Control method and device of inverter and inverter system

Abstract

The present disclosure relates to the technical field of inverters, and in particular, to a control method and device of an inverter and an inverter system. The control method of the inverter comprises: determining an output current and a voltage vector of the inverter system at a current time; inputting the output current into a fuzzy estimator to determine a disturbance estimation value of the inverter system; predicting an estimated current at a next time based on the output current, the disturbance estimation value and the voltage vector; determining a target voltage vector according to the estimated current, and adjusting a switching state of the inverter system based on the target voltage vector. Based on the fuzzy estimator, a disturbance estimation value close to an unknown disturbance term can be determined, and then a more accurate estimated current can be determined, which facilitates accurate control of the switching state of the inverter system, reduces the error between the output current and the reference current, and enhances the stability and accuracy of the output current.

Description

Technical Field

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

[0002] An inverter converts direct current (DC) to alternating current (AC). In an inverter system, the system state is determined based on the voltage vector, and then the switching states are determined based on that system state. By adjusting multiple switches, the output current can be changed.

[0003] In related technologies, the system state can be determined using a finite control set model, thereby controlling the switching of the inverter system. This approach relies on accurate system modeling. However, in real-world scenarios, due to factors such as system parameterization, external disturbances, and non-ideal characteristics during operation, the system model inevitably contains time-varying unknown disturbance terms. This results in significant prediction errors in related technologies, affecting system control and causing a large discrepancy between the inverter's converted output current and the standard sinusoidal AC current. Summary of the Invention

[0004] To overcome the problems existing in the related technologies, this disclosure provides a control method, device and inverter system for an inverter, which can solve the above problems.

[0005] According to a first aspect of the present disclosure, a control method for an inverter is provided, the method comprising: determining an output current and a voltage vector of an inverter system at a current moment; inputting the output current into a fuzzy estimator to determine a disturbance estimate of the inverter system; predicting an estimated current at a next moment based on the output current, the disturbance estimate, and the voltage vector; determining a target voltage vector based on the estimated current; and adjusting the switching state of the inverter system based on the target voltage vector.

[0006] According to a second aspect of the present disclosure, a control device for an inverter is provided, the device comprising: a determining unit configured to determine an output current and a voltage vector of an inverter system at a current moment; a fuzzy estimation unit configured to input the output current into a fuzzy estimator to determine a disturbance estimate of the inverter system; a predicting unit configured to predict an estimated current at a next moment based on the output current, the disturbance estimate, and the voltage vector; and an adjusting unit configured to determine a target voltage vector based on the estimated current and adjust the switching state of the inverter system based on the target voltage vector.

[0007] According to a third aspect of the present disclosure, an inverter system is provided, the system including an inverter circuit and a control module, the control module controlling the inverter circuit using the inverter control method described in the first aspect.

[0008] According to a fourth 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 a control method for an inverter as described in the first aspect by invoking the computer program.

[0009] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in the first aspect.

[0010] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: After constructing the fuzzy estimator, this disclosure allows the output current to be input into the fuzzy estimator to determine the disturbance estimate of the inverter system; the estimated current at the next moment is predicted based on the disturbance estimate, the output current, and the voltage vector; after determining the estimated current, the target voltage vector can be determined based on the estimated current, and the switching state of the inverter system can be adjusted based on the target voltage vector.

[0011] Based on the method proposed in this disclosure, a relatively close disturbance estimate can be determined by using a fuzzy estimator and leveraging the universal approximation property of fuzzy logic systems when the unknown disturbance term of the uncertain system is unknown. Since the disturbance estimate is relatively close to the unknown disturbance term, the estimated current determined based on the disturbance estimate is also close to the measured current at the next moment, with a small prediction error. This allows for a more accurate determination of the target voltage vector, and thus a more accurate and appropriate adjustment of the system's switching state, making the inverter system's output current more stable, more accurate, and closer to the reference current with a smaller error.

[0012] 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

[0013] 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.

[0014] Figure 1 This disclosure is a schematic diagram of the topology of a three-level NPC inverter according to an exemplary embodiment.

[0015] Figure 2 This is a schematic flowchart illustrating a control method for an inverter according to an exemplary embodiment of the present disclosure.

[0016] Figure 3 This is a schematic diagram illustrating the effect of a switching state adjustment strategy according to an exemplary embodiment of the present disclosure.

[0017] Figure 4 This is a schematic flowchart illustrating an adaptive threshold event-triggered model-free predictive control method based on fuzzy prediction period according to an exemplary embodiment of the present disclosure.

[0018] Figure 5 This is a schematic diagram of the structure of an inverter system according to an exemplary embodiment of the present disclosure.

[0019] Figure 6 This disclosure is a block diagram illustrating a control device for an inverter according to an exemplary embodiment.

[0020] Figure 7 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0021] 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.

[0022] 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.

