Permanent magnet synchronous generator parameter-free model predictive control method based on dynamic mode decomposition

Abstract

The invention relates to a permanent magnet synchronous generator parameter-free model prediction control method based on dynamic mode decomposition, which comprises the following steps: providing a target permanent magnet synchronous generator, and carrying out system identification on the target permanent magnet synchronous generator to determine model prediction state parameters for carrying out model prediction control on the target permanent magnet synchronous generator, wherein the model prediction state parameters at least comprise a state matrix A and a control matrix B. The identified and determined model prediction state parameters are configured in a generator prediction control model in a closed-loop controller so as to configure the closed-loop controller to carry out closed-loop control on the bus-bar end voltage output by the target permanent magnet synchronous generator. In the closed-loop control process, the generator prediction control model is used for model prediction control, the bus-bar end voltage output by the PMSG under the condition that initial parameters are unknown or parameters are mismatched can be effectively controlled, and the method has the advantages of being high in robustness, data driving adaptability and high in dynamic response.

Description

Technical Field

[0001] The present invention relates to a model predictive control method, in particular to a parameter-free model predictive control method for a permanent magnet synchronous generator based on dynamic mode decomposition. Background Art

[0002] The permanent magnet synchronous generator (PMSG) is a common power generation device widely used in renewable energy, particularly wind and marine power generation. The PMSG boasts high efficiency, low maintenance, and strong reliability. Its operating principle is based on the interaction between the magnetic field generated by permanent magnets and the stator windings, converting mechanical energy into electrical energy through electromagnetic induction.

[0003] Currently, PMSG control typically relies on traditional control methods, such as vector control and direct power control. These methods typically require precise modeling of the PMSG system. However, when faced with the nonlinearity, dynamic changes, and external disturbances of the PMSG system, these traditional methods often struggle to achieve optimal dynamic performance. Consequently, traditional control methods struggle to achieve optimal PMSG control.

[0004] Dynamic modal decomposition (DMD) is a data-driven dimensionality reduction method that can extract key modal information from high-dimensional dynamic system time series data and identify the system's inherent dynamic characteristics. Traditional DMD methods are mainly used in fields such as fluid dynamics, structural health monitoring, and signal processing. Specifically, DMD can effectively analyze the dynamic behavior of a system and capture its time evolution. DMDc is a method that combines dynamic modal decomposition with control. It can not only extract the system's dynamic modal information from its real-time data, but also design effective control strategies based on this information.

[0005] Unlike traditional control methods, DMDc uses a data-driven approach, utilizing system time-series data for modeling rather than relying on precise mathematical models. This allows it to adapt to the time-varying and uncertain nature of the system. However, when the initial PMSG parameters are unknown or there is a mismatch in the PMSG parameters, effectively controlling the PMSG output bus voltage remains a pressing technical challenge. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a parameter-free model predictive control method for a permanent magnet synchronous generator based on dynamic mode decomposition, which can effectively control the bus terminal voltage output by the PMSG when the initial parameters are unknown or the parameters are mismatched, and has the characteristics of strong robustness, data-driven adaptability and high dynamic response.

[0007] According to the technical solution provided by the present invention, a parameter-free model predictive control method for a permanent magnet synchronous generator based on dynamic mode decomposition, the parameter-free model predictive control method for a permanent magnet synchronous generator includes:

[0008] A target permanent magnet synchronous generator is provided, and system identification is performed on the target permanent magnet synchronous generator to determine model prediction state parameters for model predictive control of the target permanent magnet synchronous generator after system identification, wherein:

[0009] The model prediction state parameters include at least the state matrix A and the control matrix B.

[0010] During system identification, a target permanent magnet synchronous generator is configured to generate an observable dynamic response, and thereafter, model prediction state parameters corresponding to the target permanent magnet synchronous generator are determined based on at least a DMDc method, and during the identification process, the target permanent magnet synchronous generator is configured to be in a bus voltage open-loop state;

[0011] The model prediction state parameters determined by identification are configured in the generator predictive control model in the closed-loop controller to configure the closed-loop controller to perform closed-loop control on the bus terminal voltage output by the target permanent magnet synchronous generator. In the process of closed-loop control, the generator predictive control model is used to perform model predictive control, wherein:

[0012] In a k-th sampling control period of the closed-loop control, at least power generation state information of the target permanent magnet synchronous generator at time k and power generation control information at time k are loaded into a generator predictive control model, so as to perform model predictive control using the generator predictive control model and generate power generation state information at time k+1, wherein the power generation state information includes a q-axis current and a d-axis current;

[0013] Based on the power generation state information at time k+1 and the power generation state reference information at time k, the closed-loop control value function value representing the corresponding error state of the q-axis current and the d-axis current at time k+1 is calculated.

[0014] Based on the optimal state of the closed control value function value at time k+1, target rectifier switch control information for controlling the rectifier rectifier state is determined, so as to configure the bus terminal voltage output by the rectifier to be adapted to the bus reference voltage based on the target rectifier switch control information, wherein:

[0015] The power generation state reference information at time k is generated based at least on the bus reference voltage and the bus sampling voltage at time k, wherein the bus sampling voltage is generated based on voltage sampling of the bus terminal voltage output by the rectifier.

[0016] In closed-loop control, parameter mismatch control is also included, where:

[0017] When performing parameter mismatch control processing, it includes:

[0018] determining a parameter mismatch monitoring target, wherein the parameter mismatch monitoring target includes at least a q-axis current,

[0019] determining a parameter mismatch state based on a parameter mismatch monitoring target, wherein when the parameter mismatch state indicates parameter mismatch, performing online updating on model prediction state parameters, and generating online updated model prediction state parameters adapted to a target permanent magnet synchronous generator; thereafter, configuring the model prediction state parameters within a generator predictive control model, so as to perform model predictive control using the generator predictive control model in closed-loop control;

[0020] When determining the parameter mismatch state based on the parameter mismatch monitoring target, when multiple consecutive q-axis current residuals all match the parameter mismatch monitoring threshold, the parameter mismatch state is configured as parameter mismatched, wherein, for any q-axis current residual, the q-axis current residual is generated based on the difference between the corresponding q-axis currents at two adjacent moments.

[0021] For the q-axis current at time m, the q-axis current residual is:

[0022]

[0023] Among them, i q(m) is the q-axis current corresponding to time m, i q(m-1) is the q-axis current corresponding to time m-1, is the q-axis current residual corresponding to the q-axis current at time m;

[0024] and q-axis current residual The corresponding parameter mismatch monitoring threshold is:

[0025]

[0026] in, is the parameter mismatch monitoring threshold corresponding to the q-axis current at time m, is the mean value calculated based on the q-axis current corresponding to the n sampling control cycles before time m, is the standard deviation of the q-axis current calculated based on the n sampling control cycles before time m, λ m is the regulation coefficient corresponding to the q-axis current at time m;

[0027] when When , the q-axis current residual Parameter mismatch monitoring threshold match.

[0028] Adjustment coefficient λ m Determined by fuzzy control, where the adjustment coefficient λ is determinedm When , there are:

[0029] Determine the q-axis current residual The corresponding residual change rate Among them, the residual change rate Then we have: is the q-axis current residual corresponding to the q-axis current at time m-1, T s is the sampling control period;

[0030] The q-axis current residual and the residual change rate Load it into the constructed fuzzy controller, and the fuzzy controller will calculate the corresponding adjustment coefficient increment Δλ m ,

[0031] Based on the adjustment coefficient increment Δλ m , generating the adjustment coefficient λ m , then: m =λ m-1 +Δλ m , where λ m-1 is the regulation coefficient corresponding to the q-axis current at time m-1.

