Converter control method and device, equipment, storage medium and program product

Abstract

The invention discloses a converter control method and device, equipment, a storage medium and a program product. The method comprises the steps of obtaining an active power deviation value and a reactive power deviation value corresponding to a converter; respectively determining an active deviation change rate and a reactive deviation change rate according to the active power deviation value and the reactive power deviation value; inputting the active power deviation value and the active deviation change rate into a first fuzzy controller, and performing fuzzy reasoning on the active power deviation value and the active deviation change rate through a fuzzy reasoning rule corresponding to the first fuzzy controller to obtain an active droop coefficient; inputting the reactive power deviation value and the reactive power deviation change rate into a second fuzzy controller to obtain a reactive power droop coefficient; and generating a control signal of the converter according to the active droop coefficient and the reactive droop coefficient. According to the embodiment of the invention, the performance of the energy storage system can be improved.

Description

Technical Field

[0001] This application belongs to the field of power electronics technology, and in particular relates to a control method, device, equipment, storage medium and program product for a converter. Background Technology

[0002] With the rapid development of new energy technologies, their ability to interact with the power grid is gradually improving. Among them, centralized energy storage systems are one of the core infrastructures for the development of new energy technologies, capable of meeting the technical requirements for large-scale grid connection of renewable energy.

[0003] Because the grid parameters output by a centralized energy storage system differ from grid demand, multiple energy storage converters can be connected in parallel to increase the system's power capacity, and then a step-up transformer can be used to boost the system's voltage level, thus achieving grid connection. However, due to limitations in the hardware structure of the energy storage converters, the output current of different converters varies, which can lead to circulating currents between multiple converters, reducing the performance of the centralized energy storage system.

[0004] Therefore, how to control the energy storage converter to improve the performance of centralized energy storage systems is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This application provides a control method, apparatus, device, computer storage medium, and program product for a converter, which can improve the performance of an energy storage system.

[0006] In a first aspect, embodiments of this application provide a control method for a converter, applied to an energy storage system. The energy storage system includes one or more converters connected in parallel, and the converter is connected to a digital signal processor. The method includes: acquiring the active power deviation and reactive power deviation corresponding to the converter; determining the active power deviation rate of change and the reactive power deviation rate of change based on the active power deviation and reactive power deviation, respectively; inputting the active power deviation and the active power deviation rate of change into a first fuzzy controller, and performing fuzzy inference on the active power deviation and the active power deviation rate of change through the fuzzy inference rules corresponding to the first fuzzy controller to obtain an active power droop coefficient; inputting the reactive power deviation and the reactive power deviation rate of change into a second fuzzy controller, and performing fuzzy inference on the reactive power deviation and the reactive power deviation rate of change through the fuzzy inference rules corresponding to the second fuzzy controller to obtain a reactive power droop coefficient, wherein the fuzzy inference rules corresponding to the first fuzzy controller are different from the fuzzy inference rules corresponding to the second fuzzy controller; and generating a control signal for the converter based on the active power droop coefficient and the reactive power droop coefficient.

[0007] In one embodiment, the active power deviation and the rate of change of active power deviation are input into a first fuzzy controller. Fuzzy inference rules corresponding to the first fuzzy controller are used to perform fuzzy inference on the active power deviation and the rate of change of active power deviation to obtain the active power droop coefficient. This includes: determining a first membership degree corresponding to the active power deviation and a second membership degree corresponding to the rate of change of active power deviation according to a preset membership degree correspondence rule; determining a target active power membership degree according to the first membership degree and the second membership degree, following the fuzzy inference rules corresponding to the first fuzzy controller, where the fuzzy inference rules include the correspondence between the first membership degree, the second membership degree, and multiple active power membership degrees; determining an active power droop adjustment factor corresponding to the active power membership degree using a preset defuzzification algorithm; and obtaining the active power droop coefficient based on the active power droop adjustment factor and preset active power droop parameters.

[0008] In one embodiment, before obtaining the active power droop coefficient based on the active power droop adjustment factor and the preset active power droop parameter, the method further includes: obtaining the rated voltage frequency, voltage frequency threshold, active power dispatch command power, and active power limit respectively; and calculating the preset active power droop parameter based on the rated voltage frequency, voltage frequency threshold, active power dispatch command power, and active power limit.

[0009] In one embodiment, the reactive power deviation and the reactive power deviation rate of change are input into a second fuzzy controller. Fuzzy inference rules corresponding to the second fuzzy controller are used to perform fuzzy inference on the reactive power deviation and the reactive power deviation rate of change to obtain the reactive power droop coefficient. This includes: determining the third membership degree to which the reactive power deviation belongs and the fourth membership degree to which the reactive power deviation rate of change belongs, respectively, according to a preset membership degree correspondence rule; determining the target reactive power membership degree according to the third and fourth membership degrees and the fuzzy inference rules corresponding to the second fuzzy controller, wherein the fuzzy inference rules corresponding to the second fuzzy controller include the correspondence between the third and fourth membership degrees and multiple reactive power membership degrees; determining the reactive power droop adjustment factor corresponding to the reactive power membership degree through a preset defuzzification algorithm; and obtaining the reactive power droop coefficient based on the reactive power droop adjustment factor and preset reactive power droop parameters.

[0010] In one embodiment, before obtaining the reactive power droop coefficient based on the reactive power droop adjustment factor and the preset reactive power droop parameters, the method further includes: obtaining the rated voltage, voltage threshold and reactive power limit respectively.

[0011] The preset reactive power droop parameters are calculated based on the rated voltage, voltage threshold, and reactive power limit.

[0012] In one embodiment, generating a converter control signal based on the active power droop coefficient and the reactive power droop coefficient includes: calculating the voltage rotation angle based on the active power droop coefficient, the active power deviation, and the rated frequency; calculating the voltage amplitude based on the reactive power droop coefficient, the reactive power deviation, and the rated voltage; determining the active voltage and reactive voltage based on the voltage rotation angle, the voltage amplitude, and a preset voltage change strategy; inputting the difference between the active voltage and the detected active voltage of the grid, and the difference between the reactive voltage and the detected reactive voltage of the grid, into a voltage controller to obtain active current and reactive current values; and inputting the difference between the active current value and the detected active current value of the grid, and the difference between the reactive current value and the detected reactive current value of the grid, into a current controller to obtain the converter control signal.

[0013] In one embodiment, the voltage rotation angle is calculated based on the active power droop coefficient, the active power deviation, and the rated frequency, including: determining the voltage frequency based on the active power droop coefficient, the active power deviation, and the rated frequency; and obtaining the voltage rotation angle by integrating and taking the remainder of the voltage frequency.

[0014] Secondly, embodiments of this application provide a control device for a converter, the device comprising:

[0015] The acquisition module is used to acquire the active power deviation and reactive power deviation corresponding to the converter.

[0016] The first determining module is used to determine the active power deviation rate and the reactive power deviation rate based on the active power deviation and the reactive power deviation, respectively.

[0017] The second determining module is used to input the active power deviation and the active power deviation change rate into the first fuzzy controller, and to perform fuzzy inference on the active power deviation and the active power deviation change rate through the fuzzy inference rules corresponding to the first fuzzy controller to obtain the active power droop coefficient.

[0018] The third determining module is used to input the reactive power deviation and the reactive power deviation change rate into the second fuzzy controller, and to perform fuzzy inference on the reactive power deviation and the reactive power deviation change rate through the fuzzy inference rule corresponding to the second fuzzy controller to obtain the reactive power droop coefficient. The fuzzy inference rule corresponding to the first fuzzy controller is different from the fuzzy inference rule corresponding to the second fuzzy controller.

