Method, system, device and storage medium for controlling active power of wind farm

Abstract

The present disclosure provides a wind farm active power control method, system, device and storage medium, the method comprising: performing first limiting processing on the obtained initial active target value to obtain an active reference value; obtaining proportional control parameters and integral control parameters of PI control according to the active reference value and actual active feedback value; performing fuzzy adjustment on the anti-saturation threshold parameter based on the response time and the response time change amount between the current execution period and the last execution period to obtain the anti-saturation threshold parameter; and performing PI control on the active power based on the proportional control parameters, the integral control parameters and the current integral value to obtain an active given value. The present disclosure increases the anti-saturation threshold parameter fuzzy adjuster and the control mode with integral saturation limit adaptive switching as the core on the basis of the PI controller to realize the anti-saturation control of the wind farm active power, reduces the occurrence frequency of the integral saturation of the PI controller through the limiting processing, and optimizes the adaptive PI controller.

Description

Technical Field

[0001] This disclosure relates to the field of wind farm technology, and in particular to a method, system, device and storage medium for controlling the active power of a wind farm. Background Technology

[0002] Active power control in wind farms typically involves applications such as automatic generation control, primary frequency regulation, and inertial response. While the calculation methods for the overall active power target value differ across scenarios, the overall control logic remains largely the same. This includes steps such as calculating the overall active power target value or receiving commands, receiving and summing feedback on the actual power generated by wind turbines, calculating the deviation between the target and measured values, PI control, power command limiting, and optimized power allocation for wind turbines. Among these, the PI controller, as a crucial component of the entire control process, directly impacts the control effectiveness of the wind farm's active power. Traditional PI control algorithms use fixed proportional and integral control parameters, which can easily lead to unstable dynamic and static performance of the control system and an inability to adapt to complex operating scenarios such as large power deviation variations and changes in turbine response characteristics caused by different wind conditions. To address these challenges, industry scholars and experts have applied advanced algorithms such as fuzzy control and neural networks to PI control parameter tuning, enabling online real-time modification of PI parameters and improving the adaptability of wind farm active power. However, current adaptive tuning of PI control parameters is limited to proportional and integral control parameters and does not consider adaptive adjustments under abnormal conditions such as integral saturation.

[0003] Furthermore, existing wind farm active power control mostly employs traditional PI control methods with fixed set parameters. This requires technicians to determine the PI control parameters through on-site testing, and adjustments are necessary for different wind farms. Moreover, a fixed set of PI control parameters is insufficient to guarantee stable and effective control for varying target command amplitudes.

[0004] Existing publicly available adaptive active power control for wind farms uses fuzzy control rules to tune the proportional and integral parameters of PI control online, neglecting the handling of abnormal situations such as integral saturation. This may result in the overall active power response time and adjustment time of the wind farm failing to meet the grid-connected power support requirements of the local power grid in operating scenarios with high dynamic rate requirements, such as primary frequency regulation and inertia response.

[0005] The existing publicly available technology does not perform input limiting processing on the active power target value entering the PI control loop. If the target value is higher than the actual power generation of all controllable units in the wind farm, it is easy to cause integral saturation of the PI controller, which in turn leads to a deterioration in the dynamic performance of the active power control of the entire farm. Summary of the Invention

[0006] The technical problem to be solved by this disclosure is to overcome the defects of existing active power control methods that are prone to abnormal situations such as integral saturation, and to provide a control method, system, equipment and storage medium for active power in wind farms.

[0007] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0008] The first aspect of this disclosure provides a method for controlling the active power of a wind farm, the method comprising:

[0009] Obtain the initial active power target value of the wind farm;

[0010] The initial active power target value is subjected to a first limiting process to obtain an active power reference value;

[0011] Based on the active power reference value and the actual active power feedback value, obtain the proportional control parameters and integral control parameters of the PI control;

[0012] Based on the response time of the execution cycle of active power control in wind farms, and the change in response time between the current execution cycle and the previous execution cycle, fuzzy adjustment of the anti-saturation threshold parameter is performed to obtain the anti-saturation threshold parameter.

[0013] If the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes, then the anti-saturation threshold parameter is switched to the steady-state integral value of the previous execution cycle.

[0014] Based on the proportional control parameters, the integral control parameters, and the current integral value, PI control is performed on the active power to obtain the active power setpoint.

[0015] Preferably, the step of performing PI control on the active power based on the proportional control parameters, the integral control parameters, and the current integral value to obtain the active power setpoint includes:

[0016] Based on the proportional control parameters, the integral control parameters, and the current integral value, PI control is performed to convert the active power reference value into a command value.

[0017] The active power command value output by the PI control is subjected to a second limiting process to obtain the active power setpoint.

[0018] Preferably, after performing a second limiting process on the active power command value output by the PI control to obtain the active power setpoint, the control method further includes:

[0019] The active power of the wind farm is optimized and allocated based on the given active power value.

[0020] Preferably, the first limiting process includes:

[0021] Obtain the upper and lower limits of the theoretical power generation capacity of the entire wind farm, and perform amplitude limiting processing on the initial active power target value;

[0022] If the initial active power target value is greater than the upper limit of the theoretical power generation capacity of the entire field, then the upper limit of the theoretical power generation capacity of the entire field shall be used as the initial active power target value.

[0023] If the initial active power target value is less than the lower limit of the theoretical power generation capacity of the entire field, then the lower limit of the theoretical power generation capacity of the entire field will be used as the initial active power target value.

[0024] Preferably, the step of performing PI control on the active power based on the proportional control parameters, the integral control parameters, and the current integral value to obtain the active power setpoint includes:

[0025] The difference between the active power reference value and the actual active power feedback value, as well as the change in the difference between two adjacent execution cycles, are obtained. The execution cycle is synchronized with the active power control module.

[0026] Based on the difference and the change in the difference, PI parameter adaptive control is performed to obtain proportional control parameters and integral control parameters.

[0027] Preferably, the control method further includes:

[0028] The proportional control parameters and the integral control parameters are obtained based on linear piecewise functions and / or fuzzy control algorithms and / or neural network algorithms.

[0029] Preferably, the control method further includes:

[0030] After the execution cycle enters a steady state, the anti-saturation threshold parameter is fuzzy adjusted.

[0031] Preferably, the step of switching the anti-saturation threshold parameter to the steady-state integral value of the previous execution cycle if the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes includes:

[0032] At the start of the same active power control execution cycle, if the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes, the anti-saturation threshold parameter is switched to the steady-state integral value of the previous execution cycle.

[0033] Preferably, the fuzzy adjustment of the anti-saturation threshold parameter based on the response time of the execution cycle of the active power control of the wind farm and the change in response time between the current execution cycle and the previous execution cycle, to obtain the anti-saturation threshold parameter, includes:

[0034] Obtain the adjustment amount of the anti-saturation threshold parameter and the current anti-saturation threshold parameter for the current execution cycle;

[0035] The adjusted anti-saturation threshold parameter is superimposed with the current anti-saturation threshold parameter of the current execution cycle to obtain the updated anti-saturation threshold parameter.