[0032] For the closed-loop control value function, we have:

[0033]

[0034] in, is the closed-loop control value function at time k+1, is the q-axis current at time k+1, is the given value of the q-axis current at time k, is the q-axis current at time k+1, is the given value of the d-axis current at time k;

[0035] When determining the optimal state of the closed control value function at time k+1, we have:

[0036] Traverse the rectifier state and the rectifier switch state information in each rectifier state, and obtain the bus sampling voltage in each rectifier state, and then generate at least the q-axis current given value at time k based on the bus sampling voltage

[0037] Based on the generated q-axis current reference value at time k The closed-loop control value function values at time k+1 are calculated respectively, and the minimum closed-loop control value function value at time k+1 is configured as the optimal state of the closed-loop control value function value at time k+1, and the rectifier switch state information corresponding to the optimal state of the closed-loop control value function value at time k+1 is configured as the target rectifier switch control information.

[0038] Given the q-axis current at time k At least perform PI calculation on the bus reference voltage and the bus sampling voltage at time k;

[0039] The d-axis current at time k is given as Set to 0.

[0040] When performing system identification on the target permanent magnet synchronous generator, the method includes:

[0041] Constructing a DMDc state space equation corresponding to the target permanent magnet synchronous generator and configuring identification reference voltage information, wherein the identification reference voltage information includes a d-axis reference voltage and a q-axis reference voltage of multiple reference segments;

[0042] After configuring the target permanent magnet synchronous generator to generate an observable dynamic response, determining a basic voltage vector of the target permanent magnet synchronous motor when generating the dynamic response, wherein the basic voltage vector includes a d-axis response voltage and a q-axis response voltage;

[0043] Calculating open-loop identification value function values representing the d-axis response voltage, the q-axis response voltage, and the d-axis reference voltage, and the q-axis reference voltage in each reference segment, and determining the optimal value of the open-loop identification value function value in each reference segment, so as to determine the target identification rectification state of the rectifier according to the optimal value of the open-loop identification value function value in each reference segment, and then performing data sampling processing in each reference segment to collect and generate corresponding identification sampling data, wherein, for each identification sampling data, the identification sampling data includes an identification state quantity and an identification control quantity;

[0044] The identification state quantity includes identifying the d-axis current and identifying the q-axis current;

[0045] The identified control quantity includes identifying the d-axis voltage, identifying the q-axis voltage and the angular velocity of the target permanent magnet synchronous generator;

[0046] All the identification sampling data are used to solve the DMDc state space equation constructed above to obtain the model prediction state parameters after the solution.

[0047] When configuring the target permanent magnet synchronous generator to generate an observable dynamic response, at least the target permanent magnet synchronous generator is driven to rotate at a low speed by the engine;

[0048] For the d-axis reference voltage of multiple reference segments, we have:

[0049]

[0050] For the q-axis reference voltage of multiple reference segments, we have:

[0051]

[0052] in, is the bus reference voltage, is the d-axis reference voltage, is the q-axis reference voltage, f N is the fundamental frequency of the target permanent magnet synchronous generator.

[0053] During data sampling, periodic downsampling is performed in each reference segment of the d-axis reference voltage, and transient full sampling is performed when the d-axis reference voltage jumps from one reference segment to another adjacent reference segment.

[0054] For the open-loop identification value function, we have:

[0055]

[0056] Among them, J e is the open-loop identification value function, is the d-axis response voltage, is the q-axis response voltage;

[0057] When determining the optimal value of the open-loop identification value function under each reference segment, the d-axis response voltage q-axis response voltage Corresponding to the same reference segment of the d-axis reference voltage and the q-axis reference voltage.

[0058] The advantages of the present invention are as follows: for a target permanent magnet synchronous generator, the model prediction state parameters for model predictive control of the target permanent magnet synchronous generator are determined through the system identification stage. Thereafter, the model prediction state parameters are configured in the generator predictive control model of the closed-loop controller, so that in the process of closed-loop control, the generator predictive control model is used to perform model predictive control, thereby effectively controlling the bus terminal voltage output by the PMSG when the initial parameters are unknown or the parameters are mismatched, and having the characteristics of strong robustness, data-driven adaptability and high dynamic response. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 The figure is a flow chart of an embodiment of the parameter-free model predictive control method for a permanent magnet synchronous generator of the present invention.

[0060] Figure 2This is a structural block diagram of an embodiment of the present invention for controlling a target permanent magnet synchronous generator.

[0061] Figure 3 This is a schematic diagram of an embodiment of the bus terminal voltage during the system identification stage of the present invention.

[0062] Figure 4 The figure is a waveform diagram of the bus terminal voltage and torque in one embodiment of the switching process from system identification to closed-loop control of the present invention.

[0063] Figure 5 The figure is a waveform diagram of an embodiment of the bus terminal voltage and torque response under changing working conditions of the present invention. DETAILED DESCRIPTION

[0064] The present invention will be further described below with reference to specific drawings and embodiments.

[0065] In order to effectively control the bus terminal voltage output by a PMSG with unknown initial parameters, the present invention provides a parameter-free model predictive control method for a permanent magnet synchronous generator based on dynamic mode decomposition. Specifically, the parameter-free model predictive control method for a permanent magnet synchronous generator includes:

[0066] A target permanent magnet synchronous generator is provided, and system identification is performed on the target permanent magnet synchronous generator to determine model prediction state parameters for model predictive control of the target permanent magnet synchronous generator after system identification, wherein:

[0067] The model prediction state parameters include at least the state matrix A and the control matrix B.

[0068] During system identification, a target permanent magnet synchronous generator is configured to generate an observable dynamic response, and thereafter, model prediction state parameters corresponding to the target permanent magnet synchronous generator are determined based on at least a DMDc method, and during the identification process, the target permanent magnet synchronous generator is configured to be in a bus voltage open-loop state;

[0069] The model prediction state parameters determined by identification are configured in the generator predictive control model in the closed-loop controller to configure the closed-loop controller to perform closed-loop control on the bus terminal voltage output by the target permanent magnet synchronous generator. In the process of closed-loop control, the generator predictive control model is used to perform model predictive control, wherein:

[0070] In a k-th sampling control period of the closed-loop control, at least power generation state information of the target permanent magnet synchronous generator at time k and power generation control information at time k are loaded into a generator predictive control model, so as to perform model predictive control using the generator predictive control model and generate power generation state information at time k+1, wherein the power generation state information includes a q-axis current and a d-axis current;

[0071] Based on the power generation state information at time k+1 and the power generation state reference information at time k, the closed-loop control value function value representing the corresponding error state of the q-axis current and the d-axis current at time k+1 is calculated.

[0072] Based on the optimal state of the closed control value function value at time k+1, target rectifier switch control information for controlling the rectifier rectifier state is determined, so as to configure the bus terminal voltage output by the rectifier to be adapted to the bus reference voltage based on the target rectifier switch control information, wherein:

[0073] The power generation state reference information at time k is generated based at least on the bus reference voltage and the bus sampling voltage at time k, wherein the bus sampling voltage is generated based on voltage sampling of the bus terminal voltage output by the rectifier.

[0074] Figure 1 A flow chart of an embodiment of the present invention for performing parameter-free model predictive control on a permanent magnet synchronous generator is shown in the figure. As can be seen from the figure, when performing parameter-free model predictive control, a target permanent magnet synchronous generator should be provided, that is, the provided target permanent magnet synchronous generator and the corresponding connected rectifier are the control objects of the parameter-free model predictive control of the present invention. The target permanent magnet synchronous generator can be an existing commonly used permanent magnet synchronous generator, that is, the type of the target permanent magnet synchronous generator can be selected according to needs.

[0075] It should be noted that the term "parameter-free" in the present invention specifically refers to not knowing or relying on the initial parameters of the target permanent magnet synchronous generator. Furthermore, when performing parameter-free model predictive control on the target permanent magnet synchronous generator, the control objective should be to adapt the bus terminal voltage output by the target permanent magnet synchronous generator to the bus reference voltage. Adaptation of the bus terminal voltage to the bus reference voltage specifically means that the bus terminal voltage is consistent with the bus reference voltage, or that the difference between the bus terminal voltage and the bus reference voltage is within an allowable range of values. The allowable range of the difference is generally related to the control accuracy of the bus terminal voltage output by the target permanent magnet synchronous generator and can be specifically determined based on the control accuracy.

