Power control method, model training method and system of inverter

Abstract

The embodiment of the invention provides a power control method of an inverter and a model training method and system. The method is applied to a controller of an anti-countercurrent system, the anti-countercurrent system comprises an inverter set, and the method comprises the following steps: acquiring a first output total power sequence of the anti-countercurrent system; wherein the first output total power sequence comprises output total power of the anti-countercurrent system at each moment in a preset time period; inputting the first output total power sequence into a preset intelligent prediction model for processing to obtain a proportion parameter value, a differential parameter value and an integral parameter value; and controlling the power of the inverters in the inverter set according to the first output total power sequence, the proportion parameter value, the differential parameter value and the integral parameter value. The method is used for accurately controlling the output power of the inverter so as to efficiently and accurately prevent the distributed photovoltaic power generation system from countercurrent.

Description

Technical Field

[0001] The present application relates to the technical field of inverters, and in particular to a power control method, a model training method, and a system for an inverter. Background Art

[0002] In a distributed photovoltaic power generation system, when the DC power output by photovoltaic panels is converted to AC power by an inverter, if the local load's real-time power consumption is lower than the system's power generation, excess power will be fed back to the grid through the user-side distribution network, resulting in a reverse flow. To prevent this, precise control of the inverter's output power is required.

[0003] In the related art, the output power of each inverter is corrected by evenly distributing the reverse power value. However, due to the differences in the operating parameters and environmental conditions of each inverter, this strategy will cause some inverters to have power over-adjustment due to excessive adjustment. At the same time, other inverters still have the risk of reverse flow due to insufficient adjustment, making it difficult for the overall system to effectively suppress reverse flow.

[0004] Therefore, there is an urgent need for a method that can accurately control the output power of the inverter to efficiently and accurately prevent the reverse flow phenomenon in the distributed photovoltaic power generation system. Summary of the Invention

[0005] The embodiments of the present application provide a power control method, a model training method, and a system for an inverter, so as to achieve the effect of efficiently and accurately preventing backflow in a distributed photovoltaic power generation system.

[0006] In a first aspect, an embodiment of the present application provides a power control method for an inverter, the method being applied to a controller of a backflow prevention system, the backflow prevention system including a set of inverters, the method comprising:

[0007] Obtaining a first total output power sequence of the anti-backflow system; wherein the first total output power sequence includes the total output power of the anti-backflow system at each moment in a preset time period;

[0008] Inputting the first total output power sequence into a preset intelligent prediction model for processing to obtain a proportional parameter value, a differential parameter value, and an integral parameter value; wherein the proportional parameter value is used to correct an error in the total output power of the anti-backflow system, the differential parameter value is used to correct an accumulated error in the total output power of the first total output power sequence within a preset time period, and the integral parameter value is used to predict an error in the total output power of the anti-backflow system;

[0009] The powers of the inverters in the inverter set are controlled according to the first output total power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value.

[0010] In one possible implementation, the first output total power sequence is input into a preset intelligent prediction model for processing to obtain a proportional parameter value, a differential parameter value, and an integral parameter value, including:

[0011] Determining a power difference sequence based on the first total output power sequence and a preset power threshold; wherein the power difference sequence includes the power difference value of the backflow prevention system at each moment in the preset time period; the power difference value represents the power value that needs to be corrected by the inverter;

[0012] The power difference sequence is processed based on a preset intelligent prediction model to obtain a proportional parameter value, a differential parameter value, and an integral parameter value.

[0013] In one possible implementation, the power difference sequence is processed based on a preset intelligent prediction model to obtain a proportional parameter value, a differential parameter value, and an integral parameter value, including:

[0014] Processing the power difference sequence based on a preset intelligent prediction model to obtain a first power difference vector; wherein the first power difference vector represents a trend in the change of the importance of the power difference values ​​in the power difference sequence;

[0015] Processing the power difference sequence and the first power difference vector according to a preset intelligent prediction model to obtain a second power difference vector; wherein the second power difference vector represents a feature of power difference values ​​in the power difference sequence whose importance is greater than a preset threshold;

[0016] The second power difference vector is processed based on a preset intelligent prediction model to obtain a proportional parameter value, a differential parameter value, and an integral parameter value.

[0017] In one possible implementation, processing the power difference sequence based on a preset intelligent prediction model to obtain a first power difference vector includes:

[0018] Processing the power difference sequence based on a preset intelligent prediction model to obtain a third power difference vector; wherein the third power difference vector represents the importance of the power difference values ​​in the power difference sequence;

[0019] Based on a preset intelligent prediction model, the power difference sequence and the third power difference vector are processed to obtain a fourth power difference vector; wherein the fourth power difference vector represents a power difference value in the power difference sequence whose importance is greater than a preset threshold;

[0020] According to a preset intelligent prediction model, the power difference sequence, the third power difference vector, and the fourth power difference vector are processed to obtain a first power difference vector.

[0021] In one possible implementation, processing the power difference sequence and the first power difference vector according to a preset intelligent prediction model to obtain the second power difference vector includes:

[0022] Processing the power difference sequence and the first power difference vector according to a preset intelligent prediction model to obtain an attention weight; wherein the attention weight represents the importance of the first power difference vector;

[0023] The first power difference vector and the attention weight are weighted and summed according to a preset intelligent prediction model to obtain a second power difference vector.

[0024] In one possible implementation, controlling the power of inverters in the inverter set according to the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value includes:

[0025] Determining a power correction parameter based on the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value; wherein the power correction parameter represents the power percentage of the inverters in the inverter set that needs to be corrected;

[0026] The current output power of the inverters in the inverter set is obtained; and the power values ​​of the inverters in the inverter set are controlled according to the current output power and the power correction parameter.

[0027] In one possible implementation, the backflow prevention system further includes an auxiliary power supply set; and obtaining a first total output power sequence of the backflow prevention system includes:

[0028] Obtaining a second total output power sequence of the inverter set and a third total output power sequence of the auxiliary power supply set; wherein the second total output power sequence includes the total output power of the inverter set at each moment in a preset time period, and the third total output power sequence includes the total output power of the auxiliary power supply set at each moment in the preset time period;

[0029] A first total output power sequence is determined according to the second total output power sequence and the third total output power sequence.

[0030] In a second aspect, an embodiment of the present application provides a method for training a preset intelligent prediction model for a power control method of an inverter, wherein the power control method of the inverter is such as the method in the first aspect and / or various possible embodiments of the first aspect; the training method of the preset intelligent prediction model is applied to a controller of a backflow prevention system, wherein the backflow prevention system includes an auxiliary power supply set, and the training method of the preset intelligent prediction model includes:

[0031] Obtaining a total output power sequence set of the anti-backflow system, the number of connected auxiliary power supplies in the auxiliary power supply set, and the load type of the anti-backflow system; wherein the total output power sequence set includes at least one total output power sequence, and the total output power sequence includes the total output power of the anti-backflow system at each moment in a preset time period;

[0032] The total power sequences in the total power sequence set are concatenated with the corresponding number of auxiliary power supplies connected and the load type to obtain a data set to be processed;

[0033] The data set to be processed is input into the initial model for training to obtain the preset intelligent prediction model.