[0036] Preferably, before obtaining the adjustment amount of the anti-saturation threshold parameter, the following steps are included:

[0037] Construct a rule corresponding to the response time, the change in response time, and the adjustment amount of the anti-saturation threshold parameter. This rule includes at least the membership degree and membership value of the adjustment amount of the anti-saturation threshold parameter to the response time and the change in response time.

[0038] Based on the current response time, the change in response time compared to the previous execution cycle, and the rule, the adjustment amount of the anti-saturation threshold parameter is determined.

[0039] The fuzzy adjustment of the anti-saturation threshold parameter includes:

[0040] Obtain the rules;

[0041] The adjustment amount of the anti-saturation threshold parameter is obtained by using a fuzzy algorithm based on the membership degree and the membership value.

[0042] Preferably, the rules for constructing the corresponding response time, the change in response time, and the adjustment amount of the anti-saturation threshold parameter include:

[0043] Obtain the first linguistic variable of response time and its corresponding first fuzzy subset, and the second linguistic variable of response time change and its corresponding second fuzzy subset;

[0044] Construct a third linguistic variable for the adjustment of the anti-saturation threshold parameter and the corresponding trifuzzy subset;

[0045] Establish a fuzzy rule table based on a first fuzzy subset element, a second fuzzy subset element, and a third fuzzy subset element. Each third fuzzy subset element corresponds to a specific first fuzzy subset element and a specific second fuzzy subset element. The third fuzzy subset corresponds to the language variable, and the third language variable corresponds to the anti-saturation threshold parameter adjustment amount.

[0046] Construct a correspondence rule between the third fuzzy subset and the first fuzzy subset and the second fuzzy subset, wherein the rule corresponds at least to the membership degree and membership value of the third fuzzy subset to the first fuzzy subset and the second fuzzy subset.

[0047] A second aspect of this disclosure provides a control system for the active power of a wind farm, the control system comprising:

[0048] The first acquisition module is used to acquire the initial active power target value of the wind farm;

[0049] A wind farm-level controller is used to perform a first limiting process on the initial active power target value to obtain an active power reference value;

[0050] The second acquisition module is used to acquire the proportional control parameters and integral control parameters of the PI control based on the active power reference value and the actual active power feedback value.

[0051] The third acquisition module is used to perform fuzzy adjustment of the anti-saturation threshold parameter based on the response time of the execution cycle of the active power control of the wind farm and the change in response time between the current execution cycle and the previous execution cycle, so as to obtain the anti-saturation threshold parameter.

[0052] The switching module is used to switch the anti-saturation threshold parameter to the steady-state integral value of the previous execution cycle if the current integral value is higher than the anti-saturation threshold parameter and the initial active power target value changes.

[0053] A PI controller is used to perform PI control on active power to obtain an active power setpoint based on the proportional control parameters, the integral control parameters, and the current integral limit.

[0054] Preferably, the PI controller is specifically used to perform PI control based on the proportional control parameters, the integral control parameters, and the current integral value, so as to convert the active power reference value into a command value;

[0055] The active power command value output by the PI control is subjected to a second limiting process to obtain the active power setpoint.

[0056] Preferably, the control system further includes:

[0057] The optimization module is used to optimize the allocation of active power in the wind farm based on the given active power value.

[0058] Preferably, the wind farm-level controller is specifically used to obtain the upper and lower limits of the theoretical power generation capacity of the entire wind farm and to limit the initial active power target value.

[0059] If the initial active power target value is greater than the upper limit of the theoretical power generation capacity of the entire field, then the upper limit of the theoretical power generation capacity of the entire field shall be used as the initial active power target value.

[0060] If the initial active power target value is less than the lower limit of the theoretical power generation capacity of the entire field, then the lower limit of the theoretical power generation capacity of the entire field will be used as the initial active power target value.

[0061] Preferably, the PI controller is specifically used to obtain the difference between the active power reference value and the actual active power feedback value, as well as the change in the difference between two adjacent execution cycles, wherein the execution cycle is synchronized with the active power control module;

[0062] Based on the difference and the change in the difference, PI parameter adaptive control is performed to obtain proportional control parameters and integral control parameters.

[0063] Preferably, the control system further includes:

[0064] The fourth acquisition module is used to acquire the proportional control parameters and the integral control parameters based on a linear piecewise function and / or a fuzzy control algorithm and / or a neural network algorithm.

[0065] Preferably, the control system further includes:

[0066] The adjustment module is used to perform fuzzy adjustment of the anti-saturation threshold parameter after the execution cycle enters a steady state.

[0067] Preferably, the switching module is specifically used to switch the anti-saturation threshold parameter to the steady-state integral value of the previous execution cycle at the beginning of the same active power control execution cycle if the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes.

[0068] Preferably, the third acquisition module is specifically used to acquire the anti-saturation threshold parameter adjustment amount and the current anti-saturation threshold parameter of the current execution cycle;

[0069] The adjusted anti-saturation threshold parameter is superimposed with the current anti-saturation threshold parameter of the current execution cycle to obtain the updated anti-saturation threshold parameter.

[0070] Preferably, the third acquisition module is further configured to construct a corresponding rule for the response time, the change in response time, and the adjustment amount of the anti-saturation threshold parameter. The rule includes at least the membership degree and membership value of the adjustment amount of the anti-saturation threshold parameter to the response time and the change in response time.

[0071] Based on the current response time, the change in response time compared to the previous execution cycle, and the rule, the adjustment amount of the anti-saturation threshold parameter is determined.

[0072] Obtain the rules;

[0073] The adjustment amount of the anti-saturation threshold parameter is obtained by using a fuzzy algorithm based on the membership degree and the membership value.

[0074] Preferably, the third acquisition module is further configured to acquire the first linguistic variable of response time and the corresponding first fuzzy subset, and the second linguistic variable of response time change and the corresponding second fuzzy subset;

[0075] Construct a third linguistic variable for the adjustment of the anti-saturation threshold parameter and the corresponding trifuzzy subset;

[0076] Establish a fuzzy rule table based on a first fuzzy subset element, a second fuzzy subset element, and a third fuzzy subset element. Each third fuzzy subset element corresponds to a specific first fuzzy subset element and a specific second fuzzy subset element. The third fuzzy subset corresponds to the language variable, and the third language variable corresponds to the anti-saturation threshold parameter adjustment amount.

[0077] Construct a correspondence rule between the third fuzzy subset and the first fuzzy subset and the second fuzzy subset, wherein the rule corresponds at least to the membership degree and membership value of the third fuzzy subset to the first fuzzy subset and the second fuzzy subset.

[0078] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the wind farm active power control method described in the first aspect.

[0079] The fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the wind farm active power control method described in the first aspect.

[0080] The fifth aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the wind farm active power control method as described in the first aspect.

[0081] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.