[0076] Specifically, the bus terminal voltage output by the target permanent magnet synchronous generator specifically refers to the DC voltage obtained by rectifying the three-phase voltage output by the target permanent magnet synchronous generator through the rectifier and passing through the bus capacitor. Figure 2 An embodiment of obtaining the bus terminal voltage is shown in FIG. Figure 2In the figure, ZS is a rectifier connected to the target permanent magnet synchronous generator, C1 is a bus capacitor adapted to be connected to the rectifier. In specific implementation, the rectifier can adopt an existing commonly used three-phase rectifier. Generally, the rectifier can include three groups of bridge arms, each group of bridge arms includes two bridge arm switching tubes, and the bridge arm switching tubes can adopt existing commonly used switching tubes, such as IGBT devices. IGBT devices are used as bridge arm switching tubes, and the way of forming a rectifier and the way of performing rectification can be consistent with the existing technology, which will not be repeated here.

[0077] In order to control the bus terminal voltage output by the target permanent magnet synchronous generator, the target permanent magnet synchronous generator should first be system identified. The model prediction state parameters for model predictive control of the target permanent magnet synchronous generator can be determined through system identification, that is, the model prediction state parameters can be obtained after system identification, and then the model prediction state parameters can be used to perform model predictive control (MPC). In order to meet the needs of model predictive control, the model prediction state parameters of the present invention include at least a state matrix A and a control matrix B. Generally, when performing model predictive control, only the state matrix A and the control matrix B can be used. Of course, the situation of the model predictive control state parameters is subject to whether the model predictive control can be satisfied. When the model prediction state parameters are the state matrix A and the control matrix B, the method of performing model predictive control can refer to the corresponding description below.

[0078] In order to meet the requirements of system identification, the target permanent magnet synchronous generator should be configured to produce an observable dynamic response. For example, the target permanent magnet synchronous generator can be driven to rotate at a low speed by at least the engine. That is, when the target permanent magnet synchronous generator is driven to rotate at a low speed, the target permanent magnet synchronous generator can produce an observable dynamic response. The observable dynamic response specifically means that the dynamic response of the target permanent magnet synchronous generator can be measured. The dynamic response situation can be referred to the following description.

[0079] Specifically, after the target permanent magnet synchronous generator generates an observable dynamic response, the DMDc method can be used to identify and determine the model prediction state parameters. It can be understood that the model prediction state parameters should correspond to the target permanent magnet synchronous generator, that is, the model prediction state parameters should be able to reflect the main modal information of the target permanent magnet synchronous generator. The method and process of using the DMDc method to identify and determine the model prediction state parameters will be described in detail below. It should be noted that during the identification process, the target permanent magnet synchronous generator is configured in a bus voltage open-loop state, where the bus voltage open-loop state specifically means that during the identification process, the bus terminal voltage is not collected, that is, the bus terminal voltage mentioned above is not used to form an open-loop control state.

[0080] It should be understood that in order to accurately control the bus terminal voltage and achieve adaptation of the bus terminal voltage to the bus reference voltage, the bus terminal voltage of the target permanent magnet synchronous generator should be closed-loop controlled, wherein the closed-loop control corresponds to the above-mentioned open-loop control state. Therefore, during closed-loop control, the bus terminal voltage should be used, such as sampling the bus terminal voltage and obtaining the bus sampling voltage.

[0081] In order to achieve closed-loop control, a closed-loop controller should be constructed. Figure 2 The closed-loop control is the constructed closed-loop controller, which should include the generator predictive control model. The generator predictive control model can be used to execute model predictive control. Figure 2 The predicted current model in the generator predictive control model is the generator predictive control model, which mainly implements current prediction. It can be understood that the model prediction state parameters should first be configured in the generator predictive control model. After that, the generator predictive control model can perform model predictive control. Therefore, it can be seen that the closed-loop controller is used to perform closed-loop control of the bus terminal voltage of the target permanent magnet synchronous generator. During the closed-loop control process, the generator predictive control model can be used to perform model predictive control. In other words, during closed-loop control, the generator predictive control model needs to be used for model predictive control. Therefore, the model predictive control performed by the generator predictive control model is a control stage in the closed-loop control.

[0082] It should be understood that when a closed-loop controller is used to perform closed-loop control of the bus terminal voltage, it includes sequential sampling control cycles. The length of the sampling control cycles can be related to the requirements for voltage control of the bus terminal voltage and can generally be selected based on actual needs. Sampling is performed in each sampling control cycle to obtain closed-loop control sampling information. For example, in the kth sampling control cycle, sampling can obtain closed-loop control sampling information at time k. The closed-loop sampling information at time k can be the power generation state information and power generation control information at time k. It should be understood that the time interval between two adjacent sampling control cycles is a sampling control cycle Ts.

[0083] Specifically, in the kth sampling control cycle of the closed-loop control, at least the power generation state information of the target permanent magnet synchronous generator at time k and the power generation control information at time k are loaded into the generator predictive control model. Thereafter, the generator predictive control model can be used to perform model predictive control and generate the power generation state information at time k+1, wherein the power generation state information includes the q-axis current and the d-axis current. Therefore, the power generation state information at time k+1 is the q-axis current at time k+1 and the d-axis current at time k+1, and the power generation control information may include the d-axis voltage, the q-axis voltage and the angular velocity of the target permanent magnet synchronous generator.

[0084] Specifically, when the generator predictive control model performs model predictive control, there is:

[0085]

[0086] Among them, i q (k+1|k) is the q-axis current at time k+1 predicted based on the power generation state information at time k and the power generation control information at time k, i d (k+1|k) is the d-axis current at time k+1 predicted based on the power generation state information at time k and the power generation control information at time k, i q (k|k) is the q-axis current at time k, i d (k|k) is the d-axis current at time k, is the q-axis voltage at time k, is the d-axis voltage at time k, is the angular velocity at time k.

[0087] It should be noted that the q-axis current i at time k q (k|k), d-axis current i at time k d (k|k) can be directly measured and converted. Figure 1 The q-axis current i at time k is obtained. q (k|k), d-axis current i at time k d An embodiment of (k|k), specifically, when the closed-loop control is performed on the target permanent magnet synchronous generator, the operating current i of the target permanent magnet synchronous generator can be measured. s After that, the q-axis current i at time k can be obtained by converting the abc coordinate system to the dq coordinate system. q (k|k), d-axis current i at time k d (k|k), the specific method and process of converting the abc coordinate system to the dq coordinate system can be consistent with the existing technology and will not be repeated here.

[0088] Specifically, the q-axis voltage at time k is d-axis voltage at time k Angular velocity at time k It can be calculated by the commonly used calculation methods in the prior art, such as obtaining the electrical angle θ of the target permanent magnet synchronous generator at time k and the bus terminal voltage output by the rectifier. Thereafter, the q-axis voltage at time k is calculated by the calculation method. d-axis voltage at time k Angular velocity at time k The specific calculation method can adopt the existing commonly used method, such as referring to the corresponding instructions in the book "Modern Permanent Magnet Motor Control Principles and MATLAB Simulation". Figure 2 In, e a 、eb 、e c are the a-phase voltage, b-phase voltage and c-phase voltage output by the target permanent magnet synchronous generator respectively.

[0089] It should be understood that when the bus terminal voltage of the target permanent magnet synchronous motor is controlled, the rectification state of the rectifier is mainly controlled, that is, the rectification state of the rectifier is controlled so that the bus terminal voltage generated after rectification and through the bus capacitor C1 is adapted to the bus reference voltage. Therefore, when the closed-loop controller performs closed-loop control, it mainly generates rectification switch control information for controlling the rectification state of the rectifier. Figure 2 S in abc That is the rectifier switch control information that controls the rectifier rectification state.

