RTDS simulation-based DC booster control method and system for new energy grid connection

Abstract

The invention provides an RTDS simulation-based DC booster control method and system for new energy grid connection, and relates to the technical field of new energy grid connection control, and the method comprises the steps: building an RTDS-based DC booster control simulation model for new energy grid connection; operating the control simulation model; acquiring voltage and current data of a low-voltage side and voltage and current data of a high-voltage side of the direct-current booster; obtaining output data of new energy direct current grid connection; based on the voltage and current data of the low-voltage side and the high-voltage side of the direct-current booster, an abnormal analysis value of the direct-current booster is obtained through an abnormal analysis method of the direct-current booster; based on the voltage and current data of the high-voltage side and the output data of the new energy direct current grid connection, an output anomaly analysis value is obtained through an output anomaly analysis method; and controlling the direct current booster through a neural network PID control algorithm according to the abnormal analysis value and the output abnormal analysis value of the direct current booster. According to the invention, the regulation and control accuracy of the DC booster for new energy grid connection is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy grid-connected control, and in particular relates to a control method and system of a DC booster for renewable energy grid-connected based on RTDS simulation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of new energy technologies and the increasing demand for power quality from users, the demand for the access of distributed new energy and energy storage systems to distribution networks is becoming increasingly strong, while the traditional AC distribution network is increasingly unable to meet the demand for electricity in modern society. The DC distribution network is being used more and more in power systems due to its unique advantages, such as high reliability, good economy, convenience for the access of distributed new energy and high power quality.

[0004] In the development of DC power distribution, DC boosters, as the core equipment for connecting medium and low voltage buses, are the key nodes for power delivery. DC boosters mainly increase the lower DC voltage provided by batteries to the required voltage value. At present, with the increase in distribution network capacity and the increase in the number of distributed new energy sources, the demand for DC boosters, as the intermediary for new energy grid connection, is also gradually increasing.

[0005] However, when designing and debugging a large number of energy terminals and grid-connected equipment systems, it is impossible to quickly and accurately analyze the operating status and control strategy due to factors such as the excessive number of equipment and complex systems. Therefore, most current systems simulate a large number of energy terminals and their grid-connected equipment to build system simulation models for the development of key equipment such as DC distribution network control and protection.

[0006] However, most of the existing technologies currently only focus on the construction of simulation devices, and then use traditional control methods to achieve control and protection of DC distribution networks. When simulating on the RTDS platform, due to the large number of simulation devices, the amount of calculation is too large, which occupies a lot of resources, and easily leads to a large number of errors during control, making it impossible to effectively determine the operating status and ensure more accurate control. Moreover, for some complex industrial control systems, traditional control methods may be difficult to achieve ideal control effects. Summary of the invention

[0007] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a control method and system for a DC booster for new energy grid-connected based on RTDS simulation. By building a simulation model for the DC booster system for new energy grid-connected, relevant parameters of the simulation operation are obtained, and then effective regulation is performed through a neural network control algorithm based on the relevant parameters.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: A first aspect of the present invention provides a control method for a DC booster for renewable energy grid connection based on RTDS simulation.

[0009] A control method for a DC booster for a new energy grid-connected system based on RTDS simulation, comprising: According to the circuit topology and required parameters of the DC booster, a control simulation model of the DC booster for renewable energy grid connection based on RTDS is built; Run the control simulation model; obtain the voltage and current data on the low-voltage side of the DC booster and the voltage and current data on the high-voltage side; at the same time, obtain the output data of the new energy DC grid-connected; Based on the voltage and current data on the low voltage side of the DC booster and the voltage and current data on the high voltage side, an abnormal analysis value of the DC booster is obtained by using a DC booster abnormal analysis method; Based on the voltage and current data on the high-voltage side and the output data of the new energy DC grid-connected, the output abnormality analysis value is obtained through the output abnormality analysis method; According to the abnormal analysis value of the DC booster and the abnormal analysis value of the output, the DC booster is controlled by a neural network PID control algorithm.

[0010] A second aspect of the present invention provides a DC booster control system for new energy grid connection based on RTDS simulation.