[0034] In a third aspect, an embodiment of the present application provides a power control device for an inverter, the device being applied to a controller of a backflow prevention system, the backflow prevention system including an inverter set, the device comprising:

[0035] An acquisition module is used to acquire a first total output power sequence of the anti-backflow system; wherein the first total output power sequence includes the total output power of the anti-backflow system at each moment in a preset time period;

[0036] a processing module, configured to input the first total output power sequence into a preset intelligent prediction model for processing to obtain a proportional parameter value, a differential parameter value, and an integral parameter value; wherein the proportional parameter value is used to correct an error in the total output power of the anti-backflow system, the differential parameter value is used to correct an accumulated error in the total output power of the first total output power sequence within a preset time period, and the integral parameter value is used to predict an error in the total output power of the anti-backflow system;

[0037] The control module is configured to control the power of the inverters in the inverter set according to the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value.

[0038] In a possible implementation, the processing module includes:

[0039] A first processing module is configured to determine a power difference sequence based on the first total output power sequence and a preset power threshold; wherein the power difference sequence includes a power difference value of the backflow prevention system at each moment in a preset time period; and the power difference value represents a power value that needs to be corrected by the inverter;

[0040] The second processing module is used to process the power difference sequence based on a preset intelligent prediction model to obtain a proportional parameter value, a differential parameter value, and an integral parameter value.

[0041] In a possible implementation, the second processing module includes:

[0042] A third processing module is configured to process the power difference sequence based on a preset intelligent prediction model to obtain a first power difference vector; wherein the first power difference vector represents a trend in the change of the importance of the power difference values ​​in the power difference sequence;

[0043] a fourth processing module, configured to process the power difference sequence and the first power difference vector according to a preset intelligent prediction model to obtain a second power difference vector; wherein the second power difference vector represents a feature of power difference values ​​in the power difference sequence whose importance is greater than a preset threshold;

[0044] The fifth processing module is used to process the second power difference vector based on a preset intelligent prediction model to obtain a proportional parameter value, a differential parameter value, and an integral parameter value.

[0045] In a possible implementation, the third processing module includes:

[0046] Processing the power difference sequence based on a preset intelligent prediction model to obtain a third power difference vector; wherein the third power difference vector represents the importance of the power difference values ​​in the power difference sequence;

[0047] Based on a preset intelligent prediction model, the power difference sequence and the third power difference vector are processed to obtain a fourth power difference vector; wherein the fourth power difference vector represents a power difference value in the power difference sequence whose importance is greater than a preset threshold;

[0048] According to a preset intelligent prediction model, the power difference sequence, the third power difference vector, and the fourth power difference vector are processed to obtain a first power difference vector.

[0049] In a possible implementation, the fourth processing module includes:

[0050] Processing the power difference sequence and the first power difference vector according to a preset intelligent prediction model to obtain an attention weight; wherein the attention weight represents the importance of the first power difference vector;

[0051] The first power difference vector and the attention weight are weighted and summed according to a preset intelligent prediction model to obtain a second power difference vector.

[0052] In a possible implementation, the control module includes:

[0053] Determining a power correction parameter based on the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value; wherein the power correction parameter represents the power percentage of the inverters in the inverter set that needs to be corrected;

[0054] The current output power of the inverters in the inverter set is obtained; and the power values ​​of the inverters in the inverter set are controlled according to the current output power and the power correction parameter.

[0055] In a possible implementation, the backflow prevention system further includes an auxiliary power supply set; an acquisition module including:

[0056] Obtaining a second total output power sequence of the inverter set and a third total output power sequence of the auxiliary power supply set; wherein the second total output power sequence includes the total output power of the inverter set at each moment in a preset time period, and the third total output power sequence includes the total output power of the auxiliary power supply set at each moment in the preset time period;

[0057] A first total output power sequence is determined according to the second total output power sequence and the third total output power sequence.

[0058] In a fourth aspect, an embodiment of the present application provides a training device for a preset intelligent prediction model for power control of an inverter, the device being applied to a controller of an anti-backflow system, the anti-backflow system including an auxiliary power supply set, the device comprising:

[0059] An acquisition module is configured to acquire a total output power sequence set of the anti-backflow system, the number of connected auxiliary power supplies in the auxiliary power supply set, and the load type of the anti-backflow system; wherein the total output power sequence set includes at least one total output power sequence, and the total output power sequence includes the total output power of the anti-backflow system at each moment in a preset time period;

[0060] A splicing module is used to splice the total power sequences in the total power sequence set with the corresponding number of auxiliary power supply connections and load types to obtain a data set to be processed;

[0061] The training module is used to input the data set to be processed into the initial model for training to obtain a preset intelligent prediction model;

[0062] Among them, the preset intelligent prediction model is the preset intelligent prediction model in the first aspect and / or various possible implementations of the first aspect above.

[0063] In a fifth aspect, an embodiment of the present application provides a backflow prevention system, the backflow prevention system including an inverter set, an auxiliary power supply set, a smart meter, a data acquisition device, and a controller; wherein the inverter set includes at least one inverter, and the auxiliary power supply set includes at least one auxiliary power supply;

[0064] One end of the auxiliary power supply is connected to one end of the inverter through the load, and the other end of the inverter is connected to the grid system through the smart meter;

[0065] The other end of the auxiliary power supply is connected to the other end of the inverter through the data acquisition device, and the other end of the data acquisition device is connected to the smart meter; the controller is connected to the data acquisition device;

[0066] The controller is used to execute the above first aspect and / or various possible implementations of the first aspect.

[0067] In a sixth aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0068] Memory stores computer-executable instructions;

[0069] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0070] In the seventh aspect, an embodiment of the present application provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0071] In an eighth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0072] The embodiments of the present application provide a power control method, a model training method and a system for an inverter, which realizes precise power management of the anti-backflow system by constructing a dynamic PID parameter control mechanism driven by time series data. Specifically, the method first obtains the first output total power sequence of the anti-backflow system within a preset time period. The sequence is essentially a set of power fluctuation characteristics containing time dimension information. After inputting this sequence into the preset intelligent prediction model, the model deeply mines the power time series characteristics through the long short-term memory network, and dynamically generates three sets of parameter values ​​of proportion, differential and integral. According to the first output total power sequence and the PID parameter value, the inverter power in the inverter set is precisely controlled; precise and dynamic control of the inverter power is achieved, which effectively solves the problems of inaccurate power control and untimely response in traditional methods, and significantly improves the working efficiency and stability of the inverter. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0074] Figure 1 A schematic diagram of a power control method for an inverter provided in an embodiment of the present application Figure 1 ;

[0075] Figure 2 A schematic diagram of a power control method for an inverter provided in an embodiment of the present application Figure 2 ;

[0076] Figure 3 A schematic flow chart of step S202 in a power control method for an inverter provided in an embodiment of the present application;

[0077] Figure 4 A flowchart of a method for training a preset intelligent prediction model for power control of an inverter provided in an embodiment of the present application;

[0078] Figure 5 A schematic structural diagram of a power control device for an inverter provided in an embodiment of the present application;

[0079] Figure 6 A schematic diagram of the structure of a training device for a preset intelligent prediction model for inverter power control provided in an embodiment of the present application;

[0080] Figure 7 A schematic structural diagram of an anti-backflow system provided in an embodiment of the present application;

[0081] Figure 8 This is a schematic diagram of the structure of the electronic device provided in this application example.

