Self-adaptive adjustment method and system for active frequency modulation coefficient of converter of wind turbine generator

Abstract

The invention specifically discloses a self-adaptive adjustment method and system for an active frequency modulation coefficient of a converter of a wind turbine generator, and the method comprises the steps: firstly, obtaining a power margin and thermal margins of the converter and a generator in real time, and precisely sensing a comprehensive operation boundary of the unit under a current working condition; and furthermore, the multi-dimensional physical constraints are quantified into instant, short-term and long-term multi-time-scale available standby based on thermal time constant differences of different devices, so that refined evaluation on the real frequency modulation capability of the unit is completed. And finally, dynamically converting the evaluated available standby capability into an optimal adaptive frequency modulation coefficient according to actual characteristics of power grid frequency disturbance. The technical contradiction caused by a fixed coefficient is solved, so that the frequency modulation response is not blind instruction execution any more, but is an intelligent behavior tightly coupled with the self safety bearing capacity of the unit, and the effectiveness of frequency modulation and the safety of equipment can be ensured to reach the optimal balance.

Description

Technical Field

[0001] This application relates to the field of intelligent control, and more specifically, to an adaptive adjustment method and system for the active power frequency regulation coefficient of a wind turbine converter. Background Technology

[0002] With the transformation of the global energy structure, the penetration rate of renewable energy, represented by wind power, in the power system is increasing. However, wind turbines and other new energy power generation units are connected to the grid through power electronic converters, lacking the rotational inertia of traditional synchronous generators. Their large-scale integration leads to a decrease in the total inertia of the power system and a weakening of frequency stability. Therefore, requiring wind turbines to have the ability to participate in grid frequency regulation (i.e., active power frequency regulation) has become crucial to ensuring the safe and stable operation of the new power system. Currently, wind turbines participating in primary frequency regulation generally adopt a droop control strategy, which adjusts the active power output according to a certain proportion (i.e., the frequency regulation coefficient or droop coefficient) based on the deviation of the grid frequency to support the grid frequency.

[0003] In existing technologies, the frequency regulation coefficient of wind turbines is set to a fixed value. This one-size-fits-all control method has technical flaws. The problem is that a fixed frequency regulation coefficient cannot dynamically adjust the frequency regulation response capability according to the real-time operating status of the wind turbine, resulting in an irreconcilable contradiction between frequency regulation effectiveness and the turbine's own safety. Specifically, the reasons for this contradiction are as follows: On the one hand, when the available reserve capacity of the wind turbine is insufficient (e.g., due to wind speed fluctuations or equipment operating at high temperatures), a fixed, relatively aggressive frequency regulation coefficient may instruct the turbine to output power exceeding its current actual capacity. This not only fails to effectively support the grid frequency but also triggers the over-temperature and overload protection of the converter or generator, causing the turbine to disconnect from the grid, exacerbating frequency disturbances, and in severe cases threatening equipment safety. On the other hand, when the wind turbine has sufficient reserve capacity, a fixed, conservative frequency regulation coefficient set to ensure safety will limit its frequency regulation potential, resulting in slow turbine response and insufficient contribution, failing to provide the most timely and effective support to the grid, wasting frequency regulation resources, and failing to achieve the optimal balance between frequency regulation efficiency and operational safety.

[0004] Therefore, an adaptive adjustment scheme for the active power frequency regulation coefficient of the converter is desired. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide an adaptive adjustment method and system for the active power frequency regulation coefficient of a wind turbine converter.

[0006] According to one aspect of this application, an adaptive adjustment method for the active power frequency regulation coefficient of a wind turbine converter is provided, comprising: Obtain power margin, converter thermal margin, generator thermal margin, and frequency deviation; Multi-timescale thermal availability calculations are performed based on power margin, converter thermal margin, and generator thermal margin to obtain instantaneous thermal availability, short-term thermal availability, and long-term thermal availability. Based on frequency deviation, disturbance characteristics are identified and reserve capacity is dynamically weighted for instantaneous thermal reserve, short-term thermal reserve and long-term thermal reserve to obtain the final effective reserve. Based on the target frequency deviation endpoint of the final effective reserve and frequency modulation response, determine the adaptive droop coefficient; The final power command is generated based on frequency deviation, frequency dead zone, base power reference, and adaptive droop factor.

[0007] According to another aspect of this application, an adaptive adjustment and control system for the active power frequency regulation coefficient of a wind turbine converter is provided, comprising: The data acquisition module is used to acquire power margin, converter thermal margin, generator thermal margin, and frequency deviation. The backup calculation module is used to perform multi-timescale thermal availability backup calculations based on power margin, converter thermal margin, and generator thermal margin to obtain instantaneous thermal backup, short-term thermal backup, and long-term thermal backup. The dynamic weighting module is used to identify disturbance characteristics and dynamically weight reserve capacity based on frequency deviation for instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve to obtain the final effective reserve. The coefficient determination module is used to determine the adaptive droop coefficient based on the target frequency deviation endpoint of the final effective reserve and frequency modulation response; The final power generation module is used to generate the final power command based on frequency deviation, frequency dead zone, base power reference, and adaptive droop coefficient.