[0019] The generation module is used to generate control signals for the converter based on the active power droop coefficient and the reactive power droop coefficient.

[0020] Thirdly, embodiments of this application provide a control device for a converter, the device including: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the control method of the converter in the first aspect or any embodiment of the first aspect.

[0021] Fourthly, a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the control method of the converter in the first aspect or any embodiment of the first aspect.

[0022] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform a control method for a converter as described in the first aspect or any embodiment of the first aspect.

[0023] The converter control method, apparatus, device, and computer storage medium of this application embodiment allow each digital signal processor to determine the active power droop coefficient by acquiring the active power deviation and the rate of change of the active power deviation corresponding to the converter and inputting them into a first fuzzy controller. It also acquires the reactive power deviation and the rate of change of the reactive power deviation and inputs them into a second fuzzy controller to determine the reactive power droop coefficient. Based on the reactive power droop coefficient and the active power droop coefficient, a control signal is generated to control the converter, thereby achieving dynamic control of each converter. This ensures that the inverter current output by each converter in the energy storage system is balanced, thus avoiding circulating current problems in the energy storage system and improving the performance of the energy storage system. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the architecture of a centralized energy storage system provided in one embodiment of this application;

[0026] Figure 2 A flowchart illustrating a converter control method according to an embodiment of this application is shown;

[0027] Figure 3 A flowchart illustrating the determination of the active power droop coefficient according to an embodiment of this application is shown;

[0028] Figure 4 A schematic diagram of a Gaussian membership function provided in one embodiment of this application is shown;

[0029] Figure 5 A flowchart illustrating the determination of reactive power droop coefficient according to an embodiment of this application is shown;

[0030] Figure 6 A schematic flowchart illustrating the generation of control signals for a converter according to an embodiment of this application is shown;

[0031] Figure 7 A flowchart illustrating a converter control method according to an embodiment of this application is shown;

[0032] Figure 8 A schematic diagram of the hardware topology of a single digital signal processor in an energy storage system provided in one embodiment of this application is shown;

[0033] Figure 9 This is a schematic diagram of the control device for a converter provided in another embodiment of this application;

[0034] Figure 10 This is a schematic diagram of the structure of the control device for a converter provided in another embodiment of this application. Detailed Implementation

[0035] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0037] With the rapid development of new energy technologies, their ability to interact with the power grid is gradually improving. Furthermore, centralized energy storage systems are one of the core infrastructures for the development of new energy technologies, capable of meeting the technical requirements of large-scale grid connection of renewable energy.

[0038] Because the grid parameters (such as voltage level and power capacity) output by centralized energy storage systems differ from grid connection standards, two approaches can be taken: firstly, multiple power conversion systems (PCS) can be connected in parallel to increase the system's power capacity; secondly, an integrated step-up transformer can be used to increase the output voltage to match the grid level. Each power conversion system can be configured with a separate digital signal processor (DSP) for controlling it.

[0039] In one example, Figure 1 This is a schematic diagram of the architecture of a centralized energy storage system provided in one embodiment of this application, as shown below. Figure 1 As shown, the centralized energy storage system 100 includes multiple energy storage converters 110 connected in parallel. These parallel-connected energy storage converters share a DC bus output from the battery compartment 130 on the DC side and a common power grid 140 on the AC output side. Furthermore, each energy storage converter can be controlled by a separate digital signal processor 120. The digital signal processor can sample the AC signal output from the energy storage converter and control the energy storage converter based on the sampled signal, ensuring that the energy storage converter outputs AC power that conforms to grid connection standards and has controllable power.

[0040] In related technologies, digital signal processors can use virtual droop control to adjust the active and reactive power output of each energy storage converter, thereby enabling the energy storage converter to output AC power that meets grid connection standards and has controllable power.

[0041] However, due to subtle differences in the structure and sampling circuits of the energy storage converters participating in parallel operation in a centralized energy storage system (e.g., differences caused by inconsistencies in inductance, capacitance, and resistance in the structure or circuit), the output current of each energy storage converter is not completely balanced under the same power command, resulting in circulating current. Furthermore, circulating current may lead to uneven load distribution among the parallel energy storage converters, causing some devices to be lightly loaded while others are heavily loaded, affecting the system's dynamic response capability. Even worse, excessive circulating current may trigger the overcurrent protection of the energy storage converter, causing the system to malfunction. Moreover, over time, circulating current may accelerate the aging of some energy storage converters, affecting their lifespan. In addition, the uneven output current of each energy storage converter also leads to additional energy consumption between the converters, reducing the overall system efficiency.

[0042] Therefore, how to control the energy storage converter to improve the performance of centralized energy storage systems is a technical problem that urgently needs to be solved by those skilled in the art.

[0043] To address the problems of the prior art, embodiments of this application provide a control method, apparatus, device, storage medium, and program product for a converter. The control method for a converter provided in this application embodiment will be described first below.

[0044] Figure 2 A flowchart illustrating a converter control method according to an embodiment of this application is shown. Figure 2 As shown, the control method of the converter includes the following steps S210-S250:

[0045] S210. Obtain the active power deviation and reactive power deviation corresponding to the converter.

[0046] For example, the converter control method can be applied to an energy storage system, wherein the energy storage system includes one or more converters connected in parallel, and the converters are connected to a digital signal processor. Furthermore, there can be a one-to-one correspondence between the converters and the digital signal processors.

[0047] In one example, the energy storage system can be a centralized energy storage system, which may include multiple converters connected in parallel. Each converter corresponds to a separate digital signal processor. Furthermore, the converter can be an energy storage converter, or it can be an energy storage inverter.

[0048] For example, the active power deviation can characterize the deviation between the actual active power output of the converter and the rated active power, and the reactive power deviation can characterize the deviation between the actual reactive power transmitted by the converter and the rated reactive power.

[0049] The active power deviation is the difference between the rated active power and the actual active power output of the converter. The reactive power deviation can be the difference between the rated reactive power and the actual reactive power output of the converter.

[0050] Rated active power and rated reactive power can be obtained through an Energy Management System (EMS) connected to a digital signal processor. The connection between the digital signal processor and the EMS can be either a communication connection or an electrical connection.

[0051] EMS can send active power commands and reactive power commands to the digital signal processor. The active power command includes the rated active power, and the reactive power command includes the rated reactive power.

[0052] S220. Based on the active power deviation and reactive power deviation, determine the active power deviation change rate and reactive power deviation change rate respectively.

[0053] For example, the active power deviation rate can represent the rate of change of the active power deviation, and the reactive power deviation rate can represent the rate of change of the reactive power deviation.

[0054] In one example, the rate of change of active power deviation can be calculated from the derivative of the active power deviation. Similarly, the rate of change of reactive power deviation can be calculated from the derivative of the reactive power deviation.

[0055] S230. Input the active power deviation and the active power deviation change rate into the first fuzzy controller, and perform fuzzy inference on the active power deviation and the active power deviation change rate through the fuzzy inference rules corresponding to the first fuzzy controller to obtain the active power droop coefficient.

[0056] For example, the first fuzzy controller can dynamically determine the active power droop coefficient based on the active power deviation and the rate of change of the active power deviation, and then adjust the active power output of the converter.

[0057] The first fuzzy controller may include corresponding fuzzy inference rules, wherein the fuzzy inference rules included in the first fuzzy controller may be rules relating active power deviation, active power deviation rate of change, and active power droop coefficient. Further, the first fuzzy controller can perform fuzzy inference on the active power deviation and active power deviation rate of change according to the corresponding fuzzy inference rules to obtain the active power droop coefficient.

