Wind storage power station frequency control method and wind storage power station

Abstract

The invention relates to a wind storage power station frequency control method and a wind storage power station, and the method comprises the steps: constructing a power system model, and obtaining the working parameters of the power system model; the power system model comprises a wind power generation system model, an energy storage system model and a load; the actual input variable quantity of the composite controller is obtained based on the working parameters, and the key coefficient of the proportional-integral-derivative controller is adjusted according to the fuzzy rule of the fuzzy logic system; adjusting a control coefficient of the power system model output by the composite controller based on the key coefficient so as to realize dynamic adjustment of the frequency of the power system model; the composite controller comprises a fuzzy logic system and a proportional-integral-derivative controller. The wind power generation frequency is jointly adjusted through the fuzzy logic system and the proportional-integral-derivative controller, so that accurate control is quickly performed when the system frequency deviates, and the capability of the system for dealing with quick frequency disturbance is enhanced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wind power generation, and in particular to a method for controlling the frequency of a wind power station and a wind power station. Background Art

[0002] With the transformation of the global energy structure, the proportion of renewable energy has gradually increased, especially the penetration rate of wind and solar energy has increased significantly, which has also brought new challenges to the stable operation of traditional power systems.

[0003] Given that the intermittent and random nature of renewable energy sources such as wind power leads to reduced system inertia and increased frequency fluctuations, grid frequency control faces unprecedented challenges. Traditional control algorithms are unable to meet the rapidly changing system requirements, and advanced control strategies are urgently needed to ensure frequency stability and power quality. Although existing studies have proposed a variety of control methods, more intelligent and efficient solutions are still needed for complex systems with nonlinear factors, dynamic parameter changes, and renewable energy integration. Summary of the invention

[0004] In order to solve the above technical problems, the present disclosure provides a method for controlling the frequency of a wind power station and a wind power station.

[0005] In a first aspect, the present disclosure provides a method for controlling the frequency of a wind power station, comprising:

[0006] Constructing a power system model and obtaining working parameters of the power system model; the power system model includes a wind power generation system model, an energy storage system model and a load;

[0007] Based on the working parameters, the actual input variation of the composite controller is obtained, and according to the fuzzy rules of the fuzzy logic system, the key coefficients of the proportional-integral-derivative controller are adjusted;

[0008] Adjusting the control coefficient of the power system model output by the composite controller based on the key coefficient to achieve dynamic regulation of the frequency of the power system model;

[0009] Wherein, the composite controller includes a fuzzy logic system and a proportional-integral-derivative controller.

[0010] In some embodiments, before obtaining the actual input variation of the composite controller based on the working parameters and adjusting the key coefficients of the proportional integral derivative controller according to the fuzzy rules of the fuzzy logic system, the method further includes:

[0011] According to the power system model, a fuzzy rule for input variation is established through a fuzzy control algorithm;

[0012] The fuzzy rule is to use different control strategies to adjust the operating parameters of the power system model when the input variation is different.

[0013] In some embodiments, the actual input variation of the composite controller is obtained based on the working parameters, and the key coefficients of the proportional integral derivative controller are adjusted according to the fuzzy rules of the fuzzy logic system, including:

[0014] Determining the membership of the actual input variation in each fuzzy set based on the corresponding intervals of the actual input variation and the preset fuzzy sets of the input variation;

[0015] Based on each of the membership degrees, a corresponding control strategy is determined, and key coefficients of a proportional-integral-derivative controller are adjusted.

[0016] In some embodiments, the actual input variation of the composite controller is obtained based on the working parameters, and the key coefficients of the proportional integral derivative controller are adjusted according to the fuzzy rules of the fuzzy logic system, including:

[0017] The wind speed deviation, the frequency deviation and the state of charge of the energy storage system model are obtained based on the working parameters, and the key coefficients of the proportional integral differential controller are adjusted according to the fuzzy rules of the fuzzy logic system.

[0018] In some embodiments, the wind speed deviation, frequency deviation and the state of charge of the energy storage system model are obtained based on the working parameters, and the key coefficients of the proportional integral differential controller are adjusted according to the fuzzy rules of the fuzzy logic system, including:

[0019] Based on the wind speed deviation, the frequency deviation, the state of charge and the preset fuzzy set corresponding intervals of each input variation, determining the membership of the wind speed deviation, the frequency deviation and the state of charge in each fuzzy set;

[0020] Based on each of the membership degrees, a corresponding control strategy is determined, and key coefficients of a proportional-integral-derivative controller are adjusted.

[0021] In some embodiments, the key coefficients of adjusting the proportional integral derivative controller include:

[0022] Adjusting at least one of a proportional gain, an integral gain and a differential gain of the PID controller;

[0023] The formula for adjusting the proportional gain of the PID controller is as follows:

[0024]

[0025] Among them, K' p is the adjusted proportional gain; Kp，max is the maximum proportional gain; K p，min is the minimum proportional gain; Δf max is the preset maximum permissible frequency deviation; is the normalized frequency deviation, which is used to map to K p Scope of adjustment;

[0026] The formula for adjusting the integral gain of the proportional integral derivative controller is as follows:

[0027]

[0028] Among them, K i is the adjusted integral gain; ΔP is the power deviation of the system; T p is the reference time period of the integral action; α is the integral time factor adjusted as the system state changes;

[0029] The formula for adjusting the differential gain of the proportional integral derivative controller is as follows:

[0030]

[0031] Among them, K' d is the adjusted differential gain; K d，max is the maximum differential gain; K d，min is the minimum differential gain; Δf d,max / Δt ref is the maximum rate of change of frequency allowed under reference conditions.