[0082] The positive and progressive effects of this disclosure are as follows:

[0083] This disclosure adds a control method based on an anti-saturation threshold parameter fuzzy regulator and adaptive switching of integral saturation limit to the conventional adaptive PI controller to achieve anti-saturation control of active power in wind farms. By limiting the amplitude, the frequency of integral saturation of the PI controller is reduced, thus optimizing the adaptive PI controller. Attached Figure Description

[0084] Figure 1 A flowchart of the active power control method for a wind farm provided in Embodiment 1 of this disclosure;

[0085] Figure 2 This is a schematic diagram of the whole-field primary frequency modulation response curve under the condition of integral saturation before application, provided in Embodiments 1 and 2 of this disclosure.

[0086] Figure 3 This is a schematic diagram of the whole-field primary frequency modulation response curve under the condition of integral saturation after application, as provided in Embodiments 1 and 2 of this disclosure.

[0087] Figure 4 This is a schematic diagram of the control system for the active power of a wind farm provided in Embodiment 2 of this disclosure.

[0088] Figure 5 This is a schematic diagram of the electronic device used in the method for controlling the active power of a wind farm, as provided in Embodiment 3 of this disclosure. Detailed Implementation

[0089] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0090] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not be construed as an unnecessary limitation. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.

[0091] In this embodiment of the disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good morals.

[0092] Example 1

[0093] Figure 1 A flowchart of a wind farm active power control method provided in Embodiment 1 of this disclosure is shown below. Figure 1 As shown, the control methods include:

[0094] S1. Obtain the initial active power target value of the wind farm;

[0095] In this embodiment, primary frequency modulation is enabled, and the initial active power target value is calculated based on the pre-set primary frequency modulation curve according to the frequency signal received in real time.

[0096] S2. Perform the first limiting process on the initial active power target value to obtain the active power reference value;

[0097] In this embodiment, the initial active power target value is input into the wind farm-level controller for the first limiting process (e.g., input limiting process) to obtain the active power reference value.

[0098] S3. Based on the active power reference value and the actual active power feedback value, obtain the proportional control parameters and integral control parameters of the PI control;

[0099] S4. Based on the response time of the execution cycle of the active power control of the wind farm, and the change in response time between the current execution cycle and the previous execution cycle, perform fuzzy adjustment of the anti-saturation threshold parameter to obtain the anti-saturation threshold parameter.

[0100] In this embodiment, fuzzy adjustment of the anti-saturation threshold parameter, adaptive switching of the integral saturation limit, and PI control are the core components of this invention for achieving adaptive and rapid anti-saturation of active power. After each execution cycle, the response time T of that execution cycle is calculated. up (k) is passed to the PI controller, which determines the response time T of the previous execution cycle of the same pattern in the historical database based on the control mode. up (k-1), and thus the change in response time, i.e., ΔT up (k)=T up (k)-T up (k-1). The response time and the change in response time are input together into the fuzzy regulator of the anti-saturation threshold parameter, and the adjusted anti-saturation threshold parameter I is output through the fuzzy control algorithm. th (k+1) serves as one of the triggering conditions for adaptive switching of the integral saturation limit. During the fuzzification process of the input value, the fuzzy subset of the response time can be preferably selected from 3-5 linguistic variables greater than or equal to 0, and the fuzzy subset of the response time change can be preferably selected from 5-9 linguistic variables with positive and negative symmetry. The output of the fuzzy control algorithm is the adjustment amount ΔI of the anti-saturation threshold parameter. th Its fuzzy subset can be optimized from 3-7 positive and negative symmetric linguistic variables. The updated threshold parameter is obtained by superimposing the adjustment amount of the anti-saturation threshold parameter with the original value, I. th (k+1)=I th (k)+ΔI th This threshold parameter also needs to be constrained within the optimization range of the anti-saturation threshold parameter.

[0101] S5. If the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes, then switch the anti-saturation threshold parameter to the steady-state integral value of the previous execution cycle.

[0102] S6. Based on the proportional control parameters, integral control parameters, and the current integral value, perform PI control on the active power to obtain the active power setpoint.

[0103] In this embodiment, during the adaptive switching of the integral saturation limit, the default integral saturation limit is the maximum steady-state response error of the total active power, taking into account the wind turbine design requirements and extreme operating conditions. If a change in the initial active power target value is detected (i.e., entering the next execution cycle), and the current integral value of the PI controller is higher than the current anti-saturation threshold parameter, the integral saturation limit is temporarily switched from the default value to a specific value. This specific value is selected from the steady-state integral value of the previous execution cycle in the same mode.

[0104] It is worth noting that the invocation frequency of the anti-saturation threshold parameter fuzzy regulator and the PI parameter adaptive regulator differs. The PI parameter adaptive regulator is invoked synchronously with active power control, adjusting the proportional and integral control parameters multiple times within the execution cycle of the same initial active power target value. In contrast, the anti-saturation threshold parameter fuzzy regulator is specifically designed for specific control modes where PI integral desaturation may be too slow, such as the first participation in frequency regulation after a long period of free full-power operation. The invocation of this anti-saturation threshold parameter fuzzy regulator requires consideration of system control performance calculations and may only be invoked after the system has entered a steady state within one execution cycle. Similarly, the integral saturation limit adaptive switching is triggered at most once at the start of the execution cycle of the same initial active power target value.

[0105] This embodiment adds a control method based on an anti-saturation threshold parameter fuzzy regulator and adaptive switching of integral saturation limit to the conventional adaptive PI controller to achieve anti-saturation control of active power in wind farms. By limiting the amplitude, the frequency of integral saturation of the PI controller is reduced, thus optimizing the adaptive PI controller.

[0106] In an optional embodiment, S6 includes:

[0107] S6-11. Based on the proportional control parameters, integral control parameters, and the current integral value, perform PI control to convert the active power reference value into a command value.

[0108] S6-12. Perform a second limiting process on the active power command value output by the PI control to obtain the active power setpoint.

[0109] In an optional embodiment, after S62, the control method further includes:

[0110] The active power of the wind farm is optimized and allocated based on the active power setpoint.

[0111] In the specific implementation process, the PI parameter adaptive regulator and the integral saturation limit adaptive switching stage will respectively adaptively adjust the proportional control coefficient K. p Integral control coefficient K i and the current integration limit I lmtThe active power reference value is passed to the PI controller for PI control, which converts the active power reference value into a command value. The active power command value output by the PI controller is then subjected to a second limiting process (e.g., output limiting process) to obtain the final active power setpoint, which is used for the optimized allocation of unit power.

[0112] In an optional embodiment, S2 includes:

[0113] Obtain the upper and lower limits of the theoretical power generation capacity of the entire wind farm, and perform amplitude limiting processing on the initial active power target value;

[0114] If the initial active power target value is greater than the upper limit of the theoretical power generation capacity of the entire field, then the upper limit of the theoretical power generation capacity of the entire field shall be used as the initial active power target value.

[0115] If the initial active power target value is less than the lower limit of the theoretical power generation capacity of the entire field, then the lower limit of the theoretical power generation capacity of the entire field will be used as the initial active power target value.

