A control method and device for active power smoothing and reactive voltage regulation of a wind storage system

Abstract

The application discloses a control method and equipment for active power smoothing and reactive power voltage regulation of a wind storage system, and the method comprises the following steps: S1, performing adaptive noise complete set empirical mode decomposition on a wind power signal to obtain a plurality of mode components; S2, according to a wind power grid fluctuation standard, reconstructing the mode components into high-frequency components and low-frequency components, and taking the high-frequency components and the low-frequency components as a primary active power instruction of a storage system and a wind power plant grid-connected power respectively; S3, based on a real-time state of charge of the storage system and the primary active power instruction, correcting the primary active power instruction by using a fuzzy control method, and simultaneously optimizing and adjusting the state of charge of the storage system; and S4, according to the grid-connected power of the wind storage system and a grid-connected point voltage, generating a reactive power instruction of the storage system by using a fuzzy control method, so as to realize the regulation of the grid-connected point voltage.

Description

Technical Field

[0001] This application relates to the field of energy storage control technology for new energy power stations, and in particular to a control method and equipment for active power smoothing and reactive power voltage regulation in wind-storage systems. Background Technology

[0002] In recent years, with the large-scale grid connection of renewable energy sources such as wind power, their inherent randomness, volatility, and uncertainty have posed severe challenges to the safe and stable operation of the power system. Based on this, energy storage technology, with its rapid response characteristics and high-precision regulation capabilities, provides an effective solution for suppressing wind power fluctuations and preventing voltage exceedances through flexible active / reactive power support. Therefore, developing an energy storage-based coordinated control strategy that balances power smoothing and voltage regulation has become a key technological path to achieving a high proportion of wind power grid connection.

[0003] Existing research mainly revolves around two core issues: the generation method of energy storage power commands and the dynamic response control of energy storage systems. Regarding power command generation, traditional methods such as low-pass filters face difficulties in cutoff frequency tuning, while techniques based on empirical mode decomposition or wavelet packet decomposition are generally limited by mode aliasing. In terms of control strategy design, current research largely focuses on the protection mechanisms of the energy storage device itself, failing to fully consider the actual needs of the power system for energy storage regulation capabilities, resulting in limited overall control efficiency. Furthermore, research on voltage regulation using reactive power from energy storage is mainly concentrated in distribution networks, with little research on reactive power voltage regulation scenarios using energy storage at wind farm grid connection points.

[0004] Therefore, there is still a lack of a control strategy for active power smoothing and reactive power voltage regulation of grid-connected energy storage in wind farms. Summary of the Invention

[0005] This application provides a control method and device for active power smoothing and reactive power voltage regulation in wind-storage systems to solve the above-mentioned problems.

[0006] On the one hand, this application provides a control method for active power smoothing and reactive power voltage regulation in wind-storage systems. The method includes the following steps: Step S1: Perform adaptive noise complete ensemble empirical mode decomposition on the wind power signal to obtain multiple mode components; Step S2: Reconstruct the mode components into high-frequency components and low-frequency components according to the wind power grid connection fluctuation standard, and use them as the primary active power command of the energy storage system and the grid-connected power of the wind farm, respectively; Step S3: Based on the real-time state of charge of the energy storage system and the primary active power command, use a fuzzy control method to correct the primary active power command, and simultaneously optimize and adjust the state of charge of the energy storage system; Step S4: Based on the grid-connected power of the wind-storage system and the grid connection point voltage, use a fuzzy control method to generate the reactive power command of the energy storage system to achieve grid connection point voltage regulation.

[0007] In one implementation of this application, the adaptive noise complete set empirical mode decomposition in step S1 is an improved ICEEMDAN wind power decomposition method. Combined with the local mean calculation in the empirical mode decomposition process, mode components of different frequencies are gradually separated to finally obtain the residual. In addition, the noise coefficients corresponding to different order mode components are adapted and adjusted during the decomposition process to reduce mode aliasing.

