Inverter control method

Abstract

The present disclosure relates to the technical field of inverters, and in particular, to an inverter control method, device and equipment. The inverter control method comprises: determining a target current and a detected current; determining a prediction coefficient according to the detected current, the prediction coefficient being iterated by a gain equation of a recursive least square method, the gain equation adopting a scalar covariance coefficient to determine; determining a predicted current corresponding to each candidate voltage vector according to the prediction coefficient and a candidate voltage vector set; determining a target voltage vector according to a difference between the target current and the predicted current, and controlling an inverter based on the target voltage vector.

Description

Technical Field

[0001] This disclosure relates to the field of inverter technology, and in particular to an inverter control method, apparatus and equipment. Background Technology

[0002] For three-level NPC (Neutral-Point Clamped) inverters, related technologies can utilize hyperlocal models for control. Based on hyperlocal models, unmodeled parts of the hyperlocal model can be identified online, and parameters can be updated in real time. This eliminates the dependence of traditional models on system parameters, thereby enabling more accurate prediction of the current at the next moment.

[0003] However, these online parameter identification methods have shortcomings such as difficulty in implementation on a computer and difficulty in proving the stability of heuristic algorithms. In practical applications, due to the large amount of computation required, it is difficult to run them online. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides an inverter control method, apparatus, and equipment that can solve the above-mentioned problems.

[0005] According to a first aspect of the present disclosure, an inverter control method is provided, the method comprising: determining a target current and a detected current; determining prediction coefficients based on the detected current, the prediction coefficients being iterated using a gain equation obtained by recursive least squares method, the gain equation being determined using scalar covariance coefficients; determining a prediction current corresponding to each candidate voltage vector based on the prediction coefficients and a set of candidate voltage vectors; determining a target voltage vector based on the difference between the target current and the prediction current; and controlling the inverter based on the target voltage vector.

[0006] According to a second aspect of the present disclosure, an inverter control device is provided, the device comprising: a detection unit configured to determine a target current and a detection current; a determination unit configured to determine prediction coefficients based on the detection current, the prediction coefficients being iterated using a gain equation obtained by recursive least squares method, the gain equation being determined using scalar covariance coefficients; a prediction unit configured to determine a prediction current corresponding to each candidate voltage vector based on the prediction coefficients and a set of candidate voltage vectors; and a control unit configured to determine a target voltage vector based on the difference between the target current and the prediction current, and to control the inverter based on the target voltage vector.

[0007] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor and a memory; the memory being used to store a computer program; and the processor being used to execute the inverter control method as described in the first aspect by invoking the computer program.

[0008] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: The inverter control method proposed in this disclosure determines prediction coefficients based on the detected current, and then determines the predicted current based on the prediction coefficients and a candidate voltage vector set. Since the prediction coefficients are iterated through a gain equation using recursive least squares, and the gain equation is determined using scalar covariance coefficients, replacing the high-dimensional complete covariance matrix with scalar covariance coefficients to construct the gain equation significantly reduces the computational complexity, speeds up processing, and facilitates online implementation. Furthermore, because the prediction coefficients need to be iterated continuously based on the gain equation and the detected current, the error introduced by replacing the complete covariance matrix with dimensionality-reduced scalar covariance coefficients is eliminated during the iteration process. Therefore, through continuous iteration, the computational complexity is reduced, lowering the difficulty of online implementation, while accurately determining the predicted current for precise inverter control.

[0009] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0010] The accompanying drawings, which are incorporated in and form part of this disclosure, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0011] Figure 1 This disclosure is a schematic diagram of the topology of a three-level NPC inverter according to an exemplary embodiment.

[0012] Figure 2 This is a schematic flowchart illustrating an inverter control method according to an exemplary embodiment of the present disclosure.

[0013] Figure 3 This disclosure is a schematic diagram illustrating steady-state experimental results with a load inductance of 10mH and a resistance of 1Ω, according to an exemplary embodiment.

[0014] Figure 4 This disclosure is a schematic diagram illustrating transient experimental results with a load inductance of 10mH and a resistance of 1Ω, according to an exemplary embodiment.