[0058] S240. Input the reactive power deviation and the reactive power deviation change rate into the second fuzzy controller. Perform fuzzy inference on the reactive power deviation and the reactive power deviation change rate through the fuzzy inference rules corresponding to the second fuzzy controller to obtain the reactive power droop coefficient.

[0059] The fuzzy inference rules corresponding to the second fuzzy controller are different from those corresponding to the first fuzzy controller.

[0060] For example, the second fuzzy controller can dynamically determine the reactive power droop coefficient based on the reactive power deviation and the reactive power deviation rate of change, and then adjust the reactive power output of the converter.

[0061] The second fuzzy controller may include corresponding fuzzy inference rules, wherein the fuzzy inference rules included in the second fuzzy controller may be rules relating reactive power deviation, reactive power deviation rate of change, and reactive power droop coefficient. Furthermore, the second fuzzy controller can perform fuzzy inference on the reactive power deviation and reactive power deviation rate of change based on the corresponding fuzzy inference rules to obtain the reactive power droop coefficient.

[0062] S250 generates the control signal for the converter based on the active power droop coefficient and the reactive power droop coefficient.

[0063] For example, the control signal of the converter can be used to adjust the magnitude of the inverter current output by the converter.

[0064] A digital signal processor can generate control signals for the converter based on the active droop coefficient and the reactive droop coefficient, and transmit the control signals to the converter, so that the converter outputs inverter current under the action of the control signals, thereby realizing the parallel operation of the converter.

[0065] For example, in an energy storage system, each digital signal processor can acquire the active power deviation and reactive power deviation of the converter, and determine the active power deviation rate of change and the reactive power deviation rate of change, respectively, based on the active power deviation and reactive power deviation. Further, the active power deviation and the rate of change ...

[0066] In this embodiment, each digital signal processor can determine the active power droop coefficient by acquiring the active power deviation and the rate of change of the active power deviation of the converter and inputting them into the first fuzzy controller. It can also acquire the reactive power deviation and the rate of change of the reactive power deviation and input them into the second fuzzy controller to determine the reactive power droop coefficient. Based on the reactive power droop coefficient and the active power droop coefficient, a control signal is generated to control the converter, thereby achieving dynamic control of each converter. This ensures that the inverter current output by each converter in the energy storage system is balanced, thus avoiding circulating current problems in the energy storage system and improving its performance.

[0067] The effectiveness of the converter control method in this application will be explained below in conjunction with existing technologies.

[0068] Understandably, in existing technologies, when the active power command, reactive power command, or feedback received by the digital signal processor changes, the frequency and amplitude of the converter's output voltage will be adjusted to dynamically match the active and reactive power demands in the power grid. The converter can be controlled using a droop control formula, as shown in formula (1) below:

[0069]

[0070] Where ω is the current output voltage frequency of the converter, ω n The grid's rated frequency is, for example, 50 Hz; m is the active power droop factor; and P is the current active power output of the converter. ref For rated active power, |U * | represents the current output voltage amplitude of the converter, U n The rated voltage of the power grid can be set according to the transformer parameters, where n is the reactive power droop factor, and Q is the reactive power currently output by the converter. ref This is the rated reactive power.

[0071] As can be seen from the above formula (1), the adjustment of the converter is affected by the droop coefficients m and n. Although an excessively large droop coefficient can improve the power distribution accuracy and the system's response speed to power, it will lead to significant voltage frequency deviation, and in severe cases, even system instability. If the droop coefficient is too small, although it will reduce the above deviation, it will reduce the power distribution accuracy and affect the system's circulating current suppression capability. Furthermore, in the process of adjusting the droop coefficient, it is often necessary to introduce the impedance ratio r of the line where each converter is located. The corrected droop control formula is shown in the following formula (2):

[0072]

[0073] The impedance ratio is the ratio of the line resistance R to the reactance X of the converter, i.e., r = R / X.

[0074] However, the line resistance and impedance of each converter cannot be completely identical, which will lead to an imbalance in the output current of each converter, resulting in circulating current. If the impedance ratio is calculated individually for each converter to correct the droop control formula, technicians will need to check and calculate each one, which will increase labor costs significantly.

[0075] In view of this, in the embodiments of this application, each digital signal processor can obtain the active power deviation and the rate of change of the active power deviation corresponding to the converter, and input them into the first fuzzy controller to determine the active power droop coefficient. It can also obtain the reactive power deviation and the rate of change of the reactive power deviation, and input them into the second fuzzy controller to determine the reactive power droop coefficient. Then, based on the reactive power droop coefficient and the active power droop coefficient, a control signal is generated to control the converter. In this way, on the one hand, the corresponding control signal for each converter can be dynamically determined, thereby ensuring that the inverter current output by each converter is balanced, thus avoiding circulating current problems in the energy storage system and saving labor costs. On the other hand, the accuracy of the active power droop coefficient and the reactive power droop coefficient can be improved by using fuzzy control.

[0076] In some optional embodiments, in order to determine the active power droop coefficient through the first fuzzy controller, as another implementation of this application, this application also provides another implementation of determining the active power droop coefficient, as detailed in the following embodiments.

[0077] Figure 3 A schematic flowchart illustrating the determination of the active power droop coefficient according to an embodiment of this application is shown. Figure 3 As shown, the process of determining the active power droop coefficient includes the following steps:

[0078] S231. According to the preset membership degree correspondence rules, determine the first membership degree corresponding to the active power deviation and the second membership degree corresponding to the active power deviation change rate.

[0079] S232. Based on the first membership degree and the second membership degree, determine the target active membership degree according to the fuzzy inference rule corresponding to the first fuzzy controller.

[0080] S233. Determine the active droop adjustment factor corresponding to the target active membership degree through a preset defuzzification algorithm.

[0081] S234. Based on the active power droop adjustment factor and the preset active power droop parameters, obtain the active power droop coefficient.

[0082] In some embodiments, in S231, the first membership degree corresponding to the active power deviation and the second membership degree corresponding to the active power deviation change rate can be determined according to the preset membership degree correspondence rules.

[0083] For example, preset membership correspondence rules can be used to characterize the active power deviation and the correspondence between the active power deviation change rate and different membership degrees. Furthermore, these preset membership correspondence rules can be pre-set by technical personnel according to different needs.

[0084] The preset membership rule can include a first membership rule between active power deviation and membership degree, and a second membership rule between active power deviation change rate and membership degree. Furthermore, the first and second membership rules can be the same or different. That is, the active power deviation can be matched with the first membership rule to determine the first membership degree corresponding to the active power deviation, and the active power deviation change rate can be matched with the second membership rule to determine the second membership degree corresponding to the active power deviation change rate.

[0085] Membership degree can be used to describe the degree to which a feature (including active power deviation and active power deviation rate of change) belongs to a fuzzy subset. The fuzzy subsets can be preset by technical personnel according to different needs; for example, technical personnel can set the number and names of fuzzy subsets according to different requirements.

[0086] In one example, the technician can set the active power deviation (EP) to correspond to five fuzzy subsets: negative large (NL), negative small (NS), zero (ZE), positive small (PS), and positive large (PB). Furthermore, the first membership degree of the active power deviation with respect to each fuzzy subset can be calculated separately. For example, the membership degree of the active power deviation with respect to each fuzzy subset can be determined using a membership function.

[0087] Similarly, for the second membership degree corresponding to the active power deviation rate of change, technicians can set the active power deviation rate of change (ECP) to include five fuzzy subsets: negative large (NL), negative small (NS), zero (ZE), positive small (PS), and positive large (PB). Furthermore, the second membership degree of the active power deviation rate of change for each fuzzy subset can be calculated separately.

[0088] For example, the first membership degree can be a characterization of the degree to which the active power deviation belongs to different fuzzy subsets, wherein the first membership degree may include multiple subsets.