[0008] In one implementation of this application, the ICEEMDAN wind power decomposition method uses the following calculation formula:

[0009]

[0010]

[0011] In the formula, E m Indicates generated by EMD decomposition m First-order modal components, W ( k ) represents the first k Group of Gaussian white noise, N ave This represents the local mean of the power signal. ε m-1 Indicates the addition of Gaussian white noise. m IMF components E m ( W ( k The coefficient multiplied when multiplying; K The number of sequence groups; R m For the first m Group residuals; imf m For the first m One modal component.

[0012] In one implementation of this application, the process of reconstructing the high-frequency components and low-frequency components in step S2 includes setting a boundary point according to the grid connection standard, and using the sum of the modal components above the boundary point as the primary active power command of the energy storage system.

[0013] In one implementation of this application, the fuzzy control method in step S3 takes the state of charge of the energy storage system and the normalized primary active power command as inputs, and takes the power command correction coefficient as output. The power command correction coefficient is used to scale and adjust the primary active power command and simultaneously optimize the state of charge operating range of the energy storage system.

[0014] In one implementation of this application, the fuzzy control method in step S4 takes the normalized grid-connected power and grid-connected point voltage of the wind-storage system as inputs and the normalized energy storage reactive power command as output. The energy storage reactive power command is converted into the actual reactive power output value after defuzzification processing.

[0015] In one implementation of this application, the defuzzification processing of the energy storage reactive power command adopts a weighted average method to convert the fuzzy quantity output by fuzzy inference into a precise reactive power setpoint; this setpoint is combined with the upper limit of reactive power output of the energy storage system and the apparent power constraint to perform amplitude limiting processing.

[0016] In one implementation of this application, the fuzzy control used in steps S3 and S4 both employ Gaussian membership functions to fuzzify the input and output variables.

[0017] In one implementation of this application, the method further includes: constructing a fuzzy control rule table to establish the correspondence between grid connection point voltage, normalized grid-connected power and normalized energy storage reactive power, wherein the column parameters in the rule table are fuzzy sets of grid connection point voltage, the row parameters are fuzzy sets of normalized grid-connected power, and each unit parameter is the corresponding fuzzy control rule for normalized energy storage reactive power, and an energy storage reactive power adjustment command is output according to the rule table.

[0018] On the other hand, this application also provides a control device for active power smoothing and reactive power regulation in a wind power and energy storage system. The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the aforementioned control method for active power smoothing and reactive power regulation in a wind power and energy storage system.

[0019] This application provides a control method and device for active power smoothing and reactive power voltage regulation in a wind-storage system, which has the following beneficial effects:

[0020] (1) This invention is based on the improved adaptive noise complete set empirical mode decomposition (ICEEMDAN) algorithm to decompose the wind power signal, separating the original power sequence into low-frequency grid-connected components and high-frequency energy storage regulation components, thereby constructing a hierarchical control command system. Compared with conventional mode decomposition techniques, this method effectively improves the mode confusion problem in the signal decomposition process, and the obtained power components are more consistent with the dynamic response characteristics of grid connection and energy storage in actual engineering applications, providing a more accurate power allocation benchmark for subsequent optimization control.

[0021] (2) Based on the technical advantages of rapid response and flexible power regulation of energy storage systems, this invention designs a fuzzy control method that integrates high-frequency power dynamic correction and SOC optimization regulation. This method effectively avoids the risk of SOC exceeding the limit caused by excessive charge and discharge depth of energy storage units by real-time correction of high-frequency power commands and the introduction of a SOC fuzzy optimization mechanism, thereby significantly improving the dynamic regulation capability and operational reliability of the energy storage system.

[0022] (3) Based on the grid-connected power and grid-connected voltage of the wind farm, this invention designs a fuzzy control method for regulating the grid-connected voltage based on energy storage. This method effectively solves the problem of voltage exceeding the limit caused by excessive fluctuations in wind power at the grid-connected point by real-time regulation of the reactive power of the energy storage. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 A flowchart of a control method for active power smoothing and reactive power voltage regulation in a wind-storage system, provided in an embodiment of this application;

[0025] Figure 2 This application provides an overall technical roadmap for its embodiments.