[0015] Figure 5 This disclosure is a schematic diagram illustrating steady-state experimental results with a load inductance of 8mH and a resistance of 1Ω according to an exemplary embodiment.

[0016] Figure 6 This disclosure is a schematic diagram illustrating transient test results with a load inductance of 8mH and a resistance of 1Ω according to an exemplary embodiment.

[0017] Figure 7This is a block diagram illustrating an inverter control device according to an exemplary embodiment of the present disclosure.

[0018] Figure 8 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0020] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0021] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0022] To address the aforementioned technical problems, this disclosure proposes an inverter control method.

[0023] Figure 1 This is a schematic diagram of the topology of a three-level NPC inverter according to an embodiment of the present disclosure.

[0024] Figure 2 This is a schematic flowchart illustrating an inverter control method according to an embodiment of the present disclosure. The inverter control method can be used to control a three-level NPC inverter so that the inverter output current is close to the input target current.

[0025] like Figure 2 As shown, the inverter control method includes: In step S201, the target current and the detection current are determined; In step S202, prediction coefficients are determined based on the detected current. The prediction coefficients are iterated through the gain equation of the recursive least squares method, and the gain equation is determined using scalar covariance coefficients. In step S203, the predicted current corresponding to each candidate voltage vector is determined based on the prediction coefficients and the candidate voltage vector set; In step S204, a target voltage vector is determined based on the difference between the target current and the predicted current, and the inverter is controlled based on the target voltage vector. In some embodiments, a target current and a detection current are determined.

[0026] The target current is the control target for the inverter's output current. It can be determined based on user input commands or it can be a preset sinusoidal current.

[0027] The detected current is the actual current output by the inverter at the current moment, determined by a sensor.

[0028] In some embodiments, an online data-driven predictive control model can be constructed based on a data-driven online system identification strategy, utilizing a linearized dimensionality-upgrading prediction model and recursive least squares (RLS).

[0029] Based on the linearized updimensional prediction model, the system state can be mapped updimensionally, thereby transforming the inverter dynamic system into linear discrete state equations in a high-dimensional extended space.

[0030] The resulting linear discrete prediction model can use the prediction coefficients (K) and the system state containing the detected current and candidate voltage vector sets to determine the predicted current at the next moment.

[0031] Prediction coefficients can be determined based on the detected current. However, in order to transform the inverter system into a linear system, the system state is upgraded, which leads to the need to determine prediction coefficients based on the high-dimensional system state in related technologies. This results in a large amount of computation and makes it difficult to achieve online operation.

[0032] Therefore, this disclosure improves the gain equation used to determine the prediction coefficients, thereby reducing the computational load and lowering the difficulty of implementing online operation.

[0033] In some embodiments, prediction coefficients are determined based on the detected current, the prediction coefficients being iterated through a gain equation using recursive least squares, the gain equation being determined using scalar covariance coefficients.

[0034] The prediction coefficients can be determined using the gain equation of the recursive least squares (RLS) method.

[0035] In this embodiment, the product of the scalar covariance coefficient and the identity matrix is ​​used to replace the complete covariance matrix, so that the improved gain equation is related to the scalar covariance coefficient. This reduces the dimension of the high-dimensional complete covariance matrix to a one-dimensional scalar covariance coefficient, resulting in lower computational complexity, higher computational efficiency, and ease of online operation of the gain equation in this disclosure.

[0036] In some embodiments, the predicted current corresponding to each candidate voltage vector is determined based on the prediction coefficients and the candidate voltage vector set.

[0037] The predictive control model, based on the improved gain equation, can determine the predicted value of the inverter's output current at the next moment if the candidate voltage vector is adopted, i.e., the predicted current, according to the prediction coefficients and each candidate voltage vector.

[0038] For a finite set of candidate voltage vectors, the predicted current corresponding to each candidate voltage vector can be calculated separately, and then the voltage vector to be used can be determined based on the predicted current and the target current.

[0039] In some embodiments, a target voltage vector is determined based on the difference between the target current and the predicted current, and the inverter is controlled based on the target voltage vector.

