System control method and device and electronic equipment

Abstract

The invention discloses a system control method and device and electronic equipment, the method is applied to a multi-machine parallel converter, the multi-machine parallel converter comprises at least two converters connected in parallel, and the method comprises the following steps: obtaining the current load power of the multi-machine parallel converter; a target converter unit corresponding to the current load power is determined according to a pre-configured system efficiency model of the multi-machine parallel converter, and at least part of load power corresponding to a system efficiency curve of the system efficiency model is associated with at least one converter unit; and controlling the multi-machine parallel converter to start the target converter unit to work in parallel. According to the embodiment of the invention, the converter units used under different loads are determined through the reasonably configured system efficiency model, so that the scientificity of converter unit control is improved, and the working efficiency of the converter is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronic converters, and particularly to a system control method, device, and electronic device. Background Art

[0002] Currently, in photovoltaic or energy storage systems, multi-machine parallel converters are widely used. In the existing operation control of converters, the potential of the equipment has not been fully exploited. Take the over-sized converter with a three-fold overload capacity as an example. During its operation, due to factors such as bus voltage, grid-side voltage, ambient temperature, and operating temperature, the efficiency curve of the converter will change, resulting in the system operation efficiency not being constant. Currently, the conventional converter control method cannot achieve precise control of the converter, resulting in the system not always being in the optimal operation state, lacking a reasonable control basis, unable to scientifically control the converter, and causing poor overall efficiency of the photovoltaic or energy storage system. Summary of the Invention

[0003] The present invention provides a system control method, device, and electronic device to determine the converter unit used under different loads through a reasonably configured system efficiency model, facilitating the improvement of the scientific nature of converter unit control and the working efficiency of the converter.

[0004] According to one aspect of the present invention, a system control method is provided. The method is applied to a multi-machine parallel converter, which includes at least two parallel converter units. The method includes:

[0005] Obtain the current load power of the multi-machine parallel converter;

[0006] Determine the target converter unit corresponding to the current load power according to the system efficiency model pre-configured in the multi-machine parallel converter, where at least part of the load power corresponding to the system efficiency curve of the system efficiency model is associated with at least one of the converter units;

[0007] Control the multi-machine parallel converter to start the target converter unit to work in parallel.

[0008] According to another aspect of the present invention, a system control device is provided. The device is applied to a multi-machine parallel converter, which includes at least two parallel converter units. The device includes:

[0009] A power acquisition module, configured to obtain the current load power of the multi-machine parallel converter;

[0010] A target determination module, configured to determine a target converter unit corresponding to the current load power according to a pre-configured system efficiency model of the multi-converter parallel type converter, wherein at least part of the load power corresponding to the system efficiency curve of the system efficiency model is associated with at least one of the converter units;

[0011] An operation control module, configured to control the multi-converter parallel type converter to start the target converter unit to operate in parallel.

[0012] According to another aspect of the present invention, there is provided an electronic device, including:

[0013] At least one processor; and

[0014] A memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the system control method according to any embodiment of the present invention.

[0016] In the technical solution of the embodiment of the present invention, by obtaining the current load power of the multi-converter parallel type converter, determining the target converter unit started by the current load power through the system efficiency model configured for the multi-converter parallel type converter, and controlling the target converter unit in the multi-converter parallel type converter to operate in parallel, the embodiment of the present invention determines the target converter in the maximum efficiency state according to the system efficiency model, which can provide a scientific basis for converter control, enhance the accuracy of converter control, enable the multi-converter parallel type converter to continuously be in the maximum efficiency state, and improve the overall working efficiency of the system.

[0017] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0019] Figure 1 is a flowchart of a system control method according to Embodiment 1 of the present invention;

[0020] Figure 2It is a flowchart of another system control method provided according to Embodiment 2 of the present invention;

[0021] Figure 3 It is an exemplary diagram of a curve of system efficiency varying with load and bus voltage provided according to Embodiment 2 of the present invention;

[0022] Figure 4 It is an exemplary diagram of a curve of system efficiency varying with load and grid-side voltage provided according to Embodiment 2 of the present invention;

[0023] Figure 5 It is an exemplary diagram of a curve of system efficiency varying with load and ambient temperature provided according to Embodiment 2 of the present invention;

[0024] Figure 6 It is an exemplary diagram of a curve of system efficiency varying with load and running time provided according to Embodiment 2 of the present invention;

[0025] Figure 7 It is a flowchart of a system control method provided according to Embodiment 3 of the present invention;

[0026] Figure 8 It is an exemplary diagram of a system efficiency curve provided according to Embodiment 4 of the present invention;

[0027] Figure 9 It is an exemplary diagram of overload switching of an overload switching subsystem provided according to Embodiment 4 of the present invention;

[0028] Figure 10 It is a comparative example diagram of a predicted efficiency curve and an actual efficiency curve of a prediction model provided according to Embodiment 4 of the present invention;

[0029] Figure 11 It is a schematic diagram of a curve of converter efficiency varying with load provided according to Embodiment 4 of the present invention;

[0030] Figure 12 It is a schematic diagram of a loss curve when two converters are allocated different powers provided according to Embodiment 4 of the present invention;

[0031] Figure 13 It is a schematic diagram of two converters switching from the maximum efficiency mode to the overload maximum efficiency mode provided according to Embodiment 4 of the present invention;

[0032] Figure 14 It is a schematic diagram of two converters switching from the overload maximum efficiency mode to the maximum efficiency mode provided according to Embodiment 4 of the present invention;

[0033] Figure 15 It is a schematic structural diagram of a system control device provided according to Embodiment 5 of the present invention;

[0034] Figure 16 It is a schematic structural diagram of an electronic device for implementing the system control method of the embodiments of the present invention. Detailed implementation manners

[0035] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0037] In the embodiments of the present invention, a multi-inverter parallel type converter can be a system in which at least two inverter units are connected in parallel to jointly supply power to the power grid or load. The parallel-connected inverter units in the multi-inverter parallel type converter can have the same or different specifications, brands or types. The number of parallel-connected inverter units in the multi-inverter parallel type converter can be unrestricted. For example, the multi-inverter parallel type converter includes 2, 3, 5 parallel-connected inverter units, etc. The multi-inverter parallel type converter can be applied to photovoltaic or energy storage systems. The rated power Pr of the multi-inverter parallel type converter = P1r +... + PNr, where N is the number of parallel-connected inverter units in the multi-inverter parallel type converter, and Pir is the rated power of each inverter unit, i = 1, 2, 3,..., N. When a single inverter is overloaded, its power POL = M * Pr, where M is the overload multiple. The embodiments of the present invention aim to control the operation of the multi-inverter parallel type converter, explore the system efficiency of the multi-inverter parallel type converter, improve the scientificity of regulation, and optimize the overall efficiency of the photovoltaic or energy storage system.