[0026] Figure 3 Wind power decomposition diagram based on ICEEMDAN provided for embodiments of this application;

[0027] Figure 4 This is a fuzzy membership function diagram of the energy storage state of charge (SOC(t)) provided in the embodiments of this application;

[0028] Figure 5 The fuzzy membership function diagram of the primary power command for energy storage provided in the embodiments of this application;

[0029] Figure 6 The fuzzy membership function diagram of the energy storage power command correction coefficient provided in the embodiments of this application;

[0030] Figure 7 The grid-connected power fuzzy membership function diagram provided in the embodiments of this application;

[0031] Figure 8 This application provides a fuzzy membership function diagram of grid-connected voltage for embodiments of the present application.

[0032] Figure 9 The fuzzy membership function graph of energy storage reactive power provided in the embodiments of this application;

[0033] Figure 10 The energy storage system charge / discharge power diagram provided in the embodiments of this application;

[0034] Figure 11 A state-of-charge diagram of an energy storage system provided in an embodiment of this application;

[0035] Figure 12 The diagram shows the effect of the energy storage system in mitigating wind power in the embodiments of this application.

[0036] Figure 13 A diagram showing the voltage regulation results at the grid connection point of a wind farm, provided in an embodiment of this application.

[0037] Figure 14 The reactive power diagram for energy storage charging and discharging provided in the embodiments of this application;

[0038] Figure 15 This is a schematic diagram of a control device for active power smoothing and reactive power voltage regulation in a wind-storage system, provided as an embodiment of this application. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] This application provides a control method and device for active power smoothing and reactive power voltage regulation in a wind-storage system. The technical solution proposed in this application will be described in detail below with reference to the accompanying drawings.

[0041] Figure 1 This document presents a flowchart illustrating a control method for active power smoothing and reactive power voltage regulation in a wind-storage system, as provided in an embodiment of this application. Figure 1 As shown, the method mainly includes the following steps:

[0042] Step S1: Perform adaptive noise complete set empirical mode decomposition on the wind power signal to obtain multiple mode components.

[0043] Step S2: Based on the wind power grid connection fluctuation standard, the modal components are reconstructed into high-frequency components and low-frequency components, which are respectively used as the primary active power command of the energy storage system and the grid connection power of the wind farm.

[0044] Step S3: Based on the real-time state of charge of the energy storage system and the primary active power command, a fuzzy control method is used to correct the primary active power command and simultaneously optimize and adjust the state of charge of the energy storage system.

[0045] Step S4: Based on the grid-connected power and grid connection point voltage of the wind-storage system, a fuzzy control method is used to generate reactive power commands for the energy storage system in order to regulate the grid connection point voltage.

[0046] The overall technical approach of the technical solution provided in this application is as follows: Figure 2 As shown, the model includes: an upper layer based on an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to decompose and reconstruct wind power and determine the primary active power command for energy storage; a fuzzy control method is used to perform a secondary correction of the active power command based on the current state of charge (SOC) of the energy storage to avoid insufficient response capability caused by overcharging and discharging of the energy storage; a lower layer solves for the grid-connected power of the wind-storage system based on the updated power command; and considering the voltage fluctuation at the grid connection point, the reactive power of the energy storage is adjusted through a fuzzy control method to achieve effective regulation of the grid connection point voltage to meet the regulation standard of the grid connection point voltage. This invention proposes a two-layer cascaded fuzzy control strategy for active power smoothing and reactive power voltage regulation of wind-storage systems by combining ICEEMDAN and fuzzy control methods, which effectively reduces wind farm power and grid connection point voltage fluctuations and improves the response capability of the energy storage system. The specific process is as follows:

[0047] First, a wind power decomposition method based on ICEEMDAN is constructed, and its general formula is as follows:

[0048]

[0049]

[0050] In the formula, E m Indicates generated by EMD decomposition m First-order modal components, W ( k ) represents the first k Group of Gaussian white noise, N ave This represents the local mean of the power signal. ε m-1 Indicates the addition of Gaussian white noise. m IMF components E m ( W ( k The coefficient multiplied when multiplying; K The number of sequence groups; R m For the firstm Group residuals; imf m For the first m One modal component.

