Model-based Current Sequential Control Method and Device for Energy Storage Converters

Abstract

This invention relates to the field of electrical data processing and discloses a model-based prediction-based current sequence control method and device for energy storage converters. By implementing this invention, an intelligent acquisition mechanism for electrical parameters in the control circuit corresponding to the energy storage converter is established. Then, parameter processing is performed on the acquired parameter data to achieve positive and negative sequence separation of current and voltage. Furthermore, based on the established sequence model, precise data calculations are performed on the parameter processing results to achieve preliminary prediction and calculation of the control circuit's corresponding switching mode in the next sampling period / sampling time. Subsequently, data calculations and value filtering are performed on all current value sets. By comparing these values ​​with the target value, the prediction setting for the switching mode to be executed in subsequent sampling periods is improved, which helps to increase the tracking speed of each sequence current and effectively reduces the dynamic reactive power support current response time of the energy storage converter to grid faults.

Description

Technical Field

[0001] This invention relates to the field of electrical data processing technology, and in particular to a method and apparatus for current sequence control of energy storage converters based on model prediction. Background Technology

[0002] The "Technical Requirements for Energy Storage Converters in Electrochemical Energy Storage Systems" released in 2023 clearly distinguishes between symmetrical and asymmetrical voltage faults in the power grid. It requires energy storage converters to inject corresponding positive and negative sequence reactive currents according to the positive and negative sequence voltage levels of the power grid, so as to more accurately support grid voltage recovery. At the same time, the technical requirements also strictly stipulate that the dynamic reactive current support response time of the energy storage converter to grid faults shall not exceed 30ms, and it shall be able to withdraw from actively providing dynamic reactive current within 30ms after grid voltage recovery.

[0003] To address grid voltage asymmetry, numerous scholars both domestically and internationally have conducted extensive research on model predictive control methods for energy storage converters. Most proposed control methods utilize power compensation to maintain three-phase balance or approximate an ideal sine wave in voltage or current during operation, such as balancing three-phase output current and suppressing second-harmonic fluctuations in output power. However, controlling energy storage converters under grid voltage asymmetry requires simultaneously controlling multiple current variables in both positive and negative sequences, and also necessitates dynamic and rapid responses to varying usage / adjustment demands. Furthermore, current energy storage converters lack suitable control methods to meet precise control requirements. Therefore, improving the control accuracy and flexibility of control demand response is particularly crucial. Summary of the Invention

[0004] This invention provides a model-based prediction-based current sequence control method and device for energy storage converters, which can improve the control accuracy of the positive and negative sequence currents of the energy storage converter, improve the tracking speed of each sequence current, and reduce the dynamic response time of the energy storage converter to grid faults.

[0005] To address the aforementioned technical problems, the first aspect of this invention discloses a model-predictive current sequence control method for energy storage converters. This method is applied to the control circuit corresponding to the energy storage converter and includes:

[0006] According to preset sampling parameters, data acquisition operations are performed on multiple preset electrical parameters in the control circuit to obtain parameter acquisition data corresponding to each electrical parameter; the sampling parameters include the sampling period and at least one corresponding sampling time;

[0007] Perform parameter processing on the parameter acquisition data corresponding to all the electrical parameters to obtain the parameter processing results corresponding to the parameter acquisition of all the electrical parameters. The parameter processing includes at least the separation of positive and negative order for voltage and current.

[0008] According to the preset sequence model, model prediction is performed on all the parameter processing results to obtain the model prediction result corresponding to all the parameter processing results. The energy storage converter includes multiple switching modes. The model prediction result includes a set of current values ​​corresponding to the subsequent sampling time for each sampling time. The set of current values ​​corresponding to each sampling time includes the predicted current value corresponding to each switching mode.

[0009] For each set of current values ​​corresponding to each sampling time, according to the preset data filtering requirements, data calculation and numerical filtering are performed on each predicted current value in the set of current values ​​to obtain the target value value that matches the data filtering requirements at that sampling time.

[0010] Based on the target value value corresponding to each sampling time, a switching action setting is performed for the subsequent sampling period corresponding to the later sampling period, so as to set the switching mode performed by the energy storage converter in the subsequent sampling period.

[0011] As an optional implementation, in the first aspect of the present invention, all the electrical parameters include voltage and current in the control circuit; the parameter acquisition data corresponding to all the electrical parameters includes multiple voltage acquisition data and multiple current acquisition data.

[0012] The step of performing parameter processing on the parameter acquisition data corresponding to all the electrical parameters to obtain the parameter processing results corresponding to the parameter acquisition of all the electrical parameters includes:

[0013] According to the set positive and negative sequence separation algorithm, positive and negative sequence separation processing is performed on the parameter acquisition data corresponding to all the electrical parameters to obtain target separation data corresponding to each parameter acquisition data. All the target separation data include voltage positive sequence separation data, voltage negative sequence separation data, current positive sequence separation data, and current negative sequence separation data.

[0014] All the target separation data are determined as the parameter processing results corresponding to the parameter acquisition of all the electrical parameters.

[0015] As an optional implementation, in the first aspect of the present invention, the separation model includes a plurality of model prediction functions for performing current value prediction, each model prediction function matching a target separation data, and the data type of each target separation data includes any one of positive voltage sequence type, negative voltage sequence type, positive current sequence type, and negative current sequence type; all the model prediction functions include a positive current sequence function and a negative current sequence function, the positive current sequence function including a positive sequence direct axis function and a positive sequence quadrature axis function; the negative current sequence function including a negative sequence direct axis function and a negative sequence quadrature axis function;

[0016] The step of performing model prediction on all the parameter processing results according to a preset sorting model to obtain model prediction results corresponding to all the parameter processing results includes:

[0017] Based on the data type corresponding to each of the target separation data, determine the target model prediction function that matches the target separation data;

[0018] For each target separation data, according to the target model prediction function matched by the target separation data, data calculation matching the target model prediction function is performed on the target separation data to obtain the target calculation result corresponding to the target separation data;

[0019] The target calculation results corresponding to all the target separation data are determined as the model prediction results corresponding to all the parameter processing results.

[0020] As an optional implementation, in the first aspect of the present invention, the calculation formula corresponding to the positive-order direct-axis function is as follows:

[0021]

[0022] The calculation formula for the orthogonal axis function is as follows:

[0023]

[0024] The calculation formula for the negative-order direct-axis function is as follows:

[0025]

[0026] The calculation formula for the negative-order intersection axis function is as follows:

[0027]

[0028] Where P corresponds to the positive sequence component, N corresponds to the negative sequence component, R is the equivalent series resistance of the filter inductor, and T sThe sampling period is L, the grid-side filter inductance of the energy storage converter is L, k is the sampling time corresponding to the sampling period, and k+1 is the subsequent sampling time corresponding to that sampling time; ω is the grid angular frequency. The positive and negative sequence dq components of the DC-side current of the energy storage converter in different switching modes; The positive and negative sequence dq components of the DC-side voltage of the energy storage converter in different switching modes; The positive and negative sequence dq components of the AC side voltage of the energy storage converter in different switching modes are given. The predicted current value is the positive and negative sequence dq component of the current at time k+1.