[0038] Embodiment 1

[0039] Figure 1FIG. 0 is a flowchart of a system control method provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of controlling and scheduling a multi-machine parallel converter. This method can be executed by a system control device, which can be implemented in the form of hardware and / or software. The system control device can be configured locally in the multi-machine parallel converter or in the upper computer of the multi-machine parallel converter. The multi-machine parallel converter includes at least two parallel converter units. As Figure 1 shown, the method includes:

[0040] Step 110, obtain the current load power of the multi-machine parallel converter.

[0041] Wherein, the current load power may be the power provided by the output end of the multi-machine parallel converter to the load, and the current load power can be determined by direct measurement, simulation analysis or based on control strategies, etc.

[0042] In the embodiment of the present invention, the load power of the multi-machine parallel converter can be monitored, and the load power data monitored each time can be used as the current load power of the multi-machine parallel converter.

[0043] Step 120, determine the target converter unit corresponding to the current load power according to the system efficiency model pre-configured for the multi-machine parallel converter, wherein at least part of the load power corresponding to the system efficiency curve of the system efficiency model is associated with at least one converter unit.

[0044] Wherein, the system efficiency model can be a control strategy model for controlling the multi-machine parallel converter to achieve the optimal efficiency. The system efficiency model can include the multi-machine parallel converter achieving the optimal efficiency at different load powers and the converter units that enable the multi-machine parallel converter to achieve the optimal efficiency. The system efficiency model can exist in the form of a system efficiency curve, which can represent the situation of the system efficiency of the multi-machine parallel converter changing with the load under different working conditions. There may be some load powers in the system efficiency curve corresponding to the system efficiency model that have their corresponding converter units, that is, starting the associated converter unit to work at this part of the load power can make the multi-machine parallel converter reach the maximum efficiency in the system efficiency curve.

[0045] In an embodiment of the present invention, a system efficiency model of a multi-machine parallel converter can be obtained. It can be understood that different types of multi-machine parallel converters may each have their corresponding system efficiency models, and the corresponding system efficiency model can be found according to the type of the multi-machine parallel converter. The system efficiency model can exist in the form of a configuration file or configuration parameters, and the system efficiency model of the multi-machine parallel converter can be configured locally in the multi-machine parallel converter or in the upper computer of the multi-machine parallel converter. Further, the system efficiency model can exist in the form of a system efficiency curve, and the system efficiency curve can include the mapping relationship between the load power and the system efficiency. The load power in the system efficiency curve can be associated with one or more converter units of the multi-machine parallel converter, and the associated converter units can enable the multi-machine parallel converter to achieve the optimal system efficiency in the system efficiency curve at the corresponding load power.

[0046] Step 130: Control the target converter units of the multi-machine parallel converter to work in parallel.

[0047] Specifically, the target converter units determined for the multi-machine parallel converter can be controlled to work in parallel within the multi-machine parallel converter. Further, the multi-machine parallel converter can also be controlled to switch other converter units to the non-working state. It can be understood that when the target converter units are started to work in parallel, the output power can also be allocated to each target converter unit, and the output power can be determined by the performance of each target converter unit. For example, when the types of the target converter units are the same, the load power can be evenly divided according to the number of target converter units, and the evenly divided load power can be used as the output power of the target converter units.

[0048] In the embodiment of the present invention, by obtaining the current load power of the multi-machine parallel converter, determining the target converter units started by the current load power through the system efficiency model configured for the multi-machine parallel converter, and controlling the target converter units within the multi-machine parallel converter to work in parallel, the embodiment of the present invention determines the target converter units in the maximum efficiency state according to the system efficiency model, which can provide a scientific basis for converter control, enhance the accuracy of converter control, enable the multi-machine parallel converter to continuously be in the maximum efficiency state, and improve the overall working efficiency of the system.

[0049] Embodiment 2

[0050] Figure 2 is a flowchart of another system control method provided according to Embodiment 2 of the present invention. The embodiment of the present invention is a specific implementation based on the above embodiment, and describes the construction process of the system efficiency model. See Figure 2 The method provided in the embodiment of the present invention specifically includes the following steps:

[0051] Step 210: Obtain the model training data set of the multi-machine parallel converter, and train the initial neural network model as the single-machine efficiency prediction model according to the model training data set. Among them, the initial neural network model includes at least one of the following: multi-layer perceptron neural network model, long short-term memory network model, convolutional neural network model, and wavelet neural network model.

[0052] Among them, the model training data set can be the data set for training the single-machine efficiency prediction model of the multi-machine parallel converter. The model training data set can include at least one set of data, and this data includes the historical working state and the corresponding historical efficiency value of the historical working state. This data can be obtained by testing the above multi-machine parallel converter. The single-machine efficiency prediction model can be a neural network model used to predict the system efficiency of each converter in parallel in the multi-machine parallel converter. The single-machine efficiency prediction model can be the initial neural network model trained through the model training data set. The input of the single-machine efficiency prediction model can be the working state data of each converter. For example, the working state data can include load power, bus-side voltage, grid-side voltage, running time, and ambient temperature, etc. This initial neural network model can specifically be one or more of the multi-layer perceptron neural network model, long short-term memory network model, convolutional neural network model, and wavelet neural network model.

[0053] In the embodiment of the present invention, for the multi-machine parallel converter, obtain its corresponding model training data set, and the model training data set can be called to train the initial neural network model. The trained initial neural network model can be used as the single-machine efficiency prediction model. This single-machine efficiency model can be used to predict the system efficiency of each converter in parallel in the multi-machine parallel converter. The method of training the initial neural network model as the single-machine efficiency prediction model through the model training data set can include but is not limited to the gradient descent method, Adam optimization algorithm, momentum method, etc.

[0054] Step 220: Call the single-machine efficiency prediction model to respectively determine the single-machine efficiency of each converter unit in the multi-machine parallel converter under different load powers, and generate the single-machine efficiency curve corresponding to each converter unit according to the single-machine efficiency of each converter unit under different load powers.