[0011] A DC booster control system for renewable energy grid connection based on RTDS simulation, comprising: The simulation building module is configured to build a simulation model of the DC booster control for new energy grid connection based on RTDS according to the circuit topology and required parameters of the DC booster; The data acquisition module is configured to run the control simulation model; obtain the voltage and current data of the low-voltage side of the DC booster and the voltage and current data of the high-voltage side; and at the same time, obtain the output data of the new energy DC grid connection; The abnormality analysis module is configured to obtain an abnormality analysis value of the DC booster through a DC booster abnormality analysis method based on the voltage and current data on the low voltage side of the DC booster and the voltage and current data on the high voltage side; Based on the voltage and current data on the high-voltage side and the output data of the new energy DC grid-connected, the output abnormality analysis value is obtained through the output abnormality analysis method; The algorithm control module is configured to control the DC booster through a neural network PID control algorithm according to the DC booster abnormality analysis value and the output abnormality analysis value.

[0012] The third aspect of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a method as described in the first aspect of the present invention are implemented.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in a method as described in the first aspect of the present invention.

[0014] A fifth aspect of the present invention provides a computer program product comprising instructions, which, when running on a computer, enables the computer program to implement the steps in a method as described in the first aspect of the present invention when executed by a processor.

[0015] One or more of the above technical solutions have the following beneficial effects: The present invention builds a simulation model for a DC booster system for new energy grid connection, obtains relevant parameters of simulated operation, and then performs more rapid, accurate and effective regulation through a neural network control algorithm based on the relevant parameters.

[0016] By calculating the voltage difference, current difference and power difference between each moment and the previous moment, the system can accurately analyze the fluctuation of voltage, current and power on the low-voltage side and high-voltage side of the DC booster, find abnormal fluctuation problems, and draw voltage and power change curves according to the fluctuation values, intuitively displaying the operating status of the DC booster of the system during the acquisition cycle. At the same time, the precise control method based on real-time data improves the power conversion efficiency of the system, reduces energy consumption, and optimizes the overall operating efficiency.

[0017] The present invention uses a neural network PID control algorithm to perform regulation based on the obtained DC booster related parameters, and can automatically adjust the PID parameters by learning the mapping relationship between input and output, thereby adapting to different working conditions and external environmental changes, thereby improving the adaptability and robustness of the system. It can also enable the control system to better adapt to different working conditions, reduce system oscillation and overshoot, and improve the stability and response speed of the system. The neural network PID control algorithm can gradually adjust the weight of the network through training and learning, so that the error of the control system is minimized, thereby improving the control accuracy and the overall performance of the system, and can also improve the overall degree of automation.

[0018] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0020] Figure 1 This is a flow chart of a control method for a DC booster for new energy grid connection based on RTDS simulation according to Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0021] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0022] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0023] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0024] Embodiment 1 This embodiment discloses a control method for a DC booster for new energy grid connection based on RTDS simulation. Figure 1 As shown, including: According to the circuit topology and required parameters of the DC booster, a control simulation model of the DC booster for renewable energy grid connection based on RTDS is built; Run the control simulation model; obtain the voltage and current data on the low-voltage side of the DC booster and the voltage and current data on the high-voltage side; at the same time, obtain the output data of the new energy DC grid-connected; Based on the voltage and current data on the low voltage side of the DC booster and the voltage and current data on the high voltage side, an abnormal analysis value of the DC booster is obtained by using a DC booster abnormal analysis method; Based on the voltage and current data on the high-voltage side and the output data of the new energy DC grid-connected, the output abnormality analysis value is obtained through the output abnormality analysis method; According to the abnormal analysis value of the DC booster and the abnormal analysis value of the output, the DC booster is controlled by a neural network PID control algorithm.

[0025] In order to more clearly illustrate this embodiment, a control method of a DC booster for new energy grid connection based on RTDS simulation can be specifically described as follows: Step 1: According to the circuit topology and required parameters of the DC booster, build a DC booster control simulation model for renewable energy grid connection based on RTDS.

[0026] In this embodiment, by obtaining the circuit topology and related required parameters of the DC booster, a corresponding DC booster control simulation model is built through the RTDS simulation platform.