[0029] As an optional implementation, in the first aspect of the present invention, the set of current values ​​corresponding to each sampling time includes the positive-sequence direct-axis current value corresponding to the positive-sequence direct-axis function, the positive-sequence quadrature-axis current value corresponding to the positive-sequence quadrature-axis function, the negative-sequence direct-axis current corresponding to the negative-sequence direct-axis function, and the negative-sequence quadrature-axis current corresponding to the negative-sequence quadrature-axis function.

[0030] For each set of current values ​​corresponding to each sampling time, based on a pre-constructed value function, combined with preset standard current values ​​and screening requirements, difference calculation and numerical screening are performed on each predicted current value in the set of current values ​​to obtain the target value value, including:

[0031] For each sampling time, the positive sequence quadrature axis current value, the negative sequence direct axis current, the negative sequence direct axis current, and the negative sequence quadrature axis current corresponding to the sampling time are input into a pre-constructed value function to obtain a set of value values ​​corresponding to the sampling time. The set of value values ​​corresponding to each sampling time includes multiple value values, and each target value value is associated with a switching mode.

[0032] For each set of value values ​​corresponding to each sampling time, the target value value with the smallest value is selected from the set of value values, and the switching mode associated with the target value value is determined as the target switching mode.

[0033] As an optional implementation, in the first aspect of the present invention, the calculation formula corresponding to the value function is:

[0034]

[0035] in, This is the first standard value corresponding to the positive sequence component of the current. The second standard value corresponding to the positive sequence component of the current. The third standard value corresponding to the negative sequence component of the current. This is the fourth standard value corresponding to the negative sequence component of the current.

[0036] As an optional implementation, in the first aspect of the present invention, setting the switching action for the later sampling period corresponding to the target value value corresponding to each sampling time includes:

[0037] For each sampling time, the subsequent sampling time corresponding to that sampling time is determined in the subsequent sampling period;

[0038] The target switch mode corresponding to the sampling time is associated with and the mode setting is performed with the subsequent sampling time corresponding to the sampling time to obtain the association and mode setting result corresponding to the subsequent sampling time.

[0039] A second aspect of this invention discloses a model-predictive current sequence control device for an energy storage converter, the device being applied in the control circuit corresponding to the energy storage converter, the device comprising:

[0040] The data acquisition module is used to perform data acquisition operations on multiple preset electrical parameters in the control circuit according to preset sampling parameters, and obtain parameter acquisition data corresponding to each electrical parameter; the sampling parameters include the sampling period and at least one corresponding sampling time;

[0041] The parameter processing module is used to perform parameter processing on the parameter acquisition data corresponding to all the electrical parameters to obtain the parameter processing results corresponding to the parameter acquisition of all the electrical parameters. The parameter processing includes at least the separation of positive and negative order for voltage and current.

[0042] The prediction processing module is used to perform model prediction on all the parameter processing results according to the preset sequence model, and obtain the model prediction result corresponding to all the parameter processing results. The energy storage converter includes multiple switching modes. The model prediction result includes a set of current values ​​corresponding to the subsequent sampling time for each sampling time. The set of current values ​​corresponding to each sampling time includes the predicted current value corresponding to each switching mode.

[0043] The value calculation module is used to perform data calculation and value filtering on each predicted current value in the current value set corresponding to each sampling time according to preset data filtering requirements, so as to obtain the target value value that matches the sampling time with the data filtering requirements.

[0044] The setting module is used to set the switching action for the later sampling period corresponding to the target value value at each sampling time, so as to set the switching mode executed by the energy storage converter in the later sampling period.

[0045] As an optional implementation, in the second aspect of the present invention, all the electrical parameters include voltage and current in the control circuit; the parameter acquisition data corresponding to all the electrical parameters includes multiple voltage acquisition data and multiple current acquisition data.

[0046] The parameter processing module performs parameter processing on the parameter acquisition data corresponding to all the electrical parameters to obtain the parameter processing results corresponding to the parameter acquisition data of all the electrical parameters. The specific method is as follows:

[0047] According to the set positive and negative sequence separation algorithm, positive and negative sequence separation processing is performed on the parameter acquisition data corresponding to all the electrical parameters to obtain target separation data corresponding to each parameter acquisition data. All the target separation data include voltage positive sequence separation data, voltage negative sequence separation data, current positive sequence separation data, and current negative sequence separation data.

[0048] All the target separation data are determined as the parameter processing results corresponding to the parameter acquisition of all the electrical parameters.

[0049] As an optional implementation, in a second aspect of the invention, the separation model includes a plurality of model prediction functions for performing current value prediction, each model prediction function matching a target separation data, and the data type of each target separation data includes any one of positive voltage sequence type, negative voltage sequence type, positive current sequence type, and negative current sequence type; all the model prediction functions include a positive current sequence function and a negative current sequence function, the positive current sequence function including a positive sequence direct axis function and a positive sequence quadrature axis function; the negative current sequence function including a negative sequence direct axis function and a negative sequence quadrature axis function;

[0050] The prediction processing module performs model prediction on all the parameter processing results according to a preset sorting model, and obtains the model prediction result corresponding to all the parameter processing results. The specific method is as follows:

[0051] Based on the data type corresponding to each of the target separation data, determine the target model prediction function that matches the target separation data;

[0052] For each target separation data, according to the target model prediction function matched by the target separation data, data calculation matching the target model prediction function is performed on the target separation data to obtain the target calculation result corresponding to the target separation data;

[0053] The target calculation results corresponding to all the target separation data are determined as the model prediction results corresponding to all the parameter processing results.

[0054] As an optional implementation, in the second aspect of the present invention, the calculation formula corresponding to the positive-order direct-axis function is as follows:

[0055]

[0056] The calculation formula for the orthogonal axis function is as follows:

[0057]

[0058] The calculation formula for the negative-order direct-axis function is as follows:

[0059]

[0060] The calculation formula for the negative-order intersection axis function is as follows:

[0061]

[0062] Where P corresponds to the positive sequence component, N corresponds to the negative sequence component, R is the equivalent series resistance of the filter inductor, and T s The sampling period is L, the grid-side filter inductance of the energy storage converter is L, k is the sampling time corresponding to the sampling period, and k+1 is the subsequent sampling time corresponding to that sampling time; ω is the grid angular frequency. The positive and negative sequence dq components of the DC-side current of the energy storage converter in different switching modes; The positive and negative sequence dq components of the DC-side voltage of the energy storage converter in different switching modes; The positive and negative sequence dq components of the AC side voltage of the energy storage converter in different switching modes are given. The predicted current value is the positive and negative sequence dq component of the current at time k+1.