[0082] Explanation of reference numerals: 70 - anti-backflow system, 701 - inverter assembly, 702 - auxiliary power supply assembly, 703 - smart meter, 704 - data acquisition equipment, 705 - controller, 706 - load, 707 - power grid.

[0083] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0084] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0085] In a distributed photovoltaic power generation system, PV panels convert solar energy into DC power, which is then converted to AC by an inverter and preferentially supplied to local loads. When there's a dynamic imbalance between system power generation and local load demand, and if PV power generation consistently exceeds load demand, excess power will be transferred back to the grid through the user-side distribution network, creating a reverse flow phenomenon. This phenomenon can not only cause power quality issues such as grid voltage fluctuations and frequency deviations, but can also force the system to operate at a reduced capacity due to grid access regulations, significantly reducing the utilization and economic efficiency of PV power generation.

[0086] To suppress reverse flow, existing control strategies commonly use a strategy that evenly distributes the reverse flow power across all inverters. This strategy detects the total reverse flow power in the system and allocates the adjustment amount based on the number of inverters or their rated power ratio. However, this strategy has significant limitations: due to differences in operating parameters (such as maximum power point accuracy and conversion efficiency curves) and environmental conditions (such as light intensity, module temperature, and partial shading) among inverters in distributed photovoltaic systems, evenly distributing the adjustment amount cannot adapt to the individual characteristics of each inverter. For example, some inverters, due to high efficiency or favorable environmental conditions, may experience excessive load reduction when receiving the same adjustment amount, causing their power output to fall below the actual adjustable range, resulting in power overregulation. Meanwhile, other inverters, due to low efficiency or disadvantageous environmental conditions, may not receive enough adjustment to offset the reverse flow, resulting in the possibility of continued backfeeding. This uneven adjustment makes it difficult to accurately suppress reverse flow across the entire system and may even lead to secondary problems such as increased equipment wear and shortened lifespan due to frequent overregulation of some inverters.

[0087] Therefore, the present application provides a power control method, model training method and system for an inverter, which can solve the above problems.

[0088] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0089] Figure 1 A schematic diagram of a power control method for an inverter provided in an embodiment of the present application Figure 1 ,like Figure 1 As shown, the method is applied to a controller of an anti-backflow system, the anti-backflow system includes an inverter set, and the method includes:

[0090] S101. Obtain a first total output power sequence of the backflow prevention system; wherein the first total output power sequence includes the total output power of the backflow prevention system at each moment in a preset time period.

[0091] For example, a backflow prevention system is a power control device used in distributed energy systems (such as photovoltaic power plants and energy storage systems). Its core function is to monitor the power flow between the grid and the load in real time, preventing the reverse flow of power generated by the inverter into the public grid (i.e., "backflow"), thereby preventing any impact on grid stability. This system includes but is not limited to inverter clusters, controllers, data acquisition equipment, and smart meters.

[0092] For example, an inverter set refers to multiple inverter devices connected in parallel or series in a reverse flow prevention system. Each inverter is responsible for converting direct current (such as electricity generated by photovoltaic panels) into alternating current (AC) for grid-connected output.

[0093] For example, the preset period refers to a pre-set time range, such as 5 minutes, 1 hour, or 24 hours, used for periodic monitoring and analysis of system output power. The length of the preset period needs to be configured based on actual scenario requirements (such as the grid scheduling cycle and load fluctuation characteristics) to ensure real-time and accurate power control.

[0094] For example, the system collects and records the total power output of the backflow prevention system at fixed intervals (e.g., every second or every minute) during a preset period. For example, if the preset period is 10:00-10:10 and the sampling interval is 1 second, the sequence will contain 600 data points, each corresponding to a power value at a specific moment. This sequence is used to subsequently analyze the system's power fluctuation trends and backflow risks.

[0095] For example, in a power control method for a backflow prevention system, data acquisition equipment (such as contactor status detection and current sensors) is first used to obtain the connected status of auxiliary power supplies and count the number of connected auxiliary power supplies. For example, if the system is equipped with three diesel generators and two are detected to be in operation, the number of connected auxiliary power supplies is two. Subsequently, based on the rated power of each auxiliary power supply (e.g., a single diesel generator with a rated power of 100 kW), the total output power of all auxiliary power supplies is calculated by cumulative addition (e.g., 2 × 100 kW = 200 kW). Simultaneously, the output power of all inverters in the backflow prevention system is collected in real time using smart meters. The power values ​​of each inverter are added together to obtain the total output power of the inverter set (e.g., if 10 inverters each output 50 kW, the total power is 500 kW). Finally, the total inverter output power is added to the total output power of the auxiliary power supplies to obtain the total output power of the backflow prevention system. To generate a first total output power sequence, the system repeats the power acquisition and calculation process at fixed intervals within a preset period and stores the results in chronological order. For example, if the preset period is 1 hour and the sampling interval is 1 second, the sequence contains 3600 data points, each of which records the total system output power at that moment, thereby providing data support for subsequent backflow risk analysis and power regulation.

[0096] An auxiliary power supply refers to a backup power source (such as a diesel generator or energy storage battery) connected to the inverter or load in a backflow prevention system. It is used to supplement power when local load demand exceeds the inverter's output capacity. The auxiliary power supply's connection status can be monitored in real time using data acquisition equipment (such as contactor status detection and current sensors). Its rated power is the maximum output capacity specified on the device nameplate.

[0097] S102. Input the first output total power sequence into a preset intelligent prediction model for processing to obtain a proportional parameter value, a differential parameter value, and an integral parameter value; wherein the proportional parameter value is used to correct the error in the output total power of the anti-backflow system, the differential parameter value is used to correct the accumulated error in the output total power of the first output total power sequence within a preset time period, and the integral parameter value is used to predict the error in the output total power of the anti-backflow system.

[0098] For example, the preset intelligent prediction model can be an algorithmic model based on machine learning or deep learning, trained using historical data. It can predict future power trends based on the input power series and output proportional (P), integral (I), and derivative (D) parameter values. This model may use a long short-term memory (LSTM) network, a convolutional neural network (CNN), or a reinforcement learning architecture to capture nonlinear characteristics in time series.

[0099] It is understandable that the P parameter value, the I parameter value, and the D parameter value may be represented by a Proportional Integral Derivative (PID) parameter value.