[0032] In some embodiments, it also includes:

[0033] The actual input variation of the composite controller is obtained based on the working parameters, and the wind turbine speed and the pitch angle of the wind power generation system model are adjusted according to the fuzzy rules of the fuzzy logic system.

[0034] In some embodiments, adjusting the control coefficient of the power system model output by the composite controller based on the key coefficient to achieve dynamic regulation of the frequency of the power system model includes:

[0035] The inertia control coefficient of the wind power generation system model and the charge and discharge coefficient of the energy storage system model output by the composite controller are adjusted based on the key coefficients to achieve dynamic regulation of the frequency of the power system model.

[0036] In some embodiments, it also includes:

[0037] The power of the power system model is adjusted according to the wind power generation adjustment coefficient, the maximum power adjustment amount of the wind power generation system model, the energy storage system adjustment coefficient and the available charge and discharge capacity of the energy storage system model;

[0038] ΔP total =K' wind ΔP wind_available +K′ storage ΔP storage_capacity

[0039] Among them, ΔP total Indicates the total power regulation; ΔK' wind is the wind power adjustment coefficient obtained after fuzzy logic processing; ΔP wind_available Indicates the maximum power regulation that wind power can provide; ΔK′ storage is the energy storage system adjustment coefficient; ΔP storage_capacity is the currently available charge and discharge capacity of the energy storage system model.

[0040] In a second aspect, the present disclosure further provides a wind-storage power station, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the wind-storage power station frequency control methods provided in the first aspect are implemented.

[0041] Compared with the prior art, the technical solution provided by the present invention has the following advantages:

[0042] The control method of the wind power station frequency provided by the present disclosure includes: constructing a power system model, obtaining the working parameters of the power system model; the power system model includes a wind power generation system model and an energy storage system model; obtaining the actual input change of the composite controller based on the working parameters, and adjusting the key coefficient of the proportional integral differential controller according to the fuzzy rules of the fuzzy logic system; adjusting the control coefficient of the power system model output by the composite controller based on the key coefficient to realize the dynamic adjustment of the frequency of the power system model; wherein the composite controller includes a fuzzy logic system and a proportional integral differential controller. When the system is in different operating states, the power generation ratio of the wind power generation system model and the energy storage system model is different. When the frequency fluctuates to different degrees, the range of the control coefficient to be adjusted is also different. Therefore, the present disclosure determines the operating state of the current system according to the obtained system working parameters, and the key coefficient of the proportional integral differential controller can be dynamically adjusted according to the fuzzy rules in different states, and the system error is adjusted to achieve the purpose of adjusting the control coefficient of the power system model, thereby quickly and accurately controlling the system frequency. The present disclosure adjusts the frequency of wind power generation through the fuzzy logic system and the proportional integral differential controller, so as to quickly and accurately control the system frequency when the system frequency deviates, and enhance the system's ability to cope with rapid frequency disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 A flow chart of a method for controlling the frequency of a wind power station provided in an embodiment of the present disclosure;

[0046] Figure 2 A schematic diagram of the structure of a composite controller provided by an embodiment of the present disclosure;

[0047] Figure 3 A schematic structural diagram of a wind power station provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0048] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0050] The present disclosure provides a method for controlling the frequency of a wind power station. Figure 1 A flow chart of a method for controlling the frequency of a wind power station provided by an embodiment of the present disclosure, referring to Figure 1 , the control method of the wind power station frequency includes S110-S130:

[0051] S110, constructing a power system model and obtaining working parameters of the power system model; the power system model includes a wind power generation system model, an energy storage system model and a load.

[0052] First, a power system model including a wind power generation system model, an energy storage system model and a load is established. The wind power generation system model includes virtual inertia characteristics, and the energy storage system includes battery energy storage. The power system model can simulate the actual power system operation and obtain various operating parameters such as the operating status of the wind power generation system, the charge status of the energy storage system, and the speed regulation of the generator set.

[0053] The wind power generation system model and the energy storage system model are constructed in the disclosed embodiment in order to provide an accurate mathematical description and dynamic simulation platform for system control. These models contain key parameters such as the virtual inertia characteristics of the actual wind power generation system, the actual state of charge of the energy storage system, and the speed regulation of the actual generator set, so as to simulate the response of the system under different working conditions. Furthermore, through these models, the control strategy can be tested and optimized to ensure the reasonable allocation of wind power generation and energy storage system in the case of grid frequency fluctuations, thereby stabilizing the grid frequency. Once the allocation and control strategy are verified in the simulation, the strategy can be applied to the actual power system. In actual operation, the sensor will monitor the status of the power grid and the power generation system in real time, and the control strategy calculated by the model will continuously adjust the output of the wind power generation and energy storage system to stabilize the grid frequency. The control system uses the calculation results of these models to achieve accurate dynamic load response through wind power output adjustment and energy storage charging and discharging regulation.

[0054] For example, constructing a wind power generation system model requires considering the impact of wind speed changes on power generation, using the wind energy conversion coefficient and the cubic relationship of wind speed to ensure that the model can accurately reflect the instantaneous output characteristics of wind power generation and provide real-time data input for the control strategy.

[0055] The constructed wind power generation model is as follows:

[0056] P wind (t) = P wind,base +K wind ·(v(t)-v nom ) 3

[0057] Among them, P wind (t) represents wind power generation, v(t) represents real-time wind speed, v(nom) represents rated wind speed, P wind,base Indicates the rated power of the wind power generation system, K wind Represents the wind energy conversion coefficient.