[0008] Compared with existing technologies, this application provides an adaptive adjustment method and system for the active power frequency regulation coefficient of a wind turbine converter. First, it accurately senses the comprehensive operating boundary of the unit under current conditions by acquiring real-time power margin and thermal margin of the converter and generator. Then, it quantifies these multi-dimensional physical constraints into available reserves at multiple time scales (instantaneous, short-term, and long-term) based on the differences in thermal time constants of different equipment, thus completing a refined assessment of the unit's true frequency regulation capability. Finally, based on the actual characteristics of grid frequency disturbances, it dynamically transforms the assessed available reserve capacity into an optimal adaptive frequency regulation coefficient. This resolves the technical contradictions caused by fixed coefficients, making the frequency regulation response no longer a blind command execution, but an intelligent behavior closely coupled with the unit's own safety bearing capacity, ensuring that the effectiveness of frequency regulation and equipment safety can achieve an optimal balance under any operating condition. Attached Figure Description

[0009] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 This is a flowchart of an adaptive adjustment method for the active power frequency regulation coefficient of a wind turbine converter according to an embodiment of this application; Figure 2 This is a data flow diagram illustrating the adaptive adjustment method of the active power frequency regulation coefficient of the wind turbine converter according to an embodiment of this application. Figure 3 The flowchart illustrates the adaptive adjustment method for the active power frequency regulation coefficient of the wind turbine converter according to the embodiments of this application, which identifies disturbance characteristics and dynamically weights the reserve capacity of instantaneous thermal reserve, short-term thermal reserve and long-term thermal reserve based on frequency deviation to obtain the final effective reserve. Figure 4 This is a flowchart illustrating the adaptive droop coefficient determination method for the active power frequency regulation coefficient of a wind turbine converter according to embodiments of this application, based on the target frequency deviation endpoint of the final effective reserve and frequency regulation response. Figure 5 This is a block diagram of an adaptive adjustment control system for the active power frequency regulation coefficient of a wind turbine converter according to an embodiment of this application. Detailed Implementation

[0011] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0012] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0013] While this application makes various references to certain modules of the systems according to embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The modules described are merely illustrative, and different aspects of the systems and methods may use different modules.

[0014] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0015] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0016] To address the technical problems mentioned in the background, this application proposes an adaptive adjustment scheme for the active power frequency regulation coefficient of wind turbine converters. This solves the problem that existing technologies use fixed frequency regulation coefficients for wind turbines, which cannot simultaneously consider the dynamic operating state of the turbine itself and the actual frequency regulation needs of the power grid. This leads to equipment safety risks when reserves are insufficient, while wasting frequency regulation potential when reserves are sufficient. Specifically, this scheme first starts from the data source. It accurately perceives the comprehensive operating boundaries of the turbine at the aerodynamic and thermodynamic levels by acquiring power margin and thermal margin of converter and generator in real time. Then, based on the different thermal time constants of each component, these boundary constraints are quantified into available reserves at three time scales: instantaneous, short-term, and long-term, completing a refined profile of the turbine's true frequency regulation capability. Subsequently, the system identifies the specific characteristics of power grid frequency disturbances (such as the magnitude and rate of change of deviation), intelligently determines the most needed response type, and dynamically weights and fuses the three reserve capabilities to obtain a final effective reserve value most suitable for the current disturbance. Finally, the optimal adaptive droop coefficient is calculated based on this effective reserve value. With this design, the frequency regulation coefficient is no longer a fixed parameter, but a dynamic mapping between the unit's real-time capability and the actual needs of the power grid, thereby ensuring that the frequency regulation response can maximize its potential without exceeding the equipment's safety boundaries.

[0017] In the technical solution of this application, an adaptive adjustment method for the active power frequency regulation coefficient of the wind turbine converter is proposed. Figure 1 This is a flowchart of an adaptive adjustment method for the active power frequency regulation coefficient of a wind turbine converter according to an embodiment of this application. Figure 2 This is a data flow diagram illustrating the adaptive adjustment method of the active power frequency regulation coefficient of a wind turbine converter according to an embodiment of this application. Figure 1 and Figure 2As shown, the adaptive adjustment method for the active power frequency regulation coefficient of a wind turbine converter according to an embodiment of this application includes the following steps: S100, obtaining power margin, converter thermal margin, generator thermal margin, and frequency deviation; S200, performing multi-timescale thermal availability reserve calculation based on power margin, converter thermal margin, and generator thermal margin to obtain instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve; S300, based on frequency deviation, performing disturbance feature identification and dynamic weighting of reserve capacity on instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve to obtain final effective reserve; S400, determining the adaptive droop coefficient based on the final effective reserve and the target frequency deviation endpoint of the frequency regulation response; S500, generating the final power command based on frequency deviation, frequency dead zone, base power reference, and adaptive droop coefficient.

[0018] Specifically, in step S100, the power margin, converter thermal margin, generator thermal margin, and frequency deviation are obtained. It should be understood that traditional frequency regulation control uses fixed coefficients, which cannot perceive the real-time operating status of the wind turbine. For example, on a hot summer afternoon, although the wind speed is high and the turbine has high available power, the converter and generator may be operating close to their temperature limits due to the high ambient temperature. If the grid frequency drops at this time, the fixed frequency regulation coefficient will instruct the turbine to forcibly increase power output, which can easily lead to overheating and tripping of critical components, thus exacerbating grid disturbances. Therefore, in the technical solution of this application, the power margin, converter thermal margin, generator thermal margin, and frequency deviation are obtained to comprehensively and quantitatively capture the turbine's current energy reserve potential, the thermal health status of key equipment, and the frequency regulation demand signal of the external grid. This provides accurate and multi-dimensional decision-making basis for subsequent adaptive adjustment, enabling the control system to clearly recognize that, in the aforementioned high-temperature scenario, although the power margin is large, the thermal margin of the converter is very limited. This lays the foundation for formulating a frequency regulation strategy that can respond quickly without compromising equipment safety, fundamentally solving the problem of blind control.

[0019] More specifically, in the embodiments of this application, obtaining power margin, converter thermal margin, generator thermal margin, and frequency deviation includes: obtaining actual active power, available active power, converter critical point temperature, generator critical point temperature, grid frequency, and grid reference frequency; calculating the difference between available active power and actual active power as the power margin; calculating the difference between the upper limit of converter temperature and the converter critical point temperature to obtain the converter thermal margin; calculating the difference between the upper limit of generator temperature and the generator critical point temperature to obtain the generator thermal margin; and calculating the difference between the grid frequency and the grid reference frequency to obtain the frequency deviation.