[0090] It can be understood that the rectifier switch control information includes switch control signals corresponding to the switching states of the bridge arm switch tubes in multiple configurable rectifiers. The switch control signals can be digital signals. For example, when the switch control signal is "1", the corresponding connected bridge arm switch tube can be driven to be in the on state. When the switch control signal is "0", the corresponding connected bridge arm switch tube should be in the off state. Specifically, the rectification state of the rectifier refers to the rectifier rectifying the three-phase voltage generated by the target permanent magnet synchronous generator.

[0091] When the generator predictive control model is used in the closed-loop controller for model predictive control, in order to generate rectifier switch control information, in one embodiment of the present invention, a closed-loop control cost function unit should be provided. Figure 2 The value function J in bh The location is the closed-loop control value function unit, which can perform the calculation and judgment of the closed-loop control value function value. In order to generate the rectifier switch control information, the power generation state information at time k+1 should be loaded into the closed-loop control value function unit, such as Figure 2 As shown in the figure, i q (k+1), i d (k+1) are the q-axis current and d-axis current at time k+1 respectively. In order to calculate the closed-loop control value function value, the power generation state reference information at time k should also be loaded into the closed-loop control value function unit. The power generation state reference information should include the q-axis reference current and the d-axis reference current. Figure 2 middle, That is the q-axis reference current. It can be understood that the d-axis reference current Not present Figure 2 Shown in.

[0092] In specific implementation, a closed-loop control value function should be set at least in the closed-loop control value function unit. When calculating the closed-loop control value function value, the error state corresponding to the q-axis current and the d-axis current at time k+1 is calculated, that is, the error state corresponding to the q-axis current at time k+1 and the q-axis reference current at time k, and the error state corresponding to the d-axis current at time k+1 and the d-axis reference current at time k are calculated. Therefore, for the closed-loop control value function, the following is obtained:

[0093]

[0094] in, is the closed-loop control value function at time k+1, is the q-axis current at time k+1, is the given value of the q-axis current at time k, is the q-axis current at time k+1, is the given value of the d-axis current at time k.

[0095] In one embodiment of the present invention, the q-axis current at time k is given a value At least perform PI calculation on the bus reference voltage and the bus sampling voltage at time k;

[0096] The d-axis current at time k is given as Set to 0.

[0097] Figure 2 The q-axis current given value at time k is generated as shown in An embodiment of Figure 2 Middle,U dc is the bus sampling voltage, is the bus reference voltage, bus reference voltage That is to set the voltage output through the bus capacitor, so the bus reference voltage The size can be selected and determined according to the actual application requirements. When performing PI calculation, the bus voltage difference between the bus sampling voltage and the bus reference voltage should be calculated first, and then the PI calculation should be performed. The q-axis current given value at time k can be obtained in the PI calculation. The method and process of PI calculation of bus voltage difference can be consistent with the existing PI control, which will not be described here. Set to 0.

[0098] Generally, the bus sampling voltage can be consistent with the bus terminal voltage, that is, the sampling ratio is 1:1. Since the bus terminal voltage may be different in different control cycles, the q-axis current given value at time k is calculated. The bus terminal voltage should be sampled within the kth sampling control cycle and the bus sampling voltage should be obtained.

[0099] From the above description, we can know that the q-axis current given value at time k is It is related to the bus terminal voltage at time k, and the bus terminal voltage is related to the rectification state of the rectifier. Therefore, after calculating the closed-loop control value function value of the above closed-loop control value function, the optimal state of the closed-loop control value function value at time k+1 can be determined according to the rectification state of the rectifier, and the optimal state of the closed-loop control value function value at time k+1 should correspond to the rectification state of the rectifier, that is, the target rectification switch control information of the rectifier rectification state can be determined. After determining the target rectification switch control information, the above Figure 2 S in abc target value.

[0100] It should be understood that after determining the target rectifier switch control information, the target rectifier switch control information should be used to configure the rectifier state. At this time, the rectifier is in the target rectification state, so that the rectifier based on the target rectification state can make the bus terminal voltage adapt to the bus reference voltage.

[0101] In one embodiment of the present invention, when determining the optimal state of the closed control value function value at time k+1, we have:

[0102] Traverse the rectifier state and the rectifier switch state information in each rectifier state, and obtain the bus sampling voltage in each rectifier state, and then generate at least the q-axis current given value at time k based on the bus sampling voltage

[0103] Based on the generated q-axis current reference value at time k The closed-loop control value function values at time k+1 are calculated respectively, and the minimum closed-loop control value function value at time k+1 is configured as the optimal state of the closed-loop control value function value at time k+1, and the rectifier switch state information corresponding to the optimal state of the closed-loop control value function value at time k+1 is configured as the target rectifier switch control information.

[0104] It can be understood that the rectification state of the rectifier is the rectification state of the three-phase voltage generated by the rectifier to the target permanent magnet synchronous generator. From the above description, it can be seen that the rectification state of the rectifier is related to the switching state of the bridge arm switch tube. Therefore, according to the type of rectifier used, all rectification states of the rectifier and the rectification switch state information in each rectification state can be traversed. The rectification switch state information is the control information for controlling each bridge arm switch tube.

[0105] Specifically, in each rectification state, a corresponding bus sampling voltage is obtained, and then the q-axis current given value at time k can be calculated. Afterwards, the corresponding closed-loop control value function value can be calculated for the above-mentioned closed-loop control value function. Among the multiple closed-loop control value function values calculated, the smallest closed-loop control value function value is taken as the optimal state of the closed-loop control value function value. Afterwards, the rectifier switch state information corresponding to the optimal state of the closed-loop control value function value at time k+1 is configured as the target rectifier switch control information, and the target rectifier switch control information can be obtained. Therefore, within the k+1 sampling control cycles, the target rectifier switch control information is used to configure the rectifier to the corresponding target rectification state. At this time, the bus terminal voltage can be adapted to the bus reference voltage within the k+1 sampling control cycles.

[0106] In specific implementation, during closed-loop control, the above control process should be repeated. For example, after entering k+1 sampling control cycles, the target rectifier switch control information of k+2 sampling control cycles can be determined, and the rectifier is configured to be in the target rectification state according to the determined target rectifier switch control information. The control method and process of the target permanent magnet synchronous generator in subsequent sampling control cycles can be referred to the description here, and no examples will be given here one by one.

[0107] From the above description, it can be seen that the closed-loop controller should include a PI controller, a generator predictive control model and a closed-loop control value function unit; in each sampling control cycle, the generator predictive control model can be used to perform model predictive control, the PI controller can be used to realize PI calculation, and the closed-loop control value function unit can be used to perform the calculation and judgment of the above-mentioned closed-loop control value function value. The corresponding working conditions of the PI controller, the generator predictive control model and the closed-loop control value function unit can be referred to the above description and will not be repeated here.

[0108] In one embodiment of the present invention, the closed-loop control further includes parameter mismatch control processing, wherein:

[0109] When performing parameter mismatch control processing, it includes:

[0110] determining a parameter mismatch monitoring target, wherein the parameter mismatch monitoring target includes at least a q-axis current,

[0111] determining a parameter mismatch state based on a parameter mismatch monitoring target, wherein when the parameter mismatch state indicates parameter mismatch, performing online updating on model prediction state parameters, and generating online updated model prediction state parameters adapted to a target permanent magnet synchronous generator; thereafter, configuring the model prediction state parameters within a generator predictive control model, so as to perform model predictive control using the generator predictive control model in closed-loop control;

[0112] When determining the parameter mismatch state based on the parameter mismatch monitoring target, when multiple consecutive q-axis current residuals all match the parameter mismatch monitoring threshold, the parameter mismatch state is configured as parameter mismatched, wherein, for any q-axis current residual, the q-axis current residual is generated based on the difference between the corresponding q-axis currents at two adjacent moments.