[0100] For example, the proportional parameter value, the differential parameter value and the integral parameter value are the key parameters of the preset intelligent prediction model output, which correspond to different correction functions respectively. The P parameter value is used to instantly adjust the power error at the current moment, the I parameter value focuses on correcting the rate of change of the power cumulative error, and the D parameter value is a predictive adjustment of future power errors. The three work together to ensure that the corrected power of the anti-backflow system is stable and reliable.

[0101] For example, in a power control method for a reverse flow prevention system, the first output total power sequence is input into a preset intelligent prediction model (such as an LSTM, a temporal convolutional network (TCN), or a reinforcement learning controller). The model analyzes the temporal dependencies and fluctuation patterns in the sequence to infer PID parameter values ​​appropriate for the current system state. These parameters are then passed to a downstream PID controller, which adjusts the inverter output or load distribution strategy of the reverse flow prevention system in real time, ultimately achieving dynamic correction of power errors and system optimization.

[0102] In one possible implementation, a power difference sequence is determined based on the first output total power sequence and a preset power threshold; wherein the power difference sequence includes the power difference value of the backflow prevention system at each moment in a preset time period; and the power difference value represents the power value that needs to be corrected by the inverter.

[0103] In one possible implementation, a pre-configured intelligent prediction model incorporates an LSTM fused with an attention mechanism. Its input layer receives a normalized power difference sequence (e.g., scaling the power difference values ​​to a range of 0-1). The hidden layer uses a gating mechanism to capture long-term dependencies in the time series, and the output layer generates PID parameters. To improve real-time performance, the model employs a sliding window technique, using only the most recent hour's worth of data for inference (the window length can be adjusted based on system response speed). During the training phase, a transfer learning strategy is employed: the model is pre-trained on a public photovoltaic dataset and then fine-tuned using a small amount of field data. During inference, the model updates parameters every second, and a dynamic threshold mechanism is implemented to prevent frequent adjustments: parameter updates are triggered only when the prediction error exceeds 5%. A soft-start strategy is employed for PID parameters, with parameter values ​​gradually taking effect over 10 seconds to prevent sudden changes that could cause system oscillations. Furthermore, the model continuously optimizes through online learning. If a shift in the power sequence distribution (e.g., seasonal changes in sunlight) is detected, the network weights are automatically adjusted to accommodate the new pattern. Ultimately, the PID parameters work in conjunction with the control logic of the backflow prevention system to achieve millisecond-level power regulation.

[0104] S103 : Control the power of the inverters in the inverter set according to the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value.

[0105] Exemplarily, a power difference sequence is determined based on the first output total power sequence and a preset power threshold; wherein the power difference sequence includes the power difference value of the backflow prevention system at each moment in a preset time period; the power difference value represents the power value that needs to be corrected by the inverter.

[0106] For example, the total output power is compared with the PID parameter values ​​generated by a preset intelligent prediction model, and the power adjustment for the inverter set is calculated using a PID control algorithm. The specific process is as follows: the controller multiplies the error between the current total output power and the preset power threshold by the P parameter value to obtain an immediate adjustment. Simultaneously, the controller predicts the future error trend based on the rate of change (differential) of the power sequence and multiplies it by the I parameter value to obtain a trend adjustment. Furthermore, the D parameter value accumulates historical errors to eliminate long-term deviations. After adding these three together to obtain the total adjustment, the controller broadcasts the adjustment command to each inverter to dynamically adjust its output power. For example, if the current power is above the preset power threshold and is on an upward trend, the P parameter value will reduce the output, the D parameter value will suppress the increase in advance, and the I parameter value will correct any accumulated deviations, ultimately stabilizing the system output within the target range.

[0107] The embodiment of the present application provides a power control method for an inverter, which realizes precise power management of the anti-backflow system by constructing a dynamic PID parameter control mechanism driven by time series data. Specifically, the method first obtains the first output total power sequence of the anti-backflow system within a preset time period. The sequence is essentially a set of power fluctuation characteristics containing time dimension information. After inputting this sequence into the preset intelligent prediction model, the model deeply mines the power time series characteristics through the long short-term memory network, and dynamically generates three sets of parameter values ​​of proportion, differential, and integral. According to the first output total power sequence and the PID parameter value, the inverter power in the inverter set is precisely controlled; precise and dynamic control of the inverter power is achieved, which effectively solves the problems of inaccurate power control and untimely response in traditional methods, and significantly improves the working efficiency and stability of the inverter.

[0108] Figure 2 A schematic diagram of a power control method for an inverter provided in an embodiment of the present application Figure 2 ,like Figure 2 As shown, this embodiment Figure 1 Based on the embodiment, a power control method for an inverter is described in detail. The method is applied to a controller of an anti-backflow system, wherein the anti-backflow system includes an inverter set and an auxiliary power supply set. The method includes:

[0109] S201. Obtain a second total output power sequence of the inverter set and a third total output power sequence of the auxiliary power supply set; wherein the second total output power sequence includes the total output power of the inverter set at each moment in a preset time period, and the third total output power sequence includes the total output power of the auxiliary power supply set at each moment in the preset time period; determine a first total output power sequence based on the second total output power sequence and the third total output power sequence.

[0110] Optionally, the anti-inrush system further comprises a smart meter, a data acquisition device, etc.

[0111] Exemplarily, the smart meter is a high-precision electric energy metering device deployed at the output end of the inverter, which supports real-time acquisition of data such as voltage, current, power factor, and transmits the data to the controller through a communication protocol (such as Modbus, DL / T645) for generating the second total output power sequence.

[0112] Exemplarily, the data acquisition device is a hardware device integrating various input / output (I / O) interfaces, which detects the access state of the auxiliary power supply (such as a switching signal) through a digital input (Digital Input, DI) channel, and calculates the real-time output power in combination with the rated power. The DI channel is a digital input interface in the data acquisition device, which is used to receive the switching state signal of the auxiliary power supply (such as the relay contact closure indicating access and the disconnection indicating non-access), so as to determine whether the auxiliary power supply participates in power supply.

[0113] In the power control method of the anti-inrush system, the output power of each inverter in the inverter set is first acquired in real time by the smart meter, and the second total output power sequence is accumulated at a preset time interval (such as every second), which records the total output power of the inverter at each time in the preset time period. At the same time, the data acquisition device detects the access state of the auxiliary power supply (such as whether it is started) through the DI channel, counts the number of auxiliary power supplies accessing the system, and calculates the actual output power of each auxiliary power supply in combination with the rated power of each auxiliary power supply, and then generates the third total output power sequence. Finally, the second total output power sequence and the third total output power sequence are aligned by time and added to obtain the first total output power sequence of the anti-inrush system, which reflects the total output power of all power supplies (inverters + auxiliary power supplies) in the system in the preset time period.

[0114] S202, determining a power difference sequence according to the first total output power sequence and a preset power threshold; wherein the power difference sequence includes a power difference value of the anti-inrush system at each time in the preset time period; the power difference value represents the power value that needs to be corrected by the inverter; based on a preset intelligent prediction model, the power difference sequence is processed to obtain a proportional parameter value, a differential parameter value, and an integral parameter value.