[0020] It is understandable that the frequency regulation capability of wind turbines is limited by both the availability of wind energy and the physical load-bearing capacity of key components such as converters and generators. These scattered raw monitoring data, such as a specific temperature or power value, cannot directly guide complex frequency regulation decisions. Therefore, the technical solution of this application further acquires all the aforementioned data to systematically transform these discrete, multi-source raw sensor data into a set of structured core state indicators with clear physical meaning. This enables the control system to form a comprehensive and quantitative understanding of its own capability boundaries and external frequency regulation requirements. For example, on a hot summer afternoon, calculations can clearly identify that although the power margin is large, the converter thermal margin is very small, thus providing the most basic and indispensable data input for subsequently formulating a precise frequency regulation strategy that can respond to grid demands without causing equipment overheating.

[0021] Specifically, in step S200, multi-timescale thermal availability reserve calculations are performed based on power margin, converter thermal margin, and generator thermal margin to obtain instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve. It should be understood that the three core constraints of wind turbine power margin, converter thermal margin, and generator thermal margin have drastically different physical properties and time response characteristics. For example, the converter has low thermal inertia, and its temperature constraints become apparent on a timescale of seconds to minutes, while the generator has high thermal inertia, and its constraints are reflected on a longer timescale of minutes to hours. Simply treating these constraints of different dimensions as equal cannot accurately depict the true availability of the unit during a complete frequency disturbance event (from instantaneous response to sustained support). Therefore, in the technical solution of this application, multi-timescale thermal reserve calculations are further performed based on power margin, converter thermal margin, and generator thermal margin to obtain instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve. This scientifically maps constraints of different natures to three different frequency regulation response stages—instantaneous, short-term, and long-term—according to their physical time constants, thereby constructing a time-dimensional capability profile of the wind turbine. This allows the control system to clearly recognize that, on a hot summer afternoon, the unit may be able to provide 1MW of instantaneous power support (determined by the power margin), but only 0.2MW of short-term support (limited by the converter thermal margin), and even lower long-term support capability. Thus, a static capability assessment problem is transformed into a dynamic, phased list of available reserve capabilities.

[0022] More specifically, in the embodiments of this application, multi-timescale thermal availability reserve calculations are performed based on power margin, converter thermal margin, and generator thermal margin to obtain instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve, including: power equivalence of the thermal margin of the converter thermal margin and generator thermal margin to obtain converter equivalent power reserve and generator equivalent power reserve; long-term and short-term thermal reserve calculations are performed based on power margin, converter equivalent power reserve, and generator equivalent power reserve to obtain short-term thermal reserve and long-term thermal reserve; and instantaneous thermal reserve is specified based on power margin.

[0023] Specifically, the thermal margins of the converter and generator are equivalentized in power to obtain the equivalent power reserves of the converter and generator. It should be understood that when evaluating the comprehensive frequency regulation capability of wind turbines, power margin is measured in power units, while the thermal margins of the converter and generator are measured in temperature units. These two indicators with different physical dimensions cannot be directly compared and used for unified decision-making, leading to the system's inability to determine whether, under specific operating conditions, the available wind energy is insufficient or the equipment's thermal capacity reaches its limit first. Therefore, in the technical solution of this application, the thermal margins of the converter and generator are further equivalentized in power to obtain the equivalent power reserves of the converter and generator. This transforms the temperature constraints characterizing the thermal state of the equipment into power constraint indicators that the control system can directly understand and use. This enables the quantitative unification of constraints on different physical properties, allowing the system to directly compare and identify whether, under the current operating conditions, wind energy resources, converter thermal load capacity, or generator thermal load capacity becomes the bottleneck of frequency regulation response. This provides decision input of the same dimension for subsequent calculation of actual available backup based on the barrel effect.

[0024] More specifically, in a concrete example of this application, firstly, the equivalent power reserve of the converter is calculated. A pre-established steady-state thermodynamic model of the converter is used, which precisely describes the mapping relationship between the increment of the converter's output power and the steady-state temperature rise of the junction temperature of its internal key semiconductor devices (such as IGBT modules). Based on this model, the currently acquired converter thermal margin (i.e., the difference between the upper temperature limit and the current temperature) is used as input to solve for the maximum active power that the converter can stably increase before the thermal margin is exhausted; this value is determined as the converter's equivalent power reserve. Next, the equivalent power reserve of the generator is calculated. This step is similar to the converter calculation process, using a steady-state thermodynamic model of the generator, which quantifies the relationship between the increment of the generator's output power and the steady-state temperature rise of its key components (such as stator windings). The currently acquired generator thermal margin is input into this model to calculate the maximum active power that the generator can continuously generate without exceeding its temperature limit, and this value is determined as the generator's equivalent power reserve.

[0025] Specifically, long-term and short-term thermal reserves are calculated based on power margin, converter equivalent power reserve, and generator equivalent power reserve to obtain short-term and long-term thermal reserves. The long-term and short-term thermal reserves are calculated using the following formula:

[0026]

[0027] in, For power margin, and For converter equivalent power reserve and generator equivalent power reserve To obtain the minimum value, For long-term thermal backup, For short-term thermal backup.

[0028] It is understandable that the frequency regulation response capability of wind turbine units is subject to different physical constraints at different time scales. In particular, the converter, due to its low thermal inertia, becomes the bottleneck for short-term response, while the generator, due to its high thermal inertia, becomes the bottleneck for long-term continuous response. If all constraints are treated uniformly without distinction, it is impossible to accurately assess the true support capability that the unit can provide at different stages of frequency events. Therefore, in the technical solution of this application, long-term and short-term thermal reserves are further calculated based on power margin, converter equivalent power reserve, and generator equivalent power reserve to obtain short-term and long-term thermal reserves. In this way, according to the physical characteristics of each component, the core constraints at the minute (short-term) and hour (long-term) time scales are accurately identified by taking the smaller value. In this way, a general reserve capacity can be broken down into two indicators with a clear time dimension. For example, on a hot summer afternoon, the system can calculate that the short-term thermal reserve is 0.2MW (limited by the equivalent power reserve of the converter), while the long-term thermal reserve is also 0.2MW (at this time, the bottleneck is still the converter). This provides the control system with a clear capacity list: the unit can generate a maximum of 0.2MW within a few minutes, and this power level can be maintained for a long time. This provides a precise and phased decision-making basis for dynamically calling different reserve capacities according to the urgency of the disturbance.