[0055] Among them, the single-machine efficiency can be the efficiency value corresponding to each converter unit under different load powers. The single-machine efficiency curve can be the corresponding relationship between the load power and the efficiency value of each converter unit. The single-machine efficiency curve can specifically be the function mapping relationship between the responsible power and the efficiency value.

[0056] In an embodiment of the present invention, a single - machine efficiency prediction model can be invoked to predict the single - machine efficiency of each parallel converter unit in a multi - machine parallel converter. The working - state data such as the load, bus - side voltage, grid - side voltage, operating time, and ambient temperature of each converter unit can be respectively input into the single - machine efficiency prediction model. The single - machine efficiency prediction model can process the corresponding working - state data to obtain the single - machine efficiency. For each converter unit, a single - machine efficiency curve can be constructed based on the correspondence between the single - machine efficiency and the load. In this single - machine efficiency curve, the load can be the horizontal axis and the efficiency can be the vertical axis.

[0057] Step 230: Determine the parallel efficiency curve when starting different numbers of converter units according to each single - machine efficiency curve.

[0058] Specifically, after obtaining the single - machine efficiency curve of each converter unit in the multi - machine parallel converter, the parallel efficiency curve when multiple converter units operate in parallel can be determined. The parallel efficiency curve can be determined by the single - machine efficiency curves of the multiple converter units participating in parallel operation. The number of converter units participating in parallel operation can be 2, 3, …, N, where N can be the number of all converter units in the multi - machine parallel converter. That is, the parallel efficiency curve in the parallel operation state of multiple converters can be determined through the single - machine efficiency curves of each converter.

[0059] In an embodiment of the present invention, different numbers of converter units can be selected to determine the parallel efficiency curve, and the single - machine efficiency curves of the selected converter units can be used to determine the parallel efficiency curve. This determination process can be realized by means such as numerical fitting method, optimization objective - function method, modeling and simulation analysis method, aggregation modeling method, etc. Taking the data fitting method as an example, according to the efficiency values of each converter unit at each load power, the parallel efficiency curve of the parallel converter when starting the corresponding multiple converter units can be obtained by using the numerical fitting method; or, taking the optimization objective - function method as an example, an optimization objective function can be set through the single - machine efficiency curves of each converter unit, and the optimal efficiency of the parallel converter under each power load can be determined through this optimization objective function. Then, based on the optimal efficiency and the corresponding power load, the parallel efficiency curve can be constructed.

[0060] Further, for the parallel efficiency curve of the multi - machine parallel converter, different parallel efficiency curves can be determined according to the number of selected starting converter units and the types of converter units. For example, the multi - machine parallel converter may include converter unit A, converter unit B, and converter unit C. There can be 3 parallel efficiency curves corresponding to 2 converter units. For example, the parallel efficiency curves in the cases of including starting converter unit A and converter unit B, starting converter unit B and converter unit C, and starting converter unit A and converter unit C, etc.

[0061] Step 240: Determine the single - machine efficiency curves at different load powers and the partial efficiency curves with the highest efficiency within each parallel efficiency curve, and obtain the converter identifiers of the converter units to which the partial efficiency curves belong.

[0062] Among them, the partial efficiency curve can be a part of the efficiency curve with an efficiency value higher than that of other efficiency curves in the single - machine efficiency curve or the parallel efficiency curve for the same load power. This partial efficiency curve can be a part of the single - machine efficiency curve or a part of the parallel efficiency curve. Other efficiency curves can be the single - machine efficiency curves or parallel efficiency curves other than the efficiency curve currently having the partial efficiency curve. The converter identifier can indicate the information of the converter units in the multi - machine parallel converter. Different converter units can have different converter identifiers. The converter identifier can include the number, name, or type name of the converter unit, etc.

[0063] In the embodiments of the present invention, the single - machine efficiency curves and the parallel efficiency curves can be aligned according to the load power. The partial efficiency curves with the highest efficiency can be selected from all the single - machine efficiency curves and the parallel efficiency curves. It can be determined which single - machine efficiency curve or parallel efficiency curve the each partial efficiency curve belongs to. The converter unit corresponding to the above - mentioned single - machine efficiency curve or parallel efficiency curve can be obtained, and the converter identifier of this converter unit can be used as the converter identifier of the partial efficiency curve.

[0064] Step 250: Combine the partial efficiency curves into a system efficiency curve, and associate the converter identifiers with different load power ranges of the system efficiency curve according to the corresponding partial efficiency curves.

[0065] Specifically, the efficiency curves of each part can be sequentially spliced according to their corresponding load powers, and the spliced efficiency curve can be used as the system efficiency curve. The system efficiency curve can be the efficiency curve composed of the highest efficiency that a multi-parallel converter can achieve. For each part of the efficiency curve, the corresponding converter identifier can be associated with the corresponding load power in the system efficiency curve according to the load power corresponding to the part efficiency curve. For example, if the load power of the part efficiency curve is from 0.1 to 0.25, the converter identifier of this part efficiency curve can be associated with the load power range from 0.1 to 0.25 in the system efficiency curve. This association can include physical association or logical association. For example, the converter identifier and a load power range of the system efficiency curve can be stored in a configuration file, and this configuration file can be used as the association relationship between the converter identifier and the load power range of the system efficiency curve. Or, the converter identifier can also be directly marked in the corresponding load power range in the system efficiency curve.

[0066] In the embodiment of the present invention, when the part efficiency curve belongs to the single-machine efficiency curve, one converter identifier corresponding to the single-machine efficiency curve can be associated with the corresponding load power range of the system efficiency curve. When the part efficiency curve belongs to the parallel efficiency curve, multiple converter identifiers corresponding to the parallel efficiency curve can be associated with the corresponding load power range of the system efficiency curve.

[0067] Step 260: Save the system efficiency curve and the converter identifiers associated with different load power ranges as a system efficiency model.

[0068] In the embodiment of the present invention, the system efficiency curve and the converter identifiers associated with different load power ranges within the system efficiency curve can be saved as a system efficiency model. This saving method can include saving to the local of the multi-parallel converter or the upper computer or the cloud server of the multi-parallel converter.

[0069] Step 270: Obtain the current load power of the multi-parallel converter.

[0070] Step 280: Determine the target converter corresponding to the current load power according to the system efficiency model pre-configured in the multi-parallel converter, where at least part of the load power corresponding to the system efficiency curve of the system efficiency model is associated with at least one converter.

[0071] Step 290: Control the multi-parallel converter to start the target converter to work in parallel.