[0023] 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."

[0024] Inverter systems include three-level neutral point clamped (NPC) inverters. Three-level NPC inverters have advantages such as multiple output voltage levels, low harmonic content, and low voltage stress on components, making them widely used in medium- and high-power power conversion applications.

[0025] Figure 1This is a schematic diagram of the topology of a three-level NPC inverter according to an embodiment of the present disclosure.

[0026] like Figure 1 As shown, the inverter can control the three-phase output current through multiple switches, thereby converting the DC power input to the inverter system into three-phase AC power. Its AC side dynamics in a stationary coordinate system can be expressed by the following formula: (1) in, and Let the current be in the stationary coordinate system. and Let be the voltage in the stationary coordinate system, and t be the sampling time. and These are the resistor and inductor in the inverter system, respectively.

[0027] However, due to parameter changes or unknown dynamics that may occur in the inverter system, there are time-varying unknown disturbance terms in the inverter system. This makes the current prediction based on predetermined resistance and inductance have large errors, making it difficult to achieve precise control of the system state. As a result, there is a large error between the output current and the reference current, and the output AC current has poor stability.

[0028] To address the aforementioned technical problems, this disclosure proposes a control method for an inverter.

[0029] Figure 2 This is a schematic flowchart illustrating a control method for an inverter according to an embodiment of the present disclosure. This inverter control method can be used to control the switching state of an inverter system, thereby changing the output current of the inverter system so that the output current of the inverter system is closer to a reference current.

[0030] In some embodiments, the waveform of the reference current can be a sine wave.

[0031] It should be noted that in scenarios where the inverter system needs to output AC power, the reference current can be a sine wave. However, if the actual output current is not a sine wave AC current, but another waveform, or a current with specific requirements, the reference current can also change accordingly. This disclosure aims to make the current output by the inverter system approximate the reference current, without limiting the specific type of the reference current.

[0032] like Figure 2 As shown, the control methods for the inverter include: In step S201, the output current and voltage vectors of the inverter system at the current moment are determined; In step S202, the output current is input into a fuzzy estimator to determine the disturbance estimate of the inverter system; In step S203, the estimated current at the next moment is predicted based on the output current, the disturbance estimate, and the voltage vector; In step S204, a target voltage vector is determined based on the estimated current, and the switching state of the inverter system is adjusted based on the target voltage vector.

[0033] In some embodiments, the output current and voltage vectors of the inverter system at the current moment can be determined.

[0034] For ease of explanation, in the following text, the current time will be time k, and the next time will be time k+1.

[0035] The voltage vector corresponds to the switching state of the inverter system. By determining multiple voltage vectors, various switching states of the inverter system can be determined, thereby controlling the output current of the inverter system.

[0036] In some embodiments, the output current can be input into a fuzzy estimator to determine the disturbance estimate of the inverter system.

[0037] The fuzzy estimator is determined based on the fuzzy logic system. The specific determination method and examples will be introduced in detail later, and will not be elaborated here.

[0038] Based on the fuzzy estimator, the disturbance estimate of the inverter system can be determined. It should be noted that this disturbance estimate is not the unknown disturbance term of the inverter system. However, due to the universal approximation property of fuzzy logic systems, the unknown disturbance term of the inverter system can be determined by the disturbance estimate and the approximation error, which is sufficiently small to be negligible. Therefore, the disturbance estimate determined based on the fuzzy estimator can be identified as the unknown disturbance term in the inverter system.

[0039] In some embodiments, the estimated current at the next moment is predicted based on the output current, the disturbance estimate, and the voltage vector.

[0040] Voltage vectors can control the output current, while unknown disturbances in the inverter system can affect that output current.

[0041] The disturbance estimate determined by the fuzzy logic system can be used to replace the time-varying unknown disturbance term. Since the disturbance estimate is determined based on the current at the current moment, and the error between the disturbance estimate and the unknown disturbance term is extremely small, the estimated current at the next moment predicted based on the disturbance estimate is also closer to the actual measured output current at the next moment without adjusting the inverter system state.

[0042] In some embodiments, a target voltage vector is determined based on the estimated current, and the switching state of the inverter system is adjusted based on the target voltage vector.

[0043] Once the estimated current for the next moment is determined, the target voltage vector can be determined based on the estimated current. Based on a more accurate estimated current, a more precise adjustment strategy for the switching state of the inverter system can be determined to adjust the actual output current for the next moment, making the output current closer to the reference current.

[0044] For example, the target voltage vector is the voltage vector at the next moment, corresponding to the switching state of the transformer system at the next moment. By adjusting the switching state of the transformer system, the switching state of the transformer system at the current moment can be changed, thereby adjusting the actual current at the next moment, making the actual current closer to the reference current.

[0045] For example, if the estimated current is greater than the reference current, the determined target voltage vector can reduce the output current at the next moment; if the estimated current is less than the reference current, the determined target voltage vector can increase the output current at the next moment.