[0089] For example, the second membership degree can be a characterization of the degree to which the rate of change of active bias belongs to different fuzzy subsets, wherein the second membership degree may include multiple subsets.

[0090] In some embodiments, in S232, the target active membership degree is determined according to the first membership degree and the second membership degree, and in accordance with the fuzzy inference rule corresponding to the first fuzzy controller.

[0091] The fuzzy inference rules corresponding to the first fuzzy controller include the correspondence between the first membership degree, the second membership degree, and multiple active membership degrees.

[0092] For example, the fuzzy inference rule corresponding to the first fuzzy controller may include the correspondence between fuzzy subsets. That is, the fuzzy inference rule corresponding to the first fuzzy controller represents the correspondence between the fuzzy subset corresponding to the active power deviation, the fuzzy subset corresponding to the active power deviation rate of change, and the fuzzy subset corresponding to the output value of the first fuzzy controller. Further, the membership degree of each feature (including active power deviation, active power deviation rate of change, and the output value of the first fuzzy controller) to each fuzzy subset can be calculated using a fuzzy function.

[0093] In one example, the fuzzy inference rules corresponding to the first fuzzy controller can be constructed using the "IF-THEN" format. For instance, the "IF-THEN" format can be used to fuzzify the active power deviation, active power deviation rate of change, and active power membership: "IF EPis A i1 ,AND ECPis A i2 ,THENμis C i That is, when the active power deviation (EP) belongs to the fuzzy subset A i1 Furthermore, the work deviation rate (ECP) belongs to the fuzzy subset A. i2 In this case, the fuzzy subset of the fuzzy controller output (μ) is C. i Among them, the fuzzy subset A i1 Fuzzy subset A i2 And the fuzzy subset is C i Each can include five fuzzy subsets: Negative Large (NL), Negative Small (NS), Zero (ZE), Positive Small (PS), and Positive Large (PB). Specifically, the fuzzy inference rules corresponding to the first fuzzy controller can be shown in Table 1 below:

[0094] Table 1

[0095]

[0096] From Table 1, it can be determined that when EP belongs to ZE and ECP belongs to NL, μ belongs to NL. The same applies to the other rules in Table 1, which will not be repeated here.

[0097] Furthermore, the membership degree corresponding to each fuzzy subset can be determined using a membership function. For example, the membership degree can be calculated using a Gaussian membership function.

[0098] In one example, the Gaussian membership function can be expressed as shown in formula (3), and the Gaussian membership function can be expressed as follows: Figure 4 As shown:

[0099]

[0100] Where the error vector x = [EP, ECP] TThat is, an error vector x can be constructed based on the membership degree corresponding to the active power deviation and the rate of change of active power deviation; λ is the peak position of the membership degree, and σ is the membership degree width. λ and σ are selected based on simulation data and experimental data.

[0101] For example, the output of the first fuzzy controller can be used as the target active membership degree.

[0102] In some embodiments, in S233, the active droop adjustment factor corresponding to the target active membership degree can be determined by a preset defuzzification algorithm.

[0103] For example, a pre-defined defuzzification algorithm can be used to defuzzify the target active membership degree, thereby obtaining a numerically accurate active droop adjustment factor.

[0104] It is understandable that the converter needs to be controlled by precise numerical signals, while the target active power membership degree represents the degree to which the output of the first fuzzy controller belongs to different fuzzy subsets. It cannot be used to generate precise numerical signals. Therefore, the target active power membership degree is defuzzified by a defuzzification algorithm to obtain precise numerical signals.

[0105] In one example, the preset defuzzification algorithm may include the centroid method, wherein the centroid method calculation formula is shown in the following formula (4):

[0106]

[0107] Where Δm represents the active droop adjustment factor; μ represents the active membership degree; x represents the error vector; i represents the fuzzy subset; and n represents the number of fuzzy subsets. For example, if the fuzzy subsets include: negative large (NL), negative small (NS), zero (ZE), positive small (PS), and positive large (PB), then n = 5. That is, x i μ can represent the error vector corresponding to the i-th fuzzy subset. i It can represent x i The membership degree corresponding to the i-th fuzzy subset.

[0108] In some embodiments, in S234, the active power droop coefficient can be obtained based on the active power droop adjustment factor and the preset active power droop parameter.

[0109] For example, the preset active power droop factor can be determined based on the grid performance.

[0110] In some optional embodiments, the grid rated frequency, grid frequency limit, rated active power, and active power limit can be obtained separately; and a preset active power droop parameter can be calculated based on the grid rated frequency, grid frequency limit, rated active power, and active power limit.

[0111] In one example, the preset active power droop parameter m0 can be calculated using the following formula (5):

[0112] m0=(ω n -ω min ) / (P max -P ref (5)

[0113] Where, ω n Characterizes the rated frequency of the power grid, for example, the rated frequency of the power grid can be 50 Hz; ω min Characterizing the power grid frequency limit, i.e., ω min It can represent the minimum frequency allowed by the system; P max Characterized by the active power limit, that is, the maximum active power that the system is allowed to output; P ref Characterizes the rated active power.

[0114] It is understood that, in the embodiments of this application, the preset active power droop parameter calculated by the grid rated frequency, grid frequency limit, rated active power and active power limit can ensure the frequency safety of the energy storage system under extreme operating conditions, and lay a solid benchmark for adaptive adjustment of the active power droop coefficient.

[0115] For example, the sum of the active power droop adjustment factor and the preset active power droop parameter can be used as the active power droop coefficient, that is, the active power droop coefficient m can be represented by the following formula (6):

[0116] m=Δm+m0 (6)

[0117] For example, the active power droop factor can be used to adjust the active power output of the converter.

[0118] In this embodiment, according to a preset membership rule, a first membership degree corresponding to the active power deviation and a second membership degree corresponding to the active power deviation change rate are determined respectively. These first and second membership degrees are then input into a first fuzzy controller, and the target active power membership degree is determined through the fuzzy inference rule corresponding to the first fuzzy controller. Further, a preset defuzzification algorithm is used to determine the active power droop adjustment factor corresponding to the target active power membership degree; and based on the active power droop adjustment factor and preset active power droop parameters, the active power droop coefficient is obtained. It can be understood that the first fuzzy controller essentially determines the output value corresponding to the input features (including active power deviation and active power deviation change rate) under nonlinear rules. This avoids the problem of inaccurate collected features causing the reactive power droop coefficient to fail to control the current required by the converter output, thus preventing circulating current issues.

[0119] In some optional embodiments, in order to determine the reactive power droop coefficient through a second fuzzy controller, as another implementation of this application, this application also provides an implementation for determining the reactive power droop coefficient, as detailed in the following embodiments.

[0120] Figure 5 A schematic flowchart illustrating the determination of the reactive power droop coefficient according to an embodiment of this application is shown. Figure 5 As shown, the process of determining the reactive power droop coefficient includes the following steps:

[0121] S241. According to the preset membership degree correspondence rules, determine the third membership degree to which the reactive power deviation belongs and the fourth membership degree to which the reactive power deviation change rate belongs.

[0122] For example, preset membership correspondence rules can be used to characterize the active power deviation and the correspondence between the active power deviation change rate and different membership degrees. Furthermore, these preset membership correspondence rules can be pre-set by technical personnel according to different needs.

[0123] The preset membership rule can further include a third membership rule between reactive power deviation and membership, and a fourth membership rule between reactive power deviation change rate and membership. Furthermore, the third and fourth membership rules can be the same or different. That is, the reactive power deviation can be matched with the third membership rule to determine the third membership corresponding to the reactive power deviation, and the reactive power deviation change rate can be matched with the fourth membership rule to determine the fourth membership corresponding to the reactive power deviation change rate.

