Wind power plant active power control method, system and device and storage medium

Abstract

The invention provides a wind power plant active power control method, system and device and a storage medium, and the method comprises the steps: carrying out the first amplitude limiting processing of an obtained initial active target value, and obtaining an active reference value; obtaining a proportional control parameter and an integral control parameter of PI control according to the active reference value and the actual active feedback value; performing anti-saturation threshold parameter fuzzy adjustment based on the response time and the response time variation of the current execution period and the previous execution period to obtain an anti-saturation threshold parameter; and performing PI control on the active power based on the proportional control parameter, the integral control parameter and the current integral value to obtain an active given value. On the basis of the PI controller, a control mode with the anti-saturation threshold parameter fuzzy regulator and the integral saturation limit value self-adaptive switching as the core is added to achieve active power anti-saturation control over the wind power plant, the occurrence frequency of integral saturation of the PI controller is reduced through amplitude limiting processing, and the self-adaptive PI controller is optimized.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wind farms, and in particular to a method, system, device and storage medium for controlling active power of a wind farm. Background Art

[0002] Active power control of wind farms usually involves application scenarios such as automatic power generation control, primary frequency regulation and inertia response. Different scenarios have different calculation methods for the active power target value of the entire farm, but the overall control logic is basically the same, including the calculation of the active power target value of the entire farm or command reception, wind turbine actual power feedback reception and summation, target value and measured value deviation calculation, PI control, power command limit, wind turbine power optimization distribution and other steps. Among them, the PI controller, as a key link in the entire control process, will directly affect the control effect of the active power of the wind farm. The traditional PI control algorithm uses fixed proportional and integral control parameters, which easily leads to unstable dynamic and static performance of the control system and cannot adapt to various complex operating scenarios such as large power deviation variation range and changes in unit response characteristics caused by different wind conditions. In order to meet the above challenges, industry scholars and experts apply advanced algorithms such as fuzzy control and neural networks to PI control parameter setting, which can realize online real-time modification of PI parameters, thereby improving the adaptability of wind farm active power. However, the parameters of PI control adaptive setting are currently limited to proportional and integral control parameters, and adaptive adjustment under abnormal conditions such as integral saturation is not considered.

[0003] In addition, the existing wind farm active power control mostly adopts the traditional PI control method with fixed setting parameters, which requires technicians to determine the PI control parameters through on-site testing, and different wind farms need to be tested and adjusted. And for different target command change ranges, a set of fixed PI control parameters is difficult to ensure a stable and good control effect.

[0004] The existing public wind farm active power adaptive control uses fuzzy control rules to adjust the proportional and integral parameters of PI control online, ignoring the handling of abnormal situations such as integral saturation. This may result in the entire field active power response time and regulation time being difficult 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 public technology does not perform input limiting processing on the active power target value entering the PI control link. If the target value is higher than the actual power that can be generated by all controllable units in the wind farm, it will easily cause the integral saturation of the PI controller, thereby causing the dynamic performance of the active power control of the entire field to deteriorate. Summary of the invention

[0006] The technical problem to be solved by the present disclosure is to overcome the defect that the active power control method in the prior art easily causes abnormal situations such as integral saturation, and to provide a control method, system, device and storage medium for the active power of a wind farm.

[0007] The present invention solves the above technical problems through the following technical solutions:

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

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

[0010] Performing a first amplitude limiting process on the initial active power target value to obtain an active power reference value;

[0011] According to the active reference value and the actual active feedback value, a proportional control parameter and an integral control parameter of the PI control are obtained;

[0012] Based on the response time of the execution cycle of the active power control of the wind farm and the response time variation between the current execution cycle and the previous execution cycle, the anti-saturation threshold parameter is fuzzy adjusted 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, the anti-saturation threshold parameter is switched to the steady-state integral value of the previous execution cycle;

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

[0015] Preferably, the performing PI control on the active power to obtain the active given value based on the proportional control parameter, the integral control parameter and the current integral value comprises:

[0016] Based on the proportional control parameter, the integral control parameter and the current integral value, PI control is performed to convert the active 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 an active power given value.

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

[0019] The active power of the wind farm is optimally distributed according to the active given value.

[0020] Preferably, the first limiting process includes:

[0021] Obtaining the upper and lower limits of the theoretically available power of the entire wind farm, and performing a limiting process on the initial active power target value;

[0022] If the initial active power target value is greater than the upper limit value of the theoretically available power of the entire field, the upper limit value of the theoretically available power of the entire field is used as the initial active power target value;

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

[0024] Preferably, the performing PI control on the active power to obtain the active given value based on the proportional control parameter, the integral control parameter and the current integral value comprises:

[0025] Obtaining a difference between the active reference value and the actual active feedback value and a difference change value between two adjacent execution cycles, wherein the execution cycle is synchronized with the active control module;

[0026] PI parameter adaptation is performed based on the difference and the difference change value to obtain proportional control parameters and integral control parameters.

[0027] Preferably, the control method further includes:

[0028] The proportional control parameter and the integral control parameter are obtained based on a linear piecewise function and / or a fuzzy control algorithm and / or a neural network algorithm.

[0029] Preferably, the control method further includes:

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

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

[0032] At the start time 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 performing 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 response time variation between the current execution cycle and the previous execution cycle to obtain the anti-saturation threshold parameter includes:

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

[0035] The anti-saturation threshold parameter adjustment amount is superimposed on the current anti-saturation threshold parameter of the current execution cycle to obtain an updated anti-saturation threshold parameter.