[0051] Secondly, a method for allocating primary power commands for energy storage is constructed. Based on the decomposition results of wind power, the data is reconstructed according to the grid connection requirements for wind power fluctuations in my country, as shown in the table below.

[0052]

[0053] When the m When the secondary decomposition meets the grid connection requirements, the high-frequency components will be... imf 1~ imf m The sum is used as the energy storage power command, and the remaining low-frequency component and residual are used as the grid-connected power, as shown below:

[0054]

[0055]

[0056] In the formula, P ref Command to smooth out wind power consumption for energy storage; P grid Power connected to the grid; m for imf Component boundary point; n 0 is imf Total number of components; res n0 It represents the residual.

[0057] Specifically by Figure 3 Analysis shows that this invention uses ICEEMDAN to decompose wind power, which is then decomposed into 9 IMF components and 1 margin according to the frequency range from high to low. Based on the wind power fluctuation grid connection standard, [the following is omitted as it is not directly related to the analysis]. imf 1~ imf 3. The energy storage power command is reconstructed into a high-frequency component, and the remaining IMF component and margin are reconstructed into a low-frequency component as grid-connected power.

[0058] Secondly, a power command and SOC correction strategy based on fuzzy control are constructed. This specifically includes the following processes:

[0059] (1) Constructing a fuzzy control input-output calculation method. The initial power command of the fuzzy control input is normalized; the energy storage SOC is calculated considering the charging and discharging efficiency of the energy storage system; the fuzzy control output is set as a correction coefficient to correct the initial power command and optimize the SOC, as follows:

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] In the formula, Z c and Z d 0-1 variables; For charging efficiency; For discharge efficiency; For charge and discharge efficiency; This represents the initial state of charge of the energy storage. P rate This refers to the rated active power of the energy storage. E rate This refers to the rated capacity of the energy storage. n This represents the number of sampling points; The sampling time interval; P ref ( t () represents the active power command for energy storage; P ref * ( t () is the normalized active power command for energy storage; P fix ( t () represents the power command after energy storage correction; SOC fix ( t () represents the optimized state of charge for energy storage; This is the correction factor for the energy storage power command.

[0067] (2) Fuzzyization of the input and output quantities of the energy storage system. The input quantities of the energy storage fuzzy control system are the energy storage state of charge and the initial power command, and the output quantity is the power command correction coefficient. These input and output quantities are fuzzified. Based on fuzzy control theory, a Gaussian function is used as the membership function to design a fuzzy control system for energy storage. SOC ( t ) and normalized power command P ref * ( t The power command correction coefficient is used as the input for fuzzy control. For the fuzzy control strategy of the output, for the input quantity SOC (t Fuzzy sets Z, S, M, B, and L are defined, corresponding to zero, small, medium, large, and larger, respectively, representing the degree of increase from small to large within the number range [0.1, 0.9]; input quantity P ref * ( t Fuzzy sets NB, NS, Z, PS, and PB are defined, corresponding to negative large, negative small, zero, positive small, and positive large, respectively, representing the degree of increase from small to large within the number field range [-1, 1]; the output quantity... α ( t Five linguistic values ​​are defined as fuzzy sets: Z, S, M, B and L, corresponding to zero, small, medium, large and larger, respectively, representing the degree of increase from small to large within the number field range [0, 1].