[0072] In an embodiment of the present invention, by obtaining a corresponding model training data set for a multi - machine parallel converter, training an initial neural network model into a single - machine efficiency prediction model according to the model training data, calling the single - machine efficiency model to predict the single - machine efficiency of the converter units in the multi - machine parallel converter under different load powers, constructing a single - machine efficiency curve based on the single - machine efficiency of each converter unit, determining a parallel efficiency curve when different numbers of converter units are started based on the single - machine efficiency curves of each converter unit, selecting the partial efficiency curves with the highest efficiency at each load power within each single - machine efficiency curve and the parallel efficiency curve, extracting the converter identifiers of the converter units corresponding to each partial efficiency curve, merging each partial efficiency curve into a system efficiency curve, associating the converter identifier of each partial efficiency curve with the load power range corresponding to the system efficiency curve, saving the system efficiency curve as a system efficiency model, obtaining the current load power of the multi - machine parallel converter, determining the target converter unit corresponding to the current load power through the system efficiency model, and controlling the target converter unit of the multi - machine parallel converter to operate in parallel. The embodiment of the present invention can determine the target converter unit to be controlled according to the system efficiency model corresponding to the maximum efficiency curve, provide a scientific basis for the control of the converter unit, enhance the accuracy of the converter control, enable the multi - machine parallel converter to continuously be in the maximum efficiency state, and improve the overall working efficiency of the system.

[0073] Further, on the basis of the above - mentioned embodiment of the invention, obtaining the model training data set of the multi - machine parallel converter includes:

[0074] Collecting the historical working states of each converter unit in the multi - machine parallel converter and the historical efficiency values corresponding to the historical working states, where the historical working states at least include: historical load power, historical bus - side voltage, historical grid - side voltage, historical operation time, and historical ambient temperature; performing linear interpolation on the historical working states and the historical efficiency values to obtain interpolated working states and interpolated prediction efficiency values corresponding to the interpolated working states; using the historical working states, historical efficiency values, interpolated working states, and interpolated prediction efficiency values as the model training data set.

[0075] In the embodiment of the present invention, the historical working states and historical efficiency values of each converter unit in the multi - machine parallel converter can be obtained. The historical working states and historical efficiency values can be obtained through experiments. Linear interpolation can be performed on the historical working states and historical efficiency values to obtain interpolated prediction efficiency values corresponding to other interpolated working states. A model training data set can be constructed based on the historical working states, historical efficiency values, interpolated working states, and interpolated prediction efficiency values. Further, the model training data set can also include the converter identifiers of the converter units corresponding to the historical working states, historical efficiency values, interpolated working states, and interpolated prediction efficiency values respectively.

[0076] Exemplarily, since the amount of data measured through experiments is small and limited, it is difficult to comprehensively and accurately reflect the efficiency changes of the system under different conditions. Therefore, the present invention first expands the data through linear interpolation. When each factor changes alone, it can be Figure 3 , Figure 4 , Figure 5 , Figure 6 fitted into a corresponding function of efficiency varying with load. Therefore, a linear superposition method can be used to construct a comprehensive model. Let the bus voltage be V bus , the temperature be T, the grid-side voltage be V grid , and the operating time be t. Let y bus be the efficiency function considering only the change in bus voltage. Similarly, for the changes in grid-side voltage, temperature, and operating time, their efficiency functions y grid , y T , y t can also be obtained. Through the method of linear interpolation, the efficiency under different bus voltages, different grid-side voltages, different ambient temperatures, and different operating times can be calculated, and its formula can be respectively expressed as follows:

[0077]

[0078]

[0079]

[0080]

[0081] Then the comprehensive efficiency y can be approximately expressed as:

[0082]

[0083] where y0 is the efficiency curve under the reference conditions, and the reference conditions can be the conditions suitable for the normal operation of the converter unit, and the reference conditions can include the corresponding relationship between the load and efficiency corresponding to the reference bus voltage, reference grid-side voltage, reference ambient temperature, and reference operating time. Through the above method, more representative data points can be generated based on the existing small amount of data, greatly enriching the data samples and improving the diversity and integrity of the data.

[0084] Embodiment III

[0085] Figure 7 is a flowchart of a system control method provided according to Embodiment III of the present invention. Embodiment III of the present invention is a specific implementation based on the above-mentioned embodiments of the invention. The target converter unit to be controlled in the multi-machine parallel converter is determined through the current load power and the system efficiency model. AsFigure 7 As shown, the method includes:

[0086] Step 310: Obtain the current load power of the multi-machine parallel converter.

[0087] Step 320: Obtain the system efficiency curve corresponding to the system efficiency model configured for the multi-machine parallel converter.

[0088] In the embodiments of the present invention, a system efficiency model can be configured for each type of multi-machine parallel converter respectively. Different types of multi-machine parallel converters can be configured with different system efficiency models. The system efficiency model corresponding to the multi-machine parallel converter can be obtained. The system efficiency model can be configured in the upper computer or server for managing the multi-machine parallel converter, or can be configured locally in the multi-machine parallel converter. Taking the case where the system efficiency model is saved to the upper computer or server as an example, the system efficiency curve associated with the type or identification number of the multi-machine parallel converter can be found in the upper computer or server according to the type or identification number of the multi-machine parallel converter.

[0089] Step 330: Determine the target load power range to which the current load power belongs within the system efficiency curve.

[0090] In the embodiments of the present invention, the target load power range to which the value belongs can be determined in the system efficiency curve according to the value of the current load power. The target load power range can have its associated converter identification.

[0091] Step 340: Use the converter unit corresponding to the converter identification associated with the target load power range within the system efficiency curve as the target converter unit.

[0092] In the embodiments of the present invention, the converter identification associated with the target load power range of the system efficiency curve can be found, and the converter unit corresponding to the converter identification can be used as the target converter unit. According to different association methods between the load power range and the converter identification within the system efficiency curve, the converter identification associated with the target load power range can be found through different search methods. Taking the case where the load power range and the converter identification are configured in the configuration file of the system efficiency curve as an example, the converter identification associated with the target load power range can be found in the configuration file. Taking the case where the converter identification is marked in the load power range of the system efficiency curve as an example, the converter identification marked in the system efficiency curve according to the target load power can be directly obtained.

[0093] Step 350: Search for the target output power of each target converter unit in the system efficiency model according to the target load power range of each target converter unit.