[0036] Preferably, before obtaining the anti-saturation threshold parameter adjustment amount, the method includes:

[0037] Constructing a corresponding rule between the response time, the response time variation and the anti-saturation threshold parameter adjustment amount, the rule at least including a membership degree and a membership value of the anti-saturation threshold parameter adjustment amount to the response time and the response time variation;

[0038] Determine the anti-saturation threshold parameter adjustment amount based on the current response time, the response time change amount of the current response time compared to the response time of the previous execution cycle, and the rule;

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

[0040] obtaining the rule;

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

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

[0043] Obtaining a first linguistic variable of the response time and a corresponding first fuzzy subset, and a second linguistic variable of the response time variation and a corresponding second fuzzy subset;

[0044] Constructing the third language variable of the anti-saturation threshold parameter adjustment amount and the corresponding three-way fuzzy subset;

[0045] Establishing a first fuzzy subset element, a second fuzzy subset element, and a fuzzy rule table based on a third fuzzy subset element, each third fuzzy subset element corresponds to a determined first fuzzy subset element and a determined second fuzzy subset element; a third fuzzy subset corresponding to a linguistic variable, the third fuzzy variable corresponding to the anti-saturation threshold parameter adjustment amount;

[0046] A corresponding rule between the third fuzzy subset and the first and second fuzzy subsets is constructed, wherein the rule at least corresponds to the degree of membership and the value of membership of the third fuzzy subset to the first and second fuzzy subsets.

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

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

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

[0050] A second acquisition module is used to acquire a proportional control parameter and an integral control parameter of the PI control according to the active reference value and the actual active 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 response time change between the current execution cycle and the previous execution cycle to obtain the anti-saturation threshold parameter;

[0052] A switching module, configured to switch the anti-saturation threshold parameter to a steady-state integral value of a 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 the active power based on the proportional control parameter, the integral control parameter and the current integral limit value to obtain an active given value.

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

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

[0056] Preferably, the control system further comprises:

[0057] The optimization module is used to optimize the distribution of the active power of the wind farm according to the active given value.

[0058] Preferably, the wind farm level controller is specifically used to obtain the upper and lower limits of the theoretically available power of the entire wind farm, and to perform a limiting process on the initial active power target value;

[0059] If the initial active power target value is greater than the upper limit value of the theoretically available power of the entire field, the upper limit value of the theoretically available power of the entire field is used as the initial active power target value;

[0060] If the initial active power target value is less than the lower limit value of the theoretically generateable power of the entire field, the lower limit value of the theoretically generateable power 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 reference value and the actual active feedback value and the difference change value between two adjacent execution cycles, and the execution cycle is synchronized with the active control module;

[0062] PI parameter adaptation is performed based on the difference and the difference change value to obtain proportional control parameters and integral control parameters.

[0063] Preferably, the control system further comprises:

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

[0065] Preferably, the control system further comprises:

[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 start time 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 anti-saturation threshold parameter adjustment amount is superimposed on the current anti-saturation threshold parameter of the current execution cycle to obtain an updated anti-saturation threshold parameter.

[0070] Preferably, the third acquisition module is further used to construct a corresponding rule of the response time, the response time variation and the anti-saturation threshold parameter adjustment amount, the rule at least including the membership degree and membership value of the anti-saturation threshold parameter adjustment amount to the response time and the response time variation;

[0071] Determine the anti-saturation threshold parameter adjustment amount based on the current response time, the response time change amount of the current response time compared to the response time of the previous execution cycle, and the rule;

[0072] obtaining the rule;

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

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

[0075] Constructing the third language variable of the anti-saturation threshold parameter adjustment amount and the corresponding three-way fuzzy subset;

[0076] Establishing a first fuzzy subset element, a second fuzzy subset element, and a fuzzy rule table based on a third fuzzy subset element, each third fuzzy subset element corresponds to a determined first fuzzy subset element and a determined second fuzzy subset element; a third fuzzy subset corresponding to a linguistic variable, the third fuzzy variable corresponding to the anti-saturation threshold parameter adjustment amount;

[0077] A corresponding rule between the third fuzzy subset and the first and second fuzzy subsets is constructed, wherein the rule at least corresponds to the degree of membership and the value of membership of the third fuzzy subset to the first and second fuzzy subsets.

[0078] A third aspect of the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, wherein when the processor executes the computer program, the method for controlling active power of a wind farm described in the first aspect is implemented.

[0079] A fourth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for controlling active power of a wind farm according to the first aspect is implemented.

[0080] A fifth aspect of the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the method for controlling active power of a wind farm as described in the first aspect.

[0081] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.

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

[0083] The present invention adds a control method with an anti-saturation threshold parameter fuzzy regulator and adaptive switching of integral saturation limits as the core on the basis of a conventional adaptive PI controller to realize anti-saturation control of active power of a wind farm. The frequency of occurrence of integral saturation of the PI controller is reduced by limiting processing, thereby optimizing the adaptive PI controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 A flow chart of a method for controlling active power of a wind farm provided in Embodiment 1 of the present disclosure;

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

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

[0087] Figure 4 A schematic diagram of the modules of a wind farm active power control system provided in Embodiment 2 of the present disclosure.