[0068] Specifically by Figure 4 Analysis shows that this invention is based on fuzzy control theory, uses a Gaussian function as the membership function, and designs a fuzzy control input with stored SOC(t). Fuzzy sets Z, S, M, B, and L are defined, corresponding to zero, small, medium, large, and larger, respectively, representing the degree of SOC(t) increasing from small to large within the number domain [0.1, 0.9]. Figure 5 Analysis shows that this invention is based on fuzzy control theory, uses a Gaussian function as the membership function, and designs a normalized power command for energy storage. P ref * ( t As the input for fuzzy control, for the input quantity P ref * ( t Fuzzy sets are defined as NB, NS, Z, PS, and PB, corresponding to negative large, negative small, zero, positive small, and positive large, respectively, representing... P ref * ( t The degree of increase in the number field [-1, 1], respectively. Figure 6 Analysis shows that this invention is based on fuzzy control theory, uses a Gaussian function as the membership function, and designs a correction coefficient based on the energy storage power command. α(t) For the fuzzy control strategy of output, for the output quantity α ( t Five linguistic values ​​are defined as fuzzy sets: Z, S, M, B and L, corresponding to zero, small, medium, large and larger, respectively, with a number field range of [0, 1].

[0069] (3) Constructing the fuzzy control rule table. After fuzzifying the above input and output quantities, a fuzzy rule table is constructed, as shown in the table below. The column parameters of the rule table are: SOC (t ) Fuzzy set, row parameter is primary power command P ref * ( t ) Fuzzy set, where each element parameter is a correction coefficient α ( t Fuzzy control rules.

[0070]

[0071] (4) Defuzzification processing. The weighted average method is used to defuzzify the fuzzy control output to obtain the accurate value of the energy storage output power.

[0072] Secondly, a reactive power regulation strategy based on fuzzy control is constructed, and the specific process is as follows:

[0073] (1) Constructing a method for calculating fuzzy control input and output quantities

[0074] The fuzzy control input, the grid-connected power of the wind farm's energy storage system, is normalized; the fuzzy control output is set as the reactive power of the energy storage system, and the voltage at the wind farm's grid connection point is regulated, as follows:

[0075]

[0076]

[0077]

[0078]

[0079] In the formula, P wind ( t () represents the original wind power output; P wf ( t () represents the corrected grid-connected power. The normalized grid-connected power of the wind-storage system; Q ess ( t () represents the reactive power of the energy storage system; The normalized reactive power of the energy storage system; Q ess.max This represents the upper limit of reactive power in the energy storage system. S ess This represents the apparent power of the energy storage system.

[0080] (2) Fuzzyization of input and output quantities of the energy storage system. The input quantities of the energy storage fuzzy control system are the grid-connected power and grid connection point voltage of the wind-storage system, and the output quantity is the reactive power of the energy storage system. Fuzzyization is performed on these input and output quantities. Based on fuzzy control theory, a Gaussian function is used as the membership function, and a fuzzy control system is designed with the grid connection point voltage as the membership function. V ( t and normalized grid-connected power Normalized energy storage reactive power serves as the input to fuzzy control. For the fuzzy control strategy of the output, for the input quantity V(t) Fuzzy sets Z, S, M, B, and L are defined, corresponding to zero, small, medium, large, and larger, respectively, representing the degree of increase from small to large within the number range [0.92, 1.08]; input quantity Fuzzy sets Z, S, M, B, and L are defined, corresponding to zero, small, medium, large, and larger, respectively, representing the degree of increase from small to large within the number field [0, 1]; the output quantity... Five linguistic values ​​are defined as fuzzy sets: NB, NS, Z, PS, and PB, corresponding to negative large, negative small, zero, positive small, and positive large, respectively, representing the degree of increase from small to large within the number field range [-1, 1]. Specifically, [the following is a more detailed explanation of the fuzzy sets defined by NB, NS, Z, PS, and PB]. Figure 7 Analysis shows that this invention is based on fuzzy control theory, uses a Gaussian function as the membership function, and designs a system based on grid-connected power. For the fuzzy control strategy of the input, for the input quantity Five fuzzy sets of linguistic values ​​are defined: Z, S, M, B, and L, corresponding to zero, small, medium, large, and larger, respectively, with a number field range of [0, 1]. Figure 8 Analysis shows that this invention is based on fuzzy control theory, uses a Gaussian function as the membership function, and designs a system based on grid-connected voltage. V ( t A fuzzy control strategy with input as the input quantity, for the input quantity V(t) Five linguistic values, Z, S, M, B, and L, are defined as fuzzy sets, corresponding to zero, small, medium, large, and larger, respectively, with a number range of [0.92, 1.08]. Figure 9 Analysis shows that this invention is based on fuzzy control theory, uses a Gaussian function as the membership function, and designs a system for storing reactive power. As the output of fuzzy control, for the output quantity Fuzzy sets NB, NS, Z, PS, and PB are defined, corresponding to negative large, negative small, zero, positive small, and positive large, respectively, representing... The degree of increase in the number field [-1, 1] from smallest to largest.