[0058] To build an energy storage system model, it is necessary to consider the state of charge of the energy storage system, which is used to indicate the remaining power of the energy storage system. In addition, it is also necessary to consider the charging current, discharging current, charging and discharging efficiency, total battery capacity, and the sampling period (time interval) of the system.

[0059] The constructed energy storage system model is expressed as follows:

[0060]

[0061] Among them, △SOC represents the state change of the energy storage system; I ch (t) represents the charging current; I dis (t) represents the discharge current, η ch Indicates charging efficiency; η dis Indicates the discharge efficiency, C bat represents the total capacity of the battery, and △t represents the time interval.

[0062] A power system model is constructed, and the power system model can simulate the operation of the power system, so the working parameters of the power system model can be obtained therefrom.

[0063] It should be noted that the embodiments of the present disclosure do not limit the specific method of constructing the model, nor do they limit the method of obtaining the above parameters, which can be set according to actual needs.

[0064] S120, obtaining the actual input variation of the composite controller based on the working parameters, and adjusting the key coefficients of the proportional integral differential controller according to the fuzzy rules of the fuzzy logic system. The composite controller includes the fuzzy logic system and the proportional integral differential controller.

[0065] Exemplarily, the disclosed embodiment provides a composite controller, which includes a fuzzy logic system and a proportional integral differential controller. The working parameters of the power system model are the basic parameters required by the composite controller. Under different working conditions, the working parameters of the power system model are different, and the fluctuation amplitude of the system frequency is different. Therefore, the control strategy is also different. The composite controller can determine the operating state of the current system based on the obtained working parameters, and then make corresponding adjustments.

[0066] Among them, the actual input change of the composite controller can be obtained based on the working parameters, and the fuzzy logic system in the composite controller will adjust the key coefficients of the proportional-integral-differential controller according to the pre-set fuzzy rules and the actual input change, such as adjusting the proportional gain Kp, the integral gain Ki, and the differential gain Kd. Compared with the prior art, the key coefficients in the embodiment of the present disclosure are not fixed values, but can be adjusted according to the actual operating state of the power system model to match more appropriate key coefficients, thereby achieving the purpose of dynamic and precise regulation.

[0067] S130. Adjust the control coefficient of the power system model output by the composite controller based on the key coefficient to achieve dynamic regulation of the frequency of the power system model.

[0068] The control coefficient of the power system model is the target of the composite controller. When the key coefficient of the proportional integral differential controller changes dynamically, the output adjustment value of the proportional integral differential controller will also change accordingly, which can achieve the purpose of adjusting the control coefficient of the power system model. The control coefficient of the power system model directly affects the frequency change of the power system model, and affects the output of the wind power generation system model and the energy storage system model in the power system model, so as to realize the dynamic adjustment of the frequency of the power system model and stabilize the grid frequency.

[0069] Exemplarily, taking the working parameters of the power system model as wind speed, frequency, and state of charge of the energy storage system as an example, the composite controller obtains the actual input change of the composite controller and the operating state of the current system according to the above working parameters, and then inputs these working parameters into the composite controller. Based on the fuzzy rules of the preset fuzzy logic system, the corresponding control strategy can be matched in the operating state of the retaining wall system to stabilize the frequency of the power system model. Based on the corresponding control strategy, the key parameters of the proportional integral differential controller will change dynamically, and the output adjustment amount of the proportional integral differential controller is the control coefficient of the power system model, so the control coefficient will be adjusted accordingly according to the change of the key parameters to change the output of the wind power generation system model and the energy storage system model, so that the frequency of the power system model tends to be stable and the dynamic balance of the power grid is maintained.

[0070] In the embodiments of the present disclosure, when the system is in different operating states, the power generation ratios of the wind power generation system model and the energy storage system model are different. When the frequency fluctuates to different degrees, the range of the control coefficient that should be adjusted is also different. Therefore, the present disclosure determines the operating state of the current system based on the obtained system working parameters. Under different states, the key coefficients of the proportional integral differential controller can be dynamically adjusted according to fuzzy rules to adjust the system error, so as to achieve the purpose of adjusting the control coefficient of the power system model, and then quickly and accurately control the system frequency. The present disclosure jointly adjusts the frequency of wind power generation through a fuzzy logic system and a proportional integral differential controller, so as to quickly and accurately control the system frequency when the system frequency deviates, thereby enhancing the system's ability to cope with rapid frequency disturbances.

[0071] In some embodiments, before obtaining the actual input variation of the composite controller based on the working parameters and adjusting the key coefficients of the proportional integral derivative controller according to the fuzzy rules of the fuzzy logic system, the method further includes:

[0072] According to the power system model, a fuzzy rule of input variation is established through a fuzzy control algorithm; wherein the fuzzy rule is to use different control strategies to adjust the operating parameters of the power system model when the input variation is different.

[0073] For example, the composite controller is obtained by combining a fuzzy logic system with a proportional integral differential controller, so that the error and error variation of the working parameters of the power system model are analyzed based on the design and operation status of the power system model through the fuzzy logic system, and then the key coefficients of the proportional integral differential controller are dynamically adjusted, effectively improving the adaptability of the system, enabling it to stably respond to external disturbances and maintain the dynamic balance of the power grid. However, the use of a fuzzy logic system requires the establishment of fuzzy rules in advance, and then the key parameters are dynamically adjusted according to the fuzzy rules.

[0074] According to the power system model, the fuzzy rules of input variation are established through fuzzy control algorithm, where the input variation is the error and error variation of the working parameters of the power system model. In the fuzzy rules, different input variations correspond to different control strategies to achieve the purpose of dynamically adjusting the operating parameters of the power system model.