[0124] Membership degree can be used to describe the degree to which a feature (including reactive power deviation and reactive power deviation rate of change) belongs to a fuzzy subset.

[0125] In one example, the technician can set the reactive power deviation (EQ) to correspond to five fuzzy subsets: negative large (NL), negative small (NS), zero (ZE), positive small (PS), and positive large (PB). Further, the third membership degree of the active power deviation with respect to each fuzzy subset can be calculated separately. For example, the membership degree of the active power deviation with respect to each fuzzy subset can be determined using a membership function.

[0126] Similarly, for the fourth membership degree corresponding to the reactive power deviation rate of change, technicians can set the reactive power deviation rate of change (ECQ) to include five fuzzy subsets: negative large (NL), negative small (NS), zero (ZE), positive small (PS), and positive large (PB). Furthermore, the fourth membership degree of the reactive power deviation rate of change for each fuzzy subset can be calculated separately.

[0127] For example, the third membership degree can be a characterization of the degree to which reactive power deviation belongs to different fuzzy subsets, wherein the third membership degree can include multiple subsets.

[0128] For example, the fourth membership degree can be a characterization of the degree to which the reactive power deviation rate belongs to different fuzzy subsets, wherein the fourth membership degree can include multiple subsets.

[0129] S242. Based on the third and fourth membership degrees, determine the target reactive power membership degree according to the fuzzy inference rules corresponding to the second fuzzy controller.

[0130] Among them, the fuzzy inference rules corresponding to the second fuzzy controller include the correspondence between the third membership degree, the fourth membership degree and multiple reactive membership degrees.

[0131] For example, the fuzzy inference rule corresponding to the second fuzzy controller may include the correspondence between fuzzy subsets. That is, the fuzzy inference rule corresponding to the second fuzzy controller represents the correspondence between the fuzzy subset corresponding to reactive power deviation, the fuzzy subset corresponding to reactive power deviation rate of change, and the fuzzy subset corresponding to the output value of the second fuzzy controller. Further, the membership degree of each feature (including reactive power deviation, reactive power deviation rate of change, and the output value of the first fuzzy controller) to each fuzzy subset can be calculated using a fuzzy function. The fuzzy control rule corresponding to the second fuzzy controller may be different from the fuzzy control rule corresponding to the first fuzzy controller.

[0132] In one example, the fuzzy inference rules corresponding to the second fuzzy controller can also be constructed using the "IF-THEN" format. For example, the fuzzy inference rules for reactive power deviation, reactive power deviation rate of change, and reactive power membership can be fuzzified using the "IF-THEN" format: "IF EPis A i1 ,AND ECPis A i2 ,THENμis C i That is, when the reactive power deviation (EQ) belongs to the fuzzy subset A i1 Furthermore, the rate of change of work deviation (ECQ) belongs to the fuzzy subset A. i2 In this case, the fuzzy subset of the fuzzy controller output (μ) is C. i Among them, the fuzzy subset A i1 Fuzzy subset A i2 And the fuzzy subset is C i Each can include five fuzzy subsets: negative large (NL), negative small (NS), zero (ZE), positive small (PS), and positive large (PB). However, the corresponding fuzzy inference rules may differ from those shown in Table 1.

[0133] Furthermore, the membership degree corresponding to each fuzzy subset can be determined using a membership function. For example, the membership degree can be calculated using a Gaussian membership function. The calculation method for the target reactive power membership degree is similar to that for the target active power membership degree, and will not be repeated here.

[0134] For example, the output of the second fuzzy controller can be used as the target reactive power membership degree.

[0135] S243. Determine the reactive power droop adjustment factor corresponding to the target reactive power membership degree through a preset defuzzification algorithm.

[0136] For example, a pre-defined defuzzification algorithm can be used to defuzzify the target reactive power membership degree, thereby obtaining a numerically accurate reactive power droop adjustment factor.

[0137] In one example, the preset defuzzification algorithm may include the centroid method. It is understood that the calculation method for the reactive power droop adjustment factor is similar to that for the active power droop adjustment factor, and will not be repeated here.

[0138] S244. Based on the reactive power droop adjustment factor and the preset reactive power droop parameters, obtain the reactive power droop coefficient.

[0139] For example, the preset active power droop factor can be determined based on the grid performance.

[0140] In some optional embodiments, the grid rated voltage, voltage limit, and reactive power limit can be obtained separately, and the preset reactive power droop parameter can be calculated based on the grid rated voltage, the voltage threshold, and the reactive power limit.

[0141] In one example, the preset reactive power droop parameter n0 can be calculated using the following formula (7):

[0142] n0=(U n -U min ) / Q max (7)

[0143] Among them, U n Characterizing the rated voltage of the power grid; U min Characterized by voltage limitation, i.e., U min Q can represent the minimum allowable voltage of the system; max It represents the reactive power limit, that is, the minimum reactive power that the system is allowed to output.

[0144] It is understood that, in the embodiments of this application, the preset reactive power droop parameters calculated by the grid rated frequency, grid frequency limit, rated reactive power, and reactive power limit can ensure the frequency safety of the energy storage system under extreme operating conditions, and lay a solid benchmark for adaptive adjustment of the reactive power droop coefficient.

[0145] For example, the sum of the reactive power droop adjustment factor and the preset reactive power droop parameter can be used as the reactive power droop coefficient, that is, the reactive power droop coefficient n can be represented by the following formula (8):

[0146] n=Δn+n0 (8)

[0147] For example, the reactive power droop factor can be used to adjust the reactive power output of the converter.

[0148] In this embodiment, according to a preset membership rule, a third membership degree corresponding to the reactive power deviation and a fourth membership degree corresponding to the reactive power deviation change rate are determined respectively. These third and fourth membership degrees are then input into a second fuzzy controller, and the target reactive power membership degree is determined through the fuzzy inference rule corresponding to the second fuzzy controller. Further, a preset defuzzification algorithm is used to determine the reactive power droop adjustment factor corresponding to the target reactive power membership degree; and based on the reactive power droop adjustment factor and preset reactive power droop parameters, the reactive power droop coefficient is obtained. It can be understood that the second fuzzy controller essentially determines the output value corresponding to the input features (including reactive power deviation and reactive power deviation change rate) under nonlinear rules. This avoids the problem of inaccurate collected features causing the reactive power droop coefficient to fail to control the current required by the converter output, thus preventing circulating current issues.

[0149] Furthermore, in order to generate the control signal of the converter based on the active power droop coefficient and the reactive power droop coefficient, as another implementation of this application, this application also provides another implementation of the converter control method, as detailed in the following embodiments.

[0150] Figure 6 A schematic flowchart illustrating the generation of control signals for a converter according to an embodiment of this application is shown. Figure 6 As shown, the process of generating the control signal for the converter includes the following steps S251-S255:

[0151] S251. The voltage rotation angle is calculated based on the active power droop coefficient, active power deviation, and grid rated frequency.

[0152] For example, the voltage rotation angle can be used to characterize the real-time phase angle of the voltage vector.

[0153] In some alternative embodiments, the voltage frequency can be determined based on the active power droop factor, the active power deviation, and the grid rated frequency, and the voltage rotation angle can be obtained by integrating the voltage frequency and taking the remainder.

[0154] For example, combining the frequency calculation formula in formula (1), we can first calculate the product between the active power deviation and the active power droop coefficient, then calculate the sum of this product and the grid rated frequency, and use the calculated sum as the current output voltage frequency of the converter. Further, we integrate the voltage frequency with a modulus of 2π, and take the remainder of the integrated voltage frequency to obtain the voltage rotation angle.