[0088] Figure 5 A schematic diagram of the structure of an electronic device for implementing a method for controlling active power of a wind farm provided in Embodiment 3 of the present disclosure. DETAILED DESCRIPTION

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

[0090] Prefixes such as "first" and "second" are used in the embodiments of the present disclosure only to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of prefixes such as ordinal numbers to distinguish description objects in the embodiments of the present disclosure does not constitute a limitation on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and no unnecessary limitation should be constituted due to the use of such prefixes. In addition, in the description of the present embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

[0091] In the embodiments of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0092] Example 1

[0093] Figure 1 This is a flow chart of a method for controlling active power of a wind farm provided in Embodiment 1 of the present disclosure, such as Figure 1 As shown, the control method includes:

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

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

[0096] S2. performing a first amplitude limiting process on the initial active power target value to obtain an active power reference value;

[0097] In this embodiment, the initial active power target value is input into the wind farm level controller to perform a first amplitude limiting process (eg, input amplitude limiting process) to obtain an active power reference value.

[0098] S3. Obtaining proportional control parameters and integral control parameters of PI control according to the active reference value and the actual active feedback value;

[0099] S4, based on the response time of the execution cycle of the active power control of the wind farm and the response time variation 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, the fuzzy adjustment of the anti-saturation threshold parameter, the adaptive switching of the integral saturation limit and the PI control are the core links of the present invention to realize the adaptive rapid anti-saturation of active power. up (k) is passed to the PI controller, and the control mode is used to determine the response time T of the previous execution cycle of the same mode in the historical database. up (k-1), and then the response time change is obtained, that is, ΔT up (k) = T up (k)-T up The response time and the response time variation are input into the anti-saturation threshold parameter fuzzy regulator, and the adjusted anti-saturation threshold parameter I is output through the fuzzy control algorithm. th (k+1), as one of the triggering conditions for adaptive switching of integral saturation limit. In the process of fuzzifying the input value, the fuzzy subset of response time can be preferably 3-5 language variables greater than or equal to 0, and the fuzzy subset of response time change can be preferably 5-9 language variables with positive and negative symmetry. The output of the fuzzy control algorithm is the anti-saturation threshold parameter adjustment ΔI th , the fuzzy subset can be optimized to be 3-7 positive and negative symmetric language variables. The anti-saturation threshold parameter adjustment amount is superimposed on the original value to obtain the updated threshold parameter, 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, the anti-saturation threshold parameter is switched to the steady-state integral value of the previous execution cycle;

[0102] S6. Based on the proportional control parameter, the integral control parameter and the current integral value, PI control is performed on the active power to obtain an active given value.

[0103] In this embodiment, in the integral saturation limit adaptive switching link, the default integral saturation limit is the maximum steady-state response error of the entire field active power considering the design requirements and extreme operating conditions of the wind turbine. If a change in the initial active 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 triggered to temporarily switch from the default value to a specific value. The specific value selects the steady-state integral value of the previous same mode execution cycle.

[0104] It is worth noting that the calling frequency of the anti-saturation threshold parameter fuzzy regulator is different from that of the PI parameter adaptive regulator; the calling of the PI parameter adaptive regulator is synchronized with the active control, and the proportional control parameters and the integral control parameters are adjusted multiple times in the execution cycle of the same initial active target value; while the anti-saturation threshold parameter fuzzy regulator is specially designed for specific control modes where the PI integral desaturation may be too slow, such as the first participation in frequency modulation after a long period of free full-power operation. The calling of the anti-saturation threshold parameter fuzzy regulator needs to be combined with the system control performance calculation results, and it may only be called after entering a steady state in an execution cycle. Similarly, the adaptive switching of the integral saturation limit value is triggered at most once at the start time in the execution cycle of the same initial active target value.

[0105] This embodiment adds a control method with an anti-saturation threshold parameter fuzzy regulator and adaptive switching of integral saturation limit as the core on the basis of the conventional adaptive PI controller to realize anti-saturation control of the active power of the wind farm. The frequency of occurrence of integral saturation of the PI controller is reduced through limiting processing, and the adaptive PI controller is optimized.

[0106] In an optional embodiment, S6 includes:

[0107] S6-11, based on the proportional control parameter, the integral control parameter and the current integral value, PI control is performed to convert the active reference value into a command value;

[0108] S6-12, performing a second limiting process on the active power command value output by the PI control to obtain an active power given value.

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

[0110] The active power of the wind farm is optimally distributed according to the active power given value.

[0111] In the specific implementation process, the PI parameter adaptive regulator and the integral saturation limit adaptive switching link respectively adjust the proportional control coefficient K after adaptive adjustment. p , integral control coefficient K i and the current integral limit I lmtThe active reference value is transferred to the PI controller for PI control to convert the active reference value into the command value. The active command value output by the PI controller is subjected to a second limiting process (eg, output limiting process) to obtain a final active given value for optimizing the power distribution of the unit.

[0112] In an optional embodiment, S2 includes:

[0113] Obtain the upper and lower limits of the theoretically available power of the entire wind farm, and perform a limit process on the initial active power target value;

[0114] If the initial active power target value is greater than the upper limit of the theoretically available power of the entire field, the upper limit of the theoretically available power 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 theoretically available power of the entire field, the lower limit of the theoretically available power of the entire field will be used as the initial active power target value.

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

[0117] In addition, considering that the theoretical power that can be generated in the whole field may have estimation deviations due to complex changes in wind conditions, when the estimated value of the theoretical power that can be generated is higher than the actual value of the power that can be generated in the whole field, it may also cause integral saturation. Therefore, the present invention further optimizes the existing adaptive PI control logic, and the control logic mainly includes four parts, namely, PI parameter adaptive regulator, anti-saturation threshold parameter fuzzy regulator, integral saturation limit adaptive switching and PI controller. Among them, the PI parameter adaptive regulator adopts the same technical route as the existing wind farm adaptive PI control, and can select conventional variable integral function, fuzzy control, neural network and other algorithms to adaptively modify the proportional control parameters and integral control parameters based on input data such as active reference value and actual active feedback value.