[0029] Specifically, the power margin is designated as instantaneous thermal reserve. It should be understood that in the initial instant (seconds or sub-seconds) of a severe disturbance in the grid frequency, the internal temperature of power electronic and electrical equipment such as converters and generators does not change immediately due to their inherent thermal inertia. At this time, the bottleneck truly limiting the rapid increase in power output is not the thermal capacity of the equipment, but the additional aerodynamic power that can be instantaneously captured from the wind. Therefore, in the technical solution of this application, the power margin is further designated as instantaneous thermal reserve to bind the available reserve capacity in this second-level response phase to a single and direct physical constraint, namely, the aerodynamic power margin. This allows for the definition of a clear and accurate upper limit to the system's maximum instantaneous response capability. For example, on a hot summer afternoon, even with a very small converter thermal margin, if wind conditions are favorable, the unit may still have a 1MW power margin. This 1MW can serve as the initial force to cope with the sudden frequency drop, thus ensuring that in the most critical initial stage, the unit can contribute its inertial support and rapid frequency response capability to the maximum extent.

[0030] More specifically, in a concrete example of this application, firstly, a real-time calculation of the power margin is performed. The control system continuously monitors the available active power, representing the upper limit of wind energy utilization, and the actual active power currently being delivered to the grid by the generator. By calculating the difference between these two, a dynamically changing power margin value is obtained, which accurately reflects the maximum aerodynamic power that the generator can instantaneously generate without adjusting the pitch angle under the current wind conditions. Next, an instantaneous thermal reserve is assigned. The power margin value calculated in the previous step is directly assigned to the instantaneous thermal reserve parameter. This direct assignment is based on the fact that, on a second-scale timescale, the temperature rise of the converter and generator is negligible, and their thermal constraints have not yet taken effect; therefore, the instantaneous response capability of the generator is entirely determined by its aerodynamic potential. Through this step, the instantaneous thermal reserve is determined as the generator's maximum rapid power support capability without considering thermal limitations.

[0031] Specifically, in step S300, based on frequency deviation, disturbance characteristics are identified and reserve capacity is dynamically weighted for instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve to obtain the final effective reserve. It should be understood that although wind turbines have assessed available reserves at three different time scales—instantaneous, short-term, and long-term—this is merely a static capacity list. Grid frequency disturbances, however, are dynamic and varied. For example, a large-scale disconnection of a generator from the grid can cause a sharp drop in frequency, while a slow increase in load leads to a gradual decrease in frequency. These two scenarios have drastically different requirements for the urgency and continuity of power support. If reserves are called up indiscriminately, precise, targeted support cannot be achieved. Therefore, in the technical solution of this application, based on frequency deviation, disturbance characteristics are identified and reserve capacity is dynamically weighted for instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve to obtain the final effective reserve. This allows for real-time diagnosis of the severity and urgency of the current grid disturbance by analyzing the amplitude and rate of change of the frequency deviation, and dynamically assigns weights to the reserve capacity of the unit at three different time scales accordingly, ultimately synthesizing an effective reserve value most suitable for the current disturbance scenario. In this way, a static capacity list can be transformed into a dynamic, tailor-made effective reserve value for the current disturbance. For example, on a hot summer afternoon, when the frequency drops sharply, the system assigns a very high weight to the 1MW instantaneous reserve, making the final effective reserve close to 1MW to provide strong support; while when the frequency drops slowly, the 0.2MW short-term and long-term reserves are assigned higher weights, making the final effective reserve close to 0.2MW to provide stable support, thereby realizing intelligent and refined scheduling of frequency regulation resources.

[0032] Figure 3 This document describes a flowchart illustrating the adaptive adjustment method for the active power frequency regulation coefficient of a wind turbine converter, based on frequency deviation. It describes the process of identifying disturbance characteristics and dynamically weighting reserve capacity for instantaneous, short-term, and long-term thermal reserves to obtain the final effective reserve. For example... Figure 3 As shown, step S300 includes: S310, identifying grid frequency disturbance characteristics of the frequency deviation to obtain the absolute value of the frequency deviation and the frequency change rate; S320, calculating multi-time-scale reserve dynamic weights based on the absolute value of the frequency deviation and the frequency change rate to obtain instantaneous reserve weights, short-term reserve weights, and long-term reserve weights; S330, dynamically weighting and fusing the instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve based on the instantaneous reserve weights, short-term reserve weights, and long-term reserve weights to obtain the final effective reserve.

[0033] Specifically, in step S310, the frequency deviation is subjected to grid frequency disturbance characteristic identification to obtain the absolute value of the frequency deviation and the frequency change rate. It should be understood that the original frequency deviation signal itself can only reflect the degree of frequency deviation from the rated value, but cannot reveal the dynamic trend of disturbance development. For example, a -0.2Hz frequency deviation may be formed by a slow, stable load increase, or it may be a snapshot of a large-capacity unit disconnecting from the grid and its frequency rapidly collapsing. These two situations pose completely different threats to system stability and require entirely different response speeds. Therefore, in the technical solution of this application, the frequency deviation is further subjected to grid frequency disturbance characteristic identification to obtain the absolute value of the frequency deviation and the frequency change rate. This allows two orthogonal characteristic indicators that can comprehensively characterize the static severity (depth) and dynamic urgency (speed) of the disturbance to be extracted from a single frequency deviation signal. This enables the control system to not only know that a frequency deviation has occurred, but also the degree of deviation and the rate of change of deviation. For example, it can clearly distinguish between a non-emergency situation with high deviation and low rate of change and an emergency situation with high deviation and high rate of change, thus providing a basis for subsequent intelligent matching of instantaneous, short-term, or long-term backup resources.