[0040] The difference between the predicted current and the target current corresponding to multiple candidate voltage vectors can be calculated separately, and the candidate voltage vector corresponding to the predicted current with the smallest difference can be selected as the target voltage vector.

[0041] After determining the target voltage vector, the switching state can be controlled based on the target voltage vector to achieve control of the inverter.

[0042] This disclosure predicts the current at the next time step using a prediction model corresponding to the prediction coefficients. Unlike related technologies, where the gain equation used to determine the prediction coefficients requires a complete high-dimensional covariance matrix, resulting in significant computational complexity and difficulty in online implementation, this disclosure uses scalar covariance coefficients instead of the high-dimensional covariance matrix. This makes the gain equation correlated with the scalar covariance coefficients, significantly reducing computational complexity and facilitating online implementation. Furthermore, since the prediction coefficients of this disclosure can be iterated based on the gain equation determined by the scalar covariance coefficients, the prediction coefficients after multiple iterations can eliminate the errors caused by replacing the high-dimensional covariance matrix with scalar covariance coefficients, resulting in more accurate prediction coefficients and thus ensuring the accuracy of the predicted current.

[0043] In some embodiments, coordinate system transformation can be performed on the three-phase current.

[0044] The three-phase detection current can be converted to The coordinate system and transformation relationship are as follows: (1) (2) Where k represents the current time (k), i represents the current, and the original three-phase coordinate system corresponds to a, b, and c respectively. After transformation, it is: Coordinate system.

[0045] In some embodiments, determining the prediction coefficients based on the detected current includes: normalizing the detected current; determining the scalar covariance coefficients using the normalized detected current; and determining the prediction coefficients based on the scalar covariance coefficients.

[0046] To improve the numerical stability of online identification iteration and facilitate numerical calculation to avoid numerical overshoot, the system state (including current and voltage) of the inverter can be normalized.

[0047] The normalization expression is:

[0048] in, As the reference current, The reference voltage, and They are respectively shaft and The normalized state corresponding to the current of the shaft. and They are respectively shaft and The normalized state corresponding to the voltage of the axis.

[0049] In some embodiments, determining the predicted current corresponding to each candidate voltage vector based on the predicted coefficients and the candidate voltage vector set includes: determining the predicted current set corresponding to the next time step based on the predicted coefficients at the current time step and the parameter upscaling matrix set at the current time step; wherein the parameter upscaling matrix set includes the detected current at the current time step and the previous time step step, as well as the candidate voltage vector set.

[0050] An observation matrix can be constructed based on the system state of the inverter; wherein the observation matrix contains the current detection current at the current moment and the previous detection current; and the voltage vector state to be input is added to the observation matrix to form an upgraded matrix.

[0051] The expression for the constructed low-dimensional observation matrix is: (3) The observation matrix includes the current time (k). shaft and The normalized current state of the axis also includes the current at the previous time step (k-1). shaft and The current normalization state of the axis.

[0052] By adding the normalized input of the voltage vector at the current time (k) to the low-dimensional observation matrix, we can obtain the parameter up-dimensional matrix: (4) In some embodiments, a current prediction model is constructed based on an upgraded matrix.

[0053] against and Two current components can be used to establish two separate current prediction models: (5) in, , respectively corresponding shaft and The prediction coefficient under the axis, the value of which is related to the current time k; and This is the predicted normalized current at the next time step.

[0054] Based on the predicted normalized current and the reference current, the predicted current at the next time step can be determined: (6) Based on this prediction model, given the prediction coefficient K and the system state z (including the detected current at the current time and the previous time, as well as the candidate voltage vector set), the predicted current corresponding to each candidate voltage vector at the next time can be determined.

[0055] In some embodiments, the gain equation includes: the gain equation constructed by replacing the multidimensional covariance matrix with the product of the scalar covariance coefficients and the identity matrix.

[0056] Traditional RLS gain equations require a complete covariance matrix. It is certain that, since the complete covariance matrix is ​​a high-dimensional 7×7 matrix, the computational cost is relatively large. The traditional RLS gain can be expressed as: (7) Where L is used as the recursive gain value, which is used to compensate the prediction coefficients by combining the current error through the gain equation; It is a complete 7×7 covariance matrix; The dimension-upgrading matrix is ​​determined based on the system state. This is the transpose of the upgraded matrix; The forgetting factor can control the trade-off between "historical data" and "new data" in RLS. For example, it can be set to 1 to avoid forgetting historical data and improve the stability of the predicted current.