[0118] In an optional embodiment, S6 includes:

[0119] S6-21, obtaining the difference between the active reference value and the actual active feedback value and the difference change value between two adjacent execution cycles, and the execution cycle is synchronized with the active control module;

[0120] S6-22, based on the difference and the difference change value, PI parameter adaptation is performed to obtain proportional control parameters and integral control parameters.

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

[0122] Based on the linear piecewise function and / or the fuzzy control algorithm and / or the neural network algorithm, the proportional control parameter and the integral control parameter are obtained.

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

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

[0125] In an optional embodiment, S5 includes:

[0126] 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, 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, obtaining the anti-saturation threshold parameter adjustment amount and the current anti-saturation threshold parameter of the current execution cycle;

[0129] S4-2, superimposing the anti-saturation threshold parameter adjustment amount and the current anti-saturation threshold parameter of the current execution cycle to obtain an updated anti-saturation threshold parameter.

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

[0131] S4-0, constructing a corresponding rule for response time, response time variation, and anti-saturation threshold parameter adjustment amount, the rule at least including the membership degree and membership value of the anti-saturation threshold parameter adjustment amount to the response time and the response time variation amount;

[0132] S4-01, determining an anti-saturation threshold parameter adjustment amount based on a current response time, a change in the current response time compared to a response time in a previous execution cycle, and a rule;

[0133] The fuzzy adjustment of anti-saturation threshold parameters in S4 includes:

[0134] Get rules;

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

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

[0137] Obtaining a first linguistic variable of the response time and a corresponding first fuzzy subset, and a second linguistic variable of the response time variation and a corresponding second fuzzy subset;

[0138] Constructing the third language variable of the anti-saturation threshold parameter adjustment amount and the corresponding three-way fuzzy subset;

[0139] Establishing a first fuzzy subset element, a second fuzzy subset element and a fuzzy rule table based on a third fuzzy subset element, each third fuzzy subset element corresponds to a determined first fuzzy subset element and a determined second fuzzy subset element; a third fuzzy subset corresponding to a linguistic variable, and a third linguistic variable corresponding to an anti-saturation threshold parameter adjustment amount;

[0140] A corresponding rule between the third fuzzy subset and the first and second fuzzy subsets is constructed, wherein the rule at least corresponds 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 in which a wind farm participates in primary frequency regulation is selected. Considering that the wind farm is more likely to experience PI controller integral saturation when directly switching to the primary frequency regulation power limiting mode triggered by frequency disturbance in the free full-generation working mode, this is used as a test condition for specific description, including active 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 the existing wind farm active control technology, the present invention adds a PI controller input limiting link and optimizes the adaptive PI controller. Specifically, the primary frequency regulation is enabled, and the frequency signal received in real time is set based on the frequency limiting signal. The initial active target value is calculated based on a good frequency modulation curve; the initial active target value is input into the input limiting link of the wind farm field-level controller. If the initial active target value is higher than the upper limit of the theoretically generateable power of the entire field estimated by the system in real time, the initial active target value will be forced to be limited to the upper limit of the theoretically generateable power of the entire field; similarly, if the initial active target value is lower than the lower limit of the theoretically generateable power of the entire field, the initial active target value will also be forced to be limited to the lower limit of the theoretically generateable power of the entire field to ensure that the output active reference value is within the theoretically controllable range of the system; the active reference value, the actual active feedback value, and the system control performance calculation results are input into the pre-adaptive adjustment link of the PI control together. This link is divided into two branches with different calling frequencies, one of which is the PI parameter adaptive regulator, and the other is the anti-saturation threshold parameter fuzzy regulator and the integral saturation limit adaptive switching. Among them, the PI parameter adaptive regulator is synchronously called with the active control, and the proportional control parameter K is adjusted multiple times during the execution cycle of the same initial active target value. p , integral control parameter K i , and then modify the active given value for multiple times. To ensure that the PI parameters are adjusted within a short active control period, this embodiment uses a linear piecewise function similar to the idea of ​​a variable integral function to adjust the proportional control parameter K p , integral control parameter K i After the anti-saturation threshold parameter fuzzy regulator enters the steady state according to the current execution cycle (kth execution cycle), the output response time T up (k) and the response time change ΔT obtained by subtracting the data from the previous execution cycle up (k), using fuzzy control algorithm to combat saturation threshold parameter I th(k+1) is corrected. Considering that the response time requirement of a frequency modulation is generally not more than 9 seconds, for example, in this embodiment, the fuzzy subset of the response time is set to {Z, S, M, L, U}, which respectively represent the five language variables of zero, small, medium, large, and over-limit. The fuzzy subset of the response time change is {NU, NB, NM, NS, ZO, PS, PM, PB, PU}, which respectively represent the nine language variables of negative over-limit, negative large, negative medium, negative small, zero, positive small, positive medium, positive large, and positive over-limit. Output variable anti-saturation threshold parameter adjustment amount ΔI th The fuzzy subset of is the conventional {NB, NM, NS, ZO, PS, PM, PB}, which consists of 7 linguistic variables. The fuzzy rules of the anti-saturation threshold parameter regulator are shown in Table 1:

[0142] Table 1

[0143]

[0144] The membership degree and membership value of the output variable are obtained by using this rule. The anti-saturation threshold parameter adjustment can be calculated by defuzzification using the centroid method. The updated threshold parameter is obtained by iterating with the original value. th (k+1)=I th (k)+ΔI th , further limit the threshold parameter to ensure that it is within the optimization range, the anti-saturation threshold parameter after limiting It will be applied to the next execution cycle, i.e. the k+1th execution cycle. If it is detected that the initial active target value has changed, it will enter the k+1th execution cycle, and the integral saturation limit adaptive switching link will determine 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 It is the steady-state integral value of the previous same-mode execution cycle; otherwise, it keeps the default value, that is, the maximum steady-state response error of the entire field active power considering the wind turbine design requirements and extreme operating conditions.