[0046] This disclosure uses a fuzzy logic system and a fuzzy estimator to determine the disturbance estimate of the inverter system. Since the disturbance estimate is very close to the actual unknown disturbance term of the inverter system, the estimated current at the next moment can be predicted relatively accurately using the disturbance estimate, output current and voltage vector. Accurate prediction of the estimated current at the next moment allows the actual output current at the next moment to be adjusted by adjusting the control strategy of the inverter system, so that the actual output current is closer to the reference current.

[0047] Based on the embodiments of this disclosure, the control method reduces its dependence on an accurate system mathematical model, improves its robustness under uncertain system parameters and external disturbances, and can more accurately predict the estimated current at the next moment, thereby precisely adjusting the switching state to adjust the actual output current.

[0048] In some embodiments, the method further includes: constructing a hyperlocal model of the inverter system, the hyperlocal model including output current, input voltage vector and unknown disturbance term; approximating the unknown disturbance term according to a fuzzy logic system and constructing the fuzzy estimator.

[0049] To break free from dependence on precise mathematical models (e.g., for accurate load resistance) ,inductance (depending on the system), an ultra-local model can be constructed. Through the ultra-local model, the unknown dynamics and nonlinear disturbances of the system are concentrated into unknown disturbance terms, and the relationship between the unknown disturbance terms and the output current and voltage vectors is established.

[0050] For example, a hyperlocal model could be: (2) in, For unknown disturbance terms; This is the state vector of the inverter system, including the input current in the stationary coordinate system; b is the control input, which includes the voltage vector in the stationary coordinate system; b is the constant input gain.

[0051] Based on the relationship between the voltage vector and the output current constructed by this hyperlocal model, it can be seen that if the unknown disturbance term can be determined, a relatively accurate voltage vector can be determined according to the required output current.

[0052] Since the unknown disturbance term is variable and not constant, a fuzzy logic system can be used to approximate the disturbance term, reducing the prediction error caused by the unknown disturbance and more accurately predicting the estimated current at the next moment.

[0053] In some embodiments, constructing the fuzzy estimator includes: approximating the unknown disturbance term based on fuzzy parameters and fuzzy basis functions; and determining the fuzzy estimator based on output current, voltage vector, sampling period, fuzzy parameters, and fuzzy basis functions.

[0054] Based on the universal approximation property of fuzzy logic systems, the unknown disturbance term... It can be expressed as the sum of the weighted form of the optimal fuzzy basis function and the approximation error, as shown in the formula: (3) in, The optimal parameter vector; For fuzzy basis function vectors; This is a bounded approximation error, and due to the universal approximation property, this bounded approximation error is extremely small and can be ignored.

[0055] Fuzzy basis functions can be constructed from Gaussian membership functions: (4) in, Indicates the first Input variables In the Membership degree in the fuzzy basis of the antecedent of the rule.

[0056] By constructing a fuzzy estimator, it is possible to determine the unknown disturbance terms in the system. Online estimates.

[0057] The fuzzy estimator includes output current, voltage vector, sampling period, fuzzy parameters, and fuzzy basis functions.

[0058] In some embodiments, the fuzzy estimator is used to determine the estimated current at the next time step, the fuzzy parameters at the next time step, and the estimated disturbance value at the next time step; wherein, the estimated current at the next time step is determined based on the estimated disturbance value at the current time step, the input voltage vector at the current time step, the estimation error at the current time step, the sampling time interval, and the estimated current at the current time step; the estimation error is the difference between the estimated current and the measured current; the fuzzy parameters at the next time step are determined based on the regularization term of the fuzzy parameters at the current time step, the estimation error at the current time step, and the fuzzy basis function at the current time step; the estimated disturbance value at the next time step is the product of the fuzzy parameters at the current time step and the fuzzy basis function at the next time step.

[0059] Based on the constructed fuzzy estimator, the estimated current at the next time step is related to the disturbance estimate at the current time step. The disturbance estimate at the current time step, in turn, affects the fuzzy parameters at the next time step, which in turn affects the fuzzy basis function at the next time step, causing the disturbance estimate at the next time step to change.

[0060] Therefore, based on the constructed fuzzy estimator, the fuzzy parameters and fuzzy basis functions can be determined more accurately, so that the determined disturbance estimate is close to the time-varying unknown disturbance term, enabling a more accurate prediction of the estimated current at the next moment.

[0061] For example, the estimated current at time k+1 (next) is the sum of the estimated current at time k (current) and the current correction; the current correction is the product of the sampling period and the rate of change of current; the rate of change of current is the sum of the disturbance estimate at time k and the voltage vector at time k, minus the estimation error at time k. The estimation error is the difference between the estimated current at time k and the measured current.

