Control method and device of energy storage converter, storage medium and electronic device

Abstract

The invention discloses a control method and device of an energy storage converter, a storage medium and an electronic device. The method comprises the following steps: acquiring an input signal received by the energy storage converter; controlling the switching state of each switch of the energy storage converter according to the input signal so as to adjust the output condition of the energy storage converter, and inputting the input signal into a pre-established state estimation model of the energy storage converter so as to obtain the analog output condition of the energy storage converter output by the state estimation model; and when a deviation value between the analog output condition and an actual output condition of the energy storage converter acquired by a preset sensor exceeds a preset range, determining a sensor fault, and feeding back and adjusting the switching state of each switch of the energy storage converter based on the analog output condition so as to control the operation state of the energy storage converter. Even if the sensor fails, the state estimation model can still provide effective operation state feedback, so that the system can maintain operation, and the overall robustness and the operation efficiency of the energy storage system are improved.

Description

Technical Field

[0001] This application relates to the field of energy storage converters, and more specifically, to a control method and apparatus for an energy storage converter, a storage medium, an electronic device, and a computer program product. Background Technology

[0002] Energy storage technology, as a core component of smart grids, microgrids, and renewable energy grid-connected systems, plays a crucial role in optimizing energy management, stabilizing grid operation, and integrating renewable energy. The Power Conversion System (PCS), as the core of the energy storage system, enables energy transfer between the energy storage device and the grid. Through its AC / DC rectification and inversion functions, the PCS can control the bidirectional flow of energy between the AC grid and the energy storage device.

[0003] In related technologies, the control of PCS, such as zero-sequence voltage injection and space vector modulation strategies, usually relies on sensors to monitor the capacitor voltage or midpoint current in real time.

[0004] However, the control methods in related technologies suffer from insufficient reliability. Summary of the Invention

[0005] This application provides a control method and apparatus for an energy storage converter, a storage medium, an electronic device, and a computer program product.

[0006] According to one aspect of the embodiments of this application, a control method for an energy storage converter is provided, applied to an energy storage converter. The method includes: acquiring input signals received by the energy storage converter, wherein the input signals include the operating states of each switch of the energy storage converter, the operating parameters of the energy storage converter, and the parameters of the connected power grid; controlling the switching states of each switch of the energy storage converter according to the input signals to adjust the output of the energy storage converter; and inputting the input signals into a pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter output by the state estimation model; if the deviation between the simulated output and the actual output of the energy storage converter collected by a pre-set sensor exceeds a preset range, determining a sensor fault, and adjusting the switching states of each switch of the energy storage converter based on the feedback of the simulated output to control the operating state of the energy storage converter.

[0007] In an exemplary embodiment, controlling the switching states of each switch of the energy storage converter according to the input signal to adjust the output of the energy storage converter includes: calculating the midpoint voltage and output current of the energy storage converter based on the input signal and the actual output of the energy storage converter collected by the sensor, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground; and, if it is determined that there is a voltage deviation in the midpoint voltage and / or a current deviation in the output current, controlling the switching states of each switch of the energy storage converter according to the voltage deviation and / or current deviation to reduce the voltage deviation and / or current deviation.

[0008] In an exemplary embodiment, before inputting the input signal into a pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter from the state estimation model, the method further includes: acquiring the circuit topology of the energy storage converter, wherein the circuit topology includes parameters and connection relationships of the switching devices, grid-connected inductors, grid-connected resistors, and grid voltage included in the energy storage converter; establishing a first relationship model of the output current of the energy storage converter based on the circuit topology, wherein the first relationship model includes expressions relating the influence of the switching devices, grid-connected inductors, grid-connected resistors, and grid voltage on the output current; establishing a second relationship model of the midpoint voltage of the energy storage converter based on the circuit topology, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground, and the second relationship model includes expressions relating the influence of the switching devices, output current, and bus capacitance on the midpoint voltage; wherein the state estimation model includes the first relationship model and the second relationship model.

[0009] In an exemplary embodiment, adjusting the switching states of each switch of the energy storage converter based on analog output feedback to control the operating state of the energy storage converter includes: determining the midpoint voltage and output current of the energy storage converter based on the analog output, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground; and, if it is determined that there is a voltage deviation in the midpoint voltage and / or a current deviation in the output current, controlling the switching states of each switch of the energy storage converter based on the voltage deviation and / or current deviation to reduce the voltage deviation and / or current deviation.

[0010] In an exemplary embodiment, the method further includes: when the deviation between the simulated output and the actual output of the energy storage converter acquired by a pre-set sensor is within a preset range: determining the first capacitor voltage at the current moment based on the change in the midpoint voltage of the energy storage converter in the simulated output and the first capacitor voltage at the previous moment, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground, and the first capacitor voltage is the capacitor voltage between the neutral point of the energy storage converter and the first bus; determining the second capacitor voltage at the current moment based on the change in the midpoint voltage of the energy storage converter in the simulated output and the second capacitor voltage at the previous moment, wherein the second capacitor voltage is the capacitor voltage between the neutral point of the energy storage converter and the second bus; updating the capacitor voltage-related parameters in the state estimation model based on the first and second capacitor voltages at the current moment; and adding a correction value to the state estimation model according to the deviation value to make the simulated output consistent with the actual output.

[0011] In an exemplary embodiment, the method further includes: when the deviation between the simulated output and the actual output of the energy storage converter acquired by a pre-set sensor is within a preset range, determining the bus capacitance at the current moment based on the bus capacitance of the energy storage converter at the previous moment acquired by the sensor, the output current of the energy storage converter, and the bus capacitance of the energy storage converter at the previous moment in the simulated output, wherein the bus capacitance is the capacitance on the DC side of the energy storage converter; statistically determining the changing characteristics of the bus capacitance based on the determined bus capacitance at each moment; and correcting the parameters related to the bus capacitance in the state estimation model based on the changing characteristics of the bus capacitance.

[0012] According to another aspect of the embodiments of this application, a control device for an energy storage converter is also provided. The device includes: a signal acquisition module, used to acquire input signals received by the energy storage converter, wherein the input signals include the operating states of each switch of the energy storage converter, the operating parameters of the energy storage converter, and the parameters of the connected power grid; an output determination module, used to control the switching states of each switch of the energy storage converter according to the input signals to adjust the output of the energy storage converter, and to input the input signals into a pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter output by the state estimation model; and a control module, used to determine a sensor fault when the deviation between the simulated output and the actual output of the energy storage converter collected by a pre-set sensor exceeds a preset range, and to adjust the switching states of each switch of the energy storage converter based on the feedback of the simulated output to control the operating state of the energy storage converter.