[0145] In the primary frequency regulation test verification of a certain wind farm in this embodiment, the PI controller triggers the primary frequency regulation power limit under the condition of integral saturation. The primary frequency regulation response curve of the entire field under the condition of integral saturation before the application of the present invention is as follows: Figure 2 As shown, the full frequency modulation response curve under the condition of integral saturation after application is as follows Figure 3As shown; it can be seen that after the application of the present invention, the primary frequency modulation response time (reaching 90% of the initial difference) is shortened from the original 43.5s to 4.4s, and the adjustment time (entering the 5% initial difference error band and meeting the 1% Pn conventional active steady-state error) is shortened from the original 59s to 6.4s, which meets the primary frequency modulation fast response requirements, and proves that the wind farm active power adaptive fast anti-saturation control method proposed in the present invention can improve the dynamic response performance of the wind farm active power, and solves the problems of response time and adjustment time not meeting the standards that may occur when directly participating in the primary frequency modulation under the condition of integral saturation of the PI controller.

[0146] The wind farm active power adaptive fast anti-saturation control method proposed in the present invention improves the dynamic response performance of the wind farm active power, effectively solves the problems of response time and regulation time not meeting the standards that may occur when the PI controller directly participates in the primary frequency modulation under the condition of integral saturation, has strong adaptability to different units, different wind conditions and different control modes, improves the overall grid-connected support capacity of the wind farm, and reduces the on-site parameter debugging workload of technical personnel.

[0147] Example 2

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

[0149] Figure 4 A module schematic diagram of a wind farm active power control system provided in Embodiment 2 of the present disclosure, such as 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] A first acquisition module 21 is used to acquire an initial active power target value of the wind farm;

[0151] In this embodiment, the primary frequency modulation is enabled, and the initial active power target value is calculated based on the 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 limit processing 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 to perform a first amplitude limiting process (eg, input amplitude limiting process) to obtain an active power reference value.

[0154] The second acquisition module 23 is used to acquire the proportional control parameter and the integral control parameter of the PI control according to the active reference value and the actual active 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 response time change between the current execution cycle and the previous execution cycle to obtain the anti-saturation threshold parameter;

[0156] In this embodiment, the fuzzy adjustment of the anti-saturation threshold parameter, the adaptive switching of the integral saturation limit and the PI control are the core links of the present invention to realize the adaptive rapid anti-saturation of active power. up (k) is passed to the PI controller, and the control mode is used to determine the response time T of the previous execution cycle of the same mode in the historical database. up (k-1), and then the response time change is obtained, that is, ΔT up (k) = T up (k)-T up The response time and the response time variation are input into the anti-saturation threshold parameter fuzzy regulator, and the adjusted anti-saturation threshold parameter I is output through the fuzzy control algorithm. th (k+1), as one of the triggering conditions for adaptive switching of integral saturation limit. In the process of fuzzifying the input value, the fuzzy subset of response time can be preferably 3-5 language variables greater than or equal to 0, and the fuzzy subset of response time change can be preferably 5-9 language variables with positive and negative symmetry. The output of the fuzzy control algorithm is the anti-saturation threshold parameter adjustment ΔI th , its fuzzy subset can be optimized to be 3-7 positive and negative symmetric language variables. The anti-saturation threshold parameter adjustment amount is superimposed on the original value to obtain the updated threshold parameter, 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] A switching module 25, configured 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 based on the proportional control parameter, the integral control parameter and the current integral limit value to obtain an active power given value.

[0159] In this embodiment, in the integral saturation limit adaptive switching link, the default integral saturation limit is the maximum steady-state response error of the entire field active power considering the design requirements and extreme operating conditions of the wind turbine. If a change in the initial active 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 triggered to temporarily switch from the default value to a specific value. The specific value selects the steady-state integral value of the previous same mode execution cycle.

[0160] It is worth noting that the calling frequency of the anti-saturation threshold parameter fuzzy regulator is different from that of the PI parameter adaptive regulator; the calling of the PI parameter adaptive regulator is synchronized with the active control, and the proportional control parameters and the integral control parameters are adjusted multiple times in the execution cycle of the same initial active target value; while the anti-saturation threshold parameter fuzzy regulator is specially designed for specific control modes where the PI integral desaturation may be too slow, such as the first participation in frequency modulation after a long period of free full-power operation. The calling of the anti-saturation threshold parameter fuzzy regulator needs to be combined with the system control performance calculation results, and it may only be called after entering a steady state in an execution cycle. Similarly, the adaptive switching of the integral saturation limit value is triggered at most once at the start time in the execution cycle of the same initial active target value.