[0094] In an embodiment of the present invention, the system efficiency model may further include the output power of the converter unit associated with each load power range. This output power can be determined by means such as simulation analysis, data fitting, or actual operation data analysis. When determining the target converter unit, the target output power corresponding to each target converter unit can also be searched for in the system efficiency model.

[0095] Step 360: Control each of the target converter units in the multi-machine parallel converter to operate in parallel according to its respective target output power, and switch other converter units in the multi-machine parallel converter to the low-power operation mode.

[0096] In an embodiment of the present invention, it is possible to control the target converter units in the multi-machine parallel converter to switch to the working mode and operate according to the determined target output power respectively, while other converter units in the multi-machine parallel converter can be switched to the low-power operation mode. In the low-power operation mode, some non-critical components inside each converter unit, such as some auxiliary power circuits and some unnecessary control chips, enter the sleep state, which can significantly reduce the energy consumption of the converter unit, while retaining the key wake-up and fast start functions to ensure that the change in power demand can be responded to within a short time.

[0097] In an embodiment of the present invention, by obtaining the current load power of the multi-machine parallel converter, extracting the system efficiency curve configured by the multi-machine parallel converter, searching for the corresponding target load power range according to the current load power in the system efficiency curve, determining the converter identifier corresponding to the target load power range in the system efficiency model, taking the converter unit corresponding to the converter identifier pair as the target converter unit, and controlling the target converter units in the multi-machine parallel converter to operate in parallel. The embodiment of the present invention determines the target converter unit in the state of maximum efficiency based on the system efficiency model, which can provide a scientific basis for converter control, enhance the accuracy of converter control, make the multi-machine parallel converter continuously in the state of maximum efficiency, and improve the overall working efficiency of the system.

[0098] Further, on the basis of the above embodiments of the invention, at least one of the following is also included:

[0099] When it is detected that the grid voltage of the multi-machine parallel converter rises or drops by more than the voltage threshold, control all converter units of the multi-machine parallel converter to operate in parallel;

[0100] When it is determined that the current load power is greater than the load threshold, control all converter units of the multi-machine parallel converter to operate in parallel.

[0101] Among them, the voltage threshold can be the voltage critical value for judging the grid-side voltage jump. When the voltage increase or decrease amount of the grid-side voltage exceeds the voltage threshold, all the converter units in the multi-machine parallel converter can be started. The load threshold can be the critical value for judging that the load exceeds the working load of some converter units, and the load threshold can be configured according to experience.

[0102] In the embodiment of the present invention, the grid voltage and / or the current load kilometer of the multi-machine parallel transformer can be continuously monitored. If it is determined that the grid voltage rises or drops beyond the voltage threshold, or if it is judged that the current load power is greater than the load threshold, all the converter units of the multi-machine parallel converter can be controlled to work in parallel, and all the converter units of the multi-machine parallel converter can be switched to the working mode so that each converter unit can provide output power.

[0103] Further, on the basis of the above-mentioned embodiment of the invention, when it is detected that the grid-side voltage of the multi-machine parallel converter rises or drops beyond the voltage threshold, the overload current value corresponding to the grid-side voltage is determined, and the overload multiple of the multi-machine parallel converter for the overload current value is obtained; each converter unit is controlled to switch to the normal working mode and output power according to the overload multiple.

[0104] In the embodiment of the present invention, when the grid-side voltage rises or drops beyond the threshold, overload control needs to be performed on the multi-machine parallel converter. Since when the grid-side voltage rises or drops, the converter needs to provide more reactive current to support the power grid. The grid-side voltage change value of the voltage rise or drop can be determined. The current change value of all converter units can be determined based on the rated current of each converter unit and the grid-side voltage change value. This current change value can be recorded as the overload current value. The rated current value of each converter unit can be obtained. The ratio of the overload current value to the sum of the rated current values is used as the overload multiple. Each converter can be controlled to switch to the normal working mode and output power according to this overload multiple.

[0105] In some embodiments of the invention, it further includes: determining that the working parameter of the first converter unit exceeds the threshold, then reducing the first output power of the first converter unit and increasing the second output power of other converter units in the multi-machine parallel converter.

[0106] In an embodiment of the present invention, a converter unit that operates under the control of a multi - machine parallel converter can be monitored to determine the operating parameters of each converter unit. The operating parameters may include parameters such as output power, temperature, and current. When it is determined that the operating parameters exceed the threshold, the converter unit can be marked as the first converter unit. The first output power of the first converter unit can be reduced, and the second output power of other converter units in the multi - machine parallel converter can be increased. It can be understood that the reduction amount of the first output power can be the same as the total increase amount of all the second output powers.

[0107] Embodiment 4

[0108] An embodiment of the present invention provides a control system that can implement the system control method provided in any embodiment of the present invention. Among them, the system may include the following subsystems:

[0109] 1. Converter operating mode diversification setting subsystem: Three different operating modes are set for each converter unit, namely normal operation mode, low - power operation mode, and standby mode. In the normal operation mode, all components of the converter unit operate at full power to meet the power demand of the load. In the low - power operation mode, some non - critical components inside the converter unit, such as some auxiliary power circuits and some unnecessary control chips, enter the sleep state, significantly reducing energy consumption while retaining key wake - up and fast - start functions to ensure that power demand changes can be responded to within a short time. In the standby mode, the converter unit only maintains the most basic wake - up and monitoring functions, consuming almost no electrical energy and further reducing the system energy consumption.

[0110] 2. Intelligent decision - making and control subsystem: The central control unit is built - in with advanced optimization algorithms and detailed and accurate efficiency models. Generally, the converter efficiency curve shows a trend of first rising and then falling as the load power increases. However, the efficiency of the system operation is not constant, and its efficiency curve is significantly affected by many factors. To ensure that the system can always make the optimal operation decision based on the accurate efficiency curve, it is necessary to measure and record the ambient temperature, bus voltage, grid - side voltage, and operation duration under the actual working conditions of the converter to predict the efficiency of the converter under different loads. Figure 3 A curve showing the variation of efficiency with load and bus voltage is given. Under the same external conditions and the same output power, the converter efficiency decreases as the bus voltage increases. Figure 4 A curve showing the variation of efficiency with load and grid - side voltage is given. Under the same external conditions and the same output power, the converter efficiency decreases as the grid - side voltage decreases. Figure 5The curve of efficiency varying with load and temperature is given. Under the same external conditions and the same output power, the converter efficiency decreases with the increase of temperature. In addition, as the operation time of the converter increases, each component will age and its various parameters will change. For example, as the operation time increases, the on-resistance and turn-on time of the controllable semiconductor devices in the converter will increase, and the parasitic resistance of the capacitor will increase, and the efficiency will further decrease. Its efficiency curve is as Figure 6 shown.