[0081] (3) Reconstruct the fuzzy control rule table. After fuzzifying the above input and output quantities, reconstruct the fuzzy rule table as shown in the table below. The column parameters of the rule table are: V(t) Fuzzy set, row parameter is grid-connected power Fuzzy set, where each element parameter is reactive power. Fuzzy control rules.

[0082]

[0083] (4) Defuzzification processing. The weighted average method is used to defuzzify the fuzzy control output to obtain the accurate value of the energy storage output power.

[0084] This invention leverages the technological advantages of rapid response and flexible power regulation in energy storage systems, designing a fuzzy control method that integrates high-frequency power dynamic correction and SOC optimization regulation. This method effectively avoids the risk of SOC exceeding limits due to excessive charge / discharge depth by correcting high-frequency power commands in real time and introducing a fuzzy SOC optimization mechanism, thereby significantly improving the dynamic regulation capability and operational reliability of the energy storage system. Specifically, it involves… Figure 10 Analysis shows that the energy storage system charges and discharges according to the initially determined high-frequency commands, resulting in a large output amplitude, which can easily lead to excessive suppression of wind power. The fuzzy control strategy for suppressing wind power, which takes into account the energy storage output level, effectively reduces the output amplitude of the energy storage system, thus improving economic efficiency. Figure 11 Analysis reveals that when the energy storage system outputs power according to the initially determined high-frequency command, the State of Charge (SOC) may approach or even exceed the limit, leading to insufficient output capacity and an inability to respond to power easing commands. This invention addresses this issue with a fuzzy control strategy for wind power easing that considers the energy storage output level. It uses fuzzy control to correct the energy storage power command. After fuzzy control optimization, the SOC of the energy storage system fluctuates between 0.3 and 0.7, ensuring that the energy storage operates within a reasonable range and improving the system's ability to respond to power commands.

[0085] This invention effectively solves the problem of voltage exceeding limits at the grid connection point caused by excessive fluctuations in wind power output by real-time regulation of energy storage reactive power. Specifically, it involves... Figure 12 Analysis shows that the fuzzy control strategy for smoothing wind power based on the energy storage output level of this invention effectively reduces the fluctuation of the original wind power, making the grid-connected wind power smoother and meeting grid connection standards. Figure 13 Analysis shows that, based on the fuzzy control strategy for wind farm grid connection voltage proposed in this invention, the voltage level is controlled within the safe range of [0.95, 1.05] pu, meeting the grid connection standards. Figure 14 Analysis shows that this invention takes into account the fluctuations in wind power and grid connection voltage, and designs a fuzzy control strategy for reactive power regulation of energy storage, controlling the energy storage to absorb / generate reactive power for voltage regulation.

[0086] The above describes a control method for active power smoothing and reactive power voltage regulation in a wind power storage system, as provided in this application. Based on the same inventive concept, this application also provides a control device for active power smoothing and reactive power voltage regulation in a wind power storage system. Figure 15 A schematic diagram of a control device for active power smoothing and reactive power voltage regulation in a wind-storage system is provided as an embodiment of this application. Figure 15 As shown, the device mainly includes: at least one processor 1501; and a memory 1502 communicatively connected to at least one processor; wherein, the memory 1502 stores instructions that can be executed by at least one processor 1501, and the instructions are executed by at least one processor 1501 to enable at least one processor 1501 to complete the aforementioned control method for active power smoothing and reactive power voltage regulation of a wind power storage system.