[0027] Among them, a common DC booster includes a low-voltage side power converter and a high-voltage side power converter, and a transformer connecting the low-voltage side power converter and the high-voltage side power converter.

[0028] The requirement parameters are mainly used to configure simulation parameters. According to the purpose and requirements of the simulation, the simulation time, sampling rate, control strategy, etc. are configured.

[0029] RTDS simulation is a high-performance power system simulation method that can simulate the behavior of the power system and is used for testing various scenarios and operations. Through control analysis through simulation models, the overall control efficiency can be improved, errors and error rates can be reduced, and the overall data of the DC booster can be better analyzed and studied.

[0030] Step 2, run the control simulation model; obtain the voltage and current data on the low-voltage side of the DC booster and the voltage and current data on the high-voltage side; at the same time, obtain the output data of the new energy DC grid-connected.

[0031] In this embodiment, after the simulation control model is built, the control simulation model is run, and the relevant simulation operation values ​​are obtained according to the simulation conditions of the simulation model.

[0032] Specifically, it includes: obtaining the AC and DC voltage and current data on the low-voltage side of the DC booster and the AC and DC voltage and current data on the high-voltage side within a certain period.

[0033] At the same time, since it is applied to the new energy DC grid connection, it is also necessary to obtain the output data of the new energy DC grid connection within a certain period so that it can be used for overall control later.

[0034] Step 3: Based on the voltage and current data on the low-voltage side of the DC booster and the voltage and current data on the high-voltage side, an abnormal analysis value of the DC booster is obtained by using a DC booster abnormal analysis method.

[0035] In this embodiment, the DC booster abnormality analysis method mainly calculates the difference between the voltage data and current data at each moment and the previous moment within a certain period, and analyzes the fluctuation range of the difference to obtain the abnormality analysis value.

[0036] Specifically, the voltage data and current data on the low-voltage side of the DC booster within a certain period are obtained, and the voltage difference and current difference between each moment and the previous moment are calculated according to the difference calculation formula.

[0037] Set an appropriate fluctuation range threshold. When the difference exceeds the fluctuation range threshold, record the difference as an abnormal analysis value.

[0038] Then, the voltage data and current data on the high-voltage side of the DC booster within a certain period are obtained and used according to the abnormality calculation formula to obtain an abnormality analysis value.

[0039] At the same time, the corresponding power value can also be calculated through the voltage data, and the power difference can be calculated.

[0040] By calculating the voltage difference, current difference and power difference between each moment and the previous moment, the system can accurately analyze the fluctuation of voltage, current and power on the low-voltage side and high-voltage side of the DC booster, find abnormal fluctuation problems, and draw voltage and power change curves according to the fluctuation values, intuitively displaying the operating status of the DC booster of the system during the acquisition cycle. At the same time, the precise control method based on real-time data improves the system's power conversion efficiency, reduces energy consumption, and optimizes overall operating efficiency.

[0041] Step 4: Based on the voltage and current data on the high-voltage side and the output data of the new energy DC grid-connected system, an output abnormality analysis value is obtained through an output abnormality analysis method.

[0042] In this embodiment, the output abnormality analysis method is mainly based on comparative analysis of the obtained high-voltage side voltage and current data and the output data according to prediction algorithms such as linear regression. First, based on the current and voltage data without abnormalities at each moment obtained in step 3, the corresponding output data is predicted by the algorithm, and then compared with the actual output data obtained. When the actual output data is too different from the predicted data, it is recorded as an output abnormal value.

[0043] Similarly, the corresponding power value can be calculated by acquiring the voltage value of the new energy DC grid-connected output in real time; and the change curve of the voltage value and power value of the new energy DC grid-connected output terminal within the acquisition period can be drawn.

[0044] By calculating the abnormal analysis value of the DC booster, detailed abnormal situation analysis is performed on the DC booster related data and the output data used for new energy grid connection, ensuring the stable operation of the DC booster under various working conditions.

[0045] By calculating the voltage and current difference on both sides of the DC booster and predicting the output data, the abnormal situation of the DC booster can be accurately identified, and potential problems can be discovered in time for control and adjustment.