[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to execute the control method of the energy storage converter described above when running.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the control method of the energy storage converter described above through the computer program.

[0015] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the methods described in various embodiments of this application.

[0016] The aforementioned control method for energy storage converters, by comparing simulated output with actual data in real time, can promptly detect sensor faults, preventing abnormal data output from faulty sensors from misleading the control system and causing operational instability or equipment damage. In the event of sensor failure, the system can smoothly transition to a control mode based on a state estimation model, reducing reliance on physical sensors, ensuring continuous and stable system operation, and avoiding the risk of system downtime due to sensor failure. Even in the event of sensor failure, the state observation-based control strategy can still provide effective operational status feedback, enabling the system to remain within a safe and controllable range, improving the overall robustness and operational efficiency of the energy storage system. By using model prediction rather than complete reliance on sensors, the intensity of sensor usage is reduced, sensor lifespan is extended, and the cost of system maintenance and sensor component replacement is lowered. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a hardware structure block diagram of the control method of the energy storage converter according to an embodiment of this application;

[0020] Figure 2 This is a flowchart of a control method for an energy storage converter according to an embodiment of this application;

[0021] Figure 2a This is a schematic diagram of the structure of an energy storage converter according to an embodiment of this application;

[0022] Figure 2b This is a signal flow diagram according to an embodiment of this application;

[0023] Figure 3 This is a second flowchart of a control method for an energy storage converter according to an embodiment of this application;

[0024] Figure 4 This is the third flowchart of a control method for an energy storage converter according to an embodiment of this application;

[0025] Figure 4a This is a waveform diagram of a modulated wave according to an embodiment of this application;

[0026] Figure 4b This is a waveform diagram of another modulated wave according to an embodiment of this application;

[0027] Figure 5 This is the fourth flowchart of a control method for an energy storage converter according to an embodiment of this application;

[0028] Figure 6 This is the fifth flowchart of a control method for an energy storage converter according to an embodiment of this application;

[0029] Figure 7 This is a flowchart of a control method for an energy storage converter according to an embodiment of this application;

[0030] Figure 8 This is a structural block diagram of a control device for an energy storage converter according to an embodiment of this application. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data used in this way can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] The methods and embodiments provided in this application can be executed on a computer terminal or similar computing device. Taking running on a computer terminal as an example, Figure 1 This is a hardware structure block diagram of the computer terminal for the control method of the energy storage converter according to an embodiment of this application. (See diagram for example.) Figure 1 As shown, a computer terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor unit (MPU) or a programmable logic device (PLD)) and a memory 104 for storing data are also shown. In one exemplary embodiment, the computer terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the computer terminal described above. For example, the computer terminal may also include components that are more complex than those described above. Figure 1 The more or fewer components shown, or having the same Figure 1 Equivalent functions or ratios shown Figure 1 The functions shown have more different configurations.

[0034] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the control method of the energy storage converter in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider for the computer terminal. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0036] As described in the background section, the control methods in related technologies suffer from insufficient reliability because they all require voltage sensors to obtain capacitor voltage information or current sensors to calculate neutral point current. This reliance on these methods has significant drawbacks. First, sensor failure can cause the control algorithm to lose necessary feedback signals, leading to the failure of neutral point voltage balance control and triggering a series of chain reactions that threaten system stability and safety. Second, the computational complexity of redundant small vector control algorithms is high, especially when high-precision sensor data is missing or abnormal; in such cases, the algorithm's effectiveness is greatly reduced, making it difficult to quickly respond to and restore the system state. Finally, existing technologies lack effective contingency plans for sensor failures. Once a sensor fails, the system often directly enters a shutdown protection mode, unable to take more flexible fault response measures to ensure the system continues to operate stably for a short period. Therefore, to address these issues, the control method for the energy storage converter proposed in this application is proposed. This method can maintain the control capability of the energy storage converter even in the event of a sensor failure, reducing reliance on physical sensors. Simultaneously, it can maintain the basic operation and protection functions of the energy storage converter during sensor failures, avoiding unnecessary system downtime and equipment damage.

[0037] This embodiment provides a control method for an energy storage converter. Figure 2 This is a flowchart of an optional control method for an energy storage converter according to an embodiment of this application, applied to an energy storage converter. The process includes the following steps S200-S220:

[0038] Step S200: Obtain the input signal received by the energy storage converter.

[0039] Specifically, all necessary information related to the operation of the energy storage converter is collected, including the operating status and parameters (such as voltage and current) of the converter's internal switches, as well as parameters of the external power grid. This information is crucial for real-time control of the converter, monitoring of its operating status, and fault diagnosis.

[0040] For example, switch status information is obtained by monitoring electrical signals from the internal switching circuits of the converter, typically including the on / off states of transistors, relays, etc., used to control the direction and amount of energy flow. Operating parameters are collected in real time by built-in sensors (such as voltage sensors, current transformers, and temperature sensors), including capacitor voltage, output current, and temperature, reflecting the converter's operating status. Grid parameters include grid voltage, frequency, and phase, which can be obtained through sensors connected to the grid or by receiving data from the grid dispatch center or monitoring system, used to adjust the synchronization and interaction between the converter and the grid.

[0041] The input signals include the operating status of each switch in the energy storage converter, the operating parameters of the energy storage converter, and the parameters of the connected power grid. Input signals refer to those signals that can directly or indirectly affect the operating status of the energy storage converter, including but not limited to switch status, operating parameters, and power grid parameters.

[0042] For example, the topology diagram of an energy storage converter can be as follows: Figure 2a As shown, it includes a first busbar and a second busbar, and includes a capacitor for the first busbar. Second bus capacitor These are connected between the first bus and the neutral point on the DC side, and between the second bus and the neutral point, respectively, to stabilize the DC side voltage, buffer energy fluctuations, and filter ripple current. Each bridge arm contains two sets of switching devices. ( )and ( The energy storage converter converts and regulates electrical energy by controlling its switching state. Clamping diodes, in a three-level topology, clamp the neutral point voltage to prevent overvoltage and ensure the switching devices operate within a safe voltage range. The AC output of the energy storage converter is connected to the grid or load to provide AC power.

[0043] Step S210: Control the switching state of each switch of the energy storage converter according to the input signal to adjust the output of the energy storage converter, and input the input signal into the pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter from the state estimation model.

[0044] For example, the signal flow diagram is as follows Figure 2b As shown, the input signal u is simultaneously input to both the actual energy storage converter and its state estimation model. The difference between the actual output y of the energy storage converter and the output y^ of the state estimation model is the deviation value e. The deviation value e is input into the state estimation model through a closed loop, and finally, the operating state of the energy storage converter is adjusted by the output control parameters of the state estimation model.