[0034] More specifically, in a concrete example of this application, firstly, the absolute value of the frequency deviation is calculated. The control system acquires the frequency deviation signal calculated from the difference between the grid frequency and the reference frequency, and applies an absolute value operation to this signal. The result of this operation is the absolute value of the frequency deviation, which quantifies the magnitude of the frequency deviation, regardless of whether it is over- or under-frequency, serving as a static indicator of the severity of the disturbance. Next, the rate of change of frequency is calculated. The control system processes the continuously acquired time-series data of the frequency deviation. Within each control cycle, the frequency deviation value at the current sampling time is taken, the frequency deviation value at the previous sampling time is subtracted, and the difference is divided by the fixed sampling time interval of the control system. The result of this division operation is the rate of change of frequency, which accurately characterizes the instantaneous speed and direction of frequency change, serving as a dynamic indicator of the development trend and urgency of the disturbance.

[0035] Specifically, in step S320, multi-timescale dynamic weight calculations of reserve capacity are performed based on the absolute value of frequency deviation and the rate of frequency change to obtain instantaneous reserve weight, short-term reserve weight, and long-term reserve weight. It should be understood that merely identifying the characteristics of grid disturbances (such as the absolute value and rate of frequency deviation) is insufficient to form control decisions. The system also needs a clear logical rule to determine which type of reserve capacity (instantaneous, short-term, or long-term) should be prioritized under the current disturbance characteristics, and what the recommended proportion should be. Without this quantitative mapping relationship, the control system cannot translate the diagnostic results into specific response strategies. Therefore, in the technical solution of this application, multi-timescale dynamic weight calculations of reserve capacity are further performed based on the absolute value of frequency deviation and the rate of frequency change to obtain instantaneous reserve weight, short-term reserve weight, and long-term reserve weight. This establishes a quantitative mapping relationship between grid disturbance characteristics and wind turbine reserve capacity mobilization strategies, that is, assigning corresponding priorities to reserve capacities at different time scales according to the severity and urgency of the disturbance. In this way, the control system can dynamically adjust tactics according to the real-time situation. For example, when a large frequency change rate is detected, the instantaneous standby will be given a very high weight through the weight mapping function to ensure that the unit provides rapid and intense power support first. When a large absolute value of frequency deviation is detected but the change rate is very small, the long-term standby will be given a higher weight to provide stable and sustained power support, thereby realizing intelligent and differentiated scheduling of different standby resources.

[0036] More specifically, in the embodiments of this application, multi-timescale reserve dynamic weight calculation is performed based on the absolute value of frequency deviation and the rate of frequency change to obtain instantaneous reserve weight, short-term reserve weight and long-term reserve weight, including: inputting the absolute value of frequency deviation and the rate of frequency change into a preset weight mapping function to obtain instantaneous reserve activation degree, short-term reserve activation degree and long-term reserve activation degree.

[0037] Specifically, the absolute value of the frequency deviation and the rate of change of the frequency are input into a preset weight mapping function to obtain the instantaneous reserve activation degree, short-term reserve activation degree, and long-term reserve activation degree. The instantaneous reserve activation degree, short-term reserve activation degree, and long-term reserve activation degree are then used to calculate the multi-time-scale reserve dynamic weight using the following formula:

[0038]

[0039]

[0040] in, , and These are instantaneous standby activation level, short-term standby activation level, and long-term standby activation level, respectively. As an instantaneous standby weight, As a short-term reserve weight, This is a long-term reserve weight.

[0041] It should be understood that there is an inherent logical correlation between the characteristics of power grid disturbances (absolute value of frequency deviation and rate of frequency change) and the required type of reserve (instantaneous, short-term, or long-term). However, this correlation is not a simple linear relationship and requires a specific mathematical or logical tool to quantify the abstract disturbance characteristics into specific activation intensities for each type of reserve capability. Therefore, in the technical solution of this application, the absolute value of frequency deviation and rate of frequency change are further input into a preset weighted mapping function to obtain the instantaneous reserve activation degree, the short-term reserve activation degree, and the long-term reserve activation degree. This performs a conversion process based on expert rules or preset logic, which maps the rate of frequency change, representing the urgency of the disturbance, primarily to the instantaneous reserve activation degree, and the absolute value of frequency deviation, representing the severity of the disturbance, primarily to the long-term reserve activation degree. The two are then combined to determine the short-term reserve activation degree. In this way, a set of original, unnormalized activation scores can be provided for subsequent weight normalization calculations. For example, in scenarios where a large-capacity unit disconnects from the grid and the frequency drops sharply, the mapping function can be used to calculate a very high instantaneous reserve activation, a medium short-term reserve activation, and a relatively low long-term reserve activation. This clearly expresses, numerically, that the most priority and urgent need is rapid instantaneous power support, providing a foundation for the final formation of a weighted strategy dominated by instantaneous reserve.

[0042] Specifically, in step S330, based on the instantaneous reserve weight, short-term reserve weight, and long-term reserve weight, the instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve are dynamically weighted and fused to obtain the final effective reserve. It should be understood that the control system has already obtained a reserve list (instantaneous, short-term, and long-term thermal reserves) representing the wind turbine's capabilities and a weight list (instantaneous, short-term, and long-term reserve weights) representing the grid demand. However, these two separate sets of data cannot directly generate a unified power target; that is, a final comprehensive decision-making process is needed to integrate what can be done and what should be done into a single, clear action instruction. Therefore, in the technical solution of this application, the instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve are further dynamically weighted and fused based on the instantaneous reserve weight, short-term reserve weight, and long-term reserve weight to obtain the final effective reserve. This is used to perform a final comprehensive decision-making calculation, accurately matching the multi-dimensional capabilities of the turbine with the single-point demand of the grid, and generating the most effective power response target value under the current disturbance. This ensures that the power reference used to calculate the frequency regulation coefficient is both within the capacity of the generating units and what the power grid needs most at present. For example, in the face of a sharp drop in frequency on a hot summer afternoon, 1MW of instantaneous reserve will be multiplied by a weight close to 1, while 0.2MW of long-term reserve will be multiplied by a weight close to 0, ultimately resulting in an effective reserve close to 1MW, thereby driving the generating units to make a strong instantaneous response.