[0075] Optionally, the use of big data to analyze historical operation data can automatically optimize and update fuzzy rules, realize self-learning and evolution of fuzzy rules, and improve the adaptability and accuracy of control strategies.

[0076] Optionally, the obtained input variation needs to be segmented to improve the sophistication and adaptability of the control strategy. For example, the input variation includes the wind speed deviation △v. According to the working parameters of the system, the wind speed v(t) of the current system can be obtained, and the target wind speed is vref, so the wind speed deviation △v=v(t)-vref can be obtained. The wind speed deviation is divided into five levels, namely very low, low, medium, high, and very high, and different wind speed deviations correspond to different control strategies. Or the input variation includes the frequency deviation △f. According to the working parameters of the system, the frequency of the current system can be obtained as f(t), and the target frequency is fnom, so the frequency deviation △f=f(t)-fnom can be obtained. The frequency deviation is divided into five levels, namely very low, low, medium, high, and very high, and different frequency deviations correspond to different control strategies. Or the input variation also controls the state of charge of the energy storage system model, and the state of charge is divided into four levels, namely almost empty, low, medium, and full, and different states of charge also correspond to different control strategies. Therefore, after quantifying the input variation, the composite controller can make a more detailed response according to different levels of deviation or state of charge. For example, when the frequency deviation is "high" or "very high", the system may need to immediately adjust the output of wind power and energy storage to quickly respond to frequency fluctuations; when the deviation is "low" or "very low", only a small adjustment may be required.

[0077] Through this quantitative classification of input variables, the composite controller can more flexibly allocate wind power and energy storage output based on the specific status of wind speed, frequency and state of charge, meeting the needs of grid frequency stability and energy storage management.

[0078] In some embodiments, the actual input variation of the composite controller is obtained based on the working parameters, and the key coefficients of the proportional integral derivative controller are adjusted according to the fuzzy rules of the fuzzy logic system, including:

[0079] Based on the corresponding intervals of the actual input variation and the preset fuzzy sets of the input variation, determining the membership of the actual input variation in each fuzzy set;

[0080] Based on each membership degree, the corresponding control strategy is determined and the key coefficients of the proportional-integral-derivative controller are adjusted.

[0081] Exemplarily, after obtaining the working parameters of the power system model, it is necessary to obtain the actual input variation input to the composite controller according to the working parameters, for example, the input variation includes wind speed deviation, frequency deviation and charge state. It is necessary to determine the corresponding intervals of these actual input variations in the preset fuzzy sets of input variations (such as "low", "medium" and "high"), and then calculate the membership of the variable in each fuzzy set. This can be accomplished through a membership function, such as a triangular, trapezoidal or Gaussian distribution, etc., which is not limited in the embodiments of the present disclosure.

[0082] By calculating the membership of the input variation in the fuzzy set and matching its corresponding control strategy, reasonable control decisions are made based on the current operating status of the power system, and the key coefficients of the proportional-integral-differential controller are adaptively adjusted.

[0083] Optionally, the center of gravity method is also needed to convert the output instructions of the fuzzy logic system into specific control instructions to ensure a smooth transition from fuzzy decision-making to actual execution, that is, to perform a defuzzification operation. Among them, the center of gravity method converts the output of the fuzzy set into a specific value, which is usually obtained by calculating the "center of gravity" of the fuzzy output set. Assuming that the possible range of the fuzzy output adjustment (such as the output of the wind power generation system or the charging and discharging of the energy storage system) is [a, b], the center of gravity method can calculate the final output value u by the following formula:

[0084]

[0085] Among them, yi represents a possible value of the fuzzy output value; μ(yi) represents the membership degree of the output value. The u obtained by this formula is a specific value, which will be used to execute actual control instructions, such as adjusting the output of wind power generation or the charging and discharging of energy storage systems.

[0086] The specific control instruction conversion process is as follows:

[0087]

[0088] Among them, K′ wind Represents the wind power adjustment coefficient obtained after fuzzy logic processing; ωi Indicates the activation degree of the fuzzy rule, reflecting the intensity of the rule being followed in the current system state; K wind,i represents the wind contribution adjustment factor determined according to the fuzzy rule, reflecting how the wind power generation system should adjust the output power; Σωi·K wind,i It represents the weighted sum of wind contribution adjustment factors in all rules; ∑ωi represents the sum of total activation degrees of all activated rules.

[0089]

[0090] Among them, ΔK′ storage Represents the adjustment coefficient of the energy storage system obtained after fuzzy logic processing; ω i Indicates the activation degree of the fuzzy rule, reflecting the intensity of the rule being followed in the current system state; K storage,i represents the energy storage operation adjustment factor determined according to the fuzzy rule, reflecting how the energy storage system should perform charging and discharging operations; ∑ωi·K storage,i represents the weighted sum of the energy storage operation adjustment factors in all rules; ∑ωi represents the sum of the total activation degrees of all activated rules.

[0091] By calculating the outputs of all activated fuzzy rules, such as the wind contribution adjustment factor K wind,i , Energy storage operation adjustment factor K storage,i and its corresponding activation ω i The weighted average of the parameters can be used to obtain clear control instructions.

[0092] Through the above steps, the composite controller can dynamically adjust the control strategies of the wind power generation system and the energy storage system according to the real-time operating status of the current power system, respond to the changes in the grid frequency in the best way, and ensure the stable operation of the power system.