[0161] This embodiment adds a control method with an anti-saturation threshold parameter fuzzy regulator and adaptive switching of integral saturation limit as the core on the basis of the conventional adaptive PI controller to realize anti-saturation control of the active power of the wind farm. The frequency of occurrence of integral saturation of the PI controller is reduced through limiting processing, and the adaptive PI controller is optimized.

[0162] In an optional embodiment, the PI controller is specifically used to perform PI control based on the proportional control parameter, the integral control parameter and the current integral value to convert the active reference value into the command value;

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

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

[0165] The optimization module is used to optimize the distribution of active power of the wind farm according to the active power given value.

[0166] In the specific implementation process, the PI parameter adaptive regulator and the integral saturation limit adaptive switching link respectively adjust the proportional control coefficient K after adaptive adjustment. p , integral control coefficient K i and the current integral limit I lmtThe active reference value is transferred to the PI controller for PI control to convert the active reference value into the command value. The active command value output by the PI controller is subjected to a second limiting process (eg, output limiting process) to obtain a final active given value for optimizing the power distribution of the unit.

[0167] In an optional embodiment, the wind farm level controller is specifically used to obtain the upper and lower limits of the theoretically available power of the entire wind farm, and to perform a limit processing on the initial active power target value;

[0168] If the initial active power target value is greater than the upper limit of the theoretically available power of the entire field, the upper limit of the theoretically available power 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 theoretically available power of the entire field, the lower limit of the theoretically available power of the entire field will be used as the initial active power target value.

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

[0171] In addition, considering that the theoretical power that can be generated in the whole field may have estimation deviations due to complex changes in wind conditions, when the estimated value of the theoretical power that can be generated is higher than the actual value of the power that can be generated in the whole field, it may also cause integral saturation. Therefore, the present invention further optimizes the existing adaptive PI control logic, and the control logic mainly includes four parts, namely, PI parameter adaptive regulator, anti-saturation threshold parameter fuzzy regulator, integral saturation limit adaptive switching and PI controller. Among them, the PI parameter adaptive regulator adopts the same technical route as the existing wind farm adaptive PI control, and can select conventional variable integral function, fuzzy control, neural network and other algorithms to adaptively modify the proportional control parameters and integral control parameters based on input data such as active reference value and actual active feedback value.

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

[0173] The PI parameters are adapted based on the difference and the change value of the difference to obtain the proportional control parameters and the integral control parameters.

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

[0175] The fourth acquisition module is used to acquire the proportional control parameter and the integral control parameter based on the linear piecewise function and / or the fuzzy control algorithm and / or the neural network algorithm.

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

[0177] The adjustment module is used to perform fuzzy adjustment of anti-saturation threshold parameters 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 time of the same active control execution cycle if the current integral value is greater than the anti-saturation threshold parameter and the initial active target value changes.

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

[0180] The anti-saturation threshold parameter adjustment amount is superimposed on the current anti-saturation threshold parameter of the current execution cycle to obtain an updated anti-saturation threshold parameter.

[0181] In an optional embodiment, the third acquisition module is further used to construct a corresponding rule of the response time, the response time variation and the anti-saturation threshold parameter adjustment amount, the rule at least including the membership degree and membership value of the anti-saturation threshold parameter adjustment amount to the response time and the response time variation;

[0182] Determine an anti-saturation threshold parameter adjustment amount based on a current response time, a change in the current response time compared to a response time in a previous execution cycle, and a rule;

[0183] Get rules;

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

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

[0186] Constructing the third language variable of the anti-saturation threshold parameter adjustment amount and the corresponding three-way fuzzy subset;

[0187] Establishing a first fuzzy subset element, a second fuzzy subset element and a fuzzy rule table based on a third fuzzy subset element, each third fuzzy subset element corresponds to a determined first fuzzy subset element and a determined second fuzzy subset element; a third fuzzy subset corresponding to a linguistic variable, and a third linguistic variable corresponding to an anti-saturation threshold parameter adjustment amount;

[0188] A corresponding rule between the third fuzzy subset and the first and second fuzzy subsets is constructed, wherein the rule at least corresponds 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 in which a wind farm participates in primary frequency regulation is selected. Considering that the wind farm is more likely to experience PI controller integral saturation when directly switching to the primary frequency regulation power limiting mode triggered by frequency disturbance in the free full-generation working mode, this is used as a test condition for specific description, including active 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 the existing wind farm active control technology, the present invention adds a PI controller input limiting link and optimizes the adaptive PI controller. Specifically, the primary frequency regulation is enabled, and the frequency signal received in real time is set based on the frequency limiting signal. The initial active target value is calculated based on a good frequency modulation curve; the initial active target value is input into the input limiting link of the wind farm field-level controller. If the initial active target value is higher than the upper limit of the theoretically generateable power of the entire field estimated by the system in real time, the initial active target value will be forced to be limited to the upper limit of the theoretically generateable power of the entire field; similarly, if the initial active target value is lower than the lower limit of the theoretically generateable power of the entire field, the initial active target value will also be forced to be limited to the lower limit of the theoretically generateable power of the entire field to ensure that the output active reference value is within the theoretically controllable range of the system; the active reference value, the actual active feedback value, and the system control performance calculation results are input into the pre-adaptive adjustment link of the PI control together. This link is divided into two branches with different calling frequencies, one of which is the PI parameter adaptive regulator, and the other is the anti-saturation threshold parameter fuzzy regulator and the integral saturation limit adaptive switching. Among them, the PI parameter adaptive regulator is synchronously called with the active control, and the proportional control parameter K is adjusted multiple times during the execution cycle of the same initial active target value. p , integral control parameter K i , and then modify the active given value for multiple times. To ensure that the PI parameters are adjusted within a short active control period, this embodiment uses a linear piecewise function similar to the idea of ​​a variable integral function to adjust the proportional control parameter K p , integral control parameter K i After the anti-saturation threshold parameter fuzzy regulator enters the steady state according to the current execution cycle (kth execution cycle), the output response time Tup (k) and the response time change ΔT obtained by subtracting the data from the previous execution cycle up (k), using fuzzy control algorithm to combat saturation threshold parameter I th (k+1) is corrected. Considering that the response time requirement of a frequency modulation is generally not more than 9 seconds, for example, in this embodiment, the fuzzy subset of the response time is set to {Z, S, M, L, U}, which respectively represent the five language variables of zero, small, medium, large, and over-limit. The fuzzy subset of the response time change is {NU, NB, NM, NS, ZO, PS, PM, PB, PU}, which respectively represent the nine language variables of negative over-limit, negative large, negative medium, negative small, zero, positive small, positive medium, positive large, and positive over-limit. Output variable anti-saturation threshold parameter adjustment amount ΔI th The fuzzy subset of is the conventional {NB, NM, NS, ZO, PS, PM, PB}, which is composed of 7 linguistic variables. The fuzzy rules of the anti-saturation threshold parameter regulator are shown in Table 1 in Example 1.