[0063] As an optional implementation, in the second aspect of the present invention, the set of current values ​​corresponding to each sampling time includes the positive-sequence direct-axis current value corresponding to the positive-sequence direct-axis function, the positive-sequence quadrature-axis current value corresponding to the positive-sequence quadrature-axis function, the negative-sequence direct-axis current corresponding to the negative-sequence direct-axis function, and the negative-sequence quadrature-axis current corresponding to the negative-sequence quadrature-axis function;

[0064] For each sampling time corresponding to the set of current values, the value calculation module performs difference calculation and value filtering on each predicted current value in the set of current values ​​according to a pre-constructed value function, combined with preset standard current values ​​and filtering requirements, to obtain the target value value. The specific method is as follows:

[0065] For each sampling time, the positive sequence quadrature axis current value, the negative sequence direct axis current, the negative sequence direct axis current, and the negative sequence quadrature axis current corresponding to the sampling time are input into a pre-constructed value function to obtain a set of value values ​​corresponding to the sampling time. The set of value values ​​corresponding to each sampling time includes multiple value values, and each target value value is associated with a switching mode.

[0066] For each set of value values ​​corresponding to each sampling time, the target value value with the smallest value is selected from the set of value values, and the switching mode associated with the target value value is determined as the target switching mode.

[0067] As an optional implementation, in the second aspect of the present invention, the calculation formula corresponding to the value function is:

[0068]

[0069] in, This is the first standard value corresponding to the positive sequence component of the current. The second standard value corresponding to the positive sequence component of the current. The third standard value corresponding to the negative sequence component of the current. This is the fourth standard value corresponding to the negative sequence component of the current.

[0070] As an optional implementation, in a second aspect of the invention, the setting module sets the switching action for the later sampling period corresponding to the target value value corresponding to each sampling time by means of:

[0071] For each sampling time, the subsequent sampling time corresponding to that sampling time is determined in the subsequent sampling period;

[0072] The target switch mode corresponding to the sampling time is associated with and the mode setting is performed with the subsequent sampling time corresponding to the sampling time to obtain the association and mode setting result corresponding to the subsequent sampling time.

[0073] A third aspect of the present invention discloses another model-predictive current sequence control device for energy storage converters, the device comprising:

[0074] Memory containing executable program code;

[0075] A processor coupled to the memory;

[0076] The processor calls the executable program code stored in the memory to execute the model prediction-based energy storage converter current sequence control method disclosed in the first aspect of the present invention.

[0077] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the model prediction-based energy storage converter current sequence control method disclosed in the first aspect of the present invention.

[0078] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0079] This invention provides a model-predictive-based current sequence control method for energy storage converters. This method is applied to the control circuit of an energy storage converter and includes: performing data acquisition operations on multiple preset electrical parameters in the control circuit according to preset sampling parameters to obtain parameter acquisition data corresponding to each electrical parameter; the sampling parameters include a sampling period and at least one corresponding sampling time; performing parameter processing on the parameter acquisition data corresponding to all electrical parameters to obtain parameter processing results corresponding to all electrical parameters, wherein the parameter processing at least includes positive and negative sequence separation for voltage and current; and performing model prediction on all parameter processing results according to a preset sequence model to obtain a result corresponding to all parameter processing results. Based on the corresponding model prediction results, the energy storage converter includes multiple switching modes. The model prediction results include a set of current values ​​corresponding to each sampling time and a set of current values ​​corresponding to each sampling time, which includes the predicted current value corresponding to each switching mode. For each set of current values ​​corresponding to each sampling time, according to preset data filtering requirements, data calculation and value filtering are performed on each predicted current value in the set of current values ​​to obtain the target value value that matches the data filtering requirements for that sampling time. Based on the target value value corresponding to each sampling time, the switching action setting is performed in the subsequent sampling period corresponding to the later sampling period to set the energy storage converter to execute the above-mentioned switching mode in the subsequent sampling period. As can be seen, by implementing this invention, an intelligent acquisition mechanism for electrical parameters in the control circuit corresponding to the energy storage converter is established. This mechanism then performs parameter processing on the acquired parameter data, realizing the positive and negative sequence separation mechanism for current and voltage in the early stage, thus improving the accuracy of this separation. Furthermore, after completing the positive and negative sequence separation of current and voltage and realizing the preliminary data preparation function, accurate data calculation can be performed on the parameter processing results based on the established sequence model. Specifically, model prediction is performed to obtain the set of current values ​​corresponding to each sampling time at subsequent sampling times, and this set of current values ​​corresponding to subsequent sampling times includes the predicted current corresponding to different switching modes. This system enables preliminary prediction and calculation of the switching mode corresponding to the control circuit in operation at the next sampling period / sampling time. Subsequently, it can perform data calculation and value filtering on all current value sets to obtain the target value value matching each sampling time. Then, based on the target value value, it can predict and set the switching mode in the subsequent sampling period. This prediction setting of the switching mode corresponding to the subsequent sampling period can improve the tracking speed of each sequence current, effectively reduce the dynamic reactive power support current response time of the energy storage converter to grid faults, and provide voltage support to the grid more timely. In other words, it is conducive to improving the fault response speed during grid faults and improving the power supply stability and reliability of the energy storage converter to the grid voltage. Attached Figure Description

[0080] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0081] Figure 1 This is a schematic flowchart of a model-predictive current sequence control method for energy storage converters disclosed in an embodiment of the present invention.

[0082] Figure 2 This is a flowchart illustrating another model-predictive current sequence control method for energy storage converters disclosed in an embodiment of the present invention.

[0083] Figure 3 This is a schematic diagram of the structure of a model-predictive current sequence control device for an energy storage converter disclosed in an embodiment of the present invention.

[0084] Figure 4 This is a schematic diagram of another model-predictive current sequence control device for energy storage converters disclosed in an embodiment of the present invention.

[0085] Figure 5 This is a schematic diagram of the structure of a control circuit disclosed in an embodiment of the present invention;

[0086] Figure 6 This is a schematic diagram of another model-predictive current sequence control device for energy storage converters disclosed in an embodiment of the present invention. Detailed Implementation

[0087] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0088] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0089] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0090] This invention discloses a model-predictive current sequence control method and device for energy storage converters. It establishes an intelligent acquisition mechanism for electrical parameters in the control circuit corresponding to the energy storage converter, enabling parameter processing of the acquired parameter data. This achieves an initial positive and negative sequence separation mechanism for current and voltage, improving the accuracy of this separation in the control circuit. Furthermore, after completing the positive and negative sequence separation and data preparation, it can perform precise data calculation based on the established sequence model, specifically by executing model prediction to obtain the set of current values ​​corresponding to each sampling time at subsequent sampling times. This set of current values ​​corresponding to subsequent sampling times includes values ​​that are positive and negative. The predicted current value corresponding to the switching mode enables preliminary prediction and calculation of the switching mode of the control circuit in operation for the next sampling period / sampling time. Subsequently, data calculation and value filtering are performed on all current value sets to obtain the target value value matching each sampling time. Then, based on this target value value, the predicted switching mode is set for the subsequent sampling period. This predicted switching mode setting for the subsequent sampling period improves the tracking speed of each sequence current, effectively reduces the dynamic reactive power support current response time of the energy storage converter to grid faults, and provides voltage support to the grid more promptly. In other words, it helps improve the fault response speed during grid faults and enhances the stability and reliability of the energy storage converter's power supply to the grid voltage. These will be explained in detail below.