[0093] In some embodiments, the actual input variation of the composite controller is obtained based on the working parameters, and the key coefficients of the proportional integral derivative controller are adjusted according to the fuzzy rules of the fuzzy logic system, including:

[0094] The wind speed deviation, frequency deviation and charge state of the energy storage system model are obtained based on the working parameters, and the key coefficients of the proportional-integral-derivative controller are adjusted according to the fuzzy rules of the fuzzy logic system.

[0095] For example, in fuzzy logic control, the corresponding relationship of the control strategy is usually reflected in the fuzzy rules. The fuzzy rules define how the fuzzy set combination of input changes corresponds to a specific control output. For example, the input changes are wind speed deviation, frequency deviation and state of charge, and the output is the output adjustment of the wind power generation system, the charge and discharge adjustment of the energy storage system, etc.

[0096] Optionally, an example of the correspondence between fuzzy rules and control strategies is given:

[0097] The specific control strategy correspondence is as follows:

[0098] Wind speed deviation Frequency Deviation State of Charge Output adjustment Charge and discharge adjustment Low (L) Negative Low (L) Increase Discharge Low (L) Zero Low (L) Hold Discharge Medium (M) Negative Medium (M) Increase Discharge Medium (M) Zero Medium (M) Hold Hold High (H) Positive High (H) Decrease Charge High (H) Zero Medium (M) Decrease Hold Medium (M) Positive Low (L) Hold Charge Low (L) Positive High (H) Hold Charge

[0099] Taking the input variation of wind speed deviation, frequency deviation, and state of charge of the power storage system model as an example, the above three input variations are divided into several fuzzy sets. For example, the wind speed deviation is divided into three fuzzy sets, and the three fuzzy sets correspond to low (L), medium (M), and high (H); the frequency deviation is divided into three fuzzy sets, corresponding to negative (Neg), zero (Zero), and positive (Pos); the state of charge is divided into three fuzzy sets, corresponding to low (L), medium (M), and high (H). The output adjustment of the control strategy is set to the output adjustment of the wind power generation system and the charge and discharge adjustment of the energy storage system. Among them, the output adjustment of the wind power generation system includes increase (Increase), hold (Hold) and decrease (Decrease), and the charge and discharge adjustment of the energy storage system includes charge (Charge), hold (Hold) and discharge (Discharge).

[0100] For example, when the wind speed deviation is low, the frequency deviation is negative, and the state of charge is low, the output of the wind power system should be increased and the energy storage system should be discharged; due to the low wind speed, increasing the wind power output can better support the grid load demand. The state of charge is low, and power needs to be released from the energy storage system to maintain the grid frequency. When the wind speed deviation is medium, the frequency deviation is zero, and the state of charge is medium, the output of the wind power system should be maintained, and the energy storage system should remain unchanged in standby mode; in the absence of obvious frequency deviation, the wind power output remains unchanged, and due to the medium state of charge, the energy storage system is in standby mode, ready to charge and discharge at any time to respond to frequency changes. When the wind speed deviation is high, the frequency deviation is positive, and the state of charge is high, the output of the wind power system should be reduced and the energy storage system should be charged; due to the high wind speed and the grid frequency is already positive, reducing the wind power output at this time can avoid the frequency increase, and the state of charge is high, and the energy storage system can use the excess power for charging to prepare for future frequency fluctuations. When the wind speed deviation is moderate, the frequency deviation is positive and the state of charge is low, the output of the wind power generation system should be maintained and the energy storage system should be charged; in this case, the wind power output remains stable to avoid frequency fluctuations, while the state of charge is low and the energy storage system can be appropriately charged when the frequency is high to maximize the reserve power.

[0101] Through the above fuzzy rules, the composite controller can intelligently adjust the output of the wind power generation system and the energy storage system under various working conditions to ensure the stability of the grid frequency and optimize the use of energy storage. It should be noted that the corresponding relationship of this control strategy can be further adjusted according to specific system requirements to better adapt to actual working conditions. The above is only an optional embodiment.

[0102] In some embodiments, the wind speed deviation, frequency deviation and charge state of the energy storage system model are obtained based on the working parameters, and the key coefficients of the proportional integral differential controller are adjusted according to the fuzzy rules of the fuzzy logic system, including:

[0103] Based on the wind speed deviation, frequency deviation, state of charge and the corresponding intervals of the fuzzy sets of each preset input variation, the membership of the wind speed deviation, frequency deviation and state of charge in each fuzzy set is determined;

[0104] Based on each membership degree, the corresponding control strategy is determined and the key coefficients of the proportional-integral-derivative controller are adjusted.

[0105] In practical applications, the composite controller will calculate the membership of each input variation according to the current system operating parameters (such as actual wind speed deviation, frequency deviation, state of charge, etc.), match the combination of multiple fuzzy rules according to the membership, and then make decisions according to the control strategy in the rules, and finally output the appropriate control action.

[0106] In some embodiments, Figure 2 A schematic diagram of the structure of a composite controller provided by an embodiment of the present disclosure, referring to Figure 2 The composite controller includes a fuzzy logic system (Fuzzy) and a proportional integral differential controller (PID Controller). Among them, the ACE and △ACE of the input change are fuzzy quantities, and the fuzzy logic system adjusts the key coefficients of the proportional integral differential controller according to the input change, such as the proportional gain K p , integral gain K i and differential gain K d The adjusted proportional coefficient is used in the proportional-integral-differential controller to adjust the output adjustment amount of the composite controller according to the system error and the error change rate, thereby achieving the purpose of dynamic and precise regulation.

[0107] Wherein, adjusting the key coefficients of the proportional-integral-derivative controller includes: adjusting at least one of the proportional gain, the integral gain and the differential gain of the proportional-integral-derivative controller.