[0113] As can be seen from the above description, for any target permanent magnet synchronous generator with unknown initial parameters, when controlling the target permanent magnet synchronous generator, a system identification operation should be performed first. After that, closed-loop control can be performed on the bus terminal voltage generated by the target permanent magnet synchronous generator. It is understandable that during the closed-loop control operation of the target permanent magnet synchronous generator, a parameter mismatch may exist, that is, the state matrix A and control matrix B determined by the above system identification cannot adapt to the current control of the bus terminal voltage output by the target permanent magnet synchronous generator. At this time, in order to improve the control accuracy of the bus terminal voltage, parameter mismatch control processing should be performed. Specifically, when in a parameter mismatch situation, the bus terminal voltage cannot adapt to the bus reference voltage.

[0114] It should be understood that when executing parameter mismatch control processing, it should first be determined whether a parameter mismatch exists. When a parameter mismatch exists, the model prediction state parameters can be updated online, and the model prediction state parameters adapted to the target permanent magnet synchronous generator can be generated online. Specifically, when determining whether a parameter mismatch exists, a parameter mismatch monitoring target should be determined, that is, the parameter mismatch monitoring target can be used to determine whether a parameter mismatch exists. In specific implementation, the parameter mismatch monitoring target includes at least the q-axis current, that is, the q-axis current in different sampling control cycles can be used to determine whether a parameter mismatch exists.

[0115] In a specific implementation, when there are multiple consecutive q-axis current residuals that all match the parameter mismatch monitoring threshold, the parameter mismatch state is configured as parameter mismatch. Specifically, parameter mismatch means that the parameter mismatch is in place. For any q-axis current residual, the q-axis current residual is generated based on the difference between the corresponding q-axis currents at two adjacent moments. For example, for the q-axis current at time k, the difference between the q-axis current at time k and the q-axis current at time k-1 forms the q-axis current residual at time k. The multiple consecutive q-axis current residuals may specifically be the q-axis current residual at time k-2, the q-axis current residual at time k-1, the q-axis current residual at time k, and the q-axis current residual at time k+1. Of course, the situation of multiple consecutive q-axis current residuals may also be other situations, which are not illustrated one by one here.

[0116] Figure 2 FIG. 1 shows an embodiment in which the parameter mismatch monitoring target uses the q-axis current and performs online updating. Figure 2In the event triggering mechanism, the online update mechanism is triggered when multiple consecutive q-axis current residuals match the parameter mismatch monitoring threshold. Generally, when three consecutive q-axis current residuals match the parameter mismatch monitoring threshold, the online update mechanism is triggered to update the aforementioned model prediction state parameters, that is, to update the aforementioned state matrix A and control matrix B. After the updated state matrix A and control matrix B, the state matrix A and control matrix B should be reconfigured within the generator predictive control model so that the generator predictive control model can be used to execute model predictive control in closed-loop control. The method and process of executing model predictive control using the generator predictive control model in closed-loop control can be referred to the corresponding description above.

[0117] It is understood that the method for updating the state matrix A and the control matrix B can be consistent with the state matrix A and the control matrix B obtained by system identification. The following is a detailed description of the process of obtaining the state matrix A and the control matrix B by system identification. The updated state matrix A and the control matrix B can be more compatible with the current operating conditions of the target permanent magnet synchronous generator, thereby improving the closed-loop control accuracy of the bus terminal voltage of the target permanent magnet synchronous generator, and thus making the bus terminal voltage compatible with the bus reference voltage.

[0118] In one embodiment of the present invention, for the q-axis current at time m, the q-axis current residual is:

[0119]

[0120] Among them, i q(m) is the q-axis current corresponding to time m, i q(m-1) is the q-axis current corresponding to time m-1, is the q-axis current residual corresponding to the q-axis current at time m;

[0121] and q-axis current residual The corresponding parameter mismatch monitoring threshold is:

[0122]

[0123] in, is the parameter mismatch monitoring threshold corresponding to the q-axis current at time m, is the mean value calculated based on the q-axis current corresponding to the n sampling control cycles before time m, is the standard deviation of the q-axis current calculated based on the n sampling control cycles before time m, λ m is the regulation coefficient corresponding to the q-axis current at time m;

[0124] when When , the q-axis current residual Parameter mismatch monitoring threshold match.

[0125] It can be seen from the above description that the q-axis current at time m specifically refers to the q-axis current sampled in the mth sampling control cycle. The q-axis current in other cases can refer to the description here. In order to improve the stability and reliability of the event trigger mechanism and avoid false triggering, in one embodiment of the present invention, the q-axis current corresponding to the first n sampling control cycles can be used. Therefore, when executing the event trigger mechanism, the target permanent magnet synchronous generator should be closed-loop controlled for at least n sampling control cycles. Specifically, n can be taken as 50. At this time, the event trigger mechanism can be executed in the 51st (m=51) sampling control cycle, and the average value can be calculated based on the q-axis current of 50 sampling control cycles. and standard deviation mean and standard deviation It can be calculated using the existing common methods, such as the corresponding q-axis current within 50 control cycles, and the average value can be calculated using statistical methods and standard deviation

[0126] It can be understood that when m is 52, the corresponding mean value is calculated using the q-axis current of 2 to 51 sampling control cycles. and standard deviation When m is other than the above, please refer to the description here. In addition, the adjustment coefficient λ m For the setting situation, please refer to the following corresponding instructions. From the above instructions, it can be seen that for each q-axis current residual, the corresponding parameter mismatch monitoring threshold can be calculated. When , the q-axis current residual should be determined Parameter mismatch monitoring threshold match.

[0127] In one embodiment of the present invention, the adjustment coefficient λ m Determined by fuzzy control, where the adjustment coefficient λ is determined m When , there are:

[0128] Determine the q-axis current residual The corresponding residual change rate Among them, the residual change rate Then we have: is the q-axis current residual corresponding to the q-axis current at time m-1, T s is the sampling control period;

[0129] The q-axis current residual and the residual change rate Load it into the constructed fuzzy controller, and the fuzzy controller will calculate the corresponding adjustment coefficient increment Δλ m ,

[0130] Based on the adjustment coefficient increment Δλ m , generating the adjustment coefficient λ m , then: m =λ m-1 +Δλ m , where λ m-1 is the regulation coefficient corresponding to the q-axis current at time m-1.

[0131] In order to further improve the accuracy of the parameter mismatch monitoring threshold, the adjustment coefficient λ of the present invention is m Determined by fuzzy control, specifically, the adjustment coefficient λ is determined by fuzzy control m When the q-axis current residual is determined, The corresponding residual change rate After that, the q-axis current residual and the residual change rate Loaded into the constructed fuzzy controller, it is understood that the fuzzy controller should be constructed in the event trigger mechanism, the fuzzy controller can be based on the q-axis current residual and the residual change rate The adjustment coefficient increment Δλ is obtained by solving m , after which the coefficient increment Δλ can be adjusted m Get the adjustment coefficient λ m The following is the solution to obtain the corresponding adjustment coefficient increment Δλ m The following examples illustrate the situation.

[0132] In specific implementation, the q-axis current residual and the residual change rate Fuzzy control is performed and divided into 5 fuzzy sets (NB / NS / ZO / PS / PB). During fuzzy control, the triangular membership function is used. The q-axis current residual can be calculated by the triangular membership function. Residual change rate Construct a 5*5 rule matrix, where the row index corresponds to the residual fuzzy set, the column index corresponds to the parameter residual change rate fuzzy set, and the q-axis current residual is used to calculate the fuzzy set. Residual change rate The activation strength is calculated based on the corresponding membership, and then the calculated activation strength is defuzzified according to the centroid method. The formula of the centroid method is as follows:

[0133]

[0134] Where N is the number of activated rules, Δλ iis the output increment corresponding to the i-th fuzzy rule, is the activation strength of the i-th rule.

[0135] In specific implementation, for the determined q-axis current residual and the residual change rate The number of activated rules N and the output increment Δλ corresponding to the i-th fuzzy rule can be determined. i and the activation strength of the i-th rule The specific determination method can be consistent with the existing technology and will not be repeated here.