[0091] Example 1

[0092] Please see Figure 1 , Figure 1 This is a flowchart illustrating a model-predictive current sequence control method for energy storage converters disclosed in an embodiment of the present invention. Figure 1 The model-predictive current sequence control method for energy storage converters described herein can be applied to the control circuits corresponding to energy storage converters, and can also be applied to model-predictive current sequence control devices for energy storage converters. This invention does not limit the application of this method. Figure 1 As shown, the model-predictive current sequence control method for energy storage converters can include the following operations:

[0093] 101. Based on the preset sampling parameters, perform data acquisition operations on multiple preset electrical parameters in the control circuit to obtain parameter acquisition data corresponding to each electrical parameter.

[0094] In this embodiment of the invention, the sampling parameters include the sampling period and at least one corresponding sampling time.

[0095] 102. Perform parameter processing on the parameter acquisition data corresponding to all electrical parameters to obtain the parameter processing results corresponding to the parameter acquisition of all electrical parameters. The parameter processing shall include at least the separation of positive and negative order for voltage and current.

[0096] 103. Based on the preset sorting model, perform model prediction on the processing results of all parameters to obtain the model prediction results corresponding to the processing results of all parameters.

[0097] In this embodiment of the invention, the energy storage converter includes multiple switching modes, and the model prediction result includes a set of current values ​​corresponding to each sampling time at a later sampling time. The set of current values ​​corresponding to each sampling time includes the predicted current value corresponding to each switching mode.

[0098] In this embodiment of the invention, the energy storage converter can be an energy storage converter based on a three-phase PWM converter. For each energy storage converter, the switching mode of the energy storage converter can be encoded according to the actual usage requirements, using coded numbers such as 100, 110, 010, 011, 001, 101 and 111, 000 to represent different switching modes.

[0099] 104. For each set of current values ​​corresponding to each sampling time, according to the preset data filtering requirements, perform data calculation and numerical filtering on each predicted current value in the set of current values ​​to obtain the target value value that matches the data filtering requirements at that sampling time.

[0100] In this embodiment of the invention, the target value obtained by the screening has a corresponding switch mode, which is used as a switch mode to be set at a later sampling time corresponding to the sampling time. That is, the switch mode associated / bound / corresponding to the target value is used as a prediction and switch mode to be set at the later sampling time.

[0101] 105. Based on the target value value corresponding to each sampling time, set the switching action for the later sampling period to be performed in the later sampling period, so as to set the energy storage converter to perform the above-mentioned switching mode in the later sampling period.

[0102] It is evident that implementation Figure 1The described model-based current sequence control method for energy storage converters establishes an intelligent acquisition mechanism for electrical parameters in the control circuit applied to the energy storage converter. This mechanism then processes the acquired parameter data, achieving a positive / negative sequence separation mechanism for current and voltage in the early stages, thus improving the accuracy of this separation. Furthermore, after completing the positive / negative sequence separation and data preparation, the method can perform precise data calculations based on the established sequence model. Specifically, it performs model prediction to obtain the set of current values ​​corresponding to each sampling time at subsequent sampling times. This set of current values ​​corresponding to subsequent sampling times includes values ​​from different switching modes. The corresponding predicted current value enables preliminary prediction and calculation of the switching mode of the control circuit in operation for the next sampling period / sampling time. Then, data calculation and value filtering can be performed on all current value sets to obtain the target value value matching each sampling time. Subsequently, the prediction setting of the switching mode in the subsequent sampling period is performed based on the target value value. This prediction setting of the switching mode in the subsequent sampling period can improve the tracking speed of each sequence current, effectively reduce the dynamic reactive power support current response time of the energy storage converter to grid faults, and provide voltage support to the grid more timely. That is, it is conducive to improving the fault response speed during grid faults and improving the power supply stability and reliability of the energy storage converter to the grid voltage.

[0103] In an optional embodiment, the method of setting the switching action to be performed in the later sampling period according to the target value value corresponding to each sampling time specifically includes:

[0104] For each sampling time, determine the subsequent sampling time corresponding to that sampling time in the subsequent sampling period;

[0105] The target switch mode corresponding to the sampling time is associated with and the mode setting is performed with the subsequent sampling time corresponding to the sampling time to obtain the association and mode setting result corresponding to the subsequent sampling time.

[0106] As can be seen, in this optional embodiment, after determining the value value corresponding to each sampling moment in the current sampling period, it can automatically realize the determination of the subsequent sampling moment, the determination of the target switching mode corresponding to the value value, and the association and mode setting of the target switching mode with the subsequent sampling moment. This realizes the predictive setting of the switching mode in the control circuit corresponding to the energy storage converter, improves the setting accuracy of the switching mode in the control circuit, and can also improve the response accuracy of the energy storage converter to grid faults. At the same time, if there is a grid fault, the prediction and setting of the switching mode can assist in the switching mode switching, improving the power supply stability and reliability of the grid voltage.

[0107] In another alternative embodiment, please refer to the figure.

[0108] Example 2

[0109] Please see Figure 2 , Figure 2 This is a flowchart illustrating another model-predictive-based current sequence control method for energy storage converters disclosed in an embodiment of the present invention. Figure 2 The model-predictive current sequence control method for energy storage converters described herein can be applied to model-predictive current sequence control devices for energy storage converters, and this invention does not limit its application. Figure 2 As shown, the model-predictive current sequence control method for energy storage converters can include the following operations:

[0110] 201. Based on the preset sampling parameters, perform data acquisition operations on multiple preset electrical parameters in the control circuit to obtain parameter acquisition data corresponding to each electrical parameter.

[0111] In this embodiment of the invention, all electrical parameters include voltage and current in the control circuit; the parameter acquisition data corresponding to all electrical parameters includes multiple voltage acquisition data and multiple current acquisition data. Furthermore, the parameter acquisition data may also include grid voltage phase information, which is used to indicate the grid voltage phase corresponding to the control circuit in the current sampling period / sampling time.

[0112] 202. Based on the set positive and negative sequence separation algorithm, perform positive and negative sequence separation processing on the parameter acquisition data corresponding to all electrical parameters to obtain the target separated data corresponding to each parameter acquisition data.

[0113] In this embodiment of the invention, all target separation data include positive voltage sequence separation data, negative voltage sequence separation data, positive current sequence separation data, and negative current sequence separation data.

[0114] In this embodiment of the invention, the positive and negative sequence separation algorithm can be the Park transformation method, also known as Park's Transformation, which is a commonly used coordinate transformation for analyzing the operation of synchronous motors. It is mainly used to convert the three-phase coordinate system of a three-phase AC power system into a two-phase coordinate system (usually the dq coordinate system), thereby simplifying the control and analysis of the motor.

[0115] 203. Determine the parameter processing results corresponding to the parameter acquisition of all target separation data and all electrical parameters.