[0116] In this embodiment, the upper limit of the PI controller input limiting stage is set to the theoretical power generation capacity of the entire wind farm, while the lower limit is consistent with the lower limit of the active power control capacity of the entire wind farm. The initial active power target value output from the active power control mode selection and target value calculation stage, after input limiting, will not exceed the theoretical power generation capacity of the entire wind farm. This avoids most cases of PI controller integral saturation caused by the initial active power target value being higher than the actual power generation capacity of the wind farm. Specifically, if the initial active power target value is greater than the upper limit of the theoretical power generation capacity of the entire wind farm, the initial active power target value will be forcibly limited to the upper limit of the theoretical power generation capacity of the entire wind farm; similarly, if the initial active power target value is less than the lower limit of the theoretical power generation capacity of the entire wind farm, the initial active power target value will also be forcibly limited to the lower limit of the theoretical power generation capacity of the entire wind farm, ensuring that the output active power reference value is within the theoretically controllable range of the system.

[0117] Furthermore, considering that the estimated theoretical power generation of the entire wind farm may deviate due to complex wind conditions, and that an estimated theoretical power generation value may be higher than the actual power generation value, integral saturation may also occur. Therefore, this invention further optimizes the existing adaptive PI control logic. This control logic mainly consists of four parts: an adaptive PI parameter regulator, an anti-saturation threshold parameter fuzzy regulator, an adaptive switching mechanism for integral saturation limits, and a PI controller. The adaptive PI parameter regulator adopts the same technical approach as existing wind farm adaptive PI control, and can adaptively modify the proportional control parameters and integral control parameters based on input data such as active power reference values ​​and actual active power feedback values, selecting algorithms such as conventional variable integral functions, fuzzy control, and neural networks.

[0118] In an optional embodiment, S6 includes:

[0119] S6-21. Obtain the difference between the active power reference value and the actual active power feedback value, as well as the change in the difference between two adjacent execution cycles. The execution cycle is synchronized with the active power control module.

[0120] S6-22. Based on the difference and the change in the difference, perform PI parameter adaptation to obtain proportional control parameters and integral control parameters.

[0121] In an optional embodiment, the control method further includes:

[0122] Proportional control parameters and integral control parameters are obtained based on linear piecewise functions and / or fuzzy control algorithms and / or neural network algorithms.

[0123] In an optional embodiment, the control method further includes:

[0124] After the execution cycle reaches a steady state, the anti-saturation threshold parameter is fuzzy adjusted.

[0125] In an optional embodiment, S5 includes:

[0126] At the start of the same active power control execution cycle, if the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes, the anti-saturation threshold parameter is switched to the steady-state integral value of the previous execution cycle.

[0127] In an optional embodiment, S4 includes:

[0128] S4-1. Obtain the anti-saturation threshold parameter adjustment amount and the current anti-saturation threshold parameter for the current execution cycle;

[0129] S4-2. The anti-saturation threshold parameter adjustment amount is superimposed with the current anti-saturation threshold parameter of the current execution cycle to obtain the updated anti-saturation threshold parameter.

[0130] In an optional embodiment, before S4-1, S4 further includes:

[0131] S4-0, Construct the corresponding rules for response time, response time change, and anti-saturation threshold parameter adjustment. The rules shall at least include the membership degree and membership value of the anti-saturation threshold parameter adjustment to the response time and response time change.

[0132] S4-01. Based on the current response time, the change in response time compared to the previous execution cycle, and the rules, determine the adjustment amount of the anti-saturation threshold parameter.

[0133] Fuzzy adjustment of the anti-saturation threshold parameter in S4 includes:

[0134] Acquisition rules;

[0135] The adjustment amount of the anti-saturation threshold parameter is obtained by using a fuzzy algorithm based on the membership degree and the membership value.

[0136] In an optional embodiment, S4-0 includes:

[0137] Obtain the first linguistic variable of response time and its corresponding first fuzzy subset, and the second linguistic variable of response time change and its corresponding second fuzzy subset;

[0138] Construct a third linguistic variable for the adjustment of the anti-saturation threshold parameter and the corresponding trifuzzy subset;

[0139] Establish a fuzzy rule table with first fuzzy subset elements, second fuzzy subset elements, and third fuzzy subset elements. Each third fuzzy subset element corresponds to a specific first fuzzy subset element and a specific second fuzzy subset element. The third fuzzy subset corresponds to the linguistic variable, and the third linguistic variable corresponds to the anti-saturation threshold parameter adjustment amount.

[0140] Construct correspondence rules between the third fuzzy subset and the first and second fuzzy subsets. The rules should at least correspond to the membership degree and membership value of the third fuzzy subset to the first and second fuzzy subsets.

[0141] In the specific implementation process, a control scenario involving wind farms in primary frequency regulation is selected. Considering that directly switching from a wind farm in free full-power operation mode to a primary frequency regulation power-limiting mode triggered by frequency disturbance is more likely to cause PI controller integral saturation, this is used as the test condition for detailed explanation. Specifically, this includes active power control mode selection and target value calculation, PI controller input limiting, adaptive PI control, PI controller output limiting, and unit power optimization allocation. Compared with existing wind farm active power control technologies, this invention adds a PI controller input limiting stage and optimizes the adaptive PI controller. Specifically, primary frequency regulation is enabled, and based on the real-time received frequency signal and a set... The initial active power target value is calculated using a good frequency regulation curve. This initial active power target value is input to the wind farm's field-level controller's input limiting stage. If the initial active power target value is higher than the upper limit of the theoretically achievable power output of the entire wind farm as estimated in real time, it will be forcibly limited to that upper limit. Similarly, if the initial active power target value is lower than the lower limit of the theoretically achievable power output, it will also be forcibly limited to that lower limit to ensure that the output active power reference value is within the theoretically controllable range of the system. The active power reference value, the actual active power feedback value, and the system control performance calculation results are input together to the PI control's pre-adaptive adjustment stage. This stage is divided into two branches with different call frequencies: one is the PI parameter adaptive regulator, and the other is the anti-saturation threshold parameter fuzzy regulator and integral saturation limit adaptive switching. The PI parameter adaptive regulator is synchronously called with the active power control, adjusting the proportional control parameter K multiple times within the execution cycle of the same initial active power target value. p Integral control parameter K i This leads to repeated modifications to the active power setpoint. To ensure that the PI parameter is adjusted within a short active power control cycle, this embodiment uses a linear piecewise function, similar in concept to a variable integral function, to adjust the proportional control parameter K. p Integral control parameter K i Adaptive modifications are made. The anti-saturation threshold parameter fuzzy regulator, after entering steady state based on the current execution cycle (the k-th execution cycle), outputs a response time T. up (k) and the change in response time ΔT obtained by subtracting the data from the previous execution cycle. up (k) Employs a fuzzy control algorithm to counteract the saturation threshold parameter I. th(k+1) is used for correction. Considering that the response time requirement for a single frequency modulation is generally no more than 9 seconds, for example, in this embodiment, the fuzzy subset of the response time is set to {Z,S,M,L,U}, representing the five linguistic variables: zero, small, medium, large, and over-limit, respectively. The fuzzy subset of the response time change is {NU,NB,NM,NS,ZO,PS,PM,PB,PU}, representing the nine linguistic variables: negative over-limit, negative large, negative medium, negative small, zero, positive small, positive medium, positive large, and positive over-limit, respectively. The output variable anti-saturation threshold parameter adjustment amount ΔI th The fuzzy subset is the conventional {NB, NM, NS, ZO, PS, PM, PB}, consisting of 7 linguistic variables. The fuzzy rules for the anti-saturation threshold parameter regulator are shown in Table 1.