[0045] Specifically, on the one hand, based on the currently collected input signals, the switching states of each switch are adjusted through a preset control algorithm to control the output of the converter and ensure that it meets the predetermined operating requirements; on the other hand, the same set of input signals is sent to the previously established state estimation model to obtain the simulated operating state of the converter output by the model, which is used for fault detection and auxiliary judgment of system stability.

[0046] For example, algorithms such as Proportional Integral Derivative (PID) and fuzzy logic control are used to analyze the input signal and calculate the appropriate switching state to regulate the output current and voltage. State estimation model update: The input signal is fed into the state estimation model. Based on the model's dynamic equations and parameters, the model outputs simulated estimates of capacitor voltage, midpoint voltage deviation, and output current, which serve as the ideal operating state of the converter under the current input conditions.

[0047] The energy conversion efficiency and output characteristics of the converter are controlled by changing the on / off state of the switching elements. The state estimation model is a mathematical model that predicts the actual operating state of the converter, such as output current and capacitor voltage, based on the input signal and known system dynamic equations.

[0048] Step S220: If the deviation between the simulated output and the actual output of the energy storage converter collected by the preset sensor exceeds the preset range, the sensor is determined to be faulty, and the switching states of each switch of the energy storage converter are adjusted based on the feedback of the simulated output to control the operating state of the energy storage converter.

[0049] Specifically, in real-time operation monitoring, if the deviation between the simulated operating state output by the state estimation model and the actual data collected by the sensors is too large, exceeding the preset safety range, this usually indicates an abnormality or malfunction of the sensing equipment. In this case, the system will adjust the switching state of the converter based on the output of the state estimation model, rather than the sensor data, to maintain the stable operation of the system.

[0050] For example, the model output is continuously compared with sensor data to calculate the deviation between the two, such as capacitor voltage difference or output current difference. When the detected deviation exceeds a preset threshold, a fault alarm mechanism is triggered. If no conventional PCS fault alarm occurs, it is determined that the sensor may be faulty, resulting in the failure to detect excessive deviation. After confirming the sensor fault, the system automatically switches to open-loop control mode, ignoring the data from the faulty sensor and relying entirely on the output of the state estimation model to adjust the switching state, ensuring that the converter operation is not severely affected by the sensor fault.

[0051] The deviation value is the numerical difference between the output of the state estimation model and the actual sensor measurement value, used to monitor the operational health of the system. The preset range is a safety deviation range pre-set by the system engineer to determine whether the difference between the model output and the actual data is acceptable; exceeding this range may indicate a system malfunction.

[0052] In this embodiment, by comparing simulated output with actual data in real time, sensor faults can be detected promptly, preventing abnormal data from faulty sensors from misleading the control system and causing operational instability or equipment damage. In the event of a sensor fault, the system can smoothly transition to a control mode based on a state estimation model, reducing reliance on physical sensors and ensuring continuous and stable system operation, thus avoiding the risk of system downtime due to sensor failure. Even in the event of sensor failure, the state observation-based control strategy can still provide effective operational status feedback, enabling the system to remain within a safe and controllable range, improving the overall robustness and operational efficiency of the energy storage system. By using model prediction rather than complete reliance on sensors, the intensity of sensor usage is reduced, sensor lifespan is extended, and the cost of system maintenance and sensor component replacement is lowered.

[0053] In one embodiment, such as Figure 3 As shown, step S210 involves controlling the switching states of each switch in the energy storage converter according to the input signal to adjust the output of the energy storage converter. This includes steps S300-S310:

[0054] Step S300: Calculate the midpoint voltage and output current of the energy storage converter based on the input signal and the actual output of the energy storage converter collected by the sensor.

[0055] The neutral point voltage is the voltage of the neutral point of the energy storage converter relative to ground.

[0056] Specifically, based on the collected input signals (including the operating status and parameters of each switch in the converter and the grid parameters) and the actual output data collected by the sensors, the midpoint voltage and output current of the converter are calculated. These two parameters are crucial for the normal operation and fault detection of the converter.

[0057] For example, real-time input signals are combined with sensor data, and mathematical models and algorithms are used to calculate the current neutral point voltage and output current. This typically involves parsing the input signal while processing the sensor data to extract key parameters. The calculation of the neutral point voltage is based on the converter's topology and operating principle. In a three-level converter, the voltage of the neutral point relative to ground, i.e., the neutral point voltage, can be derived by monitoring the voltages of the upper and lower bus capacitors and the output current, combined with the switching states. The calculation of the output current relies on acquired sensor data, such as current transformer data, combined with the switching states and grid parameters in the input signal, to ensure that the calculated output current accurately reflects the real-time output state of the converter.

[0058] The neutral point voltage is the voltage of the neutral point of the three-level converter relative to ground, reflecting the balance of capacitor voltages. The output current is the actual current delivered by the converter to the grid or load, and is one of the key indicators for evaluating converter performance.

[0059] Step S310: If it is determined that there is a voltage deviation in the midpoint voltage and / or a current deviation in the output current, the switching state of each switch of the energy storage converter is controlled according to the voltage deviation and / or current deviation in order to reduce the voltage deviation and / or current deviation.

[0060] Specifically, when a deviation is detected between the midpoint voltage or output current and the expected value (voltage deviation or current deviation), this step will use a control algorithm to adjust the switching state of the converter to correct these deviations, ensure that the converter operates in the best condition, and prevent possible system failures.

[0061] For example, the calculated midpoint voltage and output current are monitored in real time and compared with set reference values ​​to determine whether there is a voltage or current deviation. Once a deviation is detected, a preset control algorithm (e.g., PID control, fuzzy control, or adaptive control algorithm) is executed according to the magnitude and direction of the deviation to calculate the adjustment amount of the switching state, thereby correcting the deviation. The adjustment amount calculated by the control algorithm is applied to the converter's switching control system to adjust the operating state of each switch, ensuring optimization of energy conversion efficiency and output characteristics.

[0062] Voltage deviation is the difference between the actual midpoint voltage and the expected midpoint voltage, which may be caused by capacitor voltage imbalance, abnormal switching status, or sensor failure. Current deviation is the difference between the actual output current and the set output current, affecting the output quality and efficiency of the converter.

[0063] In this embodiment, by integrating input signals and sensor data, precise calculations of the midpoint voltage and output current are performed. Then, based on deviation detection, the switching state is adjusted in a timely manner. This series of steps constitutes a closed-loop control system capable of real-time monitoring and optimization of the converter's operating status. While ensuring the converter's efficient and stable operation, the system possesses self-diagnostic and adjustment capabilities, reacting rapidly to voltage or current deviations, reducing system failures, and ensuring the safe and economical operation of the power grid and energy storage system. Through this control strategy, the system can more intelligently respond to various operating condition changes, improve energy conversion efficiency, reduce maintenance costs, and enhance adaptability to grid instability factors.