[0116] In this embodiment of the invention, parameter processing includes at least the separation of positive and negative order for voltage and current.

[0117] 204. Based on the preset sorting model, perform model prediction on the processing results of all parameters to obtain the model prediction results corresponding to the processing results of all parameters.

[0118] 205. For each set of current values ​​corresponding to each sampling time, according to the preset data filtering requirements, perform data calculation and numerical filtering on each predicted current value in the set of current values ​​to obtain the target value value that matches the data filtering requirements at that sampling time.

[0119] 206. Based on the target value value corresponding to each sampling time, set the switching action for the later sampling period to be performed in the later sampling period, so as to set the energy storage converter to perform the above-mentioned switching mode in the later sampling period.

[0120] For further descriptions of steps 201 and 204-206 in this embodiment of the invention, please refer to the other specific descriptions of steps 101 and 103-105 in Embodiment 1. These descriptions will not be repeated in this embodiment of the invention.

[0121] It is evident that implementation Figure 2 The described model-based current sequence control method for energy storage converters, through a set positive and negative sequence separation algorithm (specifically using Park transformation), can transform the three-phase current and voltage of the control circuit into the dq coordinate system, making it easier to apply control algorithms and perform stability analysis, thus improving the processing efficiency for all electrical parameters. Furthermore, the transformation eliminates angle variables in the equations, simplifying the solution and reducing the computational difficulty. In addition, when a fault exists in the power system / control circuit, detecting abnormalities in the current or voltage components in the dq coordinate system can determine whether there is an imbalance or fault in the system, improving the speed of fault detection in the power system / control circuit.

[0122] In an optional embodiment, the separation model includes multiple model prediction functions for performing current value prediction. Each model prediction function matches a target separation data type, and the data type of each target separation data type includes any one of positive voltage sequence type, negative voltage sequence type, positive current sequence type, and negative current sequence type. All model prediction functions include positive current sequence functions and negative current sequence functions. The positive current sequence functions include positive sequence direct axis functions and positive sequence quadrature axis functions. The negative current sequence functions include negative sequence direct axis functions and negative sequence quadrature axis functions.

[0123] The above-mentioned method of performing model prediction on the processing results of all parameters according to the preset sorting model to obtain the model prediction results corresponding to all parameter processing results specifically includes:

[0124] Based on the data type of each target separation data, determine the target model prediction function that matches the target separation data;

[0125] For each target separation data, based on the target model prediction function that matches the target separation data, perform data calculations that match the target model prediction function to obtain the target calculation result corresponding to the target separation data;

[0126] The target calculation results corresponding to all target separation data are determined as the model prediction results corresponding to all parameter processing results.

[0127] In this optional embodiment, the calculation formula for the positive-order direct-axis function is as follows:

[0128]

[0129] The calculation formulas for the orthogonal axis functions are as follows:

[0130]

[0131] The formulas for calculating negative-order direct-axis functions are as follows:

[0132]

[0133] The calculation formula for the negative-order intersection axis function is as follows:

[0134]

[0135] Where P corresponds to the positive sequence component, N corresponds to the negative sequence component, R is the equivalent series resistance of the filter inductor, and T s Where L is the sampling period, k is the grid-side filter inductance of the energy storage converter, k is the sampling time corresponding to the sampling period, k+1 is the subsequent sampling time corresponding to the sampling time, and ω is the grid angular frequency. The positive and negative sequence dq components of the DC side current of the energy storage converter in different switching modes; The positive and negative sequence dq components of the DC side voltage of the energy storage converter in different switching modes; The positive and negative sequence dq components of the AC side voltage of the energy storage converter in different switching modes are given. The predicted current value corresponding to the positive and negative sequence dq components of the current at time k+1.

[0136] In this optional embodiment, in actual use, as described above, the energy storage converter can be configured with 8 different switching modes, counted in the range [0, 7]. For each switching mode, the corresponding encoding method is sequentially adopted, such as 100, 110, 010, 011, 001, 101 and 111, 000. Further, please refer to Table 1 below, which records... as well as The corresponding values ​​for different switching modes:

[0137] Table 1 Different switching modes value

[0138]

[0139]

[0140] In this optional embodiment, please refer to Table 2 below, which records... as well as The corresponding values ​​for different switching modes:

[0141] Table 2. Different switching modes value

[0142]

[0143] In Tables 1 and 2, θ represents the initial phase angle of the positive sequence component of the grid voltage.

[0144] As can be seen, in this optional embodiment, by using the set positive and negative sequence separation algorithm (specifically, the Park transformation), the three-phase current and voltage of the control circuit can be transformed into the dq coordinate system, which is beneficial to improving the processing efficiency for all electrical parameters and reducing the data analysis difficulty of the control circuit. It can also eliminate the angle variables in the equations through transformation, making the solution of the equations simpler and reducing the difficulty of data calculation. In addition, when there is a fault in the power system / control circuit, by detecting whether the current or voltage components in the dq coordinate system are abnormal, it is possible to determine whether there is an imbalance or fault in the system, which is beneficial to improving the fault detection speed of the power system / control circuit.

[0145] In another optional embodiment, the set of current values ​​corresponding to each sampling time includes the positive-sequence direct-axis current value corresponding to the positive-sequence direct-axis function, the positive-sequence quadrature-axis current value corresponding to the positive-sequence quadrature-axis function, the negative-sequence direct-axis current corresponding to the negative-sequence direct-axis function, and the negative-sequence quadrature-axis current corresponding to the negative-sequence quadrature-axis function.

[0146] For each set of current values ​​corresponding to each sampling time, based on a pre-constructed value function, combined with preset standard current values ​​and screening requirements, the method of performing difference calculation and numerical screening on each predicted current value in the set of current values ​​to obtain the target value value includes:

[0147] For each sampling time, the positive sequence quadrature-axis current value, negative sequence direct-axis current, negative sequence direct-axis current and negative sequence quadrature-axis current corresponding to the sampling time are input into the pre-constructed value function to obtain the set of value values ​​corresponding to the sampling time. The set of value values ​​corresponding to each sampling time includes multiple value values, and each target value value is associated with a switching mode.

[0148] For each set of value values ​​corresponding to each sampling time, the target value value with the smallest value is selected from the set of value values, and the switching mode associated with the target value value is determined as the target switching mode.

[0149] In this optional embodiment, the calculation formula corresponding to the value function is:

[0150]

[0151] in, This is the first standard value corresponding to the positive sequence component of the current. The second standard value corresponding to the positive sequence component of the current. The third standard value corresponding to the negative sequence component of the current. This is the fourth standard value corresponding to the negative sequence component of the current.

[0152] As can be seen, in this optional embodiment, the predictive setting of the switching mode in the control circuit corresponding to the energy storage converter is realized, which improves the accuracy of the switching mode setting in the control circuit. It can also improve the response accuracy of the energy storage converter to grid faults. At the same time, if there is a grid fault, the prediction and setting of the switching mode can assist in the switching mode switching, thereby improving the power supply stability and reliability of the grid voltage.

