset wind speed for the fan's self-consumption power balance. The gearbox heater, engine room heater, and anti-freezing heating belt are sorted from high to low according to their respective priority coefficients to form a dynamic heating power allocation queue.

5. The method for calculating the low-temperature start-up heating time of wind power according to claim 4, characterized in that, The specific steps for obtaining the initial heating time window for each heating element include: Obtain the upper limit of the total heating power available to the wind turbine generator set during low-temperature startup, as well as the rated power of the heating component ranked first in the dynamic heating power allocation queue; The initial heating duration of the heating component ranked first is calculated based on the ratio of the heating response baseline delay value to the priority coefficient of the first-ranked heating component to the sum of the total priority coefficients. The initial heating time of each subsequent heating component in the queue is calculated sequentially. The initial heating time of each heating component is calculated by proportionally allocating its own priority coefficient to the remaining total heating power after all the heating components before it have been allocated their time, thus obtaining the initial heating time window of each heating component.

6. The method for calculating the low-temperature start-up heating time of wind power according to claim 5, characterized in that, The specific steps for iteratively correcting the heating time window for each heating component include: After starting the heating of the first heating element according to the initial heating time window, the temperature change rate of the corresponding heated area of ​​the heating element is monitored in real time, and the slope of the temperature change rate is compared with the pre-stored standard temperature rise curve of the heating element under the same environmental conditions. If the real-time monitored rate of temperature change is lower than the corresponding slope of the standard heating curve, the heating time window of the current heating element is extended. The extended duration is equal to the remaining heating time of the current heating element multiplied by the rate deviation ratio. If the real-time monitored rate of temperature change is higher than the corresponding slope of the standard heating curve, the heating time window of the current heating element is shortened. The shortened time is equal to the remaining heating time of the current heating element multiplied by the rate deviation ratio, and the power quota saved by the shortening is transferred to the next heating element in the dynamic allocation queue of heating power.

7. The method for calculating the low-temperature start-up heating time of wind power according to claim 6, characterized in that, The specific implementation methods for ensuring that the real-time temperatures of all heating components synchronously reach the preset start-up temperature threshold include: During the iterative correction process, the target time when each heating component is expected to reach the temperature threshold is recorded, and all target times are arranged in chronological order to form a target time sequence; Calculate the time span between the earliest and latest target times in the target time sequence, and determine whether the time span is greater than the preset synchronization allowable deviation value; If the time span exceeds the preset allowable synchronization deviation value, the heating component with the latest target time is selected as the reference component, and the heating start time of other heating components is advanced one by one or their heating power distribution ratio is increased so that all target times converge within the range of the allowable synchronization deviation value.

8. The method for calculating the low-temperature start-up heating time of wind power according to claim 7, characterized in that, The specific steps for outputting the final heating start-up time include: When the absolute value of the difference between the real-time temperature of all heating components and their respective start-up temperature thresholds is less than the preset temperature dead zone value, the current system time is recorded as the candidate start-up time. Acquire historical temperature data for a complete environmental temperature fluctuation cycle from the current moment, and use the same time series decomposition method as in step "extracting the trend component and periodic component from the time difference sequence" to predict the time when the cabin temperature lowest point will occur in the next complete cycle. The candidate start time is compared with the time when the cabin temperature reaches its lowest point. If the candidate start time falls before the time when the cabin temperature reaches its lowest point, the heating start time is output as the candidate start time. Otherwise, the heating start time is delayed to the same phase time of the next temperature fluctuation cycle.

9. The method for calculating the low-temperature start-up heating time of wind power according to claim 1, characterized in that, The method for obtaining the real-time monitored component temperature change rate is as follows: Multiple temperature sensors are arranged on each heating element to collect the temperature at multiple points on the surface of the heating element and the temperature at multiple points on the housing of the key component served by the heating element. The average temperature gradient along the heat conduction path is obtained by dividing the difference between the average temperature of multiple points on the surface of the heating component and the average temperature of multiple points on the housing of the key component by the preset heat conduction path length between the surface of the heating component and the housing of the key component. The rate of temperature change is obtained by dividing the difference between the average temperature gradient at the current moment and the average temperature gradient at the previous moment by the sampling time interval.

10. The method for calculating the low-temperature start-up heating time of wind power according to claim 1, characterized in that, The dynamic adjustment method for the heating priority of each heating component in the dynamic heating power allocation queue is as follows: During the heating process, the gearbox oil temperature change rate and the air temperature change rate in the engine compartment are collected in real time, and the ratio of the gearbox oil temperature change rate to the preset expected gearbox oil temperature rise slope is calculated to obtain the gearbox heating efficiency coefficient. The ratio of the rate of change of air temperature inside the computer cabin to the preset expected rate of temperature rise in the cabin air is used to obtain the cabin heating efficiency coefficient. The gearbox heating efficiency coefficient is compared with the engine compartment heating efficiency coefficient. If the gearbox heating efficiency coefficient is less than the engine compartment heating efficiency coefficient, the gearbox heater is moved forward one position in the dynamic heating power allocation queue. If the gearbox heating efficiency coefficient is greater than the engine compartment heating efficiency coefficient, the engine compartment heater is moved forward one position in the dynamic heating power allocation queue.

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