[0062] For example, the fuzzy parameter at time k+1 is the difference between the regularization term of the fuzzy parameter at time k and the instantaneous estimation error; the instantaneous estimation error can be determined by multiplying the fuzzy basis function at time k and the estimation error at time k.

[0063] The regularization term of the fuzzy parameters prevents them from growing indefinitely, and the instantaneous estimation error can be adjusted in real time according to the current error, so that the disturbance estimate determined by the fuzzy system continuously approaches the true unknown disturbance term of the inverter system. This process allows the determined disturbance estimate to be adaptively corrected instantaneously in this embodiment to continuously approach the unknown disturbance, thereby making the predicted estimated current more accurate.

[0064] For example, the perturbation estimate at time k+1 is the product of the fuzzy parameter at time k and the fuzzy basis function at time k+1.

[0065] The expression for the fuzzy estimator can be: (5) in, To estimate the state, we need to estimate the current; The estimation error is the difference between the estimated current and the measured current. For design gain; The learning rate; is the regularization coefficient.

[0066] Based on the constructed fuzzy estimator, combined with the hyperlocal model, the unknown disturbance term can be approximated by the disturbance estimate when the inverter system is subjected to unknown disturbance changes. This reduces the prediction error of the estimated current caused by the unknown disturbance, enabling the embodiments of this disclosure to predict the estimated current more accurately. Then, based on the estimated current, the adjustment strategy for the inverter system is determined, the target voltage vector is determined, the switching state of the inverter system is adjusted, and finally the output current of the inverter system is adjusted so that the output current of the inverter system can better match the reference current.

[0067] In some embodiments, a target voltage vector can be determined from a set of candidate voltage vectors based on a comparison between the predicted estimated current and the reference current, and the switching state of the inverter system can be adjusted using the target voltage vector.

[0068] When the estimated current does not match the reference current, the target voltage vector can be determined based on the comparison results. The system can then be adjusted using the target voltage vector to make the actual current output by the system at the next moment closer to the reference current based on the estimated current.

[0069] In some embodiments, determining the target voltage vector based on the estimated current includes: determining the estimation error between the estimated current and the measured current at the current moment; maintaining the switching state of the inverter system if the estimation error is not greater than an adjustment threshold; and determining the voltage vector corresponding to the estimation error in the candidate voltage vector set as the target voltage vector if the estimation error is greater than the adjustment threshold.

[0070] However, if the switch state is adjusted every time the predicted estimated current does not match the reference current, as in the previous embodiment, the operating frequency of the switch will be high, which will easily lead to switch wear.

[0071] Therefore, in this embodiment, the estimation error can be determined. If the estimation error is not greater than the adjustment threshold, it indicates that although the estimated current does not match the reference current, it is still relatively close. The switching state of the system can be maintained without adjusting the switching state, thereby reducing the switching frequency and reducing unnecessary control updates and switching actions.

[0072] If the estimation error is greater than the adjustment threshold, the voltage vector corresponding to the estimation error in the candidate voltage vector set is determined as the target voltage vector, and the switching state is adjusted based on the target voltage vector.

[0073] In some embodiments, the method further includes: determining an upper boundary for maintaining a measurement error based on the fuzzy estimator, the measurement error being the difference between the output current at the time of the last adjustment of the inverter system and the output current at the current time; and determining an adjustment threshold at the current time based on the upper boundary.

[0074] If the adjustment threshold is a fixed value, it is easily affected by system parameter disturbances, leading to deviations in the adjustment threshold and making it impossible to adjust the switching state at the appropriate time. Therefore, this embodiment can use the output of a fuzzy estimator to reconstruct the system dynamics and determine an adaptive dynamic adjustment threshold, making the adjustment threshold more accurate and enhancing the robustness of the inverter system.

[0075] You can first define the measurement error. ;in, t represents the last time the switch state was adjusted, and t represents the current time.

[0076] Based on the constructed fuzzy estimator (e.g., Equation (5)) and the definition of measurement error, an upper bound for the fuzzy estimator's hold-up error can be derived. Therefore, a dynamically adjusted threshold is constructed, and the formula for calculating the dynamically adjusted threshold can be: (6) Where N is the number of sampling steps since the last adjustment of the switch state to the current time; T is the system sampling period. ; These are the upper bounds for the system state and the input, respectively.

[0077] Based on formula (6), the dynamic adjustment threshold can be calculated and determined, and this dynamic adjustment threshold is also determined based on the disturbance estimate determined by the fuzzy estimator. It can be seen that the disturbance estimate determined by the fuzzy estimator can be used to predict the estimated current, and can also be used to update the dynamic adjustment threshold.

[0078] Furthermore, an adjustable factor is introduced. The balance between control performance and switching frequency can be manually set, and the trigger condition is: (7) If the triggering condition is met, it indicates that the upper bound of the error maintained by the fuzzy estimator is not greater than the dynamic adjustment threshold, and the optimal switching state of the previous moment can be maintained.