[0043] More specifically, in a concrete example of this application, firstly, the reserve components at each time scale are calculated. The control system multiplies the determined instantaneous thermal reserve with its corresponding instantaneous reserve weight to obtain a weighted instantaneous reserve component; simultaneously, it multiplies the short-term thermal reserve with its corresponding short-term reserve weight to obtain a weighted short-term reserve component; and it multiplies the long-term thermal reserve with its corresponding long-term reserve weight to obtain a weighted long-term reserve component. Then, the final effective reserve is synthesized. The control system sums the weighted instantaneous reserve component, weighted short-term reserve component, and weighted long-term reserve component calculated in the previous step. The result of this summation is determined as the final effective reserve. This value represents the optimal comprehensive power support capability that can be provided under the current grid disturbance characteristics, taking into account the multi-time scale capabilities of the wind turbine units.

[0044] Specifically, in step S400, an adaptive droop coefficient is determined based on the final effective reserve and the target frequency deviation endpoint of the frequency regulation response. It should be understood that the preceding steps have calculated the optimal power response target that the wind turbine should provide under the current disturbance, i.e., the final effective reserve. However, traditional droop control determines the response slope using a fixed coefficient. This fixed slope cannot guarantee that the turbine's response capability can be accurately guided to this dynamically changing optimal target value under any operating condition, resulting in a deviation between the actual response and the ideal target. Therefore, in the technical solution of this application, an adaptive droop coefficient is further determined based on the final effective reserve and the target frequency deviation endpoint of the frequency regulation response. This completes the inverse solution from a dynamic power target to a dynamic control parameter, that is, based on the power that the turbine can and should currently provide, the optimal response slope that can achieve the target is calculated in reverse. This ensures that the control parameters are perfectly matched with the real-time capabilities of the generating unit and the real-time needs of the power grid. For example, in the heat of summer afternoons, when faced with a sharp drop in frequency, a small droop factor is calculated based on a final effective reserve of nearly 1MW, driving the generating unit to provide rapid and substantial power support. Conversely, if the grid disturbance is mild under the same operating conditions, and the calculated final effective reserve is only 0.2MW, a larger droop factor is calculated accordingly, making the unit response smoother. This achieves a measured and precise frequency regulation response, resolving the contradiction between safety and efficiency caused by fixed factors.

[0045] Figure 4 This is a flowchart illustrating the adaptive droop coefficient determination method for the active power frequency regulation coefficient of a wind turbine converter according to embodiments of this application, based on the target frequency deviation endpoint of the final effective reserve and frequency regulation response. (See flowchart for example.) Figure 4 As shown, step S400 includes: S410, determining the maximum frequency modulation power and the target frequency deviation based on the final effective standby and the target frequency deviation endpoint of the frequency modulation response; S420, calculating the initial adaptive droop coefficient based on the maximum frequency modulation power and the target frequency deviation; S430, constraining and finally determining the initial adaptive droop coefficient to obtain the adaptive droop coefficient.

[0046] Specifically, in steps S410, S420, and S430, the maximum frequency regulation power and the target frequency deviation are determined based on the final effective reserve and the target frequency deviation endpoint of the frequency regulation response; the initial adaptive droop coefficient is calculated based on the maximum frequency regulation power and the target frequency deviation; and the initial adaptive droop coefficient is constrained and finally determined to obtain the adaptive droop coefficient. It should be understood that the aforementioned steps have determined an optimal power response target for the wind turbine under a specific disturbance, namely the final effective reserve. However, the direct execution parameter for droop control is the frequency regulation coefficient, not the power value. The system needs a clear mechanism to transform this dynamic power target into an equally dynamic and executable control coefficient; otherwise, the control loop cannot be closed, and the calculated optimal power target cannot be accurately executed. Therefore, in the technical solution of this application, the adaptive droop coefficient is further obtained based on the above steps to complete the reverse solution and constraint from a dynamic power target to a dynamic control parameter. That is, based on the power that the unit can and should currently provide, the optimal response slope that can achieve the target is calculated in reverse and ensured to be within a reasonable range. This ensures that the final control parameters are fully matched with the real-time capabilities of the generating unit and the real-time needs of the power grid. For example, in the heat of summer afternoons, when faced with a sharp drop in frequency, the system, based on a final effective reserve of nearly 1MW, will calculate a small droop factor and drive the generating unit to provide rapid and substantial power support, thereby achieving a frequency regulation response that is both within its capabilities and precise in its implementation.

[0047] More specifically, in a concrete example of this application, firstly, the characteristic points of the frequency modulation response are defined. This step directly assigns the final effective reserve calculated in the previous step to the maximum frequency modulation power as the upper limit of the power for this frequency modulation response; simultaneously, a preset target frequency deviation endpoint representing the desired response saturation point is assigned to the target frequency deviation. These two values ​​together define the endpoint coordinates of the desired adaptive droop curve. Then, the initial adaptive droop coefficient is calculated. The control system divides the absolute value of the target frequency deviation determined in the previous step by the maximum frequency modulation power. The result of this division is the initial adaptive droop coefficient, which precisely represents the absolute value of the slope of the straight line connecting the origin and the desired response endpoint. Finally, the droop coefficient is constrained and finally determined. The control system compares the calculated initial adaptive droop coefficient with preset upper and lower limits for the droop coefficient. If the initial value exceeds this range, it is limited to the boundary value. This is intended to prevent system oscillation due to an excessively small coefficient or response failure due to an excessively large coefficient. The value output after this saturation limiting process is the adaptive droop coefficient that is finally used to generate the power command.