[0057] However, as mentioned earlier, the iteration of the traditional RLS gain equation involves a high-dimensional covariance matrix, which results in extremely high computational cost, slow computation speed, and difficulty in online operation.

[0058] Therefore, in this embodiment of the disclosure, scalar covariance coefficients can be used. The product of the identity matrix E and the covariance matrix is ​​used to replace the covariance matrix. This reduces the amount of computation.

[0059] make Instead of the covariance matrix in traditional RLS gain The improved gain equation in this disclosure can be obtained as follows: (8) in, For scalar update laws, the expression is: (9) As can be seen, this disclosure improves the gain equation of the recursive least squares method by using the scalar covariance coefficient p, then determines the prediction coefficient K based on the improved gain equation, and determines the prediction current based on the prediction coefficient K, the detection current and the candidate voltage vector.

[0060] The improved gain equation only needs to maintain the scalar covariance coefficient. It does not require a complete update. The covariance matrix makes the computational complexity reduced from Reduce to In this disclosed example, based on the parameter-increased-dimensional matrix, .

[0061] The following is based on Using channels as an example, this explains how to... Determining the detection current of the channel The prediction coefficient K of the channel.

[0062] In some embodiments, determining the prediction coefficients based on the detected current includes: determining the scalar update law and the prediction error at the current time; the prediction error at the current time is the difference between the predicted current determined at the previous time and the detected current at the current time; determining the prediction coefficients at the current time based on the prediction coefficients at the previous time, the scalar update law at the current time, the prediction error at the current time, and the target parameter dimension-upgrading matrix at the previous time; wherein the target parameter dimension-upgrading matrix at the previous time includes the target voltage vector at the previous time.

[0063] The normalized detection current at the current moment is: (10) The prediction error at the current moment can be determined based on the difference between the predicted current determined at the previous moment and the detected current at the current moment. for: (11) in, The prediction coefficients were determined at the previous time step. This is the upgraded matrix of the target parameters from the previous time step.

[0064] Unlike the current moment, where the target voltage vector has not yet been determined and the inverter has not yet been controlled based on the target voltage vector, the parameter up-dimensional matrix at the current moment contains a set of candidate voltage vectors composed of all candidate voltage vectors.

[0065] In the previous moment, since the target voltage vector of the previous moment has been determined and adopted, the voltage vector in the target parameter up-dimensional matrix corresponding to the previous moment is the target voltage vector of the previous moment, which is a clear and unique value.

[0066] therefore, The predicted current after using the target voltage vector at the previous time step is compared with the detected current determined by actual measurement at the current time step. The difference is the prediction error.

[0067] The prediction coefficients at the current time step are determined iteratively based on the prediction coefficients at the previous time step. Therefore, the prediction coefficients at the previous time step can be used as a basis for prediction. Scalar update law at the current moment Prediction error at the current moment and the target parameter upscaling matrix from the previous time step Determine the prediction coefficients at the current time. The target parameter upscaling matrix of the previous time step contains the target voltage vector of the previous time step.

[0068] The prediction coefficients at the current time step are the sum of the prediction coefficients from the previous time step and the updated prediction coefficient values. The updated prediction coefficient values ​​are the product of the scalar update law at the current time step, the prediction error at the current time step, and the transpose of the target parameter dimension-upgraded matrix from the previous time step. The formula is: (12) Based on this formula, the prediction coefficients for the current time can be determined after the scalar update law at the current time is determined. Therefore, the following section will continue to explain how to determine the scalar update law at the current time. .

[0069] In some embodiments, determining the scalar update law at the current moment includes: determining the scalar covariance coefficient at the current moment, and determining the scalar update law at the current moment based on the scalar covariance coefficient at the current moment and the target parameter dimension-upgrading matrix at the previous moment.