[0136] In one embodiment of the present invention, performing system identification on the target permanent magnet synchronous generator includes:

[0137] Constructing a DMDc state space equation corresponding to the target permanent magnet synchronous generator and configuring identification reference voltage information, wherein the identification reference voltage information includes a d-axis reference voltage and a q-axis reference voltage of multiple reference segments;

[0138] After configuring the target permanent magnet synchronous generator to generate an observable dynamic response, determining a basic voltage vector of the target permanent magnet synchronous motor when generating the dynamic response, wherein the basic voltage vector includes a d-axis response voltage and a q-axis response voltage;

[0139] Calculating open-loop identification value function values representing the d-axis response voltage, the q-axis response voltage, and the d-axis reference voltage, and the q-axis reference voltage in each reference segment, and determining the optimal value of the open-loop identification value function value in each reference segment, so as to determine the target identification rectification state of the rectifier according to the optimal value of the open-loop identification value function value in each reference segment, and then performing data sampling processing in each reference segment to collect and generate corresponding identification sampling data, wherein, for each identification sampling data, the identification sampling data includes an identification state quantity and an identification control quantity;

[0140] The identification state quantity includes identifying the d-axis current and identifying the q-axis current;

[0141] The identified control quantity includes identifying the d-axis voltage, identifying the q-axis voltage and the angular velocity of the target permanent magnet synchronous generator;

[0142] All the identification sampling data are used to solve the DMDc state space equation constructed above to obtain the model prediction state parameters after the solution.

[0143] Specifically, when constructing the DMDc state-space equation, we can start with the mathematical model of the target permanent magnet synchronous generator to obtain the state equation of the target permanent magnet synchronous generator. Based on the state equation, we can obtain the control matrix of the target permanent magnet synchronous generator, and then construct the state-space equation of the DMD. Based on the operating characteristics of the target permanent magnet synchronous generator, we can obtain the mathematical model of the PMSG in the rotating coordinate system, which is:

[0144]

[0145] Among them, R s is the stator resistance, L q is the q-axis inductance, L d is the d-axis inductance, ω e is the electrical angular velocity of the target permanent magnet synchronous generator, ψ f is the flux amplitude of the permanent magnet of the target permanent magnet synchronous generator, u d and u q is the stator voltage component of the dq axis, i d and i q is the stator current component of the dq axis, that is, u d is the d-axis voltage, u q is the q-axis voltage, i d is the d-axis current, i q is the q-axis current, ψ q is the q-axis magnetic flux, ψ d is the d-axis magnetic flux.

[0146] Combining the above equations (1) and (2), we can get the current differential equation:

[0147]

[0148] In formula (3), the system parameters of the target permanent magnet synchronous generator are time-varying variables. Since the speed is usually kept constant under the target permanent magnet synchronous generator working condition, it is assumed that the angular velocity ω e remains unchanged, then by extracting 4 time variables u from the coefficient matrix of the target permanent magnet synchronous generator d ,u q ,i d ,i q , we can get the fixed coefficient matrix, and then incorporate the error term into the input to form the standard DMD state space equation:

[0149]

[0150] By discretizing Equation (4), the obtained mathematical model can be used to predict the current value at the next moment. The discretized model is written as:

[0151]

[0152] in:

[0153]

[0154] The above formula (5) is the expression for executing model predictive control. In formula (6), A is the state matrix, B is the control matrix, and T s is the sampling control period.

[0155] Based on the above equations (5) and (6), the DMDc state space equation can be obtained:

[0156] X2=AX1+BU (7)

[0157] in,

[0158] The above construction constructs the DMDc state-space equation corresponding to the target permanent magnet synchronous generator. After obtaining the DMDc state-space equation, before system identification, identification reference voltage information should also be configured. This identification reference voltage information includes the d-axis reference voltage and q-axis reference voltage of multiple reference segments. Specifically, the d-axis reference voltage and q-axis reference voltage of multiple reference segments can enhance the information content of the identification reference voltage, thereby more comprehensively and accurately extracting the dynamic modes and state-space model of the target permanent magnet synchronous generator.

[0159] In one embodiment of the present invention, for the d-axis reference voltage of multiple reference segments, there is:

[0160]

[0161] For the q-axis reference voltage of multiple reference segments, we have:

[0162]

[0163] in, is the bus reference voltage, is the d-axis reference voltage, is the q-axis reference voltage, f N is the fundamental frequency of the target permanent magnet synchronous generator.

[0164] Specifically, in order to effectively extract the dynamic mode of the target permanent magnet synchronous generator, the present invention superimposes a high-frequency AC signal on the bus reference voltage. The frequency of the high-frequency AC signal is 80 times the fundamental frequency. The amplitude of the AC component of the d-axis reference voltage is 2% of the bus reference voltage, and the amplitude of the AC component of the q-axis reference voltage is 1% of the bus reference voltage. The fundamental frequency f of the target permanent magnet synchronous generator is 1. N It can be calculated from the speed characteristics of the target permanent magnet synchronous generator. The fundamental frequency f of the target permanent magnet synchronous generator can be determined specifically.N The situation is consistent with the prior art and will not be described in detail here.

[0165] In the above d-axis reference voltage and q-axis reference voltage, t represents time, measured in seconds. As can be seen from the corresponding descriptions of the d-axis reference voltage and q-axis reference voltage, each time period forms a reference segment. For example, for the d-axis reference voltage, 0 ≤ t < 0.5 is the first reference segment, and 0.5 ≤ t < 1 is the second reference segment. For other reference segments, refer to the descriptions here.

[0166] As can be seen from the above description, a low speed can be applied to the target permanent magnet synchronous generator by the engine so that the target permanent magnet synchronous generator produces an observable dynamic response. The observable dynamic response includes a basic voltage vector and a basic stator current. The basic voltage vector includes a d-axis response voltage and a q-axis response voltage. The d-axis response voltage and the q-axis response voltage can be obtained and determined by referring to the method for determining the d-axis voltage and the q-axis voltage within the power generation control information described above. The basic stator current can generally be the q-axis current and the d-axis current.

[0167] Since model predictive control is used in closed-loop control, similar to closed-loop control, an open-loop identification value function should also be set in system identification. In order to realize the calculation and judgment of the open-loop identification value function value, an open-loop identification value function unit should be set. Figure 2 In the value function J e The location is the open-loop identification value function unit. As can be seen from the diagram, after obtaining the basic voltage vector, the basic voltage vector should be loaded into the open-loop identification value function unit so that the open-loop identification value function unit can calculate the open-loop identification value function value. It can be understood that the above-mentioned multi-reference segment d-axis reference voltage and multi-reference segment q-axis reference voltage should also be set in the open-loop identification value function unit.

[0168] In one embodiment of the present invention, for the open-loop identification cost function, we have:

[0169]

[0170] Among them, J e is the open-loop identification value function, is the d-axis response voltage, is the q-axis response voltage;

[0171] When determining the optimal value of the open-loop identification value function under each reference segment, the d-axis response voltage q-axis response voltage Corresponding to the same reference segment of the d-axis reference voltage and the q-axis reference voltage.

[0172] It should be noted that the method of calculating the open-loop identification value function value can refer to the calculation process description of the closed-loop control value function value mentioned above. The difference is that when calculating the open-loop identification value function value, the open-loop identification value function value corresponding to each reference segment should be calculated separately. For example, the d-axis reference voltage and q-axis reference voltage of the multiple reference segments shown above should be calculated separately for the open-loop identification value function values of the corresponding reference segments such as 0≤t<0.5, 0.5≤t<1, etc.

[0173] From the above description, it can be seen that when calculating the open-loop identification value function value, the d-axis response voltage and the q-axis response voltage should be obtained. Specifically, when obtaining the d-axis response voltage and the q-axis response voltage, the rectification state of the rectifier should be traversed until the optimal value of the open-loop identification value function of each reference segment is obtained. Among them, the optimal value of the open-loop identification value function value of each reference segment is the minimum value of the open-loop identification value function value of each reference segment. At this time, the rectification state of the rectifier is the target identification rectification state. After determining the target identification rectification state, the open-loop identification value function unit should load the reference segment identification rectification switch control information to the rectifier. Figure 2 Q abc That is, the reference segment identification rectifier switch control information for controlling the rectifier state.