[0079] If the triggering condition is not met, the dynamic adjustment threshold is recalculated and updated. In this case, the adjustment strategy for the switch state changes to: (8) in, The optimal voltage vector at time k; The target voltage vector determined at the current moment can be, for example, the optimal voltage vector determined based on minimizing the cost function, which will be described in more detail later.

[0080] The switching state adjustment strategy corresponding to formula (8) is to maintain the original switching state when the estimation error is not greater than the dynamic adjustment threshold; and to determine the optimal voltage vector at the next moment as the target voltage vector when the estimation error is greater than the dynamic adjustment threshold. This embodiment can reduce unnecessary control updates, thereby reducing the switching frequency.

[0081] In some embodiments, determining the target voltage vector based on the estimated current includes: determining the slope and tracking error of the estimated current, the tracking error being the difference between the estimated current and a reference current; constructing a cost function that includes a slope penalty term for the estimated current and the tracking error; and determining the target voltage vector based on the cost function.

[0082] The target voltage vector is used to adjust the switching state of the inverter system. However, in some scenarios, the estimated current deviates significantly from the reference current. To adjust the estimated current to the reference current, a large number of switching states need to be modified, and a large number of switches need to be adjusted.

[0083] To further reduce the switching frequency, in this embodiment, when the switching state needs to be adjusted, the adjustment can be made not according to the optimal voltage vector, but a more gradual adjustment strategy can be determined. This reduces the number of switches to be adjusted, lowers the switching frequency, and makes the estimated current closer to the reference current.

[0084] In this embodiment, the slope and tracking error of the estimated current can be determined. The tracking error is the difference between the estimated current and the reference current, used to measure the degree of deviation between the estimated current and the reference current, and to limit the adjusted switching state so that the output current is closer to the reference current. The slope penalty term of the current is used to represent the degree of change of the output current after the switching state is adjusted. The larger the slope, the greater the degree of change, indicating that more switching states need to be adjusted.

[0085] A cost function is constructed that includes the slope penalty term of the estimated current and the tracking error. Based on the target voltage vector determined by this cost function, the tracking error and the number of switches adjusted can be comprehensively considered, so that the adjustment based on the target voltage vector can achieve both the effect of reducing the switching frequency and getting closer to the reference current.

[0086] For example, the slope of the estimated current can be defined as: (9) in, This represents the measured current at the current moment; This is the estimated current for the next moment.

[0087] The cost function can be: (10) In formula (10), the first term is the tracking error, and the second term is the current slope penalty term; where, This is a weighting coefficient. The current slope penalty term can prompt the controller to select a target voltage vector with a more gradual current change, thereby reducing the switching frequency.

[0088] Figure 3 This is a schematic diagram illustrating the effect of a switching state adjustment strategy according to an embodiment of the present disclosure.

[0089] like Figure 3 As shown, i_ref is the reference current, i_k is the measured output current, i_p is the predicted estimated current, and the dashed lines of i_p1, i_p2, i_p3 and the rest of i_p represent the output current after adjusting the switching state using multiple voltage vectors, respectively. To adjust the (triggering) threshold, t is time.

[0090] At time k, since the estimation error at the current time does not exceed the adjustment threshold at time k, the switching state remains unchanged at time k, and this switching state continues until time k+1. At time k+1, the estimated current at time k+1 is calculated as i_p, and its estimation error is greater than the adjustment threshold at time k+1. Therefore, a control update is triggered, and the switching state is adjusted through the target voltage vector to change the output current at time k+2, making it closer to the reference current. It is evident that, based on the embodiments of this disclosure, the switching frequency can be reduced, and switching losses can be lowered.

[0091] Furthermore, if it has been determined at time k+1 that the switch state needs to be adjusted, such as Figure 3As shown, there can be adjustment methods including i_p1, i_p2, i_p3, and more. Based on the above embodiments of this disclosure, the optimal target voltage vector can be determined according to the cost function including the current slope penalty term and the tracking error. Figure 3 (The voltage vector corresponding to i_p2 in the middle).

[0092] Compared to the adjustment method of i_p1, the adjustment method of i_p2 has a relatively smaller slope, requires fewer switching states to be adjusted, and can keep the adjusted output current within the preset error range of the reference current adjustment, thereby further reducing the switching frequency while getting closer to the reference current.

[0093] It should be noted that, in Figure 3 The embodiments include an embodiment that determines the timing of adjustment by adjusting a threshold, and an embodiment that determines the optimal target voltage vector by a cost function. However, both embodiments can be implemented individually or in combination with other embodiments. Figure 3 Examples of such instances should not limit the scope of protection of this disclosure.