[0108] For example, if the proportional gain is related to the frequency deviation, then the formula for adjusting the proportional gain of the proportional-integral-derivative controller is as follows:

[0109]

[0110] Among them, K' p is the adjusted proportional gain; K p，max is the maximum proportional gain; K p，min is the minimum proportional gain; Δf represents the frequency deviation; Δf max is the preset maximum permissible frequency deviation; is the normalized frequency deviation, which is used to map to K p Adjustment range.

[0111] In addition, Δf represents the difference between the actual grid frequency and the target frequency, which can be obtained through real-time frequency measurement. The fuzzy logic system can dynamically calculate the value of the proportional gain according to the current frequency deviation, thereby achieving the purpose of adjusting the proportional gain of the proportional integral differential controller, making the proportional integral differential controller's response to the frequency deviation more adaptable to the current working conditions.

[0112] The maximum proportional gain and the minimum proportional gain are used to set the upper and lower limits of the proportional gain to ensure that the adjustment does not exceed the reasonable range. max When K' p Close to K p，max ; When the frequency deviation Δf is small, K' p Close to K p，min .

[0113] For example, let the integration time T i As a function of α: T i =α.

[0114] This equation introduces a factor, α, which directly determines the integral time constant. The time span of the integral action is proportional to this variable, providing a simple method for adjusting the integral effect based on a single variable. The factor α is used instead of a fixed T i , and can be adjusted according to system status or control requirements to improve the adaptability and response flexibility of the system.

[0115] Integral gain K i is defined as follows:

[0116]

[0117] Where, T represents the system time constant; T d represents the derivative time; K p Represents proportional gain; K d Expressed as differential gain.

[0118] This equation defines the integral gain K i How to normalize the gain K' p and K' d (Indirectly through Kp and K d ) and the system time constant. By introducing the derivative time T d , ensuring that K is adjusted taking into account the proportional and derivative effects and the system dynamics i .

[0119] The formula for adjusting the integral gain of the proportional-integral-derivative controller is as follows:

[0120]

[0121] Among them, K i is the adjusted integral gain; ΔP is the power deviation of the system; T p is the reference time period of the integral action; α is the integral time factor adjusted as the system state changes.

[0122] The power deviation ΔP is the difference between the output power of each generator set and the load demand power. The required power can be measured by a real-time power measurement device such as a power sensor or a power meter to calculate the power deviation ΔP.

[0123] For example, the formula for adjusting the derivative gain of a proportional-integral-derivative controller is as follows:

[0124]

[0125] Among them, K' d is the adjusted differential gain; K d，max is the maximum differential gain; K d，min is the minimum differential gain; Δf d,max / Δt ref is the maximum rate of change of frequency allowed under reference conditions; Δf d，max Indicates the maximum frequency deviation allowed under reference conditions; Δt ref Indicates the reference time period for the integral action.

[0126] It should be noted that the above is only a method for obtaining the proportional gain, integral gain and differential gain adjusted by the fuzzy logic system provided in the embodiment of the present disclosure, and the adjusted key parameters are applied to the control process of the proportional integral differential controller. In other embodiments, there are other calculation formulas and adjustment methods, and the embodiment of the present disclosure is only used as an example.

[0127] In some embodiments, it also includes: obtaining the actual input change of the composite controller based on the working parameters, and adjusting the wind turbine speed and pitch angle of the wind power generation system model according to the fuzzy rules of the fuzzy logic system.

[0128] Exemplarily, the composite controller can also determine the current system operating state according to the current system operating parameters (such as actual wind speed deviation, frequency deviation, state of charge, etc.). Then, combined with the operating state and the calculated actual input change, the speed and pitch angle of the wind turbines in the wind power generation system and the charging and discharging strategy of the energy storage system are adjusted in real time according to the fuzzy rules of the fuzzy logic system, thereby meeting the needs of grid frequency changes.

[0129] By adjusting the speed, the output power of the wind turbine can be controlled so that it can quickly respond to the changing needs of the grid frequency, so as to improve the inertial response capability of the wind power generation system, enable the wind turbine to provide support when the system frequency fluctuates, and stabilize the grid frequency. For example, in low wind speed conditions, the speed can be appropriately reduced to optimize the wind energy capture efficiency and avoid excessive power consumption of the wind turbine for speed maintenance. In this case, the output power of the wind turbine will be reduced. In high wind speed conditions, the power output can be increased by accelerating the wind turbine speed. If the rated power is reached, the speed can be reduced to limit the power to prevent exceeding the rated output. When the grid frequency drops, the grid needs to increase power support. At this time, by appropriately increasing the wind turbine speed, the output power can be quickly increased to cope with the power gap caused by frequency fluctuations. When the grid frequency rises, the grid has excess power, and the wind turbine speed can be appropriately reduced to reduce the output power and help restore the frequency balance of the grid.

[0130] In some other embodiments, the force of wind acting on the blades is changed by adjusting the pitch angle, thereby controlling the power output of the wind turbine so that it can generate electricity stably at different wind speeds. In the case of high wind speed, the pitch angle adjustment can prevent the wind turbine from exceeding the rated power, protect the wind turbine equipment, and avoid equipment loss caused by excessive wind speed. For example, at low wind speeds, the pitch angle is generally maintained at a smaller angle so that the blades can capture as much wind energy as possible, thereby increasing the output power. At medium wind speeds, the pitch angle is gradually adjusted to the optimal angle to keep the output power of the wind turbine stable at a higher level. At this time, the wind turbine can operate within the rated power range. At high wind speeds or sudden changes in wind speed, when the wind speed exceeds the rated range or has a sudden increase, the pitch angle is rapidly increased to reduce the force on the wind turbine, prevent the output power from exceeding the rated power, and protect the wind turbine blades and transmission system. When the grid frequency decreases, the pitch angle can be appropriately reduced to increase the force and power output of the wind turbine to cope with the demand for increased load. If the grid frequency is too high, the pitch angle can be increased to reduce the force of wind on the blades, lower the power output, and alleviate the problem of excess power caused by the increase in frequency.