[0174] After determining the target identified rectification state corresponding to each reference segment, data sampling processing is performed at least under each reference segment to collect and generate corresponding identification sampling data, wherein for each identification sampling data, the identification sampling data includes an identification state quantity and an identification control quantity; in one embodiment of the present invention, the identification state quantity includes an identification d-axis current and an identification q-axis current; the identification control quantity includes an identification d-axis voltage, an identification q-axis voltage, and the angular velocity of the target permanent magnet synchronous generator; it can be seen that the identification state quantity corresponds to the above-mentioned power generation state information, and the identification control quantity corresponds to the above-mentioned power generation control information. It can be understood that the identification state quantity should correspond to the identification control quantity, that is, the identification state quantity and the identification control quantity correspond to the power generation state corresponding to the target permanent magnet synchronous generator under the same reference segment.

[0175] To effectively extract the dynamic modulus of the target permanent magnet synchronous generator, data sampling should also be performed during the transition between two adjacent reference segments. That is, during the system identification process, data sampling should be performed for each reference segment and the transition phase of adjacent reference segments. To meet the requirements of system identification while reducing the amount of data computation, one embodiment of the present invention performs periodic downsampling within each reference segment of the d-axis reference voltage during data sampling, and performs full transient sampling when the d-axis reference voltage jumps from one reference segment to another adjacent reference segment.

[0176] As can be seen from the above description, during the system identification process, the data sampling process performed forms a mixed sampling state. When periodically downsampling is performed within each reference segment, the amount of identification sampled data collected can be reduced. During periodic downsampling, data sampling can be performed at intervals of multiple sampling control cycles, such as 100 sampling control cycles, to obtain corresponding identification sampled data within each reference segment.

[0177] For two adjacent reference segments, the DC amplitude of the d-axis reference voltage will jump when entering from one reference segment to the other. For example, when entering from the first reference segment with 0≤t<0.5 and the second reference segment with 0.5≤t<1, the DC amplitude of the d-axis reference voltage will jump. To effectively extract the dynamic modulus under this jump, full transient sampling should be performed. For other situations where the DC amplitude of the d-axis reference voltage jumps, please refer to the instructions here.

[0178] Specifically, when full sampling of the transient process is performed, data sampling processing is performed once in each sampling control period, for example, full sampling is performed on the transient process corresponding to the time window (0.05s) after the jump.

[0179] It should be understood that after executing the above-mentioned mixed sampling state, the corresponding identification sampling data can be obtained. Based on all the identification sampling data, the following data matrix X1, data matrix X2, and data matrix U can be constructed, where X2 is delayed by one time step based on X1. Specifically,

[0180]

[0181] Specifically, after the above mixed sampling data sampling process, κ can be obtained. max +1 identification sampling data, Indicates the first data sampling process to the κth max The identified q-axis current is obtained by data sampling and processing. For other situations, please refer to the description here.

[0182] After constructing the above-mentioned data matrix X1, data matrix X2, and data matrix U, the DMDc state space equation constructed above can be solved to obtain the model prediction state parameters after solving. The specific method and process of solving the model prediction state parameters can be consistent with the existing technology and will not be repeated here.

[0183] Figure 2In order to facilitate the execution of the above-mentioned system identification process, a DMDc model unit and a matrix identification unit should be constructed, wherein the DMDc model unit can complete the construction of the above-mentioned DMDc state space equation, as well as the above-mentioned data sampling processing and the construction of the data matrix X1, data matrix X2, and data matrix U, and the constructed data matrix X1, data matrix X2, and data matrix U are loaded into the matrix identification unit, and the matrix identification unit is used to perform the solution of the above-mentioned DMDc state space equation, and the solved state matrix A and control matrix B are loaded into the generator predictive control model.

[0184] Figure 3 FIG. 1 shows an embodiment of the bus terminal voltage generated by the target permanent magnet synchronous generator during the system identification process. As can be seen from the figure, during the system identification process, the duration is 0 to 2.5 seconds, and the bus terminal voltage fluctuates within the range of 0 to 100V.

[0185] Figure 4 (a) shows an embodiment of the bus terminal voltage of the target permanent magnet synchronous generator in the system identification stage and the closed-loop control stage. Figure 4 , open-loop excitation specifically refers to the system identification stage mentioned above, and closed-loop control is the stage of performing closed-loop control on the bus terminal voltage. In the following figures, open-loop excitation and closed-loop control have the same meaning. Figure 3 and Figure 4 (a) It can be seen that in the system identification stage, the bus terminal voltage is low and basically stable. In the closed-loop control stage, the bus terminal voltage first surges and finally stabilizes at the bus reference voltage. Figure 4 An embodiment when the bus reference voltage is 430V. Figure 4 (b) is an embodiment of the electromagnetic torque of the target permanent magnet synchronous generator in system identification and closed-loop control.

[0186] Figure 5 , which shows an embodiment of the bus terminal voltage and electromagnetic torque of the target permanent magnet synchronous generator under varying operating conditions. As can be seen from the figure, the present invention can effectively achieve stable control of the bus terminal voltage under different operating conditions.

[0187] Depend on Figure 5As can be seen from the above description, the target permanent magnet synchronous generator will need to update the model prediction state parameters online during operation. Specifically, the method for obtaining the model prediction state parameters online is basically the same as that used in the system identification stage. The main difference lies in the different data sources used. Specifically, in the system identification stage, the identification sampling data is derived from the system response of the PMSG in the bus open-loop state. The identification sampling data can be referred to the corresponding description above. In the online update process, the data matrix is derived from the response data of the PMSG under specific actual operating conditions during the period of the q-axis current residual pulse caused by the speed change of the target permanent magnet synchronous generator. Therefore, it can be seen that the model prediction state parameters can be updated by collecting the corresponding data and using the above method. It should be understood that the collected data should be based on the requirements of the matrix identification unit to perform calculations to obtain the state matrix A and control matrix B.

[0188] In addition, it can be seen from the above drawings that in the system identification stage, the speed of the target permanent magnet synchronous generator is low, resulting in the bus terminal voltage being actually lower than the bus reference voltage. Therefore, it is necessary to increase the speed of the target permanent magnet synchronous generator so that the bus terminal voltage can adapt to the bus reference voltage. In one embodiment of the present invention, a staged dynamic adjustment control method is adopted, that is, the bus reference voltage increases with the engine speed until it reaches the target bus reference voltage, that is, the bus reference voltage is gradually adjusted until the bus reference voltage reaches the target bus reference voltage. Therefore, the bus reference voltage mentioned in the above closed-loop control is the bus reference voltage that needs to be finally achieved.

[0189] It should be noted that the aforementioned process of gradually adjusting the engine speed until the target bus reference voltage is reached does not involve a change in operating conditions, so the aforementioned online update process is not performed. In other words, the model-predicted state parameters obtained during the system identification phase are maintained. Therefore, online updates of the model-predicted state parameters primarily occur when operating condition changes lead to parameter mismatch.

[0190] As can be seen from the above description, the present invention combines DMDc and MPC to identify the model-predicted state parameters of the target permanent magnet synchronous generator, while reducing MPC's dependence on model parameters, enabling real-time prediction of the system's future dynamic behavior and, based on this, online optimization of control inputs, thereby achieving precise regulation of the bus terminal voltage. Therefore, the DMDc-based parameter-free model predictive control method for permanent magnet synchronous generators provides a new approach for efficient and stable PMSG operation. Under conditions of unknown initial parameters or PMSG parameter mismatch, this closed-loop control method for bus terminal voltage has unique advantages and exhibits strong robustness, data-driven adaptability, and high dynamic response.