[0153] In yet another alternative embodiment, please refer to Figure 5 as well as Figure 6 , Figure 5 This is a schematic diagram of the structure of a control circuit disclosed in an embodiment of the present invention; Figure 6This is a schematic diagram of another model-predictive current sequence control device for energy storage converters disclosed in an embodiment of the present invention.

[0154] In this optional embodiment, such as Figure 5 As shown, the control circuit topology consists of three main parts: a three-phase converter 501, an AC side port 502, and a DC energy storage unit 503. Wherein:

[0155] The three-phase converter 501 includes six IGBT power switching transistors S1, ..., S6 and their anti-parallel diodes; the three-phase converter 501 is used to realize the energy conversion and control between three-phase AC power supply and DC power supply.

[0156] The AC side port 502 includes three RC filter units for filtering the three-phase AC signal transmitted by the three-phase converter 501 to reduce or eliminate harmonic components in the circuit.

[0157] DC energy storage unit 503 is used to store and release electrical energy.

[0158] In this optional embodiment, the output of the DC energy storage unit 503 exhibits voltage source characteristics, so the DC voltage remains constant in steady state.

[0159] In this optional embodiment, such as Figure 6 As shown, the various modules in the diagram form a closed-loop control circuit. The modules corresponding to the prediction function and the value function are the core of the entire control structure. They can predict the current value at the next sampling time based on the current grid information using the prediction function and the value function. Then, by optimizing the value function, the optimal switching action in the next sampling period is obtained, thereby completing the control of the energy storage converter.

[0160] Example 3

[0161] Please see Figure 3 , Figure 3 This is a schematic diagram of a model-predictive current sequence control device for energy storage converters, as disclosed in an embodiment of the present invention. This model-predictive current sequence control device can be applied to the control circuit corresponding to an energy storage converter. Optionally, the model-predictive current sequence control device can be a model-predictive current sequence control terminal, device, system, or server. The server can be a local server, a remote server, or a cloud server (also known as a cloud server). When the server is not a cloud server, it can communicate with the cloud server. This embodiment of the invention does not impose any limitations. Figure 3As shown, the model-based energy storage converter current sequence control device may include a data acquisition module 301, a parameter processing module 302, a prediction processing module 303, a value calculation module 304, and a setting module 305, wherein:

[0162] The data acquisition module 301 is used to perform data acquisition operations on multiple preset electrical parameters in the control circuit according to preset sampling parameters, and obtain parameter acquisition data corresponding to each electrical parameter; the sampling parameters include the sampling period and at least one corresponding sampling time.

[0163] The parameter processing module 302 is used to perform parameter processing on the parameter acquisition data corresponding to all electrical parameters, and obtain the parameter processing results corresponding to the parameter acquisition of all electrical parameters. The parameter processing includes at least the separation of positive and negative order for voltage and current.

[0164] The prediction processing module 303 is used to perform model prediction on the processing results of all parameters according to the preset sequence model, and obtain the model prediction result corresponding to the processing results of all parameters. The energy storage converter includes multiple switching modes. The model prediction result includes a set of current values ​​corresponding to each sampling time and the set of current values ​​corresponding to each sampling time includes the predicted current value corresponding to each switching mode.

[0165] The value calculation module 304 is used to perform data calculation and value filtering on each predicted current value in the current value set corresponding to each sampling time according to the preset data filtering requirements, so as to obtain the target value value that matches the data filtering requirements at that sampling time.

[0166] The setting module 305 is used to set the switching action to be performed in the later sampling period according to the target value value corresponding to each sampling time, so as to set the energy storage converter to perform the above-mentioned switching mode in the later sampling period.

[0167] It is evident that implementation Figure 3The described model-based current sequence control device for energy storage converters features an intelligent acquisition mechanism for electrical parameters in the control circuit corresponding to the energy storage converter. This mechanism processes the acquired parameter data, achieving a positive / negative sequence separation mechanism for current and voltage in the initial stage, thus improving the accuracy of this separation. Furthermore, after completing the positive / negative sequence separation and data preparation, the device performs precise data calculations based on the established sequence model. Specifically, it performs model prediction to obtain the set of current values ​​corresponding to each sampling time at subsequent sampling times. This set of current values ​​corresponding to subsequent sampling times includes values ​​from different switching modes. The corresponding predicted current value enables preliminary prediction and calculation of the switching mode of the control circuit in operation for the next sampling period / sampling time. Then, data calculation and value filtering can be performed on all current value sets to obtain the target value value matching each sampling time. Subsequently, the prediction setting of the switching mode in the subsequent sampling period is performed based on the target value value. This prediction setting of the switching mode in the subsequent sampling period can improve the tracking speed of each sequence current, effectively reduce the dynamic reactive power support current response time of the energy storage converter to grid faults, and provide voltage support to the grid more timely. That is, it is conducive to improving the fault response speed during grid faults and improving the power supply stability and reliability of the energy storage converter to the grid voltage.

[0168] In an optional embodiment, all electrical parameters include voltage and current in the control circuit; the parameter acquisition data corresponding to all electrical parameters includes multiple voltage acquisition data and multiple current acquisition data.

[0169] The parameter processing module 302 performs parameter processing on the parameter acquisition data corresponding to all electrical parameters to obtain the parameter processing results corresponding to the parameter acquisition of all electrical parameters. The specific method is as follows:

[0170] According to the set positive and negative sequence separation algorithm, the positive and negative sequence separation processing is performed on the parameter acquisition data corresponding to all electrical parameters to obtain the target separation data corresponding to each parameter acquisition data. All target separation data include voltage positive sequence separation data, voltage negative sequence separation data, current positive sequence separation data, and current negative sequence separation data.

[0171] All target separation data are identified as parameter acquisition and processing results corresponding to all electrical parameters.

[0172] As can be seen, in this optional embodiment, by using the set positive and negative sequence separation algorithm (specifically, the Park transformation), the three-phase current and voltage of the control circuit can be transformed into the dq coordinate system, making it easier to apply the control algorithm and perform stability analysis, which is beneficial to improving the processing efficiency for all electrical parameters; it can also eliminate the angle variables in the equations through transformation, making the solution of the equations simpler and reducing the difficulty of data calculation; in addition, when there is a fault in the power system / control circuit, by detecting whether the current or voltage components in the dq coordinate system are abnormal, it is possible to determine whether there is an imbalance or fault in the system, which is beneficial to improving the fault detection speed of the power system / control circuit.

[0173] In another optional embodiment, the separation model includes multiple model prediction functions for performing current value prediction. Each model prediction function matches a target separation data type, and the data type of each target separation data type includes any one of positive voltage sequence type, negative voltage sequence type, positive current sequence type, and negative current sequence type. All model prediction functions include positive current sequence functions and negative current sequence functions. The positive current sequence functions include positive sequence direct axis functions and positive sequence quadrature axis functions. The negative current sequence functions include negative sequence direct axis functions and negative sequence quadrature axis functions.