[0131] In practical applications, the speed and pitch angle adjustments can be combined to work together. For example, when the grid frequency fluctuates greatly, the control system can first quickly adjust the pitch angle to achieve rapid power adjustment, while slowly adjusting the speed to achieve long-term stable output. This composite control method helps to achieve precise power control of the wind power system, ensuring that the system can operate efficiently and stably under different wind speeds and grid frequency fluctuations.

[0132] In some embodiments, adjusting the control coefficient of the power system model output by the composite controller based on the key coefficient to achieve dynamic regulation of the frequency of the power system model includes:

[0133] Based on the key coefficients, the inertia control coefficient of the wind power generation system model and the charge and discharge coefficient of the energy storage system model output by the composite controller are adjusted to achieve dynamic regulation of the frequency of the power system model.

[0134] The control parameters of the power system model include the inertia control coefficient of the wind power system model and the charge / discharge coefficient of the energy storage system model. Among them, the inertia control coefficient is used to adjust the response speed of the wind power system to frequency changes. By increasing the inertia control coefficient, the wind power system can provide greater instantaneous power support when the frequency changes to alleviate frequency fluctuations. The higher the inertia control coefficient, the stronger the response of the wind power system to frequency fluctuations, and it can increase or decrease the output in a short period of time, play a role in stabilizing the grid frequency and reducing the amplitude of frequency fluctuations. The adjustment of the inertia control coefficient will affect the instantaneous speed adjustment of the wind turbine, thereby affecting the instantaneous change of the generated power. The charge / discharge coefficient can determine the reaction speed and discharge / charge strength of the energy storage system model when the frequency fluctuates. For example, when the frequency is low, increasing the discharge coefficient can allow the energy storage system to quickly release more power to support frequency stability. The change in charge and discharge rate directly affects the speed of change of the state of charge. If the frequency fluctuates frequently or the amplitude is large, the state of charge of the energy storage system will change quickly, and it is necessary to reasonably control the charge and discharge coefficient to prevent the state of charge from being too low or too high. Therefore, by adjusting the charge and discharge coefficient of the energy storage system, the output power can be adjusted in time to cooperate with the wind power generation system to maintain the stability of the grid frequency. Especially when the wind power generation system is highly volatile, the energy storage system can alleviate the impact of the output fluctuation of the wind power generation system on the grid.

[0135] In some embodiments, it also includes: adjusting the power of the power system model according to the wind power generation adjustment coefficient, the maximum power adjustment amount of the wind power generation system model, the energy storage system adjustment coefficient and the available charge and discharge capacity of the energy storage system model.

[0136] According to the output of the composite controller, the proportional relationship between the output power of the wind power generation system and the output power of the energy storage system can also be optimized in real time. Considering that in the actual operation process, the power generation of the wind power generation system is constrained by the wind speed, and the power generation of the energy storage system is limited by the state of charge. Therefore, the proportional relationship of the output power can be adjusted according to the actual operating status, and the advantages of the two power generation methods can be fully utilized to complement each other to achieve efficient regulation of the grid frequency.

[0137] For example, the power allocation formula of the wind power generation system model and the energy storage system model in the power system model is as follows:

[0138] ΔP total =K' wind ΔP wind_available +K′ storage ΔP storage_capacity

[0139] Among them, ΔP total Indicates the total power regulation; ΔK' wind is the wind power adjustment coefficient obtained after fuzzy logic processing; ΔP wind_available Indicates the maximum power regulation that wind power can provide; ΔK′ storage is the energy storage system adjustment coefficient; ΔP storage_capacity is the currently available charge and discharge capacity of the energy storage system model.

[0140] Through this dynamic adjustment process, the wind power generation system can allocate regulation tasks between wind power generation and energy storage in real time according to changes in wind speed, charge state and actual needs of grid frequency, ensuring the efficiency and accuracy of frequency control.

[0141] The embodiment of the present disclosure also provides a wind power station. Figure 3 A schematic diagram of a wind power plant structure provided by the present disclosure, referring to Figure 3 , including a memory 31 and a processor 32, wherein the memory 31 stores a computer program, and when the processor 32 executes the computer program, the steps of the method for controlling the frequency of the wind power station described in any of the above embodiments are implemented.

[0142] It can be understood that the memory in this embodiment can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memory. In some embodiments, the memory stores the following elements: executable units or data structures, or their subsets, or their extended sets of operating systems and applications. In the embodiment of the present disclosure, the processor executes the steps of each embodiment of the control method of the heater provided in the embodiment of the present disclosure by calling the program or instruction stored in the memory.

[0143] After the control strategy is obtained according to the above-mentioned wind power station frequency control method, it can be applied to all power networks integrating wind power generation and energy storage facilities, especially in areas with abundant wind resources and strict requirements on grid stability.