[0046] Based on the voltage and power fluctuation values ​​obtained in real time, data-driven decisions and adjustments can be made to improve operating efficiency and reduce unnecessary energy consumption. Through real-time monitoring and dynamic adjustment, the operating state of the DC transformer can be optimized and the efficient power conversion process can be maintained. Through accurate analysis and calculation of voltage and power fluctuation values, the high quality of output power can be maintained, the grid-connected requirements can be met, stable and reliable power can be provided, and voltage and power fluctuations can be effectively controlled, power quality issues can be reduced, and the stability of power transmission and use can be improved.

[0047] Step 5: According to the abnormal analysis value of the DC booster and the output abnormal analysis value, the DC booster is controlled by a neural network PID control algorithm.

[0048] In this embodiment, the neural network PID control algorithm is mainly based on PID control adjusted by BP neural network, which is an adaptive control method combining PID control with BP (back propagation) neural network.

[0049] Step 5-1: First, define a BP neural network, whose main structure consists of an input layer, two hidden layers, and an output layer.

[0050] Step 5-2: Initialize PID parameters and set the initial gain of the PID controller (including proportional gain, integral gain and differential gain).

[0051] Step 5-3: Based on step 3 and step 4, calculate the voltage and current on both sides of the DC booster and the output error and error variation, and obtain the DC booster abnormality analysis value and the output abnormality analysis value.

[0052] Step 5-4: training the neural network, using the abnormal analysis value of the DC booster and the abnormal analysis value of the output obtained in step 5-3 to obtain the adjustment amount of the PID parameters as output through the BP neural network. The neural network then adjusts the weight through the back propagation algorithm to minimize the control error.

[0053] Step 5-5: Update the PID parameters according to the output of the neural network, that is, adjust the PID gain.

[0054] Step 5-6: Calculate the control signal using the updated PID parameters.

[0055] In each control cycle, the BP neural network can continue to adjust the PID parameters according to the new error and error change.

[0056] BP neural network automatically adjusts PID control parameters by learning the error signal of the system, thereby optimizing the control performance. And the neural network PID control algorithm can automatically adjust PID parameters by learning the mapping relationship between input and output, so as to adapt to different working conditions and external environmental changes, thereby improving the adaptability and robustness of the system. It can also enable the control system to better adapt to different working conditions, reduce system oscillation and overshoot, and improve the stability and response speed of the system.

[0057] The neural network PID control algorithm can gradually adjust the weight of the network through training and learning to minimize the error of the control system, thereby improving the control accuracy and the overall performance of the system.

[0058] For some complex industrial control systems, traditional PID control may be difficult to achieve the desired control effect. The neural network PID control algorithm can better adapt to and handle the control problems of complex systems through its powerful nonlinear mapping ability.

[0059] Traditional PID controllers require manual parameter adjustment, while the neural network PID control algorithm reduces manual intervention and improves the system's automation level through automatic learning and optimization.

[0060] Therefore, the neural network PID control algorithm significantly improves the performance and stability of the control system through its powerful adaptability and learning ability. It is suitable for various complex industrial control systems and can achieve more accurate and stable control of the entire grid-connected current booster.

[0061] Embodiment 2 The purpose of this embodiment is to provide a DC booster control system for new energy grid connection based on RTDS simulation, including: The simulation building module is configured to build a DC booster control simulation model based on RTDS according to the circuit topology and required parameters of the DC booster; The data acquisition module is configured to run the control simulation model; obtain the voltage and current data of the low-voltage side of the DC booster and the voltage and current data of the high-voltage side; and at the same time, obtain the output data of the new energy DC grid connection; The abnormality analysis module is configured to obtain an abnormality analysis value of the DC booster through a DC booster abnormality analysis method based on the voltage and current data on the low voltage side of the DC booster and the voltage and current data on the high voltage side; Based on the voltage and current data on the high-voltage side and the output data of the new energy DC grid-connected, the output abnormality analysis value is obtained through the output abnormality analysis method; The algorithm control module is configured to control the DC booster through a neural network PID control algorithm according to the DC booster abnormality analysis value and the output abnormality analysis value.

[0062] Embodiment 3 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of the above method are implemented when the processor executes the program.

[0063] Embodiment 4 The purpose of this embodiment is to provide a computer-readable storage medium.