[0142] Table 1

[0143]

[0144] Using this rule, the membership degree and membership value of the output variable are obtained. After centroid-based defuzzification, the adjustment amount of the anti-saturation threshold parameter can be calculated. Iterating with the original value yields the updated threshold parameter, I. th (k+1)=I th (k)+ΔI th Furthermore, the threshold parameter was subjected to amplitude limiting to ensure that it remained within the optimization range. The amplitude-limited anti-saturation threshold parameter was then applied. This will be applied to the next execution cycle, i.e., the (k+1)th execution cycle. Subsequently, if a change in the initial active power target value is detected, i.e., entering the (k+1)th execution cycle, the integral saturation limit adaptive switching circuit determines whether the current integral value of the PI controller is higher than the current anti-saturation threshold parameter. If so, switch the integral saturation limit I. lmt This is the steady-state integral value of the previous execution cycle in the same mode; otherwise, the default value is maintained, which is the maximum steady-state response error of the total active power considering the wind turbine design requirements and extreme operating conditions.

[0145] In this embodiment, during a primary frequency regulation test verification at a wind farm, the PI controller triggered a primary frequency regulation power limit under integral saturation. The primary frequency regulation response curve for the entire farm under integral saturation before the application of this invention is shown below. Figure 2 As shown, the overall primary frequency modulation response curve under integral saturation after application is as follows: Figure 3As shown, after the application of this invention, the primary frequency regulation response time (reaching 90% of the initial difference) is shortened from 43.5s to 4.4s, and the adjustment time (entering the 5% initial difference error band and satisfying the 1% Pn conventional active steady-state error) is shortened from 59s to 6.4s. This meets the fast response requirement of primary frequency regulation and confirms that the wind farm active power adaptive fast anti-saturation control method proposed in this invention can improve the dynamic response performance of wind farm active power and solve the problems of response time and adjustment time not meeting the standards that may occur when the PI controller directly participates in primary frequency regulation under integral saturation.

[0146] The adaptive and rapid anti-saturation control method for active power in wind farms proposed in this invention improves the dynamic response performance of active power in wind farms. It effectively solves the problems of insufficient response time and adjustment time that may occur when the PI controller directly participates in primary frequency regulation under integral saturation. It has strong adaptability to different units, different wind conditions and different control modes, and improves the grid connection support capability of wind farms as a whole, while reducing the workload of technicians in on-site parameter debugging.

[0147] Example 2

[0148] Corresponding to the aforementioned embodiment of a method for controlling the active power of a wind farm, this disclosure also provides an embodiment of a control system for the active power of a wind farm.

[0149] Figure 4 This is a schematic diagram of a control system for the active power of a wind farm provided in Embodiment 2 of this disclosure, as shown below. Figure 4 As shown, the control system includes: a first acquisition module 21, a wind farm-level controller 22, a second acquisition module 23, a third acquisition module 24, a switching module 25, and a PI controller 26;

[0150] The first acquisition module 21 is used to acquire the initial active power target value of the wind farm;

[0151] In this embodiment, primary frequency modulation is enabled, and the initial active power target value is calculated based on the pre-set primary frequency modulation curve according to the frequency signal received in real time.

[0152] The wind farm-level controller 22 is used to perform a first limiting process on the initial active power target value to obtain an active power reference value;

[0153] In this embodiment, the initial active power target value is input into the wind farm-level controller for the first limiting process (e.g., input limiting process) to obtain the active power reference value.

[0154] The second acquisition module 23 is used to acquire the proportional control parameters and integral control parameters of the PI control based on the active power reference value and the actual active power feedback value.

[0155] The third acquisition module 24 is used to perform fuzzy adjustment of the anti-saturation threshold parameter based on the response time of the execution cycle of the active power control of the wind farm and the change in response time between the current execution cycle and the previous execution cycle, so as to obtain the anti-saturation threshold parameter.

[0156] In this embodiment, fuzzy adjustment of the anti-saturation threshold parameter, adaptive switching of the integral saturation limit, and PI control are the core components of this invention for achieving adaptive and rapid anti-saturation of active power. After each execution cycle, the response time T of that execution cycle is calculated. up (k) is passed to the PI controller, which determines the response time T of the previous execution cycle of the same pattern in the historical database based on the control mode. up (k-1), and thus the change in response time, i.e., ΔT up (k)=T up (k)-T up (k-1). The response time and the change in response time are input together into the fuzzy regulator of the anti-saturation threshold parameter, and the adjusted anti-saturation threshold parameter I is output through the fuzzy control algorithm. th (k+1) serves as one of the triggering conditions for adaptive switching of the integral saturation limit. During the fuzzification process of the input value, the fuzzy subset of the response time can be preferably selected from 3-5 linguistic variables greater than or equal to 0, and the fuzzy subset of the response time change can be preferably selected from 5-9 linguistic variables with positive and negative symmetry. The output of the fuzzy control algorithm is the adjustment amount ΔI of the anti-saturation threshold parameter. th Its fuzzy subset can be optimized from 3-7 positive and negative symmetric linguistic variables. The updated threshold parameter is obtained by superimposing the adjustment amount of the anti-saturation threshold parameter with the original value, I. th (k+1)=I th (k)+ΔI th This threshold parameter also needs to be constrained within the optimization range of the anti-saturation threshold parameter.

[0157] The switching module 25 is used to switch the anti-saturation threshold parameter to the steady-state integral value of the previous execution cycle if the current integral value is higher than the anti-saturation threshold parameter and the initial active power target value changes.

[0158] The PI controller 26 is used to perform PI control on the active power to obtain the active power setpoint based on the proportional control parameters, integral control parameters and the current integral limit.

[0159] In this embodiment, during the adaptive switching of the integral saturation limit, the default integral saturation limit is the maximum steady-state response error of the total active power, taking into account the wind turbine design requirements and extreme operating conditions. If a change in the initial active power target value is detected (i.e., entering the next execution cycle), and the current integral value of the PI controller is higher than the current anti-saturation threshold parameter, the integral saturation limit is temporarily switched from the default value to a specific value. This specific value is selected from the steady-state integral value of the previous execution cycle in the same mode.