[0111] Since the amount of data measured through experiments is small and limited, it is difficult to comprehensively and accurately reflect the efficiency change of the system under different conditions. The present invention first expands the data by means of linear interpolation. Since when each factor changes alone, the above Figure 3 , Figure 4 , Figure 5 , Figure 6 can be fitted into the corresponding function of efficiency varying with load. Therefore, a comprehensive model can be constructed by means of linear superposition. Let the bus voltage be V bus , the temperature be T, the grid-side voltage be V grid , and the operation time be t. Let y bus be the efficiency function when only considering the change of bus voltage. Similarly, for the changes of grid-side voltage, temperature and operation time, the efficiency functions y grid , y T , y t can also be obtained. Through the method of linear interpolation, the efficiency under different bus voltages, different grid-side voltages, different ambient temperatures and different operation times can be calculated, and its formula can be respectively expressed by the following formula:

[0112]

[0113]

[0114]

[0115]

[0116] Then the comprehensive efficiency y can be approximately expressed as:

[0117]

[0118] where y0 is the efficiency curve under the reference conditions. Through the above method, more representative data points can be generated on the basis of the existing small amount of data, greatly enriching the data samples and improving the diversity and integrity of the data.

[0119] After completing data augmentation, the efficiency curve under different conditions is predicted by means of a neural network. The data on the known system efficiency varying with bus voltage, temperature, grid-side voltage, and operating time are collected to form a data set. Each record in this data set can be (V bus ,V grid ,T,t,x,y), where V bus is the bus voltage, V grid is the grid-side voltage, T is the ambient temperature, t is the operating time, x is the load power, and y is the corresponding efficiency. And all the data are normalized so that their range is between [0,1]. A multi-layer perceptron neural network is used as the prediction model. The input layer is set with 5 nodes, corresponding to the normalized bus voltage, grid-side voltage, temperature, operating time, and load power respectively. Its hidden layer is set with 2 - 3 layers, and the number of neurons in each layer is determined according to experiments and optimization. The output layer is set with 1 node, which is used to output the predicted value of the normalized efficiency. And the mean square error is used as the loss function to measure the difference between the model prediction value and the true value. Its formula is:

[0120]

[0121] where n is the number of samples, y i is the true value, .

[0122] Through the above method, the curve of the efficiency of a converter varying with the load output power during single operation is obtained. Its overall efficiency shows a trend of first rising and then falling with the load power. Therefore, by selecting the number of parallel-connected converters and distributing the output power of the parallel-connected converters, the overall system can always operate at the maximum efficiency with the change of the load. Taking the example from single operation to three parallel operations, Figure 8 the efficiency curves are respectively shown. From Figure 8It can be seen that as the system power increases, there is a maximum efficiency point for a single converter to three converters at a certain specific power. By deeply analyzing the efficiency characteristics of a single converter under different power outputs, as well as the mutual influence and cooperation mechanism when multiple converters are operating in parallel, it can be understood that the converter in the embodiment of the present application may include an inverter. When the central control unit receives the load power data, it will quickly compare and analyze it with the pre-set key power points P1 (the intersection point of the single-operation efficiency curve and the two-parallel-operation efficiency curve), P2 (the intersection point of the two-operation efficiency curve and the three-parallel-operation efficiency curve), etc., and based on the efficiency model, calculate the optimal converter operation plan under the current load condition through an optimization algorithm. Specifically, when the load power is lower than point P1, the central control unit will accurately select a converter with the best performance from all converters, send an instruction to it to enter the normal operation mode, and at the same time send an instruction to other converters to switch to the low-power operation mode; when the load power is between point P1 and point P2, the central control unit will, through precise calculation, select two converters to work together and reasonably distribute the power output between them to ensure the optimal system efficiency; when the load power is greater than point P2, the central control unit will instruct the three converters to run simultaneously, and dynamically and accurately adjust the output power of each converter according to the real-time characteristics of the load and the actual state of each converter to ensure that the system is always in the highest efficiency operation state. When more than three converters are in parallel, it also has the same characteristics. The system will, according to the current power state, appropriately select the number of parallel converters and distribute the power output of the converters so that the system efficiency is always in the maximum state.

[0123] When different models are in parallel, although the efficiency curves of different models are inconsistent, the overall trend is the same. At this time, the system will first conduct a detailed analysis of the efficiency curves of each model and establish a comprehensive efficiency model. After receiving the load power data, the system will, based on this model, accurately calculate the power share that each converter of different models should bear. For example, when the load power is lower than a certain specific value, the system will select a converter with the highest efficiency in this power segment from all converters of different models to put into operation, and the remaining converters will enter the low-power mode. When the load power is in different intervals, the system will, in accordance with the principle of maximizing efficiency, select an appropriate number of converters of different models to work together and dynamically and accurately distribute the power according to their respective efficiency curves, so as to ensure that the system can always achieve the maximum efficiency when different models are in parallel.

[0124] Overload switching subsystem: Refer to Figure 9, a dedicated overload detection module is set in the central control unit. On the one hand, it detects the degree of grid voltage rise or fall. When the grid suddenly undergoes low-voltage ride-through or high-voltage ride-through, the converter is switched to the overload state to support the grid by increasing the output reactive current. On the other hand, by analyzing the load power data collected by the power sensor in real time, an overload threshold is set. Once overload is detected, the central control unit immediately suspends the ongoing calculations and analyses related to efficiency optimization, and quickly sends an "overload" instruction to all converters in the low-power operation mode, standby mode, and normal power output mode. The converters that receive the "overload" instruction quickly switch from the low-power or standby mode to the normal operation mode, no longer restricted by the current power distribution scheme, and output power with maximum capacity to meet the overload demand. In the overload operation mode, the central control unit continuously monitors the operating states of each converter, including parameters such as output power, temperature, and current. If it is found that the temperature of a certain converter is too high and approaching the safety threshold, or the current is too large and may cause equipment damage, the control unit immediately performs a power reduction operation on this converter, and at the same time instructs other converters to appropriately increase the output power to maintain the overall overload capacity and ensure the safe and stable operation of the system under overload conditions. When the high and low voltage ride-through state ends or the load power decreases and remains lower than the overload threshold for a certain period of time, the central control unit determines that the overload situation has ended and instructs each converter to gradually return to the normal efficiency optimization operation mode.