[0064] In one embodiment, such as Figure 4 As shown, before step S210, the method further includes steps S400-S420:

[0065] Step S400: Obtain the circuit topology of the energy storage converter.

[0066] The circuit topology includes the switching devices, grid-connected inductor, grid-connected resistor, grid voltage parameters, and connection relationships of the energy storage converter.

[0067] Specifically, first, the system needs to understand the detailed circuit structure of the energy storage converter, including switching devices (such as insulated gate bipolar transistors, IGBTs), grid-connected inductors, grid-connected resistors, grid voltage, and other components and their interconnections. This is the foundation for building an accurate model and designing control strategies.

[0068] For example, by consulting equipment manuals, circuit diagrams, or using circuit scanning tools, clarify the internal circuit layout of the energy storage converter, including the quantity, location, and type of each switching device, as well as the parameters of the grid-connected inductor and resistor. Accurately record the key parameters of each component, including the rated voltage and current of the switching devices, the inductance value of the grid-connected inductor, the resistance value of the grid-connected resistor, the rated value and fluctuation range of the grid voltage, etc. Confirm the electrical connections between the components, which is crucial for understanding how energy flows within the system.

[0069] The circuit topology, which refers to the layout and interconnection of internal components in an energy storage converter, determines the converter's operating principle and performance characteristics. Switching devices, such as transistors and IGBTs, are responsible for energy conversion and control.

[0070] Step S410: Establish the first relationship model of the output current of the energy storage converter based on the circuit topology.

[0071] The first relational model includes the relationship between the effects of switching devices, grid-connected inductors, grid-connected resistors, and grid voltage on the output current.

[0072] Specifically, establishing a mathematical model to describe how switching devices, grid-connected inductors, grid-connected resistors, and grid voltage work together to affect the output current of an energy storage converter under a specific topology is key to understanding and predicting converter behavior.

[0073] For example, based on the topology, the applicable mathematical framework for the model is determined. For instance, it can be assumed that the switching devices are ideally on and off, with no delay or voltage drop, and that the inductors and resistors are linear. Using Kirchhoff's laws (current and voltage laws), the law of electromagnetic induction, and other physical laws, combined with the converter's operating mode, a set of equations describing the relationship between the output current and various parameters is established. These equations include the current path rules under the switching device's operating state (on, off), the influence of grid voltage on the current, and the voltage drop across the inductors and resistors in the current path. By incorporating parameters such as the switching devices, grid-connected inductor, grid-connected resistor, and grid voltage into the model, a parameterized expression of the output current is achieved, facilitating subsequent control and analysis.

[0074] Among them, the first relational model quantifies the relationship between the output current of the energy storage converter and the switching devices, grid-connected inductor, grid-connected resistor and grid voltage.

[0075] Step S420: Establish a second relationship model for the midpoint voltage of the energy storage converter based on the circuit topology.

[0076] Specifically, the midpoint voltage is a concept unique to three-level converters, reflecting the balance of capacitor voltages. This step aims to describe, through a second relational model, how switching devices, output current, and bus capacitance affect the midpoint voltage.

[0077] For example, for a three-level converter, the variation patterns of capacitor voltage and midpoint voltage under different switching modes are analyzed, as well as the influence of output current on the midpoint voltage. Based on the capacitor charging and discharging principle and circuit topology, equations describing the relationship between the midpoint voltage and the switching devices, output current, and bus capacitance are established. This typically includes the capacitor charging and discharging equations, and the formula for the influence of capacitor voltage on the midpoint voltage under switching modes. The states of the switching devices, the output current, and the parameters of the bus capacitance are incorporated into the model to form a parameterized expression for the midpoint voltage.

[0078] Among them, the neutral point voltage is the voltage of the neutral point of the energy storage converter relative to ground, and the second relationship model includes the relationship between the switching devices, output current, and bus capacitance on the neutral point voltage.

[0079] The state estimation model comprises a first relational model and a second relational model. Together, these models describe the dynamic state of the energy storage converter. Specifically, the first formula describes how the output current is affected by switching states and grid conditions, while the second formula explains the dynamic changes in the neutral point voltage imbalance, which are in turn regulated by switching states and output current. By continuously monitoring and calculating these dynamic changes, the switching states can be adjusted in real time to achieve precise control of the output current and neutral point voltage, ensuring stable and efficient energy conversion under various operating conditions. This control strategy enhances the system's robustness and efficiency, particularly in providing more accurate feedback and adjustment when dealing with sensor failures or other uncertainties.

[0080] The expression for the first relational model is as follows:

[0081]

[0082] in, Let x be the output current of the x-th phase of the energy storage converter. This represents the switching state of the first group of switching devices in phase x of the energy storage converter. This refers to the switching state of the second set of switching devices in phase x of the energy storage converter. The voltage across the first capacitor between the neutral point of the energy storage converter and the first bus is the voltage across the first capacitor. This refers to the voltage across the second capacitor between the neutral point of the energy storage converter and the second bus. This is the grid voltage. For grid connection resistor, For the grid-connected inductance, this formula represents the rate of change of the output current of the energy storage converter with respect to time. It primarily focuses on how the output current is affected by switching states, grid voltage, grid-connected inductance, and grid-connected resistance. This represents the rate of change of the output current over time. This section describes the effect of the power supply voltage on the output current under the control of the switching devices, where... and It is a switch state variable. and These represent the voltages of the two capacitors, respectively, when the switch... When conducting, The output current is affected by the grid-connected inductor when the switch... When conducting, The output current is affected by the grid-connected inductor. Indicates grid voltage The contribution of the output current variation is directly affected by the change in grid voltage. This section describes the effect of the grid-connected resistor R on the output current. The energy loss caused by the grid-connected resistor indirectly affects the rate of change of the output current.

[0083] The expression for the second relational model is as follows:

[0084]

[0085] in, This is the deviation of the midpoint voltage. This refers to the bus capacitor, which is the capacitor on the DC side of the energy storage converter. This represents the switching state of the first group of switching devices in phase x of the energy storage converter. This refers to the switching state of the second set of switching devices in phase x of the energy storage converter. Let x be the output current of the energy storage converter's phase x. This formula represents the rate of change of the neutral point voltage deviation over time. It focuses on how the neutral point voltage is affected by the switching state and the output current, reflecting the capacitor charging and discharging process. This represents the rate of change of the midpoint voltage deviation over time. This refers to the voltage deviation at the midpoint, which is the voltage fluctuation at the midpoint relative to zero or ground. This section describes the switch states. and With output current The impact on the rate of change of the midpoint voltage deviation. Changes in the switching state directly affect the charging and discharging process of the capacitor. When the two switching states are different, the capacitor will charge and discharge, thus affecting the midpoint voltage. This part represents the contribution of the capacitor (C) to the rate of change of the midpoint voltage. The larger the capacitance value, the smaller the rate of change of the midpoint voltage, meaning the capacitor can stabilize the midpoint voltage.