[0115] For example, in the power control method of the anti-backflow system, the first output total power sequence is first compared with the preset power threshold moment by moment to generate a power difference sequence. This sequence reflects the degree of deviation of the output power of the system at each moment within the preset time period from the target threshold. Subsequently, the power difference sequence is input into the preset intelligent prediction model. The model predicts the future error change trend by analyzing the periodicity, trend and randomness characteristics of the sequence, and outputs the PID parameter value. Among them, the P parameter value directly responds to the current error, the I parameter value predicts the future trend based on the error change rate, and the D parameter value eliminates long-term deviations by integrating historical errors. Finally, the PID parameter value dynamically adjusts the output power of the inverter set through a closed-loop control mechanism, so that the total output power of the system is always stable near the preset threshold to avoid backflow.

[0116] Figure 3 This is a flow chart of step S202 in a power control method for an inverter provided in an embodiment of the present application, as shown in FIG. Figure 3 As shown, step S202 includes:

[0117] S2021. Process the power difference sequence based on a preset intelligent prediction model to obtain a first power difference vector; wherein the first power difference vector represents a changing trend of the importance of the power difference values ​​in the power difference sequence.

[0118] Optionally, the preset intelligent prediction model can be an LSTM model or a TCN model, and the present application embodiment does not impose specific restrictions. It can be understood that the preset intelligent prediction model of this embodiment is obtained by training based on a large amount of historical data using the LSTM model as the initial model.

[0119] For example, the LSTM-based preset intelligent prediction model processes the power difference sequence as follows: First, the power difference sequence is input into the LSTM model's input layer, which normalizes the data (e.g., scaling it to a range of 0-1) to eliminate dimensionality differences. The data then enters the LSTM's hidden layer, where a forget gate filters out historical information that needs to be retained (e.g., the power difference trend over the past 10 minutes). The input gate updates the cell state with the current power difference value, generating a new hidden state. The first power difference vector output by the hidden layer captures key features of the sequence (e.g., the power difference's sudden change point and the frequency of periodic fluctuations) and is mapped to PID parameter values ​​through a fully connected layer. For example, if the vector indicates an upward trend in the power difference over a certain period, the LSTM generates a larger differential parameter to preemptively suppress the increase. If the vector indicates a long-term static error, a larger integral parameter is generated to gradually eliminate the deviation. Ultimately, the first power difference vector serves as the model's core output, providing a basis for dynamic adjustments in subsequent PID control.

[0120] In one example, the power difference sequence is processed based on a preset intelligent prediction model to obtain a third power difference vector, where the third power difference vector represents the importance of the power difference values in the power difference sequence. The power difference sequence and the third power difference vector are processed based on the preset intelligent prediction model to obtain a fourth power difference vector, where the fourth power difference vector represents the power difference values whose importance is greater than a preset threshold in the power difference sequence. The power difference sequence, the third power difference vector, and the fourth power difference vector are processed based on the preset intelligent prediction model to obtain the first power difference vector.

[0121] In one example, the power difference sequence is processed based on a preset intelligent prediction model to obtain a third power difference vector, where the third power difference vector represents the importance of the power difference values in the power difference sequence. The power difference sequence is input into an LSTM model, and the historical information (such as the power difference value trend in the past 10 minutes) that needs to be retained is screened through a forgetting gate. At the same time, the cell state is updated by combining the power difference value at the current time through an input gate to generate the third power difference vector.

[0122] In one example, the power difference sequence and the third power difference vector are processed based on a preset intelligent prediction model to obtain a fourth power difference vector, where the fourth power difference vector represents the power difference values whose importance is greater than a preset threshold in the power difference sequence. The third power difference vector and the original power difference sequence are input into another layer of the LSTM, and the weight of each time feature is calculated through a gating mechanism (such as an input gate) to retain only the features whose weight exceeds a preset threshold (such as 0.8) to generate the fourth power difference vector.

[0123] In one example, the third layer of the LSTM takes the original power difference sequence, the third power difference vector, and the fourth power difference vector as input to generate the first power difference vector through an output gate.

[0124] In one example, the power difference sequence and the first power difference vector are processed based on a preset intelligent prediction model to obtain a second power difference vector, where the second power difference vector represents the features whose importance is greater than a preset threshold in the power difference sequence.

[0125] For example, the hidden state outputs of all LSTM time steps (i.e., the set of first power difference vectors) are fed into an attention mechanism model for weighted fusion. The attention mechanism automatically calculates an attention weight for the hidden state at each time step. The weight depends on the impact of the power difference at that moment on the overall system state—the more critical the impact (such as excessive deviation or drastic change), the higher the weight. Finally, all hidden states are weighted and summed according to their weights to generate the final second power difference vector. For example, if the power difference value at a certain moment has a weight of 0.9 (exceeding the threshold of 0.8), the feature at that moment is retained, while features with a weight of 0.5 are suppressed.

[0126] In one example, the power difference sequence and the first power difference vector are processed according to a preset intelligent prediction model to obtain an attention weight; wherein the attention weight represents the importance of the first power difference vector; and the first power difference vector and the attention weight are weighted and summed according to the preset intelligent prediction model to obtain a second power difference vector.

[0127] Exemplarily, the preset intelligent prediction model receives the first power difference vector and the original power difference sequence as input, and uses the attention mechanism to calculate the weights of the features of each dimension. Specifically, the model generates attention weights by matching the similarity between the query vector and the key vector, for example, using dot product attention to calculate the weight distribution. Finally, the first power difference vector is element-wise multiplied by the attention weight and summed to generate a second power difference vector. For example, if the power difference value at a certain moment corresponds to a weight of 0.8 in the first power difference vector, the features at that moment will be significantly enhanced, while the features with a weight of 0.2 will be suppressed. Ultimately, the second power difference vector serves as the core output of the model, providing a more focused feature representation for subsequent PID parameter generation.

[0128] S2023. Process the second power difference vector based on a preset intelligent prediction model to obtain a proportional parameter value, a differential parameter value, and an integral parameter value.

[0129] Exemplarily, the second power difference vector is input into the fully connected layer of the preset intelligent prediction model, and the high-order features are mapped to the initial values ​​of the PID parameters through linear transformation. Subsequently, the activation function performs nonlinear adjustment on the initial values. For example, the Sigmoid function constrains the range of the proportional parameter, and the ReLU function retains the positive change trend of the differential parameter. Finally, the model output layer normalizes the parameters to ensure that the PID parameter values ​​meet the input requirements of the PID controller. For example, if the second power difference vector shows that the power difference feature weight at a certain moment is 0.95, the model will generate a larger P parameter value to quickly respond to the current error, while generating a moderate I parameter value to suppress future fluctuations, and the D parameter value will gradually eliminate long-term deviations. Ultimately, the PID parameter value dynamically adjusts the output power of the inverter set through a closed-loop control mechanism, so that the total output power of the system is stable near the preset threshold.