[0048] Specifically, in step S500, a final power command is generated based on frequency deviation, frequency dead zone, base power reference, and adaptive droop coefficient. It should be understood that the calculations of all the aforementioned steps ultimately converge on a core control parameter, namely the adaptive droop coefficient. However, this coefficient itself cannot directly drive the converter operation; it must be applied to a complete control law, combined with real-time grid frequency deviation, to generate a specific and executable power setpoint. Therefore, in the technical solution of this application, a final power command is further generated based on frequency deviation, frequency dead zone, base power reference, and adaptive droop coefficient. This completes the final execution stage of the entire adaptive frequency regulation strategy, applying the dynamically calculated frequency regulation coefficient to the standard droop control equation, calculating the power increment required for frequency regulation in real time, and combining it with the unit's basic power generation plan. This ensures that the wind turbine's converter receives a precise, dynamic, and closed-loop final power command. For example, on a hot summer afternoon, when faced with a sharp drop in frequency, the system will use a calculated small droop coefficient to convert a large frequency deviation into a positive power increment, which is then superimposed on the base power reference. This sends a clear high-power output command to the converter, ultimately translating the entire adaptive adjustment concept into the actual physical action of the equipment.

[0049] More specifically, in a concrete example of this application, firstly, a dead-zone determination of the frequency deviation is performed. The control system compares the absolute value of the frequency deviation acquired in real time with a preset frequency dead-zone threshold. If the absolute value of the frequency deviation is less than or equal to the dead-zone threshold, it is considered a normal fluctuation in the power grid, and the frequency regulation power increment is zero; if it is greater than the dead-zone threshold, the portion exceeding the dead zone is calculated as the effective frequency deviation, and the calculation proceeds to the next step. Then, the frequency regulation power increment is calculated. The control system divides the effective frequency deviation obtained in the previous step by the currently determined adaptive droop coefficient and takes a negative sign. The result of this calculation is the frequency regulation power increment required by the unit to respond to this frequency deviation. The negative sign ensures that a positive power increment is output when the frequency drops (the deviation is negative), and vice versa. Finally, the final power command is synthesized. The control system adds the frequency regulation power increment calculated in the previous step to the planned output value representing the unit when there is no frequency regulation task, i.e., the base power reference. The summation result is then determined as the final power command and sent to the wind turbine's converter control system as the active power output target for its next control cycle.

[0050] In summary, the adaptive adjustment method for the active power frequency regulation coefficient of the wind turbine converter according to the embodiments of this application is explained. First, it accurately senses the comprehensive operating boundary of the unit under the current operating conditions by acquiring the power margin and the thermal margin of the converter and generator in real time. Then, it quantifies these multi-dimensional physical constraints into available reserves at multiple time scales—instantaneous, short-term, and long-term—based on the differences in the thermal time constants of different devices, thus completing a refined assessment of the unit's actual frequency regulation capability. Finally, based on the actual characteristics of grid frequency disturbances, it dynamically transforms the assessed available reserve capacity into an optimal adaptive frequency regulation coefficient. This resolves the technical contradictions caused by fixed coefficients, making the frequency regulation response no longer a blind command execution, but an intelligent behavior closely coupled with the unit's own safety bearing capacity, ensuring that the effectiveness of frequency regulation and equipment safety can achieve an optimal balance under any operating condition.

[0051] Furthermore, an adaptive adjustment and control system for the active power frequency regulation coefficient of a wind turbine converter is also provided.

[0052] Figure 5 This is a block diagram of an adaptive adjustment control system for the active power frequency regulation coefficient of a wind turbine converter according to an embodiment of this application. Figure 5 As shown, the adaptive adjustment and control system 500 for the active power frequency regulation coefficient of the wind turbine converter according to an embodiment of this application includes: a data acquisition module 510, used to acquire power margin, converter thermal margin, generator thermal margin and frequency deviation; a standby calculation module 520, used to perform multi-timescale thermal availability standby calculation based on power margin, converter thermal margin and generator thermal margin to obtain instantaneous thermal standby, short-term thermal standby and long-term thermal standby; a dynamic weighting module 530, used to perform disturbance feature identification and dynamic weighting of standby capacity on instantaneous thermal standby, short-term thermal standby and long-term thermal standby based on frequency deviation to obtain final effective standby; a coefficient determination module 540, used to determine the adaptive droop coefficient based on the final effective standby and the target frequency deviation endpoint of the frequency regulation response; and a final power generation module 550, used to generate the final power command based on frequency deviation, frequency dead zone, base power reference and adaptive droop coefficient.

[0053] As described above, the adaptive adjustment and control system 500 for the active power frequency regulation coefficient of the wind turbine converter according to the embodiments of this application can be implemented in various wireless terminals, such as servers with adaptive adjustment and control algorithms for the active power frequency regulation coefficient of the wind turbine converter. In one possible implementation, the adaptive adjustment and control system 500 for the active power frequency regulation coefficient of the wind turbine converter according to the embodiments of this application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the adaptive adjustment and control system 500 for the active power frequency regulation coefficient of the wind turbine converter can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the adaptive adjustment and control system 500 for the active power frequency regulation coefficient of the wind turbine converter can also be one of the many hardware modules of the wireless terminal.

[0054] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. An adaptive adjustment method for the active power frequency regulation coefficient of a wind turbine converter, characterized in that, include: Obtain power margin, converter thermal margin, generator thermal margin, and frequency deviation; Multi-timescale thermal availability calculations are performed based on power margin, converter thermal margin, and generator thermal margin to obtain instantaneous thermal availability, short-term thermal availability, and long-term thermal availability. Based on frequency deviation, disturbance characteristics are identified and reserve capacity is dynamically weighted for instantaneous thermal reserve, short-term thermal reserve and long-term thermal reserve to obtain the final effective reserve. Based on the target frequency deviation endpoint of the final effective reserve and frequency modulation response, determine the adaptive droop coefficient; The final power command is generated based on frequency deviation, frequency dead zone, base power reference, and adaptive droop factor.