[0174] The prediction processing module 303 performs model prediction on the processing results of all parameters according to the preset sorting model, and obtains the model prediction result corresponding to all parameter processing results. The specific method is as follows:

[0175] Based on the data type of each target separation data, determine the target model prediction function that matches the target separation data;

[0176] For each target separation data, based on the target model prediction function that matches the target separation data, perform data calculations that match the target model prediction function to obtain the target calculation result corresponding to the target separation data;

[0177] The target calculation results corresponding to all target separation data are determined as the model prediction results corresponding to all parameter processing results.

[0178] In this optional embodiment, the calculation formula for the positive-order direct-axis function is as follows:

[0179]

[0180] The calculation formulas for the orthogonal axis functions are as follows:

[0181]

[0182] The formulas for calculating negative-order direct-axis functions are as follows:

[0183]

[0184] The calculation formula for the negative-order intersection axis function is as follows:

[0185]

[0186] Where P corresponds to the positive sequence component, N corresponds to the negative sequence component, R is the equivalent series resistance of the filter inductor, and T s Where L is the sampling period, k is the grid-side filter inductance of the energy storage converter, k is the sampling time corresponding to the sampling period, k+1 is the subsequent sampling time corresponding to the sampling time, and ω is the grid angular frequency. The positive and negative sequence dq components of the DC side current of the energy storage converter in different switching modes; The positive and negative sequence dq components of the DC side voltage of the energy storage converter in different switching modes; The positive and negative sequence dq components of the AC side voltage of the energy storage converter in different switching modes are given. The predicted current value corresponding to the positive and negative sequence dq components of the current at time k+1.

[0187] As can be seen, in this optional embodiment, by using the set positive and negative sequence separation algorithm (specifically, the Park transformation), the three-phase current and voltage of the control circuit can be transformed into the dq coordinate system, which is beneficial to improving the processing efficiency for all electrical parameters and reducing the data analysis difficulty of the control circuit. It can also eliminate the angle variables in the equations through transformation, making the solution of the equations simpler and reducing the difficulty of data calculation. In addition, when there is a fault in the power system / control circuit, by detecting whether the current or voltage components in the dq coordinate system are abnormal, it is possible to determine whether there is an imbalance or fault in the system, which is beneficial to improving the fault detection speed of the power system / control circuit.

[0188] In another optional embodiment, the set of current values ​​corresponding to each sampling time includes the positive-sequence direct-axis current value corresponding to the positive-sequence direct-axis function, the positive-sequence quadrature-axis current value corresponding to the positive-sequence quadrature-axis function, the negative-sequence direct-axis current corresponding to the negative-sequence direct-axis function, and the negative-sequence quadrature-axis current corresponding to the negative-sequence quadrature-axis function.

[0189] For each sampling time corresponding to the set of current values, the value calculation module 304 performs difference calculation and value filtering on each predicted current value in the set of current values ​​according to the pre-constructed value function, combined with the preset standard current value and screening requirements, to obtain the target value value. The specific method is as follows:

[0190] For each sampling time, the positive sequence quadrature-axis current value, negative sequence direct-axis current, negative sequence direct-axis current and negative sequence quadrature-axis current corresponding to the sampling time are input into the pre-constructed value function to obtain the set of value values ​​corresponding to the sampling time. The set of value values ​​corresponding to each sampling time includes multiple value values, and each target value value is associated with a switching mode.

[0191] For each set of value values ​​corresponding to each sampling time, the target value value with the smallest value is selected from the set of value values, and the switching mode associated with the target value value is determined as the target switching mode.

[0192] In this optional embodiment, the calculation formula corresponding to the value function is:

[0193]

[0194] in, This is the first standard value corresponding to the positive sequence component of the current. The second standard value corresponding to the positive sequence component of the current. The third standard value corresponding to the negative sequence component of the current. This is the fourth standard value corresponding to the negative sequence component of the current.

[0195] As can be seen, in this optional embodiment, the predictive setting of the switching mode in the control circuit corresponding to the energy storage converter is realized, which improves the accuracy of the switching mode setting in the control circuit. It can also improve the response accuracy of the energy storage converter to grid faults. At the same time, if there is a grid fault, the prediction and setting of the switching mode can assist in the switching mode switching, thereby improving the power supply stability and reliability of the grid voltage.

[0196] In another optional embodiment, the setting module 305 sets the switching action to be performed in the later sampling period according to the target value value corresponding to each sampling time. The specific method is as follows:

[0197] For each sampling time, determine the subsequent sampling time corresponding to that sampling time in the subsequent sampling period;

[0198] The target switch mode corresponding to the sampling time is associated with and the mode setting is performed with the subsequent sampling time corresponding to the sampling time to obtain the association and mode setting result corresponding to the subsequent sampling time.

[0199] As can be seen, in this optional embodiment, after determining the value value corresponding to each sampling moment in the current sampling period, it can automatically realize the determination of the subsequent sampling moment, the determination of the target switching mode corresponding to the value value, and the association and mode setting of the target switching mode with the subsequent sampling moment. This realizes the predictive setting of the switching mode in the control circuit corresponding to the energy storage converter, improves the setting accuracy of the switching mode in the control circuit, and can also improve the response accuracy of the energy storage converter to grid faults. At the same time, if there is a grid fault, the prediction and setting of the switching mode can assist in the switching mode switching, improving the power supply stability and reliability of the grid voltage.

[0200] Example 4

[0201] Please see Figure 4 , Figure 4 This is a schematic diagram of another model-predictive current sequence control device for energy storage converters disclosed in an embodiment of the present invention. Figure 4 As shown, the model-predictive current sequence control device for energy storage converters may include:

[0202] Memory 401 storing executable program code;

[0203] Processor 402 coupled to memory 401;

[0204] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the model prediction-based energy storage converter current sequence control method described in Embodiment 1 or Embodiment 2 of the present invention.

[0205] Example 5

[0206] This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the model prediction-based energy storage converter current sequence control method described in Embodiment 1 or Embodiment 2 of this invention.

[0207] Example 6

[0208] This invention discloses a computer program product, which includes a non-transient computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the model prediction-based energy storage converter current sequence control method described in Embodiment 1 or Embodiment 2.