[0160] It is worth noting that the invocation frequency of the anti-saturation threshold parameter fuzzy regulator and the PI parameter adaptive regulator differs. The PI parameter adaptive regulator is invoked synchronously with active power control, adjusting the proportional and integral control parameters multiple times within the execution cycle of the same initial active power target value. In contrast, the anti-saturation threshold parameter fuzzy regulator is specifically designed for specific control modes where PI integral desaturation may be too slow, such as the first participation in frequency regulation after a long period of free full-power operation. The invocation of this anti-saturation threshold parameter fuzzy regulator requires consideration of system control performance calculations and may only be invoked after the system has entered a steady state within one execution cycle. Similarly, the integral saturation limit adaptive switching is triggered at most once at the start of the execution cycle of the same initial active power target value.

[0161] This embodiment adds a control method based on an anti-saturation threshold parameter fuzzy regulator and adaptive switching of integral saturation limit to the conventional adaptive PI controller to achieve anti-saturation control of active power in wind farms. By limiting the amplitude, the frequency of integral saturation of the PI controller is reduced, thus optimizing the adaptive PI controller.

[0162] In an optional embodiment, the PI controller is specifically configured to perform PI control based on proportional control parameters, integral control parameters, and the current integral value to convert the active power reference value into a command value.

[0163] The active power command value output by the PI control is subjected to a second limiting process to obtain the active power setpoint.

[0164] In an optional embodiment, the control system further includes:

[0165] The optimization module is used to optimize the allocation of active power in the wind farm based on the active power setpoint.

[0166] In the specific implementation process, the PI parameter adaptive regulator and the integral saturation limit adaptive switching stage will respectively adaptively adjust the proportional control coefficient K. p Integral control coefficient K i and the current integration limit I lmtThe active power reference value is passed to the PI controller for PI control, which converts the active power reference value into a command value. The active power command value output by the PI controller is then subjected to a second limiting process (e.g., output limiting process) to obtain the final active power setpoint, which is used for the optimized allocation of unit power.

[0167] In an optional embodiment, the wind farm-level controller is specifically used to obtain the upper and lower limits of the theoretical power generation capacity of the entire wind farm and to limit the initial active power target value.

[0168] If the initial active power target value is greater than the upper limit of the theoretical power generation capacity of the entire field, then the upper limit of the theoretical power generation capacity of the entire field shall be used as the initial active power target value.

[0169] If the initial active power target value is less than the lower limit of the theoretical power generation capacity of the entire field, then the lower limit of the theoretical power generation capacity of the entire field will be used as the initial active power target value.

[0170] In this embodiment, the upper limit of the PI controller input limiting stage is set to the theoretical power generation capacity of the entire wind farm, while the lower limit is consistent with the lower limit of the active power control capacity of the entire wind farm. The initial active power target value output from the active power control mode selection and target value calculation stage, after input limiting, will not exceed the theoretical power generation capacity of the entire wind farm. This avoids most cases of PI controller integral saturation caused by the initial active power target value being higher than the actual power generation capacity of the wind farm. Specifically, if the initial active power target value is greater than the upper limit of the theoretical power generation capacity of the entire wind farm, the initial active power target value will be forcibly limited to the upper limit of the theoretical power generation capacity of the entire wind farm; similarly, if the initial active power target value is less than the lower limit of the theoretical power generation capacity of the entire wind farm, the initial active power target value will also be forcibly limited to the lower limit of the theoretical power generation capacity of the entire wind farm, ensuring that the output active power reference value is within the theoretically controllable range of the system.

[0171] Furthermore, considering that the estimated theoretical power generation of the entire wind farm may deviate due to complex wind conditions, and that an estimated theoretical power generation value may be higher than the actual power generation value, integral saturation may also occur. Therefore, this invention further optimizes the existing adaptive PI control logic. This control logic mainly consists of four parts: an adaptive PI parameter regulator, an anti-saturation threshold parameter fuzzy regulator, an adaptive switching mechanism for integral saturation limits, and a PI controller. The adaptive PI parameter regulator adopts the same technical approach as existing wind farm adaptive PI control, and can adaptively modify the proportional control parameters and integral control parameters based on input data such as active power reference values ​​and actual active power feedback values, selecting algorithms such as conventional variable integral functions, fuzzy control, and neural networks.

[0172] In an optional embodiment, the PI controller is specifically used to obtain the difference between the active power reference value and the actual active power feedback value, as well as the change in the difference between two adjacent execution cycles, and the execution cycle is synchronized with the active power control module.

[0173] PI parameter adaptation is performed based on the difference and the change in the difference to obtain proportional control parameters and integral control parameters.

[0174] In an optional embodiment, the control system further includes:

[0175] The fourth acquisition module is used to acquire proportional control parameters and integral control parameters based on linear piecewise functions and / or fuzzy control algorithms and / or neural network algorithms.

[0176] In an optional embodiment, the control system further includes:

[0177] The adjustment module is used to perform fuzzy adjustment of the anti-saturation threshold parameter after the execution cycle enters a steady state.

[0178] In an optional embodiment, the switching module is specifically used to switch the anti-saturation threshold parameter to the steady-state integral value of the previous execution cycle at the start of the same active power control execution cycle if the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes.

[0179] In an optional embodiment, the third acquisition module is specifically used to acquire the anti-saturation threshold parameter adjustment amount and the current anti-saturation threshold parameter of the current execution cycle;

[0180] The updated anti-saturation threshold parameter is obtained by superimposing the adjustment amount of the anti-saturation threshold parameter with the current anti-saturation threshold parameter of the current execution cycle.

[0181] In an optional embodiment, the third acquisition module is further configured to construct a corresponding rule for the response time, the change in response time, and the adjustment amount of the anti-saturation threshold parameter. The rule includes at least the membership degree and membership value of the adjustment amount of the anti-saturation threshold parameter to the response time and the change in response time.

[0182] Based on the current response time, the change in response time compared to the previous execution cycle, and the rules, determine the adjustment amount of the anti-saturation threshold parameter;

[0183] Acquisition rules;

[0184] The adjustment amount of the anti-saturation threshold parameter is obtained by using a fuzzy algorithm based on the membership degree and membership value.

[0185] In an optional embodiment, the third acquisition module is further configured to acquire the first linguistic variable of the response time and the corresponding first fuzzy subset, and the second linguistic variable of the response time change and the corresponding second fuzzy subset;

[0186] Construct a third linguistic variable for the adjustment of the anti-saturation threshold parameter and the corresponding trifuzzy subset;

[0187] Establish a fuzzy rule table with first fuzzy subset elements, second fuzzy subset elements, and third fuzzy subset elements. Each third fuzzy subset element corresponds to a specific first fuzzy subset element and a specific second fuzzy subset element. The third fuzzy subset corresponds to the linguistic variable, and the third linguistic variable corresponds to the anti-saturation threshold parameter adjustment amount.

[0188] Construct correspondence rules between the third fuzzy subset and the first and second fuzzy subsets. The rules should at least correspond to the membership degree and membership value of the third fuzzy subset to the first and second fuzzy subsets.