[0070] Based on the scalar update law of the improved RLS gain equation (see Equation (9)), the scalar update law at the current time includes the scalar covariance coefficient at the current time. and the target parameter upscaling matrix from the previous time step .

[0071] The expression for the scalar update law at the current moment is: (13) As can be seen from the expression based on this scalar update law, as long as the scalar covariance coefficient at the current moment can be determined... The scalar update law can then be determined. Therefore, the prediction coefficient at the current moment can be determined based on formula (12). To determine the predicted current.

[0072] Therefore, it is also necessary to determine the scalar covariance coefficient at the current moment.

[0073] In some embodiments, determining the scalar covariance coefficient at the current moment includes: determining the scalar covariance coefficient at the current moment based on the scalar covariance coefficient at the previous moment, the target parameter dimension-upgrading matrix of the previous moment, and the transpose matrix corresponding to the target parameter dimension-upgrading matrix.

[0074] scalar covariance coefficient It needs to be determined based on the scalar covariance coefficient of the previous time step (k-1), the target parameter dimension-upgrading matrix z of the previous time step (k-2), and the transpose of the target parameter dimension-upgrading matrix z at time step (k-2).

[0075] To facilitate identification and avoid complicating the formula, the scalar covariance coefficient for the next time step (k+1) is determined using the relevant parameters of the previous time step (k-1) and the current time step (k) through the following formula: (14) After multiple iterations of the inverter system, based on the scalar covariance coefficient The prediction coefficients can be determined relatively accurately, and thus the predicted current can be estimated relatively accurately.

[0076] In some embodiments, the initial scalar covariance coefficient of the inverter system is a preset value.

[0077] The initial scalar covariance coefficient p(0) can be a preset value, and iterative processes are performed based on this preset value.

[0078] In some embodiments, the method further includes: limiting the prediction coefficients and the scalar covariance coefficients to restrict the prediction coefficients and the scalar covariance coefficients to their respective value ranges.

[0079] The scalar covariance coefficient p and the prediction coefficient K can be limited to prevent abnormal data from causing parameter abrupt changes.

[0080] For example, restrictions can be imposed. When the scalar covariance coefficient p or the prediction coefficient K is less than the minimum limit, the minimum value is taken; when the scalar covariance coefficient p or the prediction coefficient K is greater than the maximum limit, the maximum value is taken.

[0081] In some embodiments, determining the prediction coefficients based on the detected current includes: normalizing the detected current; determining the scalar covariance coefficients using the normalized detected current; and determining the prediction coefficients based on the scalar covariance coefficients.

[0082] The specific normalization process and the method for restoring the normalized current and voltage to the actual current and voltage can be referred to the description in the above embodiments, and will not be elaborated here.

[0083] Normalization can unify the numerical scale of parameters, avoid excessively large values, and improve the stability of identification iteration and the universality of model parameters.

[0084] The above embodiments are determined to The scheme is illustrated using the predicted current of the shaft as an example. Correspondingly, The predicted current of the shaft can also be determined using a similar method.

[0085] The predicted current of the channel satisfies the following relationship: (15) For details on the implementation process of the function and role of each relation in relation (15), please refer to the above determination. The explanation of the corresponding formula in the method for predicting the current of the shaft will not be repeated here.

[0086] In some embodiments, a target voltage vector is determined based on the difference between the target current and the predicted current, and the inverter is controlled based on the target voltage vector.

[0087] This includes substituting the target current and the predicted current corresponding to each candidate voltage vector into the determined cost function, and determining the candidate voltage vector that minimizes the cost function value as the target voltage vector.

[0088] The cost function also includes the midpoint voltage.

[0089] As an example, the expression for the cost function J is: (16) in, and They are respectively shaft and The target current of the shaft; This is the weighting factor for the midpoint voltage term, and it is a preset value. Let be the midpoint voltage at time k+1.

[0090] Based on this cost function, the optimization objectives can be small output current error and balanced midpoint voltage. The voltage vectors corresponding to the 27 candidate switch vector states are compared, and the candidate voltage vector that minimizes the cost function is determined from the candidate voltage vector set. This candidate voltage vector is then determined as the target voltage vector, and the inverter is controlled based on this target voltage vector in the next cycle.