[0094] In some embodiments, determining the target voltage vector based on the cost function includes: determining the slope index and tracking error index corresponding to the candidate voltage vector set; determining the membership degree of the slope index and the tracking error index; and determining the voltage vector in the candidate voltage vector set with the largest product of the membership degrees of the slope index and the tracking error index as the target voltage vector.

[0095] Based on the previous embodiment, a weighting coefficient can be added before the current slope penalty term. This is to balance the weights of the slope penalty term. To avoid this weighting coefficient... In this embodiment, instead of cumbersome adjustments, a weightless rule based on fuzzy multi-criteria decision-making can be used to more accurately determine the optimal target voltage vector.

[0096] In some embodiments, the tracking error index and slope index corresponding to each candidate voltage vector in the candidate voltage vector set can be calculated respectively; wherein, the tracking error index is the difference between the estimated current and the reference current, and the slope index is the rate of change of the estimated current; the maximum and minimum values ​​of the tracking error index and the slope index in the candidate voltage vector set are determined; the tracking error index of any candidate voltage vector is normalized according to the maximum and minimum values ​​of the tracking error index to obtain the tracking error membership degree; the slope index of any candidate voltage vector is normalized according to the maximum and minimum values ​​of the slope index to obtain the slope membership degree; the product of the tracking error membership degree and the slope membership degree of each candidate voltage vector is calculated as the decision index of the candidate voltage vector; and the candidate voltage vector with the largest decision index is determined as the target voltage vector.

[0097] For each voltage vector in the voltage vector candidate set Tracking error metrics can be defined. and slope index The formula is as follows: (11) And map the slope index and tracking error index to normalized membership degrees: (12) in, and These are the maximum and minimum values ​​of this index among all candidate voltage vectors at the current time; A value of 1 represents the tracking error index, and a value of 2 represents the slope index.

[0098] Instead of the cost function in formula (10), the decision index can be: (13) Formula (13) is used to determine the combined score of the voltage vectors in the slope index and tracking error index in all voltage vector candidate sets, and to determine the target voltage vector based on the combined score.

[0099] This embodiment does not require explicit weighting; it can automatically balance current tracking and switching smoothness to obtain the optimal target voltage vector. .

[0100] Figure 4 This is a schematic flowchart illustrating an adaptive threshold event-triggered model-free predictive control method based on fuzzy prediction period, according to an embodiment of the present disclosure.

[0101] like Figure 4As shown, within each sampling period, the controlled model (inverter system) can collect the current at time k and update the fuzzy estimator based on the current to obtain the disturbance estimate. Based on the determined disturbance estimate, the estimated current at time k+1 can be predicted; Based on the estimated current, the estimation error is determined and input into the adaptive event triggering mechanism design module to determine the event triggering conditions (whether to trigger the adjustment of the switch state). If the trigger time is determined and the switch state needs to be adjusted, then the candidate vector is predicted, evaluated, and decided by a cost function with a predicted current slope penalty and unweighted multi-criteria decision-making, and the midpoint potential balance selection is completed to determine the optimal target voltage vector. The determined optimal target voltage vector is output to the inverter system.

[0102] For detailed implementation methods of each process step in the above steps, please refer to the corresponding paragraphs in the previous embodiments, which will not be elaborated here.

[0103] Based on Table 1, the performance of four control schemes under two operating conditions was compared through experiments. The relevant technical schemes are: (a) traditional event-triggered model predictive control, (b) event-triggered model predictive control based on sliding mode observer, and (c) event-triggered model predictive control based on neural network.

[0104]

[0105] Table 1 Based on Table 1, the four schemes were tested under two different operating conditions.

[0106] Corresponding to operating condition I: Parameter matching, the load resistance and inductance are consistent with the controller design values, that is, there are no unknown disturbance terms.

[0107] Corresponding to operating condition II: parameter mismatch, external disturbance, load inductance deviates from design value and is superimposed with external interference, and there is an unknown disturbance term.

[0108] Referring to Table 1, it can be seen that under both operating conditions, the proposed solution exhibits superior performance compared to the four related technologies: the total harmonic distortion (THD) is lower in both conditions, indicating that the output current of this solution is closer to the reference current (sine wave); the average switching frequency is also lower in both conditions. Therefore, compared to related technologies, this solution uses a lower switching frequency while achieving a closer approximation to the reference current, improving the accuracy of the output current while reducing switching losses.

[0109] Figure 5 This is a schematic diagram of the structure of an inverter system according to an embodiment of the present disclosure.

[0110] The inverter system mainly includes: a three-level NPC inverter main circuit, a voltage and current sampling circuit, a digital signal processor control board, a drive circuit, and a host computer monitoring system, as well as a control module.

[0111] like Figure 5 As shown, 1 is a power analyzer, 2 is a DC power supply, 3 is a host computer, 4 is a control unit, 5 is an oscilloscope, 6 is a three-level NPC converter, 7 is a current sensor, 8 is a voltage sensor, and 9 is a three-phase load.