[0189] In the specific implementation process, a control scenario involving wind farms in primary frequency regulation is selected. Considering that directly switching from a wind farm in free full-power operation mode to a primary frequency regulation power-limiting mode triggered by frequency disturbance is more likely to cause PI controller integral saturation, this is used as the test condition for detailed explanation. Specifically, this includes active power control mode selection and target value calculation, PI controller input limiting, adaptive PI control, PI controller output limiting, and unit power optimization allocation. Compared with existing wind farm active power control technologies, this invention adds a PI controller input limiting stage and optimizes the adaptive PI controller. Specifically, primary frequency regulation is enabled, and based on the real-time received frequency signal and a set... The initial active power target value is calculated using a good frequency regulation curve. This initial active power target value is input to the wind farm's field-level controller's input limiting stage. If the initial active power target value is higher than the upper limit of the theoretically achievable power output of the entire wind farm as estimated in real time, it will be forcibly limited to that upper limit. Similarly, if the initial active power target value is lower than the lower limit of the theoretically achievable power output, it will also be forcibly limited to that lower limit to ensure that the output active power reference value is within the theoretically controllable range of the system. The active power reference value, the actual active power feedback value, and the system control performance calculation results are input together to the PI control's pre-adaptive adjustment stage. This stage is divided into two branches with different call frequencies: one is the PI parameter adaptive regulator, and the other is the anti-saturation threshold parameter fuzzy regulator and integral saturation limit adaptive switching. The PI parameter adaptive regulator is synchronously called with the active power control, adjusting the proportional control parameter K multiple times within the execution cycle of the same initial active power target value. p Integral control parameter K i This leads to repeated modifications to the active power setpoint. To ensure that the PI parameter is adjusted within a short active power control cycle, this embodiment uses a linear piecewise function, similar in concept to a variable integral function, to adjust the proportional control parameter K. p Integral control parameter K i Adaptive modifications are made. The anti-saturation threshold parameter fuzzy regulator, after entering steady state based on the current execution cycle (the k-th execution cycle), outputs a response time T.up (k) and the change in response time ΔT obtained by subtracting the data from the previous execution cycle. up (k) Employs a fuzzy control algorithm to counteract the saturation threshold parameter I. th (k+1) is used for correction. Considering that the response time requirement for a single frequency modulation is generally no more than 9 seconds, for example, in this embodiment, the fuzzy subset of the response time is set to {Z,S,M,L,U}, representing the five linguistic variables: zero, small, medium, large, and over-limit, respectively. The fuzzy subset of the response time change is {NU,NB,NM,NS,ZO,PS,PM,PB,PU}, representing the nine linguistic variables: negative over-limit, negative large, negative medium, negative small, zero, positive small, positive medium, positive large, and positive over-limit, respectively. The output variable anti-saturation threshold parameter adjustment amount ΔI th The fuzzy subset is the conventional {NB, NM, NS, ZO, PS, PM, PB}, consisting of 7 linguistic variables. The fuzzy rules for the anti-saturation threshold parameter regulator are shown in Table 1 of Example 1.

[0190] Using this rule, the membership degree and membership value of the output variable are obtained. After centroid-based defuzzification, the adjustment amount of the anti-saturation threshold parameter can be calculated. Iterating with the original value yields the updated threshold parameter, I. th (k+1)=I th (k)+ΔI th Furthermore, the threshold parameter was subjected to amplitude limiting to ensure that it remained within the optimization range. The amplitude-limited anti-saturation threshold parameter was then applied. This will be applied to the next execution cycle, i.e., the (k+1)th execution cycle. Subsequently, if a change in the initial active power target value is detected, i.e., entering the (k+1)th execution cycle, the integral saturation limit adaptive switching circuit determines whether the current integral value of the PI controller is higher than the current anti-saturation threshold parameter. If so, switch the integral saturation limit I. lmt This is the steady-state integral value of the previous execution cycle in the same mode; otherwise, the default value is maintained, which is the maximum steady-state response error of the total active power considering the wind turbine design requirements and extreme operating conditions.

[0191] In this embodiment, during a primary frequency regulation test verification at a wind farm, the PI controller triggered a primary frequency regulation power limit under integral saturation. The primary frequency regulation response curve for the entire farm under integral saturation before the application of this invention is shown below. Figure 2 As shown, the overall primary frequency modulation response curve under integral saturation after application is as follows: Figure 3As shown, after the application of this invention, the primary frequency regulation response time (reaching 90% of the initial difference) is shortened from 43.5s to 4.4s, and the adjustment time (entering the 5% initial difference error band and satisfying the 1% Pn conventional active steady-state error) is shortened from 59s to 6.4s. This meets the fast response requirement of primary frequency regulation and confirms that the wind farm active power adaptive fast anti-saturation control method proposed in this invention can improve the dynamic response performance of wind farm active power and solve the problems of response time and adjustment time not meeting the standards that may occur when the PI controller directly participates in primary frequency regulation under integral saturation.

[0192] The adaptive and rapid anti-saturation control method for active power in wind farms proposed in this invention improves the dynamic response performance of active power in wind farms. It effectively solves the problems of insufficient response time and adjustment time that may occur when the PI controller directly participates in primary frequency regulation under integral saturation. It has strong adaptability to different units, different wind conditions and different control modes, and improves the grid connection support capability of wind farms as a whole, while reducing the workload of technicians in on-site parameter debugging.

[0193] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0194] Example 3

[0195] Figure 5 This is a schematic diagram of the structure of an electronic device according to Embodiment 3 of this disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the wind farm active power control method described in any of the above embodiments. Figure 5 The electronic device 90 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0196] like Figure 5 As shown, the electronic device 90 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 90 may include, but are not limited to: at least one processor 91, at least one memory 92, and a bus 93 connecting different system components (including memory 92 and processor 91).

[0197] Bus 93 includes a data bus, an address bus, and a control bus.

[0198] The memory 92 may include volatile memory, such as random access memory (RAM) 921 and / or cache memory 922, and may further include read-only memory (ROM) 923.

[0199] The memory 92 may also include a program tool 925 (or utility) having a set (at least one) program module 924, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0200] The processor 91 executes various functional applications and data processing by running computer programs stored in the memory 92, such as the wind farm active power control method provided in any of the above embodiments.

[0201] Electronic device 90 can also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 95. Furthermore, electronic device 90 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 96. Figure 5 As shown, network adapter 96 communicates with other modules of electronic device 90 via bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0202] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0203] Example 4

[0204] Embodiment 4 of this disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the wind farm active power control method provided in any of the above embodiments.

[0205] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0206] Example 5

[0207] Embodiment 5 of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the wind farm active power control method described in any of the above claims.