[0086] For example, please continue to see Figure 2a When using carrier stacking modulation, if the per-unit modulation voltage is m x When m xWhen S is greater than carrier C2, then x2 It is activated, and its duty cycle is 2m. x-1 When m x When S is greater than carrier C1, x1 When activated, its duty cycle is 1, and the waveform diagram of the modulated wave is shown below. Figure 4a As shown. When using carrier stacked modulation, if the per-unit modulation voltage is m x When m x When S is greater than carrier C2, then x2 When activated, its duty cycle is 0. When m... x When S is greater than carrier C1, x1 It is activated, and its duty cycle is 2m. x This model is not limited by the modulation method; the waveform diagram of the modulated wave is shown below. Figure 4b As shown.

[0087] In this embodiment, through meticulous circuit topology identification, the establishment of accurate first relationship models for output current and second relationship models for midpoint voltage, and the integration and optimization of the final state estimation model, this series of steps significantly improves the design level and operational efficiency of the energy storage converter control system. The state estimation model can predict output current and midpoint voltage in real time, providing a solid data foundation for refined system control and ensuring efficient and stable energy conversion. Even if some sensors fail, the system can estimate key operating parameters through the model, reducing absolute dependence on physical sensors and avoiding system runaway due to sensor failure, thus improving system safety and reliability. The model-based control strategy can more accurately adjust the states of switching devices, optimize output current and midpoint voltage, reduce energy loss, and improve energy conversion efficiency.

[0088] In one embodiment, such as Figure 5 As shown, step S220 involves adjusting the switching states of each switch in the energy storage converter based on the analog output feedback to control the operating state of the energy storage converter. This includes steps S500-S510:

[0089] Step S500: Determine the midpoint voltage and output current of the energy storage converter based on the simulated output.

[0090] The neutral point voltage is the voltage of the neutral point of the energy storage converter relative to ground.

[0091] Specifically, the system calculates estimated values ​​of the neutral point voltage and output current of the energy storage converter using a state estimation model. The neutral point voltage, or voltage between the converter's neutral point and ground, is a crucial indicator of the voltage balance within the converter's internal capacitors. The output current, on the other hand, is the actual current transmitted by the converter to the grid or load, directly reflecting the converter's efficiency and operating status.

[0092] For example, the real-time input signal of the energy storage converter is fed into a pre-established state estimation model, which outputs simulated values ​​of the midpoint voltage and output current based on the converter's circuit topology and dynamic equations. The model output data needs further analysis to extract the specific values ​​of the midpoint voltage and output current, which serve as the basis for subsequent control and analysis.

[0093] Step S510: If it is determined that there is a voltage deviation in the midpoint voltage and / or a current deviation in the output current, the switching state of each switch of the energy storage converter is controlled according to the voltage deviation and / or current deviation in order to reduce the voltage deviation and / or current deviation.

[0094] Specifically, when the midpoint voltage and / or output current of the analog output deviate from the target value, the system will take action to reduce these deviations by adjusting the operating states of the switches inside the converter, thereby ensuring the stable and efficient operation of the converter.

[0095] For example, the simulated values ​​output by the model are compared with the expected target values ​​to calculate the voltage deviation of the midpoint voltage and the current deviation of the output current. Once a deviation is detected, the system will activate a preset control algorithm, such as a PID controller or a fuzzy logic controller, to calculate an appropriate switching state adjustment based on the magnitude and direction of the deviation. The adjustment obtained from the control algorithm is applied to the converter's switching control system to change the on / off state of the switching devices, thereby reducing the voltage deviation of the midpoint voltage and / or the current deviation of the output current.

[0096] In this embodiment, the simulated output provided by the state estimation model serves as the basis for control. Even in the event of sensor failure, control can still be achieved through model prediction, improving the system's adaptability and robustness. When sensors fail or data is abnormal, the system can still perform control based on the state model, reducing the risk of system downtime due to equipment failure.

[0097] In one embodiment, such as Figure 6 As shown, the method further includes steps S600-S630:

[0098] If the deviation between the simulated output and the actual output of the energy storage converter acquired by pre-set sensors is within a preset range:

[0099] Step S600: Determine the first capacitor voltage at the current moment based on the change in the midpoint voltage of the energy storage converter in the simulated output and the first capacitor voltage at the previous moment.

[0100] Wherein, the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground, and the first capacitor voltage is the capacitor voltage between the neutral point of the energy storage converter and the first bus.

[0101] Specifically, when the deviation between the output of the state estimation model (simulated output) and the actual data collected by the sensor (actual output) is within an acceptable range, it indicates that the model prediction is quite consistent with the actual operating state. At this time, the system can use the model's output to update the estimated value of the capacitor voltage, especially for the estimation of the first capacitor voltage. Combining the change in the midpoint voltage and the first capacitor voltage at the previous moment, the system can deduce the first capacitor voltage at the current moment.

[0102] For example, the change of the midpoint voltage over time is extracted from the output data of the state estimation model. Based on the change in midpoint voltage and the value of the first capacitor voltage at the previous moment, the estimated value of the first capacitor voltage at the current moment is updated using an appropriate mathematical formula or algorithm. This typically involves an iterative update process of the capacitor voltage dynamic model to ensure that the estimated capacitor voltage reflects the real-time operating state of the converter.

[0103] Step S610: Determine the current voltage of the second capacitor based on the change in the midpoint voltage of the energy storage converter in the simulated output and the voltage of the second capacitor at the previous moment.

[0104] The second capacitor voltage is the capacitor voltage between the neutral point of the energy storage converter and the second bus.

[0105] Specifically, when the deviation between the model output and the actual output is within a preset range, the system can update the estimated value of the second capacitor voltage. This process relies on the change in midpoint voltage and the data of the second capacitor voltage at the previous moment, ensuring the real-time and accurate estimation of the second capacitor voltage.

[0106] Step S620: Update the parameters related to capacitor voltage in the state estimation model based on the first capacitor voltage and the second capacitor voltage at the current moment.

[0107] Specifically, after updating the capacitor voltage estimates in the first two steps, the system then uses these estimates to update the parameters of the state estimation model. This process aims to improve the model's adaptability and accuracy, ensuring that the model more accurately reflects the real-time state of the converter.