[0190] The membership degree and membership value of the output variable are obtained by using this rule. The anti-saturation threshold parameter adjustment can be calculated by defuzzification using the centroid method. The updated threshold parameter is obtained by iterating with the original value. th (k+1)=I th (k)+ΔI th , further limit the threshold parameter to ensure that it is within the optimization range, the anti-saturation threshold parameter after limiting It will be applied to the next execution cycle, i.e. the k+1th execution cycle. If it is detected that the initial active target value has changed, it will enter the k+1th execution cycle, and the integral saturation limit adaptive switching link will determine 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 It is the steady-state integral value of the previous same-mode execution cycle; otherwise, it keeps the default value, that is, the maximum steady-state response error of the entire field active power considering the wind turbine design requirements and extreme operating conditions.

[0191] In the primary frequency regulation test verification of a certain wind farm in this embodiment, the PI controller triggers the primary frequency regulation power limit under the condition of integral saturation. The primary frequency regulation response curve of the entire field under the condition of integral saturation before the application of the present invention is as follows: Figure 2 As shown, the full frequency modulation response curve under the condition of integral saturation after application is as follows Figure 3As shown; it can be seen that after the application of the present invention, the primary frequency modulation response time (reaching 90% of the initial difference) is shortened from the original 43.5s to 4.4s, and the adjustment time (entering the 5% initial difference error band and meeting the 1% Pn conventional active steady-state error) is shortened from the original 59s to 6.4s, which meets the primary frequency modulation fast response requirements, and proves that the wind farm active power adaptive fast anti-saturation control method proposed in the present invention can improve the dynamic response performance of the wind farm active power, and solves the problems of response time and adjustment time not meeting the standards that may occur when directly participating in the primary frequency modulation under the condition of integral saturation of the PI controller.

[0192] The wind farm active power adaptive fast anti-saturation control method proposed in the present invention improves the dynamic response performance of the wind farm active power, effectively solves the problems of response time and regulation time not meeting the standards that may occur when the PI controller directly participates in the primary frequency modulation under the condition of integral saturation, has strong adaptability to different units, different wind conditions and different control modes, improves the overall grid-connected support capacity of the wind farm, and reduces the on-site parameter debugging workload of technical personnel.

[0193] As for the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the units described as separate components may or may not be physically separated, and the components as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution.

[0194] Example 3

[0195] Figure 5 This is a structural schematic diagram of an electronic device shown in Example 3 of the present disclosure, the electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor, and when the processor executes the computer program, the method for controlling the active power of a wind farm described in any of the above embodiments is implemented. Figure 5 The electronic device 90 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0196] like Figure 5 As shown, the electronic device 90 may be in the form of a general-purpose computing device, for example, it may be 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 the memory 92 and the processor 91).

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

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

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

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

[0201] The electronic device 90 may also communicate with one or more external devices 94 (e.g., keyboards, pointing devices, etc.). Such communication may be performed via an input / output (I / O) interface 95. Furthermore, the electronic device 90 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 96. Figure 5 As shown, the network adapter 96 communicates with other modules of the electronic device 90 via the 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 the 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, etc.

[0202] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present 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 into multiple units / modules to be embodied.

[0203] Example 4

[0204] Embodiment 4 of the present disclosure further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for controlling the active power of a wind farm provided in any of the above embodiments is implemented.

[0205] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0206] Example 5

[0207] Embodiment 5 of the present disclosure further provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for controlling the active power of a wind farm.

[0208] Among them, the program code for executing the computer program product of the present disclosure can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0209] Although the specific embodiments of the present disclosure are described above, those skilled in the art should understand that this is only an example, and the protection scope of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, but these changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. A method for controlling active power of a wind farm, characterized in that: The control method comprises: Obtaining the initial active power target value of the wind farm; Performing a first amplitude limiting process on the initial active power target value to obtain an active power reference value; According to the active reference value and the actual active feedback value, a proportional control parameter and an integral control parameter of the PI control are obtained; Based on the response time of the execution cycle of the active power control of the wind farm and the response time variation between the current execution cycle and the previous execution cycle, the anti-saturation threshold parameter is fuzzy adjusted 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, the anti-saturation threshold parameter is switched to the steady-state integral value of the previous execution cycle; Based on the proportional control parameter, the integral control parameter and the current integral value, PI control is performed on the active power to obtain an active given value.