[0144] The composite controller combines the fuzzy logic system with the proportional-integral-differential controller, further combining the advantages of the traditional proportional-integral-differential controller with the adaptive and nonlinear processing capabilities of the fuzzy logic system. The performance of the composite controller is enhanced, especially when the system process is dynamically uncertain or nonlinear. The design of the composite controller, especially the fuzzy rules combined with the self-learning function, enables the system to intelligently adjust the control strategy according to the real-time operating conditions, which not only improves the control accuracy, but also increases the flexibility and intelligence level of the control strategy. Through the highly integrated composite fuzzy logic control strategy, the wind storage system's ability to adjust the grid frequency and response speed are significantly improved, and the comprehensive utilization efficiency of renewable energy is optimized.

[0145] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0146] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the frequency of a wind power station, characterized in that: include: Constructing a power system model and obtaining working parameters of the power system model; the power system model includes a wind power generation system model, an energy storage system model and a load; Based on the working parameters, the actual input variation of the composite controller is obtained, and according to the fuzzy rules of the fuzzy logic system, the key coefficients of the proportional-integral-derivative controller are adjusted; Adjusting the control coefficient of the power system model output by the composite controller based on the key coefficient to achieve dynamic regulation of the frequency of the power system model; Wherein, the composite controller includes a fuzzy logic system and a proportional-integral-derivative controller.

2. The control method according to claim 1, characterized in that: Before obtaining the actual input variation of the composite controller based on the working parameters and adjusting the key coefficients of the proportional integral differential controller according to the fuzzy rules of the fuzzy logic system, the method further includes: According to the power system model, a fuzzy rule for input variation is established through a fuzzy control algorithm; The fuzzy rule is to use different control strategies to adjust the operating parameters of the power system model when the input variation is different.

3. The control method according to claim 2, characterized in that: The actual input variation of the composite controller is obtained based on the working parameters, and the key coefficients of the proportional integral differential controller are adjusted according to the fuzzy rules of the fuzzy logic system, including: Determining the membership of the actual input variation in each fuzzy set based on the corresponding intervals of the actual input variation and the preset fuzzy sets of the input variation; Based on each of the membership degrees, a corresponding control strategy is determined, and key coefficients of a proportional-integral-derivative controller are adjusted.

4. The control method according to claim 3, characterized in that: The actual input variation of the composite controller is obtained based on the working parameters, and the key coefficients of the proportional integral differential controller are adjusted according to the fuzzy rules of the fuzzy logic system, including: The wind speed deviation, the frequency deviation and the state of charge of the energy storage system model are obtained based on the working parameters, and the key coefficients of the proportional integral differential controller are adjusted according to the fuzzy rules of the fuzzy logic system.

5. The control method according to claim 4, characterized in that: The wind speed deviation, frequency deviation and the state of charge of the energy storage system model are obtained based on the working parameters, and the key coefficients of the proportional integral differential controller are adjusted according to the fuzzy rules of the fuzzy logic system, including: Based on the wind speed deviation, the frequency deviation, the state of charge and the preset fuzzy set corresponding intervals of each input variation, determining the membership of the wind speed deviation, the frequency deviation and the state of charge in each fuzzy set; Based on each of the membership degrees, a corresponding control strategy is determined, and key coefficients of a proportional-integral-derivative controller are adjusted.

6. The control method according to claim 5, characterized in that: The key coefficients for adjusting the proportional-integral-derivative controller include: Adjusting at least one of a proportional gain, an integral gain and a differential gain of the PID controller; The formula for adjusting the proportional gain of the PID controller is as follows: Among them, K' p is the adjusted proportional gain; K p，max is the maximum proportional gain; K p，min is the minimum proportional gain; Δf max is the preset maximum permissible frequency deviation; is the normalized frequency deviation, which is used to map to K p Scope of adjustment; The formula for adjusting the integral gain of the proportional integral derivative controller is as follows: Among them, K i is the adjusted integral gain; ΔP is the power deviation of the system; T p is the reference time period of the integral action; α is the integral time factor adjusted as the system state changes; The formula for adjusting the differential gain of the proportional integral derivative controller is as follows: Among them, K' d is the adjusted differential gain; K d，max is the maximum differential gain; K d，min is the minimum differential gain; Δf d,max / Δt ref is the maximum rate of change of frequency allowed under reference conditions.

7. The control method according to claim 1, characterized in that: Also includes: The actual input variation of the composite controller is obtained based on the working parameters, and the wind turbine speed and the pitch angle of the wind power generation system model are adjusted according to the fuzzy rules of the fuzzy logic system.

8. The control method according to claim 1, characterized in that: Adjusting the control coefficient of the power system model output by the composite controller based on the key coefficient to achieve dynamic regulation of the frequency of the power system model includes: The inertia control coefficient of the wind power generation system model and the charge and discharge coefficient of the energy storage system model output by the composite controller are adjusted based on the key coefficients to achieve dynamic regulation of the frequency of the power system model.

9. The control method according to claim 8, characterized in that: Also includes: The power of the power system model is adjusted according to the wind power generation adjustment coefficient, the maximum power adjustment amount of the wind power generation system model, the energy storage system adjustment coefficient and the available charge and discharge capacity of the energy storage system model; ΔP total =K' wind ·ΔP wind_available +K s ' torage ·ΔP storage_capacity Among them, ΔP total Indicates the total power regulation; ΔK' wind is the wind power adjustment coefficient obtained after fuzzy logic processing; ΔP wind_available Indicates the maximum power regulation that wind power generation can provide; ΔK s ' torage is the energy storage system adjustment coefficient; ΔP storage_capacity is the currently available charge and discharge capacity of the energy storage system model.

10. A wind power station, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for controlling the frequency of a wind power station as claimed in any one of claims 1 to 9 when executing the computer program.

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