[0108] For example, recursive least squares (RLS) or other parameter adaptive algorithms are used to update capacitor voltage-related parameters in the model based on the first and second capacitor voltages at the current moment. After the parameters are updated, the state estimation model is validated. By comparing the model output with the actual output, the improvement in the model's prediction accuracy is ensured. If the deviation between the model prediction and the actual data is still large, the system should further optimize the parameter update algorithm to improve the accuracy and stability of capacitor voltage-related parameters.

[0109] For example, the voltage of the first capacitor in the model Second capacitor voltage The initial state can be set to half of the DC side voltage, and subsequent states use the following system iteration values.

[0110]

[0111]

[0112] in, and These are the first bus capacitor voltages at times k+1 and k, respectively, used in the state estimation model. and These are the second bus capacitor voltages at times k+1 and k, respectively, used in the state estimation model. and These are the estimated voltage differences between the first and second bus capacitors at times k and k-1, respectively. When the system detects a midpoint voltage deviation... When the voltage of the first bus capacitor changes, Adjustments will be made based on this change. If the estimated value of the midpoint voltage deviation increases at the current time (k) relative to the previous time (k-1), then the voltage of the first bus capacitor will be adjusted. The voltage will increase accordingly, and vice versa. This reflects that when the voltage of the first bus capacitor is relatively high, the system tends to use control strategies to allow charge to flow to the second bus capacitor in order to rebalance the capacitor voltage. Similarly, the voltage of the second bus capacitor... The update strategy is the opposite of that of the upper bus. If the midpoint voltage deviation increases at the current moment, then the second bus capacitor voltage... The voltage will decrease if the voltage is too high, and increase if the voltage is too low. This update strategy ensures a relative balance in capacitor voltage, avoiding device overvoltage and system efficiency degradation caused by voltage imbalance.

[0113] These two formulas embody the maintenance strategy of dynamic voltage balance of capacitors in energy storage converters. By monitoring changes in the neutral point voltage deviation in real time and adjusting the values ​​of the upper and lower bus capacitor voltages accordingly, the system can proactively correct capacitor voltage imbalances and maintain the capacitor voltages within a stable range. This improves the stability and efficiency of converter operation and reduces losses during energy conversion.

[0114] And, in step S630, a correction value is added to the state estimation model based on the deviation value so that the simulated output is consistent with the actual output.

[0115] Specifically, to further improve the model's prediction accuracy, the system adds correction values ​​to the state estimation model based on the deviation between the model output and the actual output. This correction process ensures that the model predictions are as consistent as possible with the actual operating state, reducing model prediction errors.

[0116] For example, the deviations between the midpoint voltage and output current predicted by the model and the actual sensor data are calculated in real time. Using the deviation values, the output of the state estimation model is adjusted through iterative correction algorithms (such as least squares or gradient descent) until the deviation between the simulated output and the actual output falls within an acceptable range.

[0117] For example, the average value of the variables in the circuit is used to replace their instantaneous values ​​within a control cycle, so as to achieve the purpose of representing the nonlinear system as an equivalent linear system. The average value of the switching state values ​​is determined by the specific modulation method, and each operating state of the three-level PCS corresponds to a unique combination of switching sequences. Considering that the general control frequency is much larger than the reference voltage frequency, the modulation wave can be considered to be basically the same within a control cycle. Therefore, the state model can be obtained by substituting the duty cycle expression of the switching function into the above equation, and the output current error is used as feedback to correct the observer. The state estimation model after introducing closed-loop control is as follows:

[0118]

[0119] Among them, G 1、 G1 is the control feedback gain, and e is the deviation between the simulated output and the actual output of the energy storage converter as collected by pre-set sensors. This is the correction value for the output current. This is the correction value for the midpoint voltage, i.e. The other parameters in the formula have been described above and can be directly referred to the above embodiments without further explanation.

[0120] In this embodiment, within the allowable range of deviation, the capacitor voltage estimate is updated using the midpoint voltage change and the capacitor voltage value from the previous moment, improving the accuracy and real-time performance of the state estimation model. By updating the capacitor voltage-related parameters in the model in real time, the state estimation model can adapt to changes in the converter's operating state, improving the adaptability and prediction accuracy of the control strategy. Based on the deviation between the simulated output and the actual output, a correction value is added to the state estimation model, achieving self-optimization adjustment between the model's prediction results and the actual operating state, further ensuring the stability of system operation and the accuracy of control.

[0121] In one embodiment, such as Figure 7 As shown, the method further includes steps S700-S720:

[0122] If the deviation between the simulated output and the actual output of the energy storage converter acquired by pre-set sensors is within a preset range:

[0123] Step S700: Determine the current bus capacitance based on the bus capacitance of the energy storage converter at the previous moment, the output current of the energy storage converter, and the bus capacitance of the energy storage converter at the previous moment in the simulated output situation.

[0124] Among them, the bus capacitor is the capacitor on the DC side of the energy storage converter.

[0125] Specifically, when the deviation between the output of the state estimation model (simulated output) and the data collected by actual sensors (actual output) remains within a preset range, it indicates that the model prediction is largely consistent with the actual operating state. Based on this, the system can use the bus capacitance value collected by sensors at the previous moment, the converter's output current, and the bus capacitance value predicted by the model at the previous moment to calculate the current bus capacitance value. This step is crucial for real-time monitoring and control of the energy storage converter's capacitance state, helping to maintain system stability and optimize energy conversion efficiency.

[0126] For example, the model output is continuously compared with sensor data to calculate the deviation value, ensuring it does not exceed a preset range. This typically involves setting a reasonable threshold as a benchmark for judging the accuracy of the model output. Using the actual bus capacitance value, actual output current, and model-predicted bus capacitance value from the previous moment, combined with the working principle and circuit model of the energy storage converter, the bus capacitance value is updated through an iterative algorithm or mathematical model. Specifically, the current bus capacitance value can be calculated using the dynamic equation of capacitor voltage, which considers the physical processes of capacitor charging and discharging, as well as the influence of switching states on capacitor charging and discharging.

[0127] Step S710: Based on the determined bus capacitance at each moment, statistically determine the variation characteristics of the bus capacitance.

[0128] Specifically, based on continuously acquiring bus capacitance values, the system will statistically analyze the variation patterns of these capacitance values ​​over time, i.e., their variation characteristics. This process not only helps to understand the charging and discharging dynamics of the capacitors but also helps to identify potential signs of capacitor aging or other performance degradation, providing data support for capacitor maintenance and optimization strategies.

[0129] For example, the bus capacitance value is continuously recorded at every moment, which typically involves using a data logger or internal memory to store the capacitance value data stream. Statistical analysis methods, such as time series analysis, trend analysis, or machine learning algorithms, are then employed to identify patterns or trends in the bus capacitance value's changes. This helps predict the future state of the bus capacitance and allows for proactive measures to address potential capacitance performance degradation.