2. The method for controlling active power of a wind farm according to claim 1, characterized in that: The performing PI control on the active power to obtain the active given value based on the proportional control parameter, the integral control parameter and the current integral value comprises: Based on the proportional control parameter, the integral control parameter and the current integral value, PI control is performed to convert the active 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 an active power given value.

3. The method for controlling active power of a wind farm according to claim 2, characterized in that: After performing a second limiting process on the active command value output by the PI control to obtain an active given value, the control method further includes: The active power of the wind farm is optimally distributed according to the active given value.

4. The method for controlling active power of a wind farm according to claim 1, characterized in that: The first clipping process comprises: Obtaining the upper and lower limits of the theoretically available power of the entire wind farm, and performing a limiting process on the initial active power target value; If the initial active power target value is greater than the upper limit value of the theoretically available power of the entire field, the upper limit value of the theoretically available power of the entire field is used as the initial active power target value; If the initial active power target value is less than the lower limit value of the theoretically generateable power of the entire field, the lower limit value of the theoretically generateable power of the entire field will be used as the initial active power target value.

5. The method for controlling active power of a wind farm according to claim 1, characterized in that: The performing PI control on the active power to obtain the active given value based on the proportional control parameter, the integral control parameter and the current integral value comprises: Obtaining a difference between the active reference value and the actual active feedback value and a difference change value between two adjacent execution cycles, wherein the execution cycle is synchronized with the active control module; PI parameter adaptation is performed based on the difference and the difference change value to obtain proportional control parameters and integral control parameters.

6. The method for controlling active power of a wind farm according to claim 1, characterized in that: The control method further comprises: The proportional control parameter and the integral control parameter are obtained based on a linear piecewise function and / or a fuzzy control algorithm and / or a neural network algorithm.

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

8. The method for controlling active power of a wind farm according to claim 1, characterized in that: If the current integral value is greater than the anti-saturation threshold parameter and the initial active target value changes, switching the anti-saturation threshold parameter to the steady-state integral value of the previous execution cycle includes: At the start time 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 active power of a wind farm according to claim 1, characterized in that: The step of performing fuzzy adjustment of anti-saturation threshold parameters based on the response time of the execution cycle of the active power control of the wind farm and the response time variation between the current execution cycle and the previous execution cycle to obtain the anti-saturation threshold parameters includes: Obtaining the anti-saturation threshold parameter adjustment amount and the current anti-saturation threshold parameter of the current execution cycle; The anti-saturation threshold parameter adjustment amount is superimposed on the current anti-saturation threshold parameter of the current execution cycle to obtain an updated anti-saturation threshold parameter.

10. The method for controlling active power of a wind farm according to claim 9, characterized in that: Before obtaining the anti-saturation threshold parameter adjustment amount, the method includes: Constructing a corresponding rule between the response time, the response time variation and the anti-saturation threshold parameter adjustment amount, the rule at least including a membership degree and a membership value of the anti-saturation threshold parameter adjustment amount to the response time and the response time variation; Determine the anti-saturation threshold parameter adjustment amount based on the current response time, the response time change amount of the current response time compared to the response time of the previous execution cycle, and the rule; The fuzzy adjustment of the anti-saturation threshold parameter comprises: obtaining the rule; The anti-saturation threshold parameter adjustment amount is obtained by using a fuzzy algorithm according to the membership degree and the membership value.

11. The method for controlling active power of a wind farm according to claim 10, characterized in that: The corresponding rules for constructing the response time, the response time variation, and the anti-saturation threshold parameter adjustment amount include: Obtaining a first linguistic variable of the response time and a corresponding first fuzzy subset, and a second linguistic variable of the response time variation and a corresponding second fuzzy subset; Constructing the third language variable of the anti-saturation threshold parameter adjustment amount and the corresponding three-way fuzzy subset; Establishing a first fuzzy subset element, a second fuzzy subset element, and a fuzzy rule table based on a third fuzzy subset element, each third fuzzy subset element corresponds to a determined first fuzzy subset element and a determined second fuzzy subset element; a third fuzzy subset corresponding to a linguistic variable, the third fuzzy variable corresponding to the anti-saturation threshold parameter adjustment amount; A corresponding rule between the third fuzzy subset and the first and second fuzzy subsets is constructed, wherein the rule at least corresponds to the degree of membership and the value of membership of the third fuzzy subset to the first and second fuzzy subsets.

12. A wind farm active power control system, characterized in that: The control system comprises: A first acquisition module is used to acquire an initial active power target value of the wind farm; A wind farm level controller, configured to perform a first amplitude limiting process on the initial active power target value to obtain an active power reference value; A second acquisition module is used to acquire a proportional control parameter and an integral control parameter of the PI control according to the active reference value and the actual active 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 response time change between the current execution cycle and the previous execution cycle to obtain the anti-saturation threshold parameter; A switching module, configured to switch the anti-saturation threshold parameter to a steady-state integral value of a 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 the active power based on the proportional control parameter, the integral control parameter and the current integral limit value to obtain an active given value.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, characterized in that: When the processor executes the computer program, the method for controlling the active power of a wind farm according to any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for controlling active power of a wind farm according to any one of claims 1 to 11 is implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for controlling active power of a wind farm according to any one of claims 1 to 11 is implemented.

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