[0125] Furthermore, in the embodiment of the present invention, predictions are made on the curve of the efficiency of a single converter varying with the load. Figure 10 The predicted efficiency curve and the measured efficiency curve under specific working conditions are respectively shown. Among them, the solid line is the predicted efficiency curve determined by using the prediction model provided by the present invention, and the dashed line is the measured efficiency curve. It can be clearly seen from this that the predicted efficiency curve is extremely close to the actually measured efficiency curve, which strongly verifies the effectiveness of the prediction model provided by the embodiment of the present invention.

[0126] After successfully obtaining the efficiency curve of each converter under this working condition, the number of converters connected in parallel can be flexibly selected according to the current power demand, and the output power of each converter can be accurately regulated, so as to maximize the system efficiency. Specifically, in this embodiment, the rated power of the converter is 200 kW, and there are two converters connected in parallel. Since the operating time and other parameters of these two converters are different, according to the predicted efficiency curve method described in the present invention, the predicted efficiency curve is as Figure 11 shown. Assuming that the current demand power is 180 kW, the loss magnitudes of the two converters when they are allocated different powers can be obtained according to their efficiency curves, as Figure 12As shown, the abscissa represents the power output of the first converter, with the unit of kilowatt. Through the precise calculation of the method of the present invention, when the power output of one converter is 95.4 kW and the power output of the other is 84.6 kW, the efficiency of the entire system can reach the maximum value.

[0127] When the converter faces an overload situation, the system can respond quickly. In this embodiment, there are also two converters with a rated power of 200 kW in parallel. However, the current required power increases from 180 kW to 380 kW. At this time, to meet the power demand, each of the two converters is allocated a power of 190 kW. The converter quickly switches from the mode of pursuing maximum efficiency output to the maximum power output mode, ensuring that the system can still operate stably under overload conditions, meet the actual power demand, and automatically return to the maximum efficiency output mode after the overload situation ends. Figure 13 and Figure 14 respectively show the waveforms when the system switches from the maximum efficiency mode to the maximum power output mode under overload conditions and from the overload maximum power output mode to the maximum efficiency output mode. From Figure 13 and Figure 14 it can be seen that the system can operate stably and respond quickly.

[0128] Embodiment Five

[0129] Figure 15 is a schematic structural diagram of a system control device provided according to Embodiment Five of the present invention. This device is applied to a multi-machine parallel converter. The multi-machine parallel converter includes at least two parallel converter units. As Figure 15 shown, this device includes:

[0130] A power acquisition module 410, configured to acquire the current load power of the multi-machine parallel converter.

[0131] A target determination module 420, configured to determine the target converter unit corresponding to the current load power according to the system efficiency model pre-configured for the multi-machine parallel converter, where at least part of the load power corresponding to the system efficiency curve of the system efficiency model is associated with at least one converter unit.

[0132] A working control module 430, configured to start the target converter unit of the multi-machine parallel converter to work in parallel.

[0133] In an embodiment of the present invention, a current load power of a multi - machine parallel converter is obtained through a power acquisition module. A target determination module determines a target converter unit started for the current load power through a system efficiency model configured for the multi - machine parallel converter. A working control module controls the target converter unit in the multi - machine parallel converter to work in parallel. The embodiment of the present invention determines a target converter in a maximum - efficiency state based on the system efficiency model, which can provide a scientific basis for converter control, enhance the accuracy of converter control, enable the multi - machine parallel converter to continuously be in a maximum - efficiency state, and improve the overall working efficiency of the system.

[0134] Based on the above - mentioned embodiment of the invention, it further includes: a model configuration module, and this model configuration module includes:

[0135] A prediction model unit, which is used to obtain a model training data set of the multi - machine parallel converter, and train an initial neural network model as a single - machine efficiency prediction model according to the model training data set. Among them, the initial neural network model includes at least one of the following: a multi - layer perceptron neural network model, a long - short - term memory network model, a convolutional neural network model, and a wavelet neural network model.

[0136] A single - machine efficiency unit, which is used to call the single - machine efficiency prediction model to respectively determine the single - machine efficiency of each converter unit in the multi - machine parallel converter under different load powers, and generate a single - machine efficiency curve corresponding to each converter unit according to the single - machine efficiency of each converter unit under different load powers.

[0137] A parallel efficiency unit, which is used to determine a parallel efficiency curve when starting different numbers of converter units according to each single - machine efficiency curve.

[0138] A curve selection unit, which is used to determine the partial efficiency curves with the highest efficiency in each single - machine efficiency curve and each parallel efficiency curve under different load powers, and obtain the converter identifiers of the converters to which each partial efficiency curve belongs.

[0139] A system efficiency unit, which is used to merge each partial efficiency curve into a system efficiency curve, associate each converter identifier with different load power ranges of the system efficiency curve according to the corresponding partial efficiency curve; save the system efficiency curve and the converter identifiers associated with different load power ranges as a system efficiency model.

[0140] Based on the above - mentioned embodiment of the invention, obtaining the model training data set of the multi - machine parallel converter in the prediction model unit includes:

[0141] Collect the historical working states of each converter unit in the multi-machine parallel-connected converter and the historical efficiency values corresponding to the historical working states, where the historical working states at least include: historical load power, historical bus-side voltage, historical grid-side voltage, historical operating time, and historical ambient temperature;

[0142] Perform linear interpolation on the historical working states and historical efficiency values to obtain the interpolated working states and the interpolated predicted efficiency values corresponding to the interpolated working states;

[0143] Use the historical working states, historical efficiency values, interpolated working states, and interpolated predicted efficiency values as the model training data set.

[0144] Based on the above-mentioned invention embodiments, the target determination module 420 includes:

[0145] A curve loading unit for obtaining the system efficiency curve corresponding to the system efficiency model configured for the multi-machine parallel-connected converter.

[0146] A load range unit for determining the target load power range to which the current load power belongs within the system efficiency curve.

[0147] A target search unit for using the converter corresponding to the converter identifier associated with the target load power range within the system efficiency curve as the target converter unit.

[0148] Based on the above-mentioned invention embodiments, the system efficiency model further includes the output power of the converter units associated with different load power ranges. The working control module 430 is specifically configured to: search for the target output power of each target converter unit within the system efficiency model according to the target load power range of each target converter unit; control each target converter unit in the multi-machine parallel-connected converter to work in parallel according to its respective target output power, and switch other converter units in the multi-machine parallel-connected converter to the low-power operation mode.