[0130] Step S720: Based on the changing characteristics of the bus capacitance, correct the parameters related to the bus capacitance in the state estimation model.

[0131] Specifically, based on the changing characteristics of the bus capacitance value, the system will adjust the parameters related to the bus capacitance in the state estimation model to improve the model's prediction accuracy and stability. This process is part of model adaptation and optimization, aiming to make the model more closely resemble the actual operating state of the converter, thereby improving control performance.

[0132] For example, by utilizing the changing characteristics of the bus capacitance, parameters in the model that need adjustment can be identified, such as the equivalent series resistance, capacitance value, and charging / discharging efficiency of the capacitor. The model parameters are then updated in real time using recursive least squares (RLS) or other adaptive parameter update algorithms to ensure that the model accurately reflects the dynamic characteristics of the capacitor. After the parameters are updated, the model's predictive ability is re-verified by comparing the model predictions with actual operating data to evaluate the degree of improvement in model performance. If the model performance does not meet expectations, the parameter update algorithm should be further adjusted, the model structure optimized, and the prediction accuracy improved.

[0133] For example, to further reduce the difference between the nominal and actual values ​​of parameters in the model, the capacitance value C is updated in real time using the recursive least squares method, thereby improving the accuracy of the model.

[0134]

[0135] in, and These are the capacitance identification parameters at times k+1 and k, respectively. This is the actual measured value of the capacitor voltage at time k. This is the estimated value of the capacitor voltage at time k. It is a control cycle. It is the load current at time k. This is the gain coefficient. When the sensor malfunctions, the actual measured value is inaccurate, therefore the above iteration needs to be terminated. This formula describes the algorithm that describes how the capacitance value C is adaptively updated in real time within each control cycle based on the actual measured midpoint voltage deviation and the estimated midpoint voltage deviation value from the state estimation model. This represents the capacitance identification parameter at the current time (k), which is the current estimate of the capacitance C. The accuracy of the capacitance identification parameter is crucial for the state estimation model, affecting the model's prediction of capacitor voltage changes. This represents the capacitance identification parameter at the next time step (k+1), i.e., the updated estimated capacitance value. This represents the actual measured value of the capacitor voltage at time k, which is data directly obtained from the sensor and is used to evaluate the accuracy of the state estimation model. The estimated value of the capacitor voltage at time k is based on the prediction of the state estimation model and is used to compare with the actual measured value to find possible deviations. The load current at time k is denoted by k. The magnitude of the load current affects the charging and discharging process of the capacitor, and thus affects the change in the midpoint voltage. The control cycle refers to the time interval between each control decision. The selection of the control cycle should take into account the system's response speed and computing power. This represents the gain factor, used to adjust the magnitude of capacitor value updates. The choice of gain factor requires a trade-off between update speed and stability; an excessively large gain factor may cause issues. This could lead to oscillations in the estimated capacitance value, while an excessively small value could cause this. This could cause the estimated value to lag behind the actual value. This formula reflects an adaptive dynamic update mechanism; by comparing the actual measured value of the capacitor voltage with the estimated value from the state estimation model, the system can adjust the estimated value of the capacitor identification parameter C in real time. If there is a significant difference between the actual measured value and the estimated value, it indicates that the capacitor value estimation in the model may be inaccurate and needs to be corrected. When the deviation of the actual measured midpoint voltage exceeds the estimated value from the model, i.e. >0), which means that the actual value of the capacitor may be greater than the current estimate, in the formula. This will increase. Conversely, if the actual measured midpoint voltage deviation is less than the model estimate, i.e. ( If <0), then the actual value of the capacitor may be less than the current estimate, as shown in the formula. This will reduce [the impact of the current loss]. Through this recursive update mechanism, the state estimation model can correct the estimated capacitance value online, improving prediction accuracy. Especially in the event of sensor failure or data anomalies, it can still maintain control of the energy storage converter through model estimation, ensuring stable system operation. This method of real-time adaptively updating capacitance parameters enhances the robustness and adaptability of the energy storage converter control system, which is of great significance for improving the stability and efficiency of the system in complex environments.

[0136] In this embodiment, when the model prediction matches the actual operating state, the system can determine the bus capacitance value in real time and accurately, providing a solid data foundation for capacitor voltage balance control. Through statistical analysis of the bus capacitance variation characteristics, the system can identify the capacitor's health status and performance degradation trends, providing a scientific basis for capacitor maintenance and optimization strategies. Utilizing the capacitance variation characteristics to correct model parameters improves the prediction accuracy and stability of the state estimation model, enhances the adaptability and robustness of the model-based control strategy, and ensures the converter's efficient and stable operation under complex conditions.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0138] This embodiment also provides a control device for an energy storage converter, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0139] Figure 8 This is a structural block diagram of an optional control device for an energy storage converter according to an embodiment of this application. Figure 8 As shown, it includes:

[0140] The signal acquisition module 801 is used to acquire the input signals received by the energy storage converter. The input signals include the operating status of each switch of the energy storage converter, the operating parameters of the energy storage converter, and the parameters of the connected power grid.

[0141] The output determination module 802 is used to control the switching state of each switch of the energy storage converter according to the input signal to adjust the output of the energy storage converter, and to input the input signal into the pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter output by the state estimation model.

[0142] The control module 803 is used to determine a sensor fault when the deviation between the simulated output and the actual output of the energy storage converter collected by a pre-set sensor exceeds a preset range, and to adjust the switching state of each switch of the energy storage converter based on the feedback of the simulated output to control the operating state of the energy storage converter.

[0143] In an exemplary embodiment, the output determination module 802 is further configured to: calculate the midpoint voltage and output current of the energy storage converter based on the input signal and the actual output of the energy storage converter collected by the sensor, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground. If a voltage deviation exists in the midpoint voltage and / or a current deviation exists in the output current, the switching state of each switch of the energy storage converter is controlled according to the voltage deviation and / or current deviation to reduce the voltage deviation and / or current deviation.

[0144] In an exemplary embodiment, the above-described apparatus is further configured to: acquire the circuit topology of the energy storage converter, wherein the circuit topology includes parameters and connection relationships of the switching devices, grid-connected inductor, grid-connected resistor, and grid voltage included in the energy storage converter; establish a first relationship model of the output current of the energy storage converter based on the circuit topology, wherein the first relationship model includes expressions relating the influence of the switching devices, grid-connected inductor, grid-connected resistor, and grid voltage on the output current; and establish a second relationship model of the midpoint voltage of the energy storage converter based on the circuit topology, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground, and the second relationship model includes expressions relating the influence of the switching devices, output current, and bus capacitance on the midpoint voltage. The state estimation model includes both the first and second relationship models.