Claims

1. A parameter-free model predictive control method for a permanent magnet synchronous generator based on dynamic mode decomposition, characterized in that: The parameter-free model predictive control method for a permanent magnet synchronous generator comprises: A target permanent magnet synchronous generator is provided, and system identification is performed on the target permanent magnet synchronous generator to determine model prediction state parameters for model predictive control of the target permanent magnet synchronous generator after system identification, wherein: The model prediction state parameters include at least the state matrix A and the control matrix B. During system identification, a target permanent magnet synchronous generator is configured to generate an observable dynamic response, and thereafter, model prediction state parameters corresponding to the target permanent magnet synchronous generator are determined based on at least a DMDc method, and during the identification process, the target permanent magnet synchronous generator is configured to be in a bus voltage open-loop state; The model prediction state parameters determined by identification are configured in the generator predictive control model in the closed-loop controller to configure the closed-loop controller to perform closed-loop control on the bus terminal voltage output by the target permanent magnet synchronous generator. In the process of closed-loop control, the generator predictive control model is used to perform model predictive control, wherein: In a k-th sampling control period of the closed-loop control, at least power generation state information of the target permanent magnet synchronous generator at time k and power generation control information at time k are loaded into a generator predictive control model, so as to perform model predictive control using the generator predictive control model and generate power generation state information at time k+1, wherein the power generation state information includes a q-axis current and a d-axis current; Based on the power generation state information at time k+1 and the power generation state reference information at time k, the closed-loop control value function value representing the corresponding error state of the q-axis current and the d-axis current at time k+1 is calculated. Based on the optimal state of the closed control value function value at time k+1, target rectifier switch control information for controlling the rectifier rectifier state is determined, so as to configure the bus terminal voltage output by the rectifier to be adapted to the bus reference voltage based on the target rectifier switch control information, wherein: The power generation state reference information at time k is generated based at least on the bus reference voltage and the bus sampling voltage at time k, wherein the bus sampling voltage is generated based on voltage sampling of the bus terminal voltage output by the rectifier.

2. The method according to claim 1, wherein: In closed-loop control, parameter mismatch control is also included, where: When performing parameter mismatch control processing, it includes: determining a parameter mismatch monitoring target, wherein the parameter mismatch monitoring target includes at least a q-axis current, determining a parameter mismatch state based on a parameter mismatch monitoring target, wherein when the parameter mismatch state indicates parameter mismatch, performing online updating on model prediction state parameters, and generating online updated model prediction state parameters adapted to a target permanent magnet synchronous generator; thereafter, configuring the model prediction state parameters within a generator predictive control model, so as to perform model predictive control using the generator predictive control model in closed-loop control; When determining the parameter mismatch state based on the parameter mismatch monitoring target, when multiple consecutive q-axis current residuals all match the parameter mismatch monitoring threshold, the parameter mismatch state is configured as parameter mismatched, wherein, for any q-axis current residual, the q-axis current residual is generated based on the difference between the corresponding q-axis currents at two adjacent moments.

3. The method according to claim 2, wherein: For the q-axis current at time m, the q-axis current residual is: Among them, i q(m) is the q-axis current corresponding to time m, i q(m-1) is the q-axis current corresponding to time m-1, is the q-axis current residual corresponding to the q-axis current at time m; and q-axis current residual The corresponding parameter mismatch monitoring threshold is: in, is the parameter mismatch monitoring threshold corresponding to the q-axis current at time m, is the mean value calculated based on the q-axis current corresponding to the n sampling control cycles before time m, is the standard deviation of the q-axis current calculated based on the n sampling control cycles before time m, λ m is the regulation coefficient corresponding to the q-axis current at time m; when When , the q-axis current residual Parameter mismatch monitoring threshold match.

4. The method according to claim 3, wherein: Adjustment coefficient λ m Determined by fuzzy control, where the adjustment coefficient λ is determined m When , there are: Determine the q-axis current residual The corresponding residual change rate Among them, the residual change rate Then we have: is the q-axis current residual corresponding to the q-axis current at time m-1, T s is the sampling control period; The q-axis current residual and the residual rate of change Load it into the constructed fuzzy controller, and the fuzzy controller will calculate the corresponding adjustment coefficient increment Δλ m , Based on the adjustment coefficient increment Δλ m , generating the adjustment coefficient λ m , then: m =λ m-1 +Δλ m , where λ m-1 is the regulation coefficient corresponding to the q-axis current at time m-1.

5. The method according to claim 1, wherein: For the closed-loop control value function, we have: in, is the closed-loop control value function at time k+1, is the q-axis current at time k+1, is the given value of the q-axis current at time k, is the q-axis current at time k+1, is the given value of the d-axis current at time k; When determining the optimal state of the closed control value function at time k+1, we have: Traverse the rectifier state and the rectifier switch state information in each rectifier state, and obtain the bus sampling voltage in each rectifier state, and then generate at least the q-axis current given value at time k based on the bus sampling voltage Based on the generated q-axis current reference value at time k The closed-loop control value function values at time k+1 are calculated respectively, and the minimum closed-loop control value function value at time k+1 is configured as the optimal state of the closed-loop control value function value at time k+1, and the rectifier switch state information corresponding to the optimal state of the closed-loop control value function value at time k+1 is configured as the target rectifier switch control information.

6. The method according to claim 5, wherein: Given the q-axis current at time k At least perform PI calculation on the bus reference voltage and the bus sampling voltage at time k; The d-axis current at time k is given as Set to 0.

7. The method according to any one of claims 1 to 6, characterized in that: When performing system identification on the target permanent magnet synchronous generator, the method includes: Constructing a DMDc state space equation corresponding to the target permanent magnet synchronous generator and configuring identification reference voltage information, wherein the identification reference voltage information includes a d-axis reference voltage and a q-axis reference voltage of multiple reference segments; After configuring the target permanent magnet synchronous generator to generate an observable dynamic response, determining a basic voltage vector of the target permanent magnet synchronous motor when generating the dynamic response, wherein the basic voltage vector includes a d-axis response voltage and a q-axis response voltage; Calculating open-loop identification value function values representing the d-axis response voltage, the q-axis response voltage, and the d-axis reference voltage, and the q-axis reference voltage in each reference segment, and determining the optimal value of the open-loop identification value function value in each reference segment, so as to determine the target identification rectification state of the rectifier according to the optimal value of the open-loop identification value function value in each reference segment, and then performing data sampling processing in each reference segment to collect and generate corresponding identification sampling data, wherein, for each identification sampling data, the identification sampling data includes an identification state quantity and an identification control quantity; The identification state quantity includes identifying the d-axis current and identifying the q-axis current; The identified control quantity includes identifying the d-axis voltage, identifying the q-axis voltage and the angular velocity of the target permanent magnet synchronous generator; All the identification sampling data are used to solve the DMDc state space equation constructed above to obtain the model prediction state parameters after the solution.

8. The method according to claim 7, wherein: When configuring the target permanent magnet synchronous generator to generate an observable dynamic response, at least the target permanent magnet synchronous generator is driven to rotate at a low speed by the engine; For the d-axis reference voltage of multiple reference segments, we have: For the q-axis reference voltage of multiple reference segments, we have: in, is the bus reference voltage, is the d-axis reference voltage, is the q-axis reference voltage, f N is the fundamental frequency of the target permanent magnet synchronous generator.

9. The method according to claim 8, wherein During data sampling, periodic downsampling is performed in each reference segment of the d-axis reference voltage, and when the d-axis reference voltage jumps from one reference segment to another adjacent reference segment, transient full sampling is performed.

10. The method according to claim 8, wherein: For the open-loop identification value function, we have: Among them, J e is the open-loop identification value function, is the d-axis response voltage, is the q-axis response voltage; When determining the optimal value of the open-loop identification value function under each reference segment, the d-axis response voltage q-axis voltage Corresponding to the same reference segment of the d-axis reference voltage and the q-axis reference voltage.

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