[0155] For example, to ensure the voltage rotation angle is positive, a corresponding compensation value can be set to compensate for the voltage rotation angle, where the compensation value is determined according to the characteristics of the power grid. For instance, in a three-phase AC power grid, the phase difference between phases A, B, and C can be 2 / 3π. Therefore, compensation values ​​can be set to 0, -2 / 3π, and 2 / 3π. Furthermore, when the calculated voltage rotation angle is -π / 3, 2 / 3π can be selected as the compensation value to ensure that the voltage rotation angle is positive.

[0156] In this embodiment, the voltage frequency is determined by combining the active power droop coefficient, active power deviation, and rated frequency. This aligns with the grid coupling characteristics and allows the active power state to be dynamically reflected through frequency, indirectly guiding active power balance and achieving closed-loop regulation. Furthermore, the rotation angle is obtained by integrating the voltage frequency, ensuring a strict match between the voltage phase and the current frequency. The remainder ensures that the corresponding phase maintains its periodic characteristics, simplifying subsequent calculations.

[0157] S252. The voltage amplitude is calculated based on the reactive power droop coefficient, reactive power deviation, and rated voltage.

[0158] For example, by combining the voltage amplitude calculation formula in formula (1), we can first calculate the product between the reactive power deviation and the reactive power droop coefficient, then calculate the sum of the product and the grid rated voltage, and use the calculated sum as the voltage amplitude of the converter's current output.

[0159] S253. Determine the active voltage and reactive voltage based on the voltage rotation angle, voltage amplitude, and preset voltage change strategy.

[0160] For example, a preset voltage transformation strategy can be used to transform time-varying variables (voltage, current, flux linkage, etc.) of a three-phase AC system into DC components in a rotating dq coordinate system. In one example, the preset voltage transformation strategy can be an abc / dq transformation, where the abc / dq transformation includes the Clark transformation and the Park transformation.

[0161] For example, the sine value corresponding to the voltage rotation angle can be calculated, and the product between the voltage amplitude and the sine value can be calculated. The product is then used as the three-phase voltage value. Furthermore, the active voltage value and reactive voltage corresponding to the three-phase voltage value can be determined by a preset voltage change strategy.

[0162] S254. Input the difference between the active voltage and the detected active voltage of the power grid, and the difference between the reactive voltage and the detected reactive voltage of the power grid into the voltage controller to obtain the active current value and the reactive current value.

[0163] For example, the detected active voltage of the power grid can be determined by sampling the three-phase voltage of the power grid and then using an abc / dq transformation. Similarly, the detected reactive voltage of the power grid can be determined by sampling the three-phase voltage of the power grid and then using an abc / dq transformation.

[0164] For example, the voltage controller can be a voltage outer loop controller, which can generate current inner loop commands through a proportional-integral (PI) controller via voltage commands and feedback. The voltage controller may include a first PI controller and a second PI controller. The difference between the active voltage and the detected active voltage from the grid can be input into the first PI controller to obtain the active current value. Similarly, the difference between the reactive voltage and the detected reactive voltage from the grid can be input into the second PI controller to obtain the reactive current value.

[0165] S255. Input the difference between the active current value and the detected active current value of the power grid, and the difference between the reactive current value and the detected reactive current value of the power grid, into the current controller to obtain the control signal of the converter.

[0166] For example, the detected active current value of the power grid can be determined by sampling the three-phase current of the power grid and then using an abc / dq transformation. Similarly, the detected reactive current value of the power grid can be determined by sampling the three-phase current of the power grid and then using an abc / dq transformation.

[0167] For example, the current controller can be an inner-loop current controller, which can generate a control signal through a PI controller via current commands and feedback. The current controller may include a third PI controller and a fourth PI controller. The difference between the active current value and the detected active current value of the power grid can be input to the third PI controller, and the difference between the reactive current value and the detected reactive current value of the power grid can be input to the fourth PI controller to obtain the control signal.

[0168] The control signal can be a modulated wave. In one example, the control signal can be a pulse width modulation (PWM) signal. Alternatively, the control signal can be a space vector pulse width modulation (SPWM) signal.

[0169] For example, the voltage rotation angle can be calculated based on the active power droop coefficient, active power deviation, and grid rated frequency; the voltage amplitude can be calculated based on the reactive power droop coefficient, reactive power deviation, and grid rated voltage. Based on the voltage rotation angle, voltage amplitude, and preset voltage change strategy, the active voltage and reactive voltage are determined. The differences between the active voltage and the grid's detected active voltage, and the differences between the reactive voltage and the grid's detected reactive voltage, are then input to the voltage controller to obtain the active current value and reactive current value. The differences between the active current value and the grid's detected active current value, and the differences between the reactive current value and the grid's detected reactive current value, are then input to the current controller to obtain the converter's control signal.

[0170] In this embodiment, the voltage rotation angle and voltage amplitude are dynamically adjusted by the droop coefficient to achieve adaptive decoupling control of power, frequency, and voltage. Furthermore, the voltage controller accurately compensates for grid voltage deviation, and the current controller can quickly track active and reactive current commands, forming a dual closed-loop collaborative mechanism. This not only improves the system's anti-interference and stability but also ensures that the converter output current has low harmonics and is strictly synchronized with the grid, significantly enhancing grid connection reliability and power quality.

[0171] Below, in conjunction with Figure 7 , Figure 8 The following examples illustrate the control methods for converters.

[0172] Figure 7 A flowchart illustrating a converter control method according to an embodiment of this application is shown; Figure 8 A schematic diagram of the hardware topology of a single digital signal processor in an energy storage system provided in one embodiment of this application is shown.

[0173] For example, in step S701, the digital signal processor 120 can receive the active power control command P* and the reactive power control command Q* sent by the EMS, wherein the active power control command P* may include the rated active power P. ref The reactive power control command Q* may include the rated active power Q. ref .

[0174] Furthermore, the digital signal processor 120 can execute step S702, based on the current output active power P of the converter and the rated active power P.ref Calculate the active power deviation EP and the active power deviation change rate ECP. The current output active power P of the converter can be obtained through methods such as... Figure 8 As shown, the signal acquisition module 801 in the energy storage system acquires the three-phase current and three-phase voltage in the power grid, and calculates the current active power P output by the converter through the power calculation module 802. Simultaneously, the power calculation module 802 can also calculate the current reactive power Q output by the converter. The digital signal processor 120 can execute step S703, based on the current reactive power Q output by the converter and the rated reactive power Q... ref Calculate the reactive power deviation EQ and the reactive power deviation change rate ECQ.

[0175] Further, steps S704 and S705 are executed, namely, the active droop coefficient is calculated by the first fuzzy controller and the reactive droop coefficient is calculated by the second fuzzy controller. According to the above embodiment, the active droop coefficient m and the reactive droop coefficient n can be represented by the following formulas (10) and (9):

[0176]

[0177] Further, in step S706, the voltage frequency ω and voltage deflection angle ωt can be calculated according to the droop control formula; in step S707, the voltage amplitude |U| can be calculated according to the droop control formula. * |. This can be achieved by combining the above formula (1) and Figure 8 A schematic diagram of the droop control formula topology in signal processor 120. Where, for example... Figure 8 As shown, after calculating the voltage frequency ω, the voltage frequency can be integrated using the integration module 804 (1 / s). Furthermore, with 2π as the modulus, the remainder of the integrated voltage frequency is taken using the remainder module 805. The voltage deflection angle ωt is then obtained by converting the modulo-taken voltage frequency.