[0149] In some invention embodiments, an abnormal handling module is further included, which is used for at least one of the following: when it is detected that the grid-side voltage of the multi-machine parallel-connected converter rises or drops beyond the voltage threshold, controlling all converter units of the multi-machine parallel-connected converter to work in parallel; when it is determined that the current load power is greater than the load threshold, controlling all converter units of the multi-machine parallel-connected converter to work in parallel.

[0150] In some invention embodiments, controlling all converter units of the multi-machine parallel-connected converter to work in parallel specifically includes: when it is detected that the grid-side voltage of the multi-machine parallel-connected converter rises or drops beyond the voltage threshold, determining the overload current value corresponding to the grid-side voltage, and obtaining the overload multiple of the multi-machine parallel-connected converter for the overload current value; controlling each converter unit to switch to the normal working mode and output power according to the overload multiple.

[0151] Based on the above embodiments of the invention, it further includes a power adjustment module, which is used to determine that the operating parameters of the first converter unit exceed the threshold, then reduce the first output power of the first converter unit, and increase the second output power of other converter units in the multi-machine parallel converter.

[0152] The system control device provided by the embodiments of the present invention can execute the system control method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0153] Embodiment Six

[0154] Figure 16 It is a schematic structural diagram of an electronic device for implementing the system control method of the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0155] As Figure 16 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0156] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0157] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the system control method.

[0158] In some embodiments, the system control method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the system control method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the system control method by any other suitable means (e.g., by means of firmware).

[0159] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0160] The computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0161] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0162] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0163] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0164] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0165] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0166] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A system control method, characterized in that: Applied to a multi-machine parallel converter, the multi-machine parallel converter includes at least two parallel converters, and the method includes: Obtaining the current load power of the multi-machine parallel converter; Determine the target converter unit corresponding to the current load power according to the pre-configured system efficiency model of the multi-machine parallel converter, wherein at least part of the load power corresponding to the system efficiency curve of the system efficiency model is associated with at least one of the converter units; The multi-machine parallel converter is controlled to start the target converter units to work in parallel.

2. The method according to claim 1, characterized in that: Also includes: Acquire a model training data set of the multi-machine parallel converter, and train an initial neural network model as a single-machine efficiency prediction model according to the model training data set, wherein the initial neural network model includes at least one of the following: a multi-layer perceptron neural network model, a long short-term memory network model, a convolutional neural network model, and a wavelet neural network model; Calling the single-machine efficiency prediction model to respectively determine the single-machine efficiency of each converter unit in the multi-machine parallel converter under different load powers, and generating a single-machine efficiency curve corresponding to each converter unit according to the single-machine efficiency of each converter unit under different load powers; Determining a parallel efficiency curve when starting different numbers of the converter units according to each of the single-machine efficiency curves; Determine each of the single-machine efficiency curves and a partial efficiency curve with the highest efficiency in each of the parallel efficiency curves under different load powers, and obtain a converter identifier of the converter to which each of the partial efficiency curves belongs; Combining the partial efficiency curves into a system efficiency curve, and associating the converter identifiers with different load power ranges of the system efficiency curve according to the corresponding partial efficiency curve; The system efficiency curve and the identifiers of the converters associated with the different load power ranges are saved as the system efficiency model.

3. The method according to claim 1 or 2, characterized in that: The step of obtaining a model training data set for the multi-machine parallel converter includes: Collecting the historical working status of each converter unit in the multi-machine parallel converter and the historical efficiency value corresponding to the historical working status, wherein the historical working status at least includes: historical load power, historical bus side voltage, historical grid side voltage, historical operating time and historical ambient temperature; Performing linear interpolation on the historical working state and the historical efficiency value to obtain an interpolated working state and an interpolated predicted efficiency value corresponding to the interpolated working state; The historical working state, the historical efficiency value, the interpolated working state and the interpolated predicted efficiency value are used as the model training data set.

4. The method according to claim 1 or 2, characterized in that: The step of determining the target converter unit corresponding to the current load power according to the pre-configured system efficiency model of the multi-machine parallel converter includes: Acquire the system efficiency curve corresponding to the system efficiency model configured for the multi-machine parallel converter; Determine a target load efficiency range to which the current load power belongs within the system efficiency curve; The converter corresponding to the converter identifier associated with the target load efficiency range in the system efficiency curve is used as the target converter unit.

5. The method according to claim 4, characterized in that: The system efficiency model further includes output powers of converter units associated with different load power ranges, and controlling the multi-machine parallel converter to start the target converter units to work in parallel includes: Searching for a target output power of each of the target converter units in the system efficiency model according to a target load power range of each of the target converter units; The target converter units in the multi-machine parallel converter are controlled to operate in parallel according to their respective target output powers, and other converter units in the multi-machine parallel converter are switched to a low power consumption operation mode.

6. The method according to claim 1, characterized in that: Also includes at least one of the following: When it is detected that the grid-side voltage of the multi-machine parallel converter rises or drops beyond a voltage threshold, all the converter units of the multi-machine parallel converter are controlled to operate in parallel; When it is determined that the current load power is greater than a load threshold, all the converter units of the multi-machine parallel converter are controlled to operate in parallel.

7. The method according to claim 6, characterized in that: The controlling all the converter units of the multi-machine parallel converter to operate in parallel comprises: When it is detected that the grid-side voltage of the multi-machine parallel converter rises or drops beyond a voltage threshold, an overload current value corresponding to the grid-side voltage is determined, and an overload multiple of the multi-machine parallel converter for the overload current value is obtained; Control each of the converter units to switch to a normal operating mode and output power according to the overload multiple.

8. The method according to claim 6 or 7, characterized in that: Also includes: If it is determined that the operating parameter of the first converter unit exceeds a threshold, the first output power of the first converter unit is reduced, and the second output power of other converter units in the multi-machine parallel converter is increased.

9. A system control device, characterized in that: Applicable to a multi-machine parallel converter, the multi-machine parallel converter includes at least two parallel converter units, and the device includes: A power acquisition module, used to acquire the current load power of the multi-machine parallel converter; a target determination module, configured to determine a target converter unit corresponding to the current load power according to a preconfigured system efficiency model of the multi-machine parallel converter, wherein at least part of the load power corresponding to a system efficiency curve of the system efficiency model is associated with at least one of the converter units; The working control module is used to control the multi-machine parallel converter to start the target converter units to work in parallel.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the system control method according to any one of claims 1 to 8.

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