[0112] When the control unit 4 controls the converter 6 using the method proposed in this disclosure, it can output a stable three-phase AC power to the three-phase load 9 and maintain good control performance even under parameter mismatch and external disturbance conditions.

[0113] Corresponding to the embodiments of the inverter control method of this disclosure, this disclosure also provides embodiments of the corresponding inverter control device.

[0114] Please see Figure 6 , Figure 6 This is a block diagram of the control device for an inverter in one embodiment of this disclosure. Figure 6 As shown, the inverter's control device includes: The determining unit 610 is configured to determine the output current and voltage vectors of the inverter system at the current moment; Fuzzy estimation unit 620 is configured to input the output current into a fuzzy estimator to determine a disturbance estimate of the inverter system. The prediction unit 630 is configured to predict the estimated current at the next moment based on the output current, the disturbance estimate, and the voltage vector; The adjustment unit 640 is configured to determine a target voltage vector based on the estimated current and adjust the switching state of the inverter system based on the target voltage vector.

[0115] In some embodiments, the apparatus is further configured to: construct a hyperlocal model of the inverter system, the hyperlocal model including output current, input voltage vector and unknown disturbance term; approximate the unknown disturbance term according to a fuzzy logic system, and construct the fuzzy estimator.

[0116] In some embodiments, constructing the fuzzy estimator includes: approximating the unknown disturbance term based on fuzzy parameters and fuzzy basis functions; and determining the fuzzy estimator based on output current, voltage vector, sampling period, fuzzy parameters, and fuzzy basis functions.

[0117] In some embodiments, the fuzzy estimator is used to determine the estimated current at the next time step, the fuzzy parameters at the next time step, and the estimated disturbance value at the next time step; wherein, the estimated current at the next time step is determined based on the estimated disturbance value at the current time step, the input voltage vector at the current time step, the estimation error at the current time step, the sampling time interval, and the estimated current at the current time step; the estimation error is the difference between the estimated current and the measured current; the fuzzy parameters at the next time step are determined based on the regularization term of the fuzzy parameters at the current time step, the estimation error at the current time step, and the fuzzy basis function at the current time step; the estimated disturbance value at the next time step is the product of the fuzzy parameters at the current time step and the fuzzy basis function at the next time step.

[0118] In some embodiments, determining the target voltage vector based on the estimated current includes: determining the estimation error between the estimated current and the measured current at the current moment; maintaining the switching state of the inverter system if the estimation error is not greater than an adjustment threshold; and determining the voltage vector corresponding to the estimation error in the candidate voltage vector set as the target voltage vector if the estimation error is greater than the adjustment threshold.

[0119] In some embodiments, the apparatus is further configured to: determine an upper boundary for maintaining a measurement error based on the fuzzy estimator, the measurement error being the difference between the output current at the time when the inverter system was last adjusted and the output current at the current time; and determine an adjustment threshold at the current time based on the upper boundary.

[0120] In some embodiments, determining the target voltage vector based on the estimated current includes: determining the slope and tracking error of the estimated current, the tracking error being the difference between the estimated current and a reference current; constructing a cost function that includes a slope penalty term for the estimated current and the tracking error; and determining the target voltage vector based on the cost function.

[0121] In some embodiments, determining the target voltage vector based on the cost function includes: calculating the tracking error index and slope index corresponding to each candidate voltage vector in the candidate voltage vector set; wherein the tracking error index is the difference between the estimated current and the reference current, and the slope index is the rate of change of the estimated current; determining the maximum and minimum values ​​of the tracking error index and the slope index in the candidate voltage vector set; normalizing the tracking error index of any candidate voltage vector according to the maximum and minimum values ​​of the tracking error index to obtain a tracking error membership degree; normalizing the slope index of any candidate voltage vector according to the maximum and minimum values ​​of the slope index to obtain a slope membership degree; calculating the product of the tracking error membership degree and the slope membership degree of each candidate voltage vector as the decision index of the candidate voltage vector; and determining the candidate voltage vector with the largest decision index as the target voltage vector.

[0122] 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.

[0123] 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 a control method for an inverter as described in any of the above embodiments by invoking the computer program.

[0124] 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.

[0125] 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.

[0126] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 7As 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).