[0178] In step S708, the three-phase voltage U* can be calculated based on the voltage rotation angle and voltage amplitude. For example... Figure 8 As shown, the voltage rotation angle can be compensated using preset compensation values ​​(0, -2 / 3π, and 2 / 3π) to obtain the compensated voltage rotation angle. The sine value of the voltage rotation angle is then calculated using the sine calculation module 806. Further, the sine value of the voltage rotation angle is compared with the voltage amplitude |U... * By multiplying these products, we obtain the three-phase voltage.

[0179] In step S709, the three-phase voltage can be converted into d-axis voltage V using Clark transformation and Park transformation, respectively. d*(i.e., active voltage) and q-axis voltage V q *(i.e., reactive voltage). For example... Figure 8 As shown, the active voltage V can be obtained by using the abc / dq transformation module 803 to change U*. d *and reactive voltage V q *

[0180] In step S710, the active current value i can be generated by the voltage outer loop controller 807. d *and reactive current value i d *.like Figure 8 As shown, the voltage outer loop controller 807 may include a first PI controller 8071 and a second PI controller 8072. It can control the active voltage and the detected active voltage i of the power grid. d The difference between the two values ​​is input into the first PI controller to obtain the active current value. It can also input the reactive voltage and the detected reactive voltage i from the power grid. q The difference between the two values ​​is input into the second PI controller to obtain the reactive current value. For example, Figure 8 As shown, after the signal acquisition module 801 in the energy storage system acquires the three-phase current and three-phase voltage in the power grid, the detected active current value i of the power grid can be calculated by the abc / dq conversion module 803. d The detected reactive current value i of the power grid q .

[0181] In step S711, a PWM signal can be generated by the current inner loop controller 808. For example... Figure 8 As shown, the current inner loop controller 808 may include a third PI controller 8081 and a fourth PI controller 8082. The difference between the active current value and the detected active current value of the grid can be input to the third PI controller, and the difference between the reactive current value and the detected reactive current value of the grid can be input to the fourth PI controller, thereby obtaining a PWM signal to control the converter 110.

[0182] It is understandable that parallel current sharing control of multiple converters often relies on additional communication lines or requires additional hardware circuitry, while pure software control strategies often employ droop control. However, the design of the droop coefficient in droop control schemes depends on engineer experience and is difficult to adapt to different application scenarios and operating conditions. In this embodiment, each digital signal processor can acquire the active power deviation and rate of change of the corresponding converter and input them into the first fuzzy controller to determine the active power droop coefficient. Similarly, it acquires the reactive power deviation and rate of change of the reactive power deviation and inputs them into the second fuzzy controller to determine the reactive power droop coefficient. Based on the reactive power droop coefficient and the active power droop coefficient, a control signal is generated to control the converter, thereby achieving dynamic control of each converter. This ensures that the inverter current output by each converter in the energy storage system is balanced, thus avoiding circulating current problems in the energy storage system and improving its performance.

[0183] Based on the converter control method provided in the above embodiments, this application also provides a specific implementation of the converter control device 900. Please refer to the following embodiments.

[0184] First see Figure 9 The control device for the converter provided in this application embodiment includes the following modules:

[0185] The acquisition module 901 is used to acquire the active power deviation and reactive power deviation corresponding to the converter.

[0186] The first determining module 902 is used to determine the active power deviation rate and the reactive power deviation rate based on the active power deviation and the reactive power deviation, respectively.

[0187] The second determining module 903 is used to input the active power deviation and the active power deviation change rate into the first fuzzy controller, and to perform fuzzy inference on the active power deviation and the active power deviation change rate through the fuzzy inference rules corresponding to the first fuzzy controller to obtain the active power droop coefficient.

[0188] The third determining module 904 is used to input the reactive power deviation and the reactive power deviation change rate into the second fuzzy controller, and to perform fuzzy inference on the reactive power deviation and the reactive power deviation change rate through the fuzzy inference rule corresponding to the second fuzzy controller to obtain the reactive power droop coefficient. The fuzzy inference rule corresponding to the first fuzzy controller is different from the fuzzy inference rule corresponding to the second fuzzy controller.

[0189] The generation module 905 is used to generate control signals for the converter based on the active power droop coefficient and the reactive power droop coefficient.

[0190] As one implementation of this application, the first determining module 902 inputs the active power deviation and the active power deviation change rate into the first fuzzy controller in the following manner, and performs fuzzy inference on the active power deviation and the active power deviation change rate through the fuzzy inference rules corresponding to the first fuzzy controller to obtain the active power droop coefficient: according to the preset membership degree correspondence rules, the first membership degree corresponding to the active power deviation and the second membership degree corresponding to the active power deviation change rate are determined respectively; according to the first membership degree and the second membership degree, the target active power membership degree is determined according to the fuzzy inference rules corresponding to the first fuzzy controller, and the fuzzy inference rules corresponding to the first fuzzy controller include the correspondence between the first membership degree, the second membership degree and multiple active power membership degrees; the active power droop adjustment factor corresponding to the target active power membership degree is determined through the preset defuzzification algorithm; and the active power droop coefficient is obtained according to the active power droop adjustment factor and the preset active power droop parameters.

[0191] As one implementation of this application, before obtaining the active power droop coefficient based on the active power droop adjustment factor and the preset active power droop parameters, the first determining module 902 is further configured to: obtain the grid rated frequency, grid frequency limit, rated active power and active power limit respectively; and calculate the preset active power droop parameters based on the grid rated frequency, grid frequency limit, rated active power and active power limit.

[0192] As one implementation of this application, the second determining module 903 inputs the reactive power deviation and the reactive power deviation change rate into the second fuzzy controller in the following manner, and performs fuzzy inference on the reactive power deviation and the reactive power deviation change rate through the fuzzy inference rules corresponding to the second fuzzy controller to obtain the reactive power droop coefficient: according to the preset membership degree correspondence rules, the third membership degree to which the reactive power deviation belongs and the fourth membership degree to which the reactive power deviation change rate belongs are determined respectively; according to the third membership degree and the fourth membership degree, the target reactive power membership degree is determined according to the fuzzy inference rules corresponding to the second fuzzy controller, and the fuzzy inference rules corresponding to the second fuzzy controller include the correspondence between the third membership degree, the fourth membership degree and multiple reactive power membership degrees; through the preset defuzzification algorithm, the reactive power droop adjustment factor corresponding to the target reactive power membership degree is determined; and the reactive power droop coefficient is obtained according to the reactive power droop adjustment factor and the preset reactive power droop parameters.

[0193] As one implementation of this application, before obtaining the reactive power droop coefficient based on the reactive power droop adjustment factor and the preset reactive power droop parameters, the second determining module 903 is further configured to: obtain the grid rated voltage, voltage threshold and reactive power limit respectively; and calculate the preset reactive power droop parameters based on the grid rated voltage, voltage threshold and reactive power limit.

[0194] As one implementation of this application, the generation module 905 generates the converter control signal according to the active power droop coefficient and the reactive power droop coefficient in the following manner: The voltage rotation angle is calculated based on the active power droop coefficient, the active power deviation, and the grid rated frequency; the voltage amplitude is calculated based on the reactive power droop coefficient, the reactive power deviation, and the grid rated voltage; the active voltage and reactive voltage are determined based on the voltage rotation angle, the voltage amplitude, and the preset voltage change strategy; the difference between the active voltage and the grid's detected active voltage, and the difference between the reactive voltage and the grid's detected reactive voltage are input to the voltage controller to obtain the active current value and the reactive current value; the difference between the active current value and the grid's detected active current value, and the difference between the reactive current value and the grid's detected reactive current value, are input to the current controller to obtain the converter control signal.

[0195] As one implementation of this application, the generation module 905 calculates the voltage rotation angle based on the active power droop coefficient, the active power deviation, and the rated frequency in the following manner: the voltage frequency is determined based on the active power droop coefficient, the active power deviation, and the rated frequency of the power grid; the voltage rotation angle is obtained by integrating and taking the remainder of the voltage frequency.