[0087] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0088] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0089] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A control method for active power smoothing and reactive power voltage regulation in a wind-storage system, characterized in that, The method includes the following steps: Step S1: Perform adaptive noise complete set empirical mode decomposition on the wind power signal to obtain multiple mode components; Step S2: Based on the wind power grid connection fluctuation standard, the modal components are reconstructed into high-frequency components and low-frequency components, which are respectively used as the primary active power command of the energy storage system and the grid-connected power of the wind farm. Step S3: Based on the real-time state of charge of the energy storage system and the primary active power command, a fuzzy control method is used to correct the primary active power command and simultaneously optimize and adjust the state of charge of the energy storage system. Step S4: Based on the grid-connected power and grid connection point voltage of the wind-storage system, a fuzzy control method is used to generate a reactive power command for the energy storage system in order to regulate the grid connection point voltage. The fuzzy control method uses the normalized grid-connected power and grid connection point voltage of the wind-storage system as inputs and the normalized energy storage reactive power command as output. The energy storage reactive power command is converted into an actual reactive power output value after defuzzification processing. The method further includes: constructing a fuzzy control rule table to establish the correspondence between grid connection point voltage, normalized grid-connected power and normalized energy storage reactive power. The column parameters in the rule table are fuzzy sets of grid connection point voltage, the row parameters are fuzzy sets of normalized grid-connected power, and the unit parameters are the corresponding fuzzy control rules for normalized energy storage reactive power. The method outputs energy storage reactive power adjustment commands based on the rule table.

2. The control method for active power smoothing and reactive power voltage regulation in a wind-storage system according to claim 1, characterized in that, The adaptive noise complete set empirical mode decomposition in step S1 is an improved ICEEMDAN wind power decomposition method. Combined with the local mean calculation in the empirical mode decomposition process, the mode components of different frequencies are gradually separated to obtain the residual. In addition, the noise coefficients corresponding to different order mode components are adapted and adjusted during the decomposition process to reduce mode aliasing.

3. The control method for active power smoothing and reactive power voltage regulation in a wind-storage system according to claim 2, characterized in that, The ICEEMDAN wind power decomposition method uses the following formula: In the formula, E m Indicates generated by EMD decomposition m First-order modal components, W ( k ) represents the first k Group of Gaussian white noise, N ave This represents the local mean of the power signal. ε m-1 Indicates the addition of Gaussian white noise. m IMF components E m ( W ( k The coefficient multiplied when multiplying; K The number of sequence groups; R m For the first m Group residuals; imf m For the first m One modal component.

4. The control method for active power smoothing and reactive power voltage regulation in a wind-storage system according to claim 1, characterized in that, The process of reconstructing high-frequency and low-frequency components in step S2 includes setting a boundary point according to the grid connection standard, and using the sum of modal components above the boundary point as the primary active power command of the energy storage system.

5. The control method for active power smoothing and reactive power voltage regulation in a wind-storage system according to claim 1, characterized in that, The fuzzy control method in step S3 takes the state of charge of the energy storage system and the normalized primary active power command as inputs and the power command correction coefficient as outputs. The power command correction coefficient is used to scale and adjust the primary active power command and simultaneously optimize the state of charge operating range of the energy storage system.

6. The control method for active power smoothing and reactive power voltage regulation in a wind-storage system according to claim 1, characterized in that, The defuzzification processing of energy storage reactive power commands adopts a weighted average method to convert the fuzzy quantity output by fuzzy inference into a precise reactive power setpoint. This setpoint is combined with the upper limit of reactive power output of the energy storage system and the apparent power constraint for amplitude limiting processing.

7. The control method for active power smoothing and reactive power voltage regulation in a wind-storage system according to claim 1, characterized in that, The fuzzy control used in steps S3 and S4 employs Gaussian membership functions to fuzzify the input and output variables.

8. A control device for active power smoothing and reactive power voltage regulation in a wind-storage system, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a control method for active power smoothing and reactive power regulation of a wind-storage system as described in any one of claims 1-7.

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