[0209] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0210] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0211] Finally, it should be noted that the model-predictive current sequence control method and device for energy storage converters disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A model-predictive current sequence control method for energy storage converters, characterized in that, The method is applied to the control circuit corresponding to the energy storage converter, and the method includes: According to preset sampling parameters, data acquisition operations are performed on multiple preset electrical parameters in the control circuit to obtain parameter acquisition data corresponding to each electrical parameter; the sampling parameters include the sampling period and at least one corresponding sampling time; Perform parameter processing on the parameter acquisition data corresponding to all the electrical parameters to obtain the parameter processing results corresponding to the parameter acquisition of all the electrical parameters. The parameter processing includes at least the separation of positive and negative order for voltage and current. According to the preset sequence model, model prediction is performed on all the parameter processing results to obtain the model prediction result corresponding to all the parameter processing results. The energy storage converter includes multiple switching modes. The model prediction result includes a set of current values ​​corresponding to the subsequent sampling time for each sampling time. The set of current values ​​corresponding to each sampling time includes the predicted current value corresponding to each switching mode. For each set of current values ​​corresponding to each sampling time, according to the preset data filtering requirements, data calculation and numerical filtering are performed on each predicted current value in the set of current values ​​to obtain the target value value that matches the data filtering requirements at that sampling time. Based on the target value value corresponding to each sampling time, a switching action setting is performed in the subsequent sampling period corresponding to the later sampling period to set the switching mode performed by the energy storage converter in the subsequent sampling period; All the electrical parameters include the voltage and current in the control circuit; the parameter acquisition data corresponding to all the electrical parameters includes multiple voltage acquisition data and multiple current acquisition data. The step of performing parameter processing on the parameter acquisition data corresponding to all the electrical parameters to obtain the parameter processing results corresponding to the parameter acquisition of all the electrical parameters includes: According to the set positive and negative sequence separation algorithm, positive and negative sequence separation processing is performed on the parameter acquisition data corresponding to all the electrical parameters to obtain target separation data corresponding to each parameter acquisition data. All the target separation data include voltage positive sequence separation data, voltage negative sequence separation data, current positive sequence separation data, and current negative sequence separation data. All the target separation data are determined as the parameter processing results corresponding to the parameter acquisition of all the electrical parameters; The separation model includes multiple model prediction functions for performing current value prediction. Each model prediction function matches a type of target separation data. The data type of each target separation data includes any one of positive voltage sequence type, negative voltage sequence type, positive current sequence type, and negative current sequence type. All model prediction functions include positive current sequence functions and negative current sequence functions. The positive current sequence function includes a positive sequence direct axis function and a positive sequence quadrature axis function. The negative current sequence function includes a negative sequence direct axis function and a negative sequence quadrature axis function. The step of performing model prediction on all the parameter processing results according to a preset sorting model to obtain model prediction results corresponding to all the parameter processing results includes: Based on the data type corresponding to each of the target separation data, determine the target model prediction function that matches the target separation data; For each target separation data, according to the target model prediction function matched by the target separation data, data calculation matching the target model prediction function is performed on the target separation data to obtain the target calculation result corresponding to the target separation data; The target calculation results corresponding to all the target separation data are determined as the model prediction results corresponding to all the parameter processing results.

2. The model-predictive-based current sequence control method for energy storage converters according to claim 1, characterized in that, The calculation formula for the positive-sequence direct-axis function is as follows: The calculation formula for the orthogonal axis function is as follows: The calculation formula for the negative-order direct-axis function is as follows: The calculation formula for the negative-order intersection axis function is as follows: Where P corresponds to the positive-sequence component, N corresponds to the negative-sequence component, and R is the equivalent series resistance of the filter inductor. The sampling period is... L ω is the grid-side filter inductance of the energy storage converter, k is the sampling time corresponding to the sampling period, k+1 is the subsequent sampling time corresponding to the sampling time; ω is the grid angular frequency. The positive and negative sequence dq components of the DC-side current of the energy storage converter in different switching modes; The positive and negative sequence dq components of the DC-side voltage of the energy storage converter in different switching modes; The positive and negative sequence dq components of the AC side voltage of the energy storage converter in different switching modes are given. The predicted current value is the positive and negative sequence dq component of the current at time k+1.

3. The model-predictive-based current sequence control method for energy storage converters according to claim 1, characterized in that, The set of current values ​​corresponding to each sampling time includes the positive sequence direct axis current value corresponding to the positive sequence direct axis function, the positive sequence quadrature axis current value corresponding to the positive sequence quadrature axis function, the negative sequence direct axis current corresponding to the negative sequence direct axis function, and the negative sequence quadrature axis current corresponding to the negative sequence quadrature axis function. For each set of current values ​​corresponding to each sampling time, based on a pre-constructed value function, combined with preset standard current values ​​and screening requirements, difference calculation and numerical screening are performed on each predicted current value in the set of current values ​​to obtain the target value value, including: For each sampling time, the positive sequence quadrature axis current value, the negative sequence direct axis current, the negative sequence direct axis current, and the negative sequence quadrature axis current corresponding to the sampling time are input into a pre-constructed value function to obtain a set of value values ​​corresponding to the sampling time. The set of value values ​​corresponding to each sampling time includes multiple value values, and each target value value is associated with a switching mode. For each set of value values ​​corresponding to each sampling time, the target value value with the smallest value is selected from the set of value values, and the switching mode associated with the target value value is determined as the target switching mode.

4. The model-predictive-based current sequence control method for energy storage converters according to claim 3, characterized in that, The calculation formula corresponding to the value function is: in, This is the first standard value corresponding to the positive sequence component of the current. The second standard value corresponding to the positive sequence component of the current. The third standard value corresponding to the negative sequence component of the current. This is the fourth standard value corresponding to the negative sequence component of the current.

5. The model-predictive-based current sequence control method for energy storage converters according to any one of claims 1-4, characterized in that, The step of setting a switching action for the later sampling period corresponding to the target value value corresponding to each sampling time includes: For each sampling time, the subsequent sampling time corresponding to that sampling time is determined in the subsequent sampling period; The target switch mode corresponding to the sampling time is associated with and the mode setting is performed with the subsequent sampling time corresponding to the sampling time to obtain the association and mode setting result corresponding to the subsequent sampling time.

6. A model-predictive current sequence control device for an energy storage converter, characterized in that, The device is applied in the control circuit corresponding to the energy storage converter, and the device is used to execute the model prediction-based energy storage converter current sequence control method as described in any one of claims 1-5, and the device includes: The data acquisition module is used to perform data acquisition operations on multiple preset electrical parameters in the control circuit according to preset sampling parameters, and obtain parameter acquisition data corresponding to each electrical parameter; the sampling parameters include the sampling period and at least one corresponding sampling time; The parameter processing module is used to perform parameter processing on the parameter acquisition data corresponding to all the electrical parameters to obtain the parameter processing results corresponding to the parameter acquisition of all the electrical parameters. The parameter processing includes at least the separation of positive and negative order for voltage and current. The prediction processing module is used to perform model prediction on all the parameter processing results according to the preset sequence model, and obtain the model prediction result corresponding to all the parameter processing results. The energy storage converter includes multiple switching modes. The model prediction result includes a set of current values ​​corresponding to the subsequent sampling time for each sampling time. The set of current values ​​corresponding to each sampling time includes the predicted current value corresponding to each switching mode. The value calculation module is used to perform data calculation and value filtering on each predicted current value in the current value set corresponding to each sampling time according to preset data filtering requirements, so as to obtain the target value value that matches the sampling time with the data filtering requirements. The setting module is used to set the switching action for the later sampling period corresponding to the target value value at each sampling time, so as to set the switching mode executed by the energy storage converter in the later sampling period.

7. A model-predictive current sequence control device for an energy storage converter, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the model prediction-based energy storage converter current sequence control method as described in any one of claims 1-5.

8. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the model prediction-based energy storage converter current sequence control method as described in any one of claims 1-5.

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