[0130] S203. Determine a power correction parameter based on the first total output power sequence, a proportional parameter value, a differential parameter value, and an integral parameter value; wherein the power correction parameter represents the power percentage of the inverters in the inverter set that needs to be corrected; obtain the current output power of the inverters in the inverter set; and control the power value of the inverters in the inverter set based on the current output power and the power correction parameter.

[0131] Exemplarily, the first total output power sequence is compared with a preset power threshold moment by moment to generate a power difference sequence. Based on the power difference sequence, the output power of the inverter is adjusted.

[0132] For example,

[0133]

[0134] in, Calculate the value for the integral term; is the I parameter value; is the power difference sequence; It is the time interval within the preset time period.

[0135]

[0136] in, Calculate the value for the differential term; is the D parameter value; is the current power difference value of the power difference sequence; is the power difference value of the previous moment of the power difference sequence; It is the time interval within the preset time period.

[0137]

[0138] in, is the PID adjustment amount; is the P parameter value; is the current power difference value of the power difference sequence; Calculate the value for the integral term; Calculate the value for the derivative term.

[0139] Convert the PID adjustment amount into a power correction parameter in percentage form; adjust the power value of each inverter according to the correction parameter ratio based on the current output power of each inverter, and ensure that the power is within a safe range through clamping operation.

[0140] The embodiment of the present application provides a power control method for an inverter, which obtains the second total output power sequence of the inverter set and the third total output power sequence of the auxiliary power supply set. These two sequences respectively record in detail the total output power of the inverter set and the auxiliary power supply set at each moment in a preset time period, providing comprehensive and accurate data support for subsequent analysis. Then, the first total output power sequence is determined based on the two sequences, and then the power difference sequence is calculated in combination with the preset power threshold. The power difference value intuitively reflects the power value that needs to be corrected by the inverter, and clarifies the direction and amplitude of the power adjustment. Subsequently, the power difference sequence is processed using a preset intelligent prediction model to obtain a proportional parameter value, a differential parameter value, and an integral parameter value; based on these parameters, the power correction parameter is determined to clarify the power percentage that needs to be corrected by the inverter; finally, the current output power of the inverter is obtained, and the final power value is determined in combination with the power correction parameter. Accurate dynamic control of the inverter power is achieved, effectively solving technical problems such as inaccurate power control and response lag in traditional methods, and significantly improving the working performance and stability of the inverter.

[0141] Based on the above embodiments, the embodiments of the present application also provide an operable interface; the operable interface includes a configuration interface, an operation analysis interface, a data collection interface, an efficiency comparison interface, an abnormal alarm interface, and a decision suggestion interface.

[0142] The operational interface includes six core modules: the configuration interface allows users to manually set the auxiliary power supply rated power, inverter parameters, etc., and supports batch import and export and version management; the operation analysis interface displays the total power, inverter status and auxiliary power supply load rate in real time, and intuitively presents the operation trend through dynamic line charts and status indicators, and supports full-screen viewing and data backtracking; the data collection interface continuously collects information such as system total power and equipment status, provides field custom sorting and historical data query functions, and supports filtering by time range or equipment ID; the efficiency comparison interface compares the auxiliary power supply fuel efficiency and inverter power conversion efficiency through a bar chart, generates an optimization suggestion report and supports export; the abnormal alarm interface monitors power fluctuations, control oscillations and other abnormalities in real time, highlights unhandled alarms in red, supports audio and visual prompts and log export; the decision suggestion interface provides suggestions for equipment load optimization, parameter adjustment, etc. based on historical data, marks the adoption rate and optimization effect, and forms a closed-loop management process of "configuration-monitoring-analysis-optimization". All interfaces are quickly navigated through the top tab bar to ensure operational consistency.

[0143] Figure 4 A flow chart of a training method for a preset intelligent prediction model of a power control method for an inverter provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the power control method of the inverter is the method provided in the above method embodiment. The training method of the preset intelligent prediction model is applied to the controller of the anti-backflow system, and the anti-backflow system includes an auxiliary power supply set. The training method of the preset intelligent prediction model includes:

[0144] S401. Obtain the total output power sequence set of the anti-backflow system, the number of auxiliary power supplies connected in the auxiliary power supply set, and the load type of the anti-backflow system; wherein the total output power sequence set includes at least one total output power sequence, and the total output power sequence includes the total output power of the anti-backflow system at each moment in a preset time period.

[0145] For example, a smart meter or power sensor collects the total output power of the backflow prevention system in real time, generating time series data based on preset time periods (e.g., every second or every minute). Simultaneously, the number of online auxiliary power devices is counted, and load types are identified through load characteristic analysis. The collected total power series, number of auxiliary power supplies, and load type information are stored in a database and archived by time or load type to form a historical data set.

[0146] Perform statistical analysis on the total output power sequence (such as mean, peak value, and fluctuation rate), analyze the system power supply reliability based on the number of auxiliary power sources, and optimize the power allocation strategy based on load type data.

[0147] Generate reports including total power sequence characteristics, available auxiliary power quantity, and load type distribution, providing a basis for system operation and maintenance or parameter adjustment.

[0148] S402 : Concatenate the total power sequences in the total power sequence set with the corresponding number of connected auxiliary power supplies and load types to obtain a data set to be processed.

[0149] For example, use a timestamp or sequence ID as an index to ensure that each total output power series corresponds to a unique number of auxiliary power supplies and load type. Add the number of auxiliary power supplies (scalar value) and load type (classification label) as new columns to each row of the total power series. Check the concatenated data for missing values ​​or time misalignments, and correct or mark any abnormal data. Store the validated data in a pre-defined format (such as CSV or JSON) to form a dataset that can be directly used by machine learning models or analytical tools.

[0150] S403: Input the data set to be processed into the initial model for training to obtain a preset intelligent prediction model.

[0151] For example, the processed data set is normalized (eg, power values ​​are scaled to a range of 0-1), missing values ​​are filled (eg, forward filling), and feature engineering is performed (eg, power volatility is extracted as a new feature).

[0152] The initial model can be an LSTM model or an RNN model, and this application does not make any specific restrictions. It can be understood that the embodiment of this application adopts the LSTM model as the initial model. First, the hyperparameters of the LSTM model are randomly initialized.

[0153] The dataset to be processed is divided into a training set and a validation set. The model output is calculated through forward propagation, and backpropagation is used to adjust parameters to minimize the loss function (such as mean squared error) until the model performance converges. The model's generalization ability is evaluated using the test set. If performance falls short, hyperparameters (such as the learning rate and number of network layers) are adjusted and retrained. The trained model parameters and structure are exported to a file (such as H5 or ONNX format) to obtain the preset intelligent prediction model.