[0091] To verify the robustness of the control algorithm of this invention, we conducted experimental verification of the method.

[0092] Figure 3 This is a schematic diagram illustrating the steady-state experimental results with a load inductance of 10mH and a resistance of 1Ω, according to an embodiment of this disclosure. Figure 4 This is a schematic diagram illustrating the transient test results with a load inductance of 10mH and a resistance of 1Ω, according to an embodiment of the present disclosure. Figure 5 This is a schematic diagram illustrating the steady-state experimental results with a load inductance of 8mH and a resistance of 1Ω, according to an embodiment of this disclosure. Figure 6 This is a schematic diagram illustrating transient test results with a load inductance of 8mH and a resistance of 1Ω, according to an embodiment of the present disclosure.

[0093] The experiment compared two different operating conditions with loads of 10 mH and 8 mH, and conducted steady-state and transient current tests to verify the results. Figures 3-6As shown in the experiment, with a load of 10mH and a current of 12A, the total harmonic distortion (THD) of the output current is approximately 2.37%; with a load of 8mH and a current of 12A, the THD of the output current is approximately 2.89%. These experimental results verify the effectiveness of this strategy, demonstrating that good output performance can be achieved without depending on the given load parameters.

[0094] Corresponding to the embodiments of the inverter control method of this disclosure, this disclosure also provides embodiments of corresponding inverter control devices.

[0095] Please see Figure 7 , Figure 7 This is a block diagram of an inverter control device according to one embodiment of this disclosure. Figure 7 As shown, the inverter control device includes: The detection unit 710 is configured to determine the target current and the detection current; The determining unit 720 is configured to determine prediction coefficients based on the detected current, the prediction coefficients being iterated through a gain equation using recursive least squares, the gain equation being determined using scalar covariance coefficients; Prediction unit 730 is configured to determine the predicted current corresponding to each candidate voltage vector based on the prediction coefficients and the candidate voltage vector set; The control unit 740 is configured to determine a target voltage vector based on the difference between the target current and the predicted current, and to control the inverter based on the target voltage vector.

[0096] In some embodiments, the gain equation includes: the gain equation constructed by replacing the multidimensional covariance matrix with the product of the scalar covariance coefficients and the identity matrix.

[0097] In some embodiments, determining the predicted current corresponding to each candidate voltage vector based on the predicted coefficients and the candidate voltage vector set includes: determining the predicted current set corresponding to the next time step based on the predicted coefficients at the current time step and the parameter upscaling matrix set at the current time step; wherein the parameter upscaling matrix set includes the detected current at the current time step and the previous time step step, as well as the candidate voltage vector set.

[0098] In some embodiments, determining the prediction coefficients based on the detected current includes: determining the scalar update law and the prediction error at the current time; the prediction error at the current time is the difference between the predicted current determined at the previous time and the detected current at the current time; determining the prediction coefficients at the current time based on the prediction coefficients at the previous time, the scalar update law at the current time, the prediction error at the current time, and the target parameter dimension-upgrading matrix at the previous time; wherein the target parameter dimension-upgrading matrix at the previous time includes the target voltage vector at the previous time.

[0099] In some embodiments, determining the scalar update law at the current moment includes: determining the scalar covariance coefficient at the current moment, and determining the scalar update law at the current moment based on the scalar covariance coefficient at the current moment and the target parameter dimension-upgrading matrix at the previous moment.

[0100] In some embodiments, determining the scalar covariance coefficient at the current moment includes: determining the scalar covariance coefficient at the current moment based on the scalar covariance coefficient at the previous moment, the target parameter dimension-upgrading matrix of the previous moment, and the transpose matrix corresponding to the target parameter dimension-upgrading matrix.

[0101] In some embodiments, the apparatus is further configured to: limit the prediction coefficients and the scalar covariance coefficients to restrict the prediction coefficients and the scalar covariance coefficients to their respective value ranges.

[0102] In some embodiments, determining the prediction coefficients based on the detected current includes: normalizing the detected current; determining the scalar covariance coefficients using the normalized detected current; and determining the prediction coefficients based on the scalar covariance coefficients.