[0208] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0209] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. A method for controlling the active power of a wind farm, characterized in that, The control method includes: Obtain the initial active power target value of the wind farm; The initial active power target value is subjected to a first limiting process to obtain an active power reference value; Based on the active power reference value and the actual active power feedback value, obtain the proportional control parameters and integral control parameters of the PI control; Based on the response time of the execution cycle of active power control in wind farms, and the change in response time between the current execution cycle and the previous execution cycle, fuzzy adjustment of the anti-saturation threshold parameter is performed to obtain the anti-saturation threshold parameter. If the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes, then the anti-saturation threshold parameter is switched to the steady-state integral value of the previous execution cycle. Based on the proportional control parameters, the integral control parameters, and the current integral value, PI control is performed on the active power to obtain the active power setpoint. The anti-saturation threshold parameter is fuzzy adjusted based on the response time of the execution cycle of the active power control of the wind farm and the change in response time between the current execution cycle and the previous execution cycle, in order to obtain the anti-saturation threshold parameter, which includes: Obtain the adjustment amount of the anti-saturation threshold parameter and the current anti-saturation threshold parameter for the current execution cycle; The updated anti-saturation threshold parameter is obtained by superimposing the adjustment amount of the anti-saturation threshold parameter with the current anti-saturation threshold parameter of the current execution cycle; Before obtaining the adjustment amount of the anti-saturation threshold parameter, the following steps are included: Construct a rule corresponding to the response time, the change in response time, and the adjustment amount of the anti-saturation threshold parameter. This rule includes at least the membership degree and membership value of the adjustment amount of the anti-saturation threshold parameter to the response time and the change in response time. Based on the current response time, the change in response time compared to the previous execution cycle, and the rule, the adjustment amount of the anti-saturation threshold parameter is determined; The fuzzy adjustment of the anti-saturation threshold parameter includes: Obtain the rules; The adjustment amount of the anti-saturation threshold parameter is obtained by using a fuzzy algorithm based on the membership degree and the membership value.

2. The method for controlling the active power of a wind farm as described in claim 1, characterized in that, The step of performing PI control on active power based on the proportional control parameter, the integral control parameter, and the current integral value to obtain the active power setpoint includes: Based on the proportional control parameters, the integral control parameters, and the current integral value, PI control is performed to convert the active power reference value into a command value. The active power command value output by the PI control is subjected to a second limiting process to obtain the active power setpoint.

3. The method for controlling the active power of a wind farm as described in claim 2, characterized in that, After performing a second limiting process on the active power command value output by the PI control to obtain the active power setpoint, the control method further includes: The active power of the wind farm is optimized and allocated based on the given active power value.

4. The method for controlling the active power of a wind farm as described in claim 1, characterized in that, The first limiting process includes: Obtain the upper and lower limits of the theoretical power generation capacity of the entire wind farm, and perform amplitude limiting processing on the initial active power target value; If the initial active power target value is greater than the upper limit of the theoretical power generation capacity of the entire field, then the upper limit of the theoretical power generation capacity of the entire field shall be used as the initial active power target value. If the initial active power target value is less than the lower limit of the theoretical power generation capacity of the entire field, then the lower limit of the theoretical power generation capacity of the entire field will be used as the initial active power target value.

5. The method for controlling the active power of a wind farm as described in claim 1, characterized in that, The step of performing PI control on active power based on the proportional control parameter, the integral control parameter, and the current integral value to obtain the active power setpoint includes: The difference between the active power reference value and the actual active power feedback value, as well as the change in the difference between two adjacent execution cycles, are obtained. The execution cycle is synchronized with the active power control module. Based on the difference and the change in the difference, PI parameter adaptive control is performed to obtain proportional control parameters and integral control parameters.

6. The method for controlling the active power of a wind farm as described in claim 1, characterized in that, The control method further includes: The proportional control parameters and the integral control parameters are obtained based on linear piecewise functions and / or fuzzy control algorithms and / or neural network algorithms.

7. The method for controlling the active power of a wind farm as described in claim 1, characterized in that, The control method further includes: After the execution cycle enters a steady state, the anti-saturation threshold parameter is fuzzy adjusted.

8. The method for controlling the active power of a wind farm as described in claim 1, characterized in that, The step of switching the anti-saturation threshold parameter to the steady-state integral value of the previous execution cycle if the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes includes: At the start of the same active power control execution cycle, if the current integral value is greater than the anti-saturation threshold parameter and the initial active power target value changes, the anti-saturation threshold parameter is switched to the steady-state integral value of the previous execution cycle.

9. The method for controlling the active power of a wind farm as described in claim 1, characterized in that, The rules for constructing the corresponding response time, response time change, and anti-saturation threshold parameter adjustment include: Obtain the first linguistic variable of response time and its corresponding first fuzzy subset, and the second linguistic variable of response time change and its corresponding second fuzzy subset; Construct a third linguistic variable for the adjustment of the anti-saturation threshold parameter and the corresponding trifuzzy subset; Establish a fuzzy rule table based on a first fuzzy subset element, a second fuzzy subset element, and a third fuzzy subset element. Each third fuzzy subset element corresponds to a specific first fuzzy subset element and a specific second fuzzy subset element. The third fuzzy subset corresponds to the language variable, and the third language variable corresponds to the anti-saturation threshold parameter adjustment amount. Construct a correspondence rule between the third fuzzy subset and the first fuzzy subset and the second fuzzy subset, wherein the rule corresponds at least to the membership degree and membership value of the third fuzzy subset to the first fuzzy subset and the second fuzzy subset.

10. A control system for the active power of a wind farm, characterized in that, The control system includes: The first acquisition module is used to acquire the initial active power target value of the wind farm; A wind farm-level controller is used to perform a first limiting process on the initial active power target value to obtain an active power reference value; The second acquisition module is used to acquire the proportional control parameters and integral control parameters of the PI control based on the active power reference value and the actual active power feedback value. The third acquisition module is used to perform fuzzy adjustment of the anti-saturation threshold parameter based on the response time of the execution cycle of the active power control of the wind farm and the change in response time between the current execution cycle and the previous execution cycle, so as to obtain the anti-saturation threshold parameter. The switching module is used to switch the anti-saturation threshold parameter to the steady-state integral value of the previous execution cycle if the current integral value is higher than the anti-saturation threshold parameter and the initial active power target value changes. A PI controller is used to perform PI control on active power based on the proportional control parameters, the integral control parameters, and the current integral limit to obtain an active power setpoint. The third acquisition module is specifically used to acquire the anti-saturation threshold parameter adjustment amount and the current anti-saturation threshold parameter of the current execution cycle; The updated anti-saturation threshold parameter is obtained by superimposing the adjustment amount of the anti-saturation threshold parameter with the current anti-saturation threshold parameter of the current execution cycle; The third acquisition module is further configured to construct a corresponding rule for the response time, the change in response time, and the adjustment amount of the anti-saturation threshold parameter. The rule includes at least the membership degree and membership value of the adjustment amount of the anti-saturation threshold parameter to the response time and the change in response time. Based on the current response time, the change in response time compared to the previous execution cycle, and the rule, the adjustment amount of the anti-saturation threshold parameter is determined; Obtain the rule; use a fuzzy algorithm based on the membership degree and the membership value to obtain the adjustment amount of the anti-saturation threshold parameter.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the method for controlling the active power of a wind farm as described in any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for controlling the active power of the wind farm as described in any one of claims 1 to 9.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for controlling the active power of a wind farm as described in any one of claims 1 to 9.

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