[0196] Figure 10 A schematic diagram of the hardware structure of the control device for the converter provided in an embodiment of this application is shown.

[0197] The control device of the converter may include a processor 1001 and a memory 1002 storing computer program instructions.

[0198] Specifically, the processor 1001 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0199] Memory 1002 may include mass storage for data or instructions. For example, and not limitingly, memory 1002 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1002 may include removable or non-removable (or fixed) media. Where appropriate, memory 1002 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 1002 is non-volatile solid-state memory.

[0200] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0201] The processor 1001 reads and executes computer program instructions stored in the memory 1002 to implement any of the converter control methods in the above embodiments.

[0202] In one example, the converter's control device may further include a communication interface 1003 and a bus 1010. For example, Figure 10 As shown, the processor 1001, memory 1002, and communication interface 1003 are connected through bus 1010 and complete communication with each other.

[0203] The communication interface 1003 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0204] Bus 1010 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1010 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0205] The converter's control device can execute the converter control method in this application embodiment based on the active power deviation and reactive power deviation corresponding to the converter, thereby achieving a combination of Figure 2 and Figure 9 The control method for the converter is described.

[0206] Furthermore, in conjunction with the converter control methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the converter control methods in the above embodiments.

[0207] This application also provides a computer program product, including a computer program, which, when executed, implements any of the converter control methods described in the above embodiments.

[0208] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0209] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0210] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0211] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0212] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A control method for a converter, characterized in that, Applied to energy storage systems, the energy storage system comprising one or more converters connected in parallel, the converters being connected to a digital signal processor, the method includes: Obtain the active power deviation and reactive power deviation corresponding to the converter; Based on the active power deviation and the reactive power deviation, the active power deviation change rate and the reactive power deviation change rate are determined respectively. The active power deviation and the active power deviation change rate are input into the first fuzzy controller. Fuzzy inference is performed on the active power deviation and the active power deviation change rate through the fuzzy inference rules corresponding to the first fuzzy controller to obtain the active power droop coefficient. The reactive power deviation and the reactive power deviation change rate are input into the second fuzzy controller. Fuzzy inference is performed on the reactive power deviation and the reactive power deviation change rate through the fuzzy inference rule corresponding to the second fuzzy controller to obtain the reactive power droop coefficient. The fuzzy inference rule corresponding to the first fuzzy controller is different from the fuzzy inference rule corresponding to the second fuzzy controller. The control signal for the converter is generated based on the active power droop coefficient and the reactive power droop coefficient.

2. The method according to claim 1, characterized in that, The active power deviation and the rate of change of active power deviation are input into a first fuzzy controller. Fuzzy inference is then performed on the active power deviation and the rate of change of active power deviation using the fuzzy inference rules corresponding to the first fuzzy controller to obtain the active power droop coefficient, including: According to the preset membership degree correspondence rules, the first membership degree corresponding to the active power deviation and the second membership degree corresponding to the active power deviation change rate are determined respectively. Based on the first membership degree and the second membership degree, and in accordance with the fuzzy inference rules corresponding to the first fuzzy controller, the target active membership degree is determined. The fuzzy inference rules corresponding to the first fuzzy controller include the correspondence between the first membership degree, the second membership degree and multiple active membership degrees. The active droop adjustment factor corresponding to the target active membership degree is determined by a preset defuzzification algorithm. The active power droop coefficient is obtained based on the active power droop adjustment factor and the preset active power droop parameters.

3. The method according to claim 2, characterized in that, Before obtaining the active power droop coefficient based on the active power droop adjustment factor and the preset active power droop parameters, the method further includes: Obtain the grid rated frequency, grid frequency limit, rated active power, and active power limit respectively; The preset active power droop parameter is calculated based on the grid rated frequency, the grid frequency limit, the rated active power, and the active power limit.

4. The method according to claim 1, characterized in that, The process involves inputting the reactive power deviation and the reactive power deviation rate of change into a second fuzzy controller, and then performing fuzzy inference on the reactive power deviation and the reactive power deviation rate of change using the fuzzy inference rules corresponding to the second fuzzy controller to obtain the reactive power droop coefficient, including: According to the preset membership degree correspondence rules, the third membership degree to which the reactive power deviation belongs and the fourth membership degree to which the reactive power deviation change rate belongs are determined respectively. Based on the third membership degree and the fourth membership degree, the target reactive power membership degree is determined according to the fuzzy inference rule corresponding to the second fuzzy controller. The fuzzy inference rule corresponding to the second fuzzy controller includes the correspondence between the third membership degree, the fourth membership degree and multiple reactive power membership degrees. The reactive power droop adjustment factor corresponding to the target reactive power membership degree is determined by a preset defuzzification algorithm. The reactive power droop coefficient is obtained based on the reactive power droop adjustment factor and the preset reactive power droop parameters.

5. The method according to claim 4, characterized in that, Before obtaining the reactive power droop coefficient based on the reactive power droop adjustment factor and the preset reactive power droop parameters, the method further includes: Obtain the grid rated voltage, voltage threshold, and reactive power limit respectively; The preset reactive power droop parameter is calculated based on the grid rated voltage, the voltage threshold, and the reactive power limit.

6. The method according to claim 1, characterized in that, The step of generating control signals for the converter based on the active power droop coefficient and the reactive power droop coefficient includes: The voltage rotation angle is calculated based on the active power droop coefficient, active power deviation, and grid rated frequency. The voltage amplitude is calculated based on the reactive power droop coefficient, reactive power deviation, and grid rated voltage. The active voltage and reactive voltage are determined based on the voltage rotation angle, the voltage amplitude, and the preset voltage change strategy. The difference between the active voltage and the detected active voltage of the power grid, and the difference between the reactive voltage and the detected reactive voltage of the power grid are input into the voltage controller to obtain the active current value and the reactive current value. The difference between the active current value and the detected active current value of the power grid, and the difference between the reactive current value and the detected reactive current value of the power grid, are input into the current controller to obtain the control signal of the converter.

7. The method according to claim 6, characterized in that, The calculation of the voltage rotation angle based on the active power droop coefficient, active power deviation, and rated frequency includes: The voltage frequency is determined based on the active power droop coefficient, the active power deviation, and the grid rated frequency. The voltage rotation angle is obtained by integrating the voltage frequency and taking the remainder.

8. A control device for a converter, characterized in that, The device includes: The acquisition module is used to acquire the active power deviation and reactive power deviation corresponding to the converter. The first determining module is used to determine the active power deviation rate and the reactive power deviation rate based on the active power deviation and the reactive power deviation, respectively. The second determining module is used to input the active power deviation and the active power deviation change rate into the first fuzzy controller, and to perform fuzzy inference on the active power deviation and the active power deviation change rate through the fuzzy inference rules corresponding to the first fuzzy controller to obtain the active power droop coefficient. The third determining module is used to input the reactive power deviation and the reactive power deviation change rate into the second fuzzy controller, and to perform fuzzy inference on the reactive power deviation and the reactive power deviation change rate through the fuzzy inference rule corresponding to the second fuzzy controller to obtain the reactive power droop coefficient. The fuzzy inference rule corresponding to the first fuzzy controller is different from the fuzzy inference rule corresponding to the second fuzzy controller. The generation module is used to generate the control signal of the converter based on the active power droop coefficient and the reactive power droop coefficient.

9. A control device for a converter, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the converter control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the converter control method as described in any one of claims 1-7.

11. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device causes the electronic device to perform the control method of the converter as described in any one of claims 1-7.

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