[0154] The embodiment of the application provides a training method of a preset intelligent prediction model for power control of an inverter. First, an output total power sequence set of an anti-flow system is acquired. The set covers the output total power of the anti-flow system at each moment within a preset period, thereby providing detailed and real-time basic data for model training. Meanwhile, the number of connected auxiliary power supplies in the auxiliary power supply set and the load type of the anti-flow system are collected. Next, the output total power sequence, the number of connected auxiliary power supplies and the load type are spliced to form a data set to be processed. This step effectively integrates multi-dimensional data and enhances the data representation capability. Finally, the data set to be processed is input into an initial model for training. Through continuous optimization of model parameters, a preset intelligent prediction model is finally obtained. The model can realize intelligent prediction and accurate control of PID parameters, thereby effectively improving the power regulation accuracy and adaptability of the inverter, realizing dynamic and stable control of the inverter power, and meeting the demand of a complex and variable power system.

[0155] Figure 5 The structure diagram of the power control device of the inverter provided by the embodiment of the application is shown as Figure 5 The device is applied to a controller of an anti-flow system. The anti-flow system includes a set of inverters. The power control device 50 of the inverter provided by the embodiment of the application includes:

[0156] The acquisition module 501 is configured to acquire a first output total power sequence of the anti-flow system. The first output total power sequence includes the output total power of the anti-flow system at each moment within a preset period.

[0157] The processing module 502 is configured to input the first output total power sequence into a preset intelligent prediction model for processing, so as to obtain a proportional parameter value, a differential parameter value and an integral parameter value. The proportional parameter value is used to correct the error of the output total power of the anti-flow system. The differential parameter value is used to correct the cumulative error of the output total power of the first output total power sequence within the preset period. The integral parameter value is used to predict the error of the output total power of the anti-flow system.

[0158] The control module 503 is configured to control the power of the inverters in the set of inverters according to the first output total power sequence, the proportional parameter value, the differential parameter value and the integral parameter value.

[0159] In a possible implementation, the processing module 502 includes:

[0160] The first processing module 5021 is configured to determine a power difference sequence according to the first output total power sequence and a preset power threshold. The power difference sequence includes a power difference value of the anti-flow system at each moment within the preset period. The power difference value represents the power value that needs to be corrected by the inverter.

[0161] The second processing module 5022 is used to process the power difference sequence based on a preset intelligent prediction model to obtain a proportional parameter value, a differential parameter value, and an integral parameter value.

[0162] In a possible implementation, the second processing module 5022 includes:

[0163] The third processing module 50221 is configured to process the power difference sequence based on a preset intelligent prediction model to obtain a first power difference vector; wherein the first power difference vector represents a trend in the importance of the power difference values ​​in the power difference sequence;

[0164] A fourth processing module 50222 is configured to process the power difference sequence and the first power difference vector according to a preset intelligent prediction model to obtain a second power difference vector; wherein the second power difference vector represents a feature of power difference values ​​in the power difference sequence whose importance is greater than a preset threshold;

[0165] The fifth processing module 50223 is configured to process the second power difference vector based on a preset intelligent prediction model to obtain a proportional parameter value, a differential parameter value, and an integral parameter value.

[0166] In a possible implementation, the third processing module 50221 includes:

[0167] Processing the power difference sequence based on a preset intelligent prediction model to obtain a third power difference vector; wherein the third power difference vector represents the importance of the power difference values ​​in the power difference sequence;

[0168] Based on a preset intelligent prediction model, the power difference sequence and the third power difference vector are processed to obtain a fourth power difference vector; wherein the fourth power difference vector represents a power difference value in the power difference sequence whose importance is greater than a preset threshold;

[0169] According to a preset intelligent prediction model, the power difference sequence, the third power difference vector, and the fourth power difference vector are processed to obtain a first power difference vector.

[0170] In a possible implementation, the fourth processing module 50222 includes:

[0171] Processing the power difference sequence and the first power difference vector according to a preset intelligent prediction model to obtain an attention weight; wherein the attention weight represents the importance of the first power difference vector;

[0172] The first power difference vector and the attention weight are weighted and summed according to a preset intelligent prediction model to obtain a second power difference vector.

[0173] In a possible implementation, the control module 503 includes:

[0174] Determining a power correction parameter based on the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value; wherein the power correction parameter represents the power percentage of the inverters in the inverter set that needs to be corrected;

[0175] The current output power of the inverters in the inverter set is obtained; and the power values ​​of the inverters in the inverter set are controlled according to the current output power and the power correction parameter.

[0176] In a possible implementation, the backflow prevention system further includes an auxiliary power supply set; the acquisition module 501 includes:

[0177] Obtaining a second total output power sequence of the inverter set and a third total output power sequence of the auxiliary power supply set; wherein the second total output power sequence includes the total output power of the inverter set at each moment in a preset time period, and the third total output power sequence includes the total output power of the auxiliary power supply set at each moment in the preset time period;

[0178] A first total output power sequence is determined according to the second total output power sequence and the third total output power sequence.

[0179] The present embodiment provides a power control device for an inverter, which can execute the method provided in the above method embodiment. The implementation principle and technical effects thereof are similar, and are not described in detail in this embodiment.

[0180] Figure 6 A structural diagram of a training device for a preset intelligent prediction model for power control of an inverter provided in an embodiment of the present application, such as Figure 6 As shown, the device is applied to a controller of an anti-backflow system, the anti-backflow system includes an auxiliary power supply set, and the device 60 includes:

[0181] An acquisition module 601 is configured to acquire a total output power sequence set of the backflow prevention system, the number of connected auxiliary power supplies in the auxiliary power supply set, and the load type of the backflow prevention system; wherein the total output power sequence set includes at least one total output power sequence, and the total output power sequence includes the total output power of the backflow prevention system at each moment in a preset time period;

[0182] A splicing module 602 is configured to splice the total power sequences in the total power sequence set with the corresponding number of connected auxiliary power supplies and load types to obtain a data set to be processed;

[0183] The training module 603 is used to input the data set to be processed into the initial model for training to obtain a preset intelligent prediction model;

[0184] The training device for the preset intelligent prediction model of power control of the inverter provided in this embodiment can execute the method provided in the method embodiments, and has similar implementation principles and technical effects. Details are not described herein again.

[0185] Figure 7 A structural schematic diagram of an anti-reflux system provided in this embodiment is shown in FIG. 7. The anti-reflux system 70 includes an inverter set 701, an auxiliary power supply set 702, a smart meter 703, a data acquisition device 704, and a controller 705. The inverter set 701 includes at least one inverter, and the auxiliary power supply set 702 includes at least one auxiliary power supply.

[0186] One end of the auxiliary power supply is connected to one end of the inverter through a load 706, and the other end of the inverter is connected to a power grid 707 system through the smart meter 703.

[0187] The other end of the auxiliary power supply is connected to the other end of the inverter through the data acquisition device 704, the other end of the data acquisition device 704 is connected to the smart meter 703, and the controller 705 is connected to the data acquisition device 704.

[0188] The specific implementation process of the controller can be referred to the method embodiments, and has similar implementation principles and technical effects. Details are not described herein again.

[0189] Figure 8 A structural schematic diagram of an electronic device provided in this embodiment is shown in FIG. 8. As shown in FIG. 8, the electronic device 80 provided in this embodiment includes at least one processor 801 and a memory 802. Optionally, the device 80 further includes a communication component 803. The processor 801, the memory 802, and the communication component 803 are connected through a bus 804. Figure 8

[0190] In the specific implementation process, the at least one processor 801 executes the computer execution instructions stored in the memory 802, so that the at least one processor 801 executes the method described above.