[0127] 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 (GPA), or any combination thereof.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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. A control method for an inverter, characterized in that, The method includes: Determine the output current and voltage vectors of the inverter system at the current moment; The output current is input into a fuzzy estimator to determine the disturbance estimate of the inverter system; Based on the output current, the disturbance estimate, and the voltage vector, predict the estimated current at the next moment; The target voltage vector is determined based on the estimated current, and the switching state of the inverter system is adjusted based on the target voltage vector; wherein, The method further includes: A hyperlocal model of the inverter system is constructed, which includes the output current, the input voltage vector, and the unknown disturbance term. The unknown disturbance term is approximated using a fuzzy logic system, and the fuzzy estimator is constructed accordingly; wherein, The construction of the fuzzy estimator includes: The unknown disturbance term is approximated based on fuzzy parameters and fuzzy basis functions; The fuzzy estimator is determined based on the output current, voltage vector, sampling period, fuzzy parameters, and fuzzy basis functions. The fuzzy estimator is used to determine the estimated current, the fuzzy parameters, and the estimated disturbance value at the next time step; wherein, The estimated current at the next moment is determined based on the disturbance estimate at the current moment, the voltage vector input at the current moment, the estimation error at the current moment, the sampling time interval, and the estimated current at the current moment; the estimation error is the difference between the estimated current and the measured current. The fuzzy parameters for the next time step are determined based on the regularization term of the fuzzy parameters for the current time step, the estimation error for the current time step, and the fuzzy basis function for the current time step. The disturbance estimate for the next time step is the product of the fuzzy parameter at the current time step and the fuzzy basis function at the next time step.

2. The method according to claim 1, characterized in that, Determining the target voltage vector based on the estimated current includes: Determine the estimation error between the estimated current and the measured current at the current moment; If the estimation error is not greater than the adjustment threshold, the switching state of the inverter system is maintained; If the estimation error is greater than the adjustment threshold, the voltage vector corresponding to the estimation error in the candidate voltage vector set is determined as the target voltage vector.

3. The method according to claim 2, characterized in that, The method further includes: The upper boundary of the measurement error is determined based on the fuzzy estimator, whereby the measurement error is the difference between the output current at the last time the inverter system was adjusted and the output current at the current time. The adjustment threshold for the current moment is determined based on the upper boundary.

4. The method according to claim 1, characterized in that, Determining the target voltage vector based on the estimated current includes: Determine the slope and tracking error of the estimated current, wherein the tracking error is the difference between the estimated current and the reference current, and the slope is the ratio of the difference of the estimated current in the discrete time domain to the sampling period; Construct a cost function that includes the slope penalty term of the estimated current and the tracking error; The target voltage vector is determined based on the cost function.

5. The method according to claim 4, characterized in that, Determining the target voltage vector based on the cost function includes: Calculate the tracking error index and slope index for each candidate voltage vector in the candidate voltage vector set; wherein, the tracking error index is the difference between the estimated current and the reference current, and the slope index is the rate of change of the estimated current; Determine the maximum and minimum values ​​of the tracking error index and the maximum and minimum values ​​of the slope index in the candidate voltage vector set; The tracking error index of any candidate voltage vector is normalized based on the maximum and minimum values ​​of the tracking error index to obtain the tracking error membership degree; the slope index of any candidate voltage vector is normalized based on the maximum and minimum values ​​of the slope index to obtain the slope membership degree. The product of the tracking error membership degree and the slope membership degree for each candidate voltage vector is calculated and used as the decision index for the candidate voltage vector. The candidate voltage vector with the largest decision index is determined as the target voltage vector.

6. A control device for an inverter, characterized in that, The device includes: The determination unit is configured to determine the output current and voltage vectors of the inverter system at the current moment; A fuzzy estimation unit is configured to input the output current into a fuzzy estimator to determine a disturbance estimate of the inverter system. The prediction unit is configured to predict the estimated current at the next moment based on the output current, the disturbance estimate, and the voltage vector; The adjustment unit is configured to determine a target voltage vector based on the estimated current, and to adjust the switching state of the inverter system based on the target voltage vector; wherein, The device is also configured to: A hyperlocal model of the inverter system is constructed, which includes the output current, the input voltage vector, and the unknown disturbance term. The unknown disturbance term is approximated using a fuzzy logic system, and the fuzzy estimator is constructed accordingly; wherein, The construction of the fuzzy estimator includes: The unknown disturbance term is approximated based on fuzzy parameters and fuzzy basis functions; The fuzzy estimator is determined based on the output current, voltage vector, sampling period, fuzzy parameters, and fuzzy basis functions. The fuzzy estimator is used to determine the estimated current, the fuzzy parameters, and the estimated disturbance value at the next time step; wherein, The estimated current at the next moment is determined based on the disturbance estimate at the current moment, the voltage vector input at the current moment, the estimation error at the current moment, the sampling time interval, and the estimated current at the current moment; the estimation error is the difference between the estimated current and the measured current. The fuzzy parameters for the next time step are determined based on the regularization term of the fuzzy parameters for the current time step, the estimation error for the current time step, and the fuzzy basis function for the current time step. The disturbance estimate for the next time step is the product of the fuzzy parameter at the current time step and the fuzzy basis function at the next time step.

7. An inverter system, characterized in that, The system includes an inverter circuit and a control module, wherein the control module controls the inverter circuit using the inverter control method described in any one of claims 1 to 5.

Images