[0064] A computer-readable storage medium stores a computer program, which executes the steps of the above method when executed by a processor.

[0065] Embodiment 5 The purpose of this embodiment is to provide a computer program product containing instructions, which, when running on a computer, enables the computer to execute the methods and functions involved in any of the above embodiments. The steps involved in the apparatus of the above embodiment correspond to the method embodiment 1, and the specific implementation method can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0066] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0067] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A control method for a DC booster for new energy grid connection based on RTDS simulation, characterized in that: include: According to the circuit topology and required parameters of the DC booster, a control simulation model of the DC booster for renewable energy grid connection based on RTDS is built; Run the control simulation model; obtain the voltage and current data on the low-voltage side of the DC booster and the voltage and current data on the high-voltage side; at the same time, obtain the output data of the new energy DC grid-connected; Based on the voltage and current data on the low voltage side of the DC booster and the voltage and current data on the high voltage side, an abnormal analysis value of the DC booster is obtained by using a DC booster abnormal analysis method; Based on the voltage and current data on the high-voltage side and the output data of the new energy DC grid-connected, the output abnormality analysis value is obtained through the output abnormality analysis method; According to the abnormal analysis value of the DC booster and the abnormal analysis value of the output, the DC booster is controlled by a neural network PID control algorithm.

2. A control method for a DC booster for new energy grid connection based on RTDS simulation as claimed in claim 1, characterized in that: The required parameters are specifically configured simulation parameters, and the simulation time, sampling rate and control strategy are configured according to the purpose and requirements of the simulation.

3. A control method for a DC booster for new energy grid connection based on RTDS simulation as claimed in claim 1, characterized in that: The DC booster abnormality analysis method mainly calculates the difference between the voltage data and current data at each moment and the previous moment within a certain period, and analyzes the fluctuation range of the difference to obtain the abnormality analysis value.

4. A method for controlling a DC booster for a new energy grid-connected system based on RTDS simulation as claimed in claim 1, characterized in that: The output abnormality analysis method is mainly based on comparative analysis of the obtained high-voltage side voltage and current data and the output data according to a linear regression prediction algorithm. According to the obtained current and voltage data without abnormalities at each moment, the corresponding output data is predicted by the algorithm, and then compared with the actual output data obtained. When the difference between the actual output data and the predicted data is too large, it is recorded as an output abnormal value.

5. A method for controlling a DC booster for grid-connected renewable energy based on RTDS simulation as claimed in claim 1, characterized in that: The neural network PID control algorithm is mainly based on PID control tuned by BP neural network.

6. A control method for a DC booster for new energy grid connection based on RTDS simulation as claimed in claim 1, characterized in that: The neural network PID control algorithm has the following specific steps: Define the BP neural network, whose main structure consists of an input layer, two hidden layers, and an output layer; initialize PID parameters and set the initial gain of the PID controller; Obtaining a DC booster abnormality analysis value and an output abnormality analysis value; The PID parameter adjustment is obtained as output through the BP neural network; the neural network then adjusts the weights through the back propagation algorithm to minimize the control error; According to the output of the BP neural network, the PID parameters are updated, that is, the PID gain is adjusted; The control signal is calculated using the updated PID parameters.

7. A DC booster control system for renewable energy grid connection based on RTDS simulation, characterized in that: include: The simulation building module is configured to build a simulation model of the DC booster control for new energy grid connection based on RTDS according to the circuit topology and required parameters of the DC booster; The data acquisition module is configured to run the control simulation model; obtain the voltage and current data of the low-voltage side of the DC booster and the voltage and current data of the high-voltage side; and at the same time, obtain the output data of the new energy DC grid connection; The abnormality analysis module is configured to obtain an abnormality analysis value of the DC booster through a DC booster abnormality analysis method based on the voltage and current data on the low voltage side of the DC booster and the voltage and current data on the high voltage side; Based on the voltage and current data on the high-voltage side and the output data of the new energy DC grid-connected, the output abnormality analysis value is obtained through the output abnormality analysis method; The algorithm control module is configured to control the DC booster through a neural network PID control algorithm according to the DC booster abnormality analysis value and the output abnormality analysis value.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are performed.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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