2. The adaptive adjustment method for the active power frequency regulation coefficient of the wind turbine converter according to claim 1, characterized in that, Obtain power margin, converter thermal margin, generator thermal margin, and frequency deviation, including: Obtain actual active power, available active power, converter key point temperature, generator key point temperature, grid frequency, and grid reference frequency; The difference between the available active power and the actual active power is calculated as the power margin. The difference between the upper limit of the converter temperature and the temperature at the critical point of the converter is calculated to obtain the thermal margin of the converter. Calculate the difference between the upper limit of generator temperature and the temperature at the critical point of the generator to obtain the generator thermal margin; The frequency deviation is obtained by calculating the difference between the grid frequency and the grid reference frequency.

3. The adaptive adjustment method for the active power frequency regulation coefficient of the wind turbine converter according to claim 1, characterized in that, Multi-timescale thermal reserve availability calculations are performed based on power margin, converter thermal margin, and generator thermal margin to obtain instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve, including: The thermal margins of the converter and the generator are equivalent in power to obtain the equivalent power reserve of the converter and the equivalent power reserve of the generator. Long-term and short-term thermal reserves are calculated based on power margin, converter equivalent power reserve and generator equivalent power reserve to obtain short-term thermal reserves and long-term thermal reserves. Designated as instantaneous thermal reserve based on power margin.

4. The adaptive adjustment method for the active power frequency regulation coefficient of the wind turbine converter according to claim 3, characterized in that, Long-term and short-term thermal reserves are calculated based on power margin, converter equivalent power reserve, and generator equivalent power reserve to obtain short-term and long-term thermal reserves. This includes: calculating long-term and short-term thermal reserves based on power margin, converter equivalent power reserve, and generator equivalent power reserve using the following formula: in, For power margin, and For converter equivalent power reserve and generator equivalent power reserve To obtain the minimum value, For long-term thermal backup, For short-term thermal backup.

5. The adaptive adjustment method for the active power frequency regulation coefficient of the wind turbine converter according to claim 1, characterized in that, Based on frequency deviation, disturbance characteristics are identified and reserve capacity is dynamically weighted for instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve to obtain the final effective reserve, including: The frequency deviation is identified by power grid frequency disturbance characteristics to obtain the absolute value of the frequency deviation and the rate of frequency change. Multi-timescale reserve dynamic weight calculation is performed based on the absolute value of frequency deviation and the rate of frequency change to obtain instantaneous reserve weight, short-term reserve weight and long-term reserve weight. Based on instantaneous reserve weight, short-term reserve weight, and long-term reserve weight, instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve are dynamically weighted and fused to obtain the final effective reserve.

6. The adaptive adjustment method for the active power frequency regulation coefficient of the wind turbine converter according to claim 5, characterized in that, Multi-timescale reserve dynamic weights are calculated based on the absolute value of frequency deviation and the rate of frequency change to obtain instantaneous reserve weights, short-term reserve weights, and long-term reserve weights, including: The absolute value of the frequency deviation and the rate of change of the frequency are input into a preset weight mapping function to obtain the instantaneous reserve activation degree, short-term reserve activation degree and long-term reserve activation degree. The dynamic weights of instantaneous reserve activation, short-term reserve activation, and long-term reserve activation are calculated using the following formula: in, , and These are instantaneous standby activation level, short-term standby activation level, and long-term standby activation level, respectively. As an instantaneous standby weight, As a short-term reserve weight, This is a long-term reserve weight.

7. The adaptive adjustment method for the active power frequency regulation coefficient of the wind turbine converter according to claim 1, characterized in that, Based on the target frequency deviation endpoint of the final effective reserve and frequency modulation response, the adaptive droop coefficient is determined, including: Based on the target frequency deviation endpoint of the final effective reserve and frequency modulation response, determine the maximum frequency modulation power and the target frequency deviation; Calculate the initial adaptive droop coefficient based on the maximum frequency modulation power and the target frequency deviation; The initial adaptive droop coefficient is constrained and finally determined to obtain the adaptive droop coefficient.

8. An adaptive adjustment system for the active power frequency regulation coefficient of a wind turbine converter, characterized in that, include: The data acquisition module is used to acquire power margin, converter thermal margin, generator thermal margin, and frequency deviation. The backup calculation module is used to perform multi-timescale thermal availability backup calculations based on power margin, converter thermal margin, and generator thermal margin to obtain instantaneous thermal backup, short-term thermal backup, and long-term thermal backup. The dynamic weighting module is used to identify disturbance characteristics and dynamically weight reserve capacity based on frequency deviation for instantaneous thermal reserve, short-term thermal reserve, and long-term thermal reserve to obtain the final effective reserve. The coefficient determination module is used to determine the adaptive droop coefficient based on the target frequency deviation endpoint of the final effective reserve and frequency modulation response; The final power generation module is used to generate the final power command based on frequency deviation, frequency dead zone, base power reference, and adaptive droop coefficient.

9. An adaptive adjustment system for the active power frequency regulation coefficient of a wind turbine converter, characterized in that, The dynamic weighting module includes: The power equivalent unit is used to perform power equivalence on the thermal margin of the converter and the thermal margin of the generator to obtain the equivalent power reserve of the converter and the equivalent power reserve of the generator. The backup calculation unit is used to perform long-term and short-term thermal backup calculations based on power margin, converter equivalent power backup and generator equivalent power backup to obtain short-term thermal backup and long-term thermal backup. Margin designation unit, used to designate instantaneous thermal standby based on power margin.

10. An adaptive adjustment system for the active power frequency regulation coefficient of a wind turbine converter, characterized in that, The coefficient determination module includes: The deviation endpoint confirmation unit is used to determine the maximum frequency modulation power and the target frequency deviation based on the target frequency deviation endpoint of the final effective standby and frequency modulation response; The droop coefficient calculation unit is used to calculate the initial adaptive droop coefficient based on the maximum frequency modulation power and the target frequency deviation; The droop coefficient constraint unit is used to constrain and finally determine the initial adaptive droop coefficient to obtain the adaptive droop coefficient.

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