[0103] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0104] Embodiments of this disclosure also provide an electronic device, including: a processor and a memory; the memory for storing a computer program; and the processor for executing an inverter control method as described in any of the above embodiments by invoking the computer program.

[0105] Embodiments of this disclosure also provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the inverter control method as described in any of the above embodiments.

[0106] Embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements the methods described in any of the foregoing embodiments.

[0107] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 8 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system).

[0108] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0109] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0110] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0111] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0112] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0113] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0114] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

[0115] It should be noted that, in this document, relational terms such as "first" and "second" are used only 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. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0116] The methods and apparatus provided in the embodiments of this disclosure have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this disclosure. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this disclosure. Therefore, the content of this specification should not be construed as a limitation of this disclosure.

Claims

1. An inverter control method, characterized in that, The method includes: Determine the target current and the detection current; The prediction coefficients are determined based on the detected current. The prediction coefficients are iterated through the gain equation using the recursive least squares method. The gain equation is determined using scalar covariance coefficients. The predicted current corresponding to each candidate voltage vector is determined based on the prediction coefficients and the candidate voltage vector set; The target voltage vector is determined based on the difference between the target current and the predicted current, and the inverter is controlled based on the target voltage vector.

2. The method according to claim 1, characterized in that, The gain equation includes: The gain equation is constructed by replacing the multidimensional covariance matrix with the product of the scalar covariance coefficients and the identity matrix.

3. The method according to claim 1, characterized in that, The step of determining the predicted current corresponding to each candidate voltage vector based on the prediction coefficients and the candidate voltage vector set includes: The predicted current set for the next time step is determined based on the prediction coefficients at the current time step and the parameter upscaling matrix set at the current time step; wherein, the parameter upscaling matrix set includes the detected current at the current time step and the previous time step step, as well as the candidate voltage vector set.

4. The method according to claim 3, characterized in that, The step of determining the prediction coefficient based on the detected current includes: Determine the scalar update law and the prediction error at the current moment; the prediction error at the current moment is the difference between the predicted current determined at the previous moment and the detected current at the current moment. The prediction coefficients for the current time are determined based on the prediction coefficients of the previous time step, the scalar update law of the current time step, the prediction error of the current time step, and the target parameter dimension-upgrading matrix of the previous time step; wherein the target parameter dimension-upgrading matrix of the previous time step contains the target voltage vector of the previous time step.

5. The method according to claim 4, characterized in that, The scalar update law for determining the current moment includes: Determine the scalar covariance coefficient at the current time, and determine the scalar update law at the current time based on the scalar covariance coefficient at the current time and the target parameter dimension-upgrading matrix at the previous time.

6. The method according to claim 5, characterized in that, Determining the scalar covariance coefficient at the current moment includes: The scalar covariance coefficients at the current time are determined based on the scalar covariance coefficients of the previous time step, the target parameter upscaling matrix of the time step preceding the previous time step, and the transpose of the target parameter upscaling matrix.

7. The method according to claim 1, characterized in that, The method further includes: The prediction coefficients and the scalar covariance coefficients are limited to their respective ranges.

8. The method according to claim 1, characterized in that, The step of determining the prediction coefficient based on the detected current includes: The detected current is normalized. The scalar covariance coefficient is determined using the normalized detection current, and the prediction coefficient is determined based on the scalar covariance coefficient.

9. An inverter control device, characterized in that, The device includes: The detection unit is configured to determine the target current and the detection current; The determining unit is configured to determine prediction coefficients based on the detected current, the prediction coefficients being iterated through a gain equation using recursive least squares, the gain equation being determined using scalar covariance coefficients; The prediction unit is configured to determine the predicted current corresponding to each candidate voltage vector based on the prediction coefficients and the candidate voltage vector set; The control unit is configured to determine a target voltage vector based on the difference between the target current and the predicted current, and to control the inverter based on the target voltage vector.

10. An electronic device, characterized in that, include: Processor, memory; The memory is used to store computer programs; The processor is configured to execute the inverter control method as described in any one of claims 1-8 by invoking the computer program.

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