[0191] The specific implementation process of the processor 801 can be referred to the method embodiments, and has similar implementation principles and technical effects. Details are not described herein again.

[0192] ​In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0193] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0194] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0195] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0196] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0197] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0198] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium may be an integral part of the processor. The processor and the readable storage medium may reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium may reside in a device as discrete components.

[0199] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0200] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0201] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0202] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0203] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0204] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A power control method for an inverter, characterized in that: The method is applied to a controller of a backflow prevention system, wherein the backflow prevention system includes an inverter set, and the method includes: Obtaining a first total output power sequence of the backflow prevention system; wherein the first total output power sequence includes the total output power of the backflow prevention system at each moment in a preset time period; Inputting the first total output power sequence into a preset intelligent prediction model for processing to obtain a proportional parameter value, a differential parameter value, and an integral parameter value; wherein the proportional parameter value is used to correct an error in the total output power of the anti-backflow system, the differential parameter value is used to correct an accumulated error in the total output power of the first total output power sequence within a preset time period, and the integral parameter value is used to predict an error in the total output power of the anti-backflow system; The power of the inverters in the inverter set is controlled according to the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value.

2. The method according to claim 1, characterized in that Inputting the first output total power sequence into a preset intelligent prediction model for processing to obtain a proportional parameter value, a differential parameter value, and an integral parameter value, including: Determining a power difference sequence based on the first total output power sequence and a preset power threshold; wherein the power difference sequence includes the power difference value of the backflow prevention system at each moment in a preset time period; the power difference value represents the power value that needs to be corrected by the inverter; The power difference sequence is processed based on the preset intelligent prediction model to obtain the proportional parameter value, the differential parameter value, and the integral parameter value.

3. The method according to claim 2, characterized in that Processing the power difference sequence based on the preset intelligent prediction model to obtain the proportional parameter value, the differential parameter value, and the integral parameter value includes: Processing the power difference sequence based on the preset intelligent prediction model to obtain a first power difference vector; wherein the first power difference vector represents a trend in the change of the importance of the power difference values ​​in the power difference sequence; Processing the power difference sequence and the first power difference vector according to the preset intelligent prediction model to obtain a second power difference vector; wherein the second power difference vector represents a feature of power difference values ​​in the power difference sequence whose importance is greater than a preset threshold; The second power difference vector is processed based on the preset intelligent prediction model to obtain the proportional parameter value, the differential parameter value, and the integral parameter value.

4. The method according to claim 3, characterized in that Processing the power difference sequence based on the preset intelligent prediction model to obtain a first power difference vector includes: Processing the power difference sequence based on the preset intelligent prediction model to obtain a third power difference vector; wherein the third power difference vector represents the importance of the power difference values ​​in the power difference sequence; Based on the preset intelligent prediction model, the power difference sequence and the third power difference vector are processed to obtain a fourth power difference vector; wherein the fourth power difference vector represents a power difference value in the power difference sequence whose importance is greater than a preset threshold; The power difference sequence, the third power difference vector, and the fourth power difference vector are processed according to the preset intelligent prediction model to obtain the first power difference vector.

5. The method according to claim 3, characterized in that Processing the power difference sequence and the first power difference vector according to the preset intelligent prediction model to obtain a second power difference vector includes: Processing the power difference sequence and the first power difference vector according to the preset intelligent prediction model to obtain an attention weight; wherein the attention weight represents the importance of the first power difference vector; The first power difference vector and the attention weight are weightedly summed according to the preset intelligent prediction model to obtain the second power difference vector.

6. The method according to claim 1, characterized in that Controlling the power of the inverters in the inverter set according to the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value includes: Determining a power correction parameter according to the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value; wherein the power correction parameter represents the power percentage of the inverters in the inverter set that needs to be corrected; The current output power of the inverters in the inverter set is obtained; and the power values ​​of the inverters in the inverter set are controlled according to the current output power and the power correction parameter.

7. The method according to any one of claims 1 to 6, characterized in that The anti-backflow system further includes an auxiliary power supply set; obtaining a first total output power sequence of the anti-backflow system includes: Obtaining a second total output power sequence of the inverter set and a third total output power sequence of the auxiliary power supply set; wherein the second total output power sequence includes the total output power of the inverter set at each moment in a preset time period, and the third total output power sequence includes the total output power of the auxiliary power supply set at each moment in the preset time period; The first total output power sequence is determined according to the second total output power sequence and the third total output power sequence.

8. A method for training a preset intelligent prediction model for an inverter power control method, wherein the inverter power control method is the method according to any one of claims 1 to 7, characterized in that: The training method of the preset intelligent prediction model is applied to a controller of an anti-backflow system, wherein the anti-backflow system includes an auxiliary power supply set. The training method of the preset intelligent prediction model includes: Obtaining a total output power sequence set of the backflow prevention system, the number of connected auxiliary power supplies in the auxiliary power supply set, and a load type of the backflow prevention system; wherein the total output power sequence set includes at least one total output power sequence, and the total output power sequence includes the total output power of the backflow prevention system at each moment in a preset time period; The total power sequences in the total power sequence set are concatenated with the corresponding number of auxiliary power supply connections and load types to obtain a data set to be processed; The data set to be processed is input into the initial model for training to obtain a preset intelligent prediction model.

9. A power control device for an inverter, characterized in that: The device is applied to a controller of a backflow prevention system, wherein the backflow prevention system includes an inverter assembly, and the device includes: An acquisition module, configured to acquire a first total output power sequence of the anti-backflow system; wherein the first total output power sequence includes the total output power of the anti-backflow system at each moment in a preset time period; a processing module, configured to input the first total output power sequence into a preset intelligent prediction model for processing to obtain a proportional parameter value, a differential parameter value, and an integral parameter value; wherein the proportional parameter value is used to correct an error in the total output power of the anti-backflow system, the differential parameter value is used to correct an accumulated error in the total output power of the first total output power sequence within a preset time period, and the integral parameter value is used to predict an error in the total output power of the anti-backflow system; A control module is configured to control the power of the inverters in the inverter set according to the first total output power sequence, the proportional parameter value, the differential parameter value, and the integral parameter value.

10. A backflow prevention system, characterized in that: The backflow prevention system includes an inverter set, an auxiliary power supply set, a smart meter, a data acquisition device, and a controller; wherein the inverter set includes at least one inverter, and the auxiliary power supply set includes at least one auxiliary power supply; One end of the auxiliary power supply is connected to one end of the inverter through a load, and the other end of the inverter is connected to the power grid system through the smart meter; The other end of the auxiliary power supply is connected to the other end of the inverter through the data acquisition device, and the other end of the data acquisition device is connected to the smart meter; the controller is connected to the data acquisition device; The controller is configured to execute the method according to any one of claims 1 to 7.

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