[0145] In an exemplary embodiment, the control module 803 is further configured to: determine the midpoint voltage and output current of the energy storage converter based on the analog output, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground. If a voltage deviation exists in the midpoint voltage and / or a current deviation exists in the output current, the control module controls the switching states of each switch of the energy storage converter based on the voltage deviation and / or current deviation to reduce the voltage deviation and / or current deviation.

[0146] In an exemplary embodiment, the apparatus is further configured to: when the deviation between the simulated output and the actual output of the energy storage converter acquired by a pre-set sensor is within a preset range: determine the current first capacitor voltage based on the change in the midpoint voltage of the energy storage converter in the simulated output and the first capacitor voltage at the previous moment, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground, and the first capacitor voltage is the capacitor voltage between the neutral point of the energy storage converter and the first bus. determine the current second capacitor voltage based on the change in the midpoint voltage of the energy storage converter in the simulated output and the second capacitor voltage at the previous moment, wherein the second capacitor voltage is the capacitor voltage between the neutral point of the energy storage converter and the second bus. update the capacitor voltage-related parameters in the state estimation model based on the current first and second capacitor voltages. And, based on the deviation value, add a correction value to the state estimation model to make the simulated output consistent with the actual output.

[0147] In an exemplary embodiment, the apparatus is further configured to: determine the current bus capacitance based on the bus capacitance of the energy storage converter at the previous moment, the output current of the energy storage converter, and the bus capacitance of the energy storage converter at the previous moment in the simulated output, provided that the deviation between the simulated output and the actual output of the energy storage converter acquired by a pre-set sensor is within a preset range; wherein the bus capacitance is the DC-side capacitance of the energy storage converter; statistically determine the changing characteristics of the bus capacitance based on the determined bus capacitance at each moment; and correct the parameters related to the bus capacitance in the state estimation model based on the changing characteristics of the bus capacitance.

[0148] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.

[0149] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:

[0150] S1, acquire the input signals received by the energy storage converter, wherein the input signals include the operating status of each switch of the energy storage converter, the operating parameters of the energy storage converter, and the parameters of the connected power grid.

[0151] S2 controls the switching states of each switch of the energy storage converter according to the input signal to adjust the output of the energy storage converter, and inputs the input signal into the pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter from the state estimation model.

[0152] S3, if the deviation between the simulated output and the actual output of the energy storage converter collected by the pre-set sensor exceeds the preset range, determine that the sensor is faulty, and adjust the switching state of each switch of the energy storage converter based on the feedback of the simulated output to control the operating state of the energy storage converter.

[0153] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0154] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0155] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0156] S1, acquire the input signals received by the energy storage converter, wherein the input signals include the operating status of each switch of the energy storage converter, the operating parameters of the energy storage converter, and the parameters of the connected power grid.

[0157] S2 controls the switching states of each switch of the energy storage converter according to the input signal to adjust the output of the energy storage converter, and inputs the input signal into the pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter from the state estimation model.

[0158] S3, if the deviation between the simulated output and the actual output of the energy storage converter collected by the pre-set sensor exceeds the preset range, determine that the sensor is faulty, and adjust the switching state of each switch of the energy storage converter based on the feedback of the simulated output to control the operating state of the energy storage converter.

[0159] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0160] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium storing the computer program product, wherein the computer program, when executed by a processor, implements the steps of the methods in various embodiments of this application.

[0161] Optionally, in this embodiment, the computer program described above can be configured to perform the following steps when executed by a processor:

[0162] S1, acquire the input signals received by the energy storage converter, wherein the input signals include the operating status of each switch of the energy storage converter, the operating parameters of the energy storage converter, and the parameters of the connected power grid.

[0163] S2 controls the switching states of each switch of the energy storage converter according to the input signal to adjust the output of the energy storage converter, and inputs the input signal into the pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter from the state estimation model.

[0164] S3, if the deviation between the simulated output and the actual output of the energy storage converter collected by the pre-set sensor exceeds the preset range, determine that the sensor is faulty, and adjust the switching state of each switch of the energy storage converter based on the feedback of the simulated output to control the operating state of the energy storage converter.

[0165] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0166] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0167] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A control method for an energy storage converter, characterized in that, Applied to energy storage converters, the method includes: The input signal received by the energy storage converter is acquired, wherein the input signal includes the operating status of each switch of the energy storage converter, the operating parameters of the energy storage converter, and the parameters of the connected power grid; The switching states of each switch of the energy storage converter are controlled according to the input signal to adjust the output of the energy storage converter, and the input signal is input into the pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter output by the state estimation model. If the deviation between the simulated output and the actual output of the energy storage converter acquired by the pre-set sensor exceeds a preset range, the sensor is determined to be faulty, and the switching states of each switch of the energy storage converter are adjusted based on the simulated output to control the operating state of the energy storage converter.

2. The control method for the energy storage converter according to claim 1, characterized in that, The step of controlling the switching states of each switch of the energy storage converter according to the input signal to adjust the output of the energy storage converter includes: Based on the input signal and the actual output of the energy storage converter collected by the sensor, the midpoint voltage and output current of the energy storage converter are calculated, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground; If a voltage deviation is found in the midpoint voltage and / or a current deviation is found in the output current, the switching state of each switch of the energy storage converter is controlled according to the voltage deviation and / or the current deviation to reduce the voltage deviation and / or the current deviation.

3. The control method for the energy storage converter according to claim 1, characterized in that, Before inputting the input signal into the pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter from the state estimation model, the method further includes: Obtain the circuit topology of the energy storage converter, wherein the circuit topology includes the parameters and connection relationships of the switching devices, grid-connected inductor, grid-connected resistor, and grid voltage included in the energy storage converter; Based on the circuit topology, a first relationship model is established for the output current of the energy storage converter, wherein the first relationship model includes the relationship between the switching device, the grid-connected inductor, the grid-connected resistor, and the grid voltage on the output current; A second relationship model for the midpoint voltage of the energy storage converter is established based on the circuit topology, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground, and the second relationship model includes the relationship between the switching devices, the output current, and the bus capacitance on the midpoint voltage; The state estimation model includes the first relation model and the second relation model.

4. The control method for the energy storage converter according to claim 1, characterized in that, The method of adjusting the switching states of each switch of the energy storage converter based on the feedback of the simulated output to control the operating state of the energy storage converter includes: The midpoint voltage and output current of the energy storage converter are determined based on the simulated output, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground; If a voltage deviation is found in the midpoint voltage and / or a current deviation is found in the output current, the switching state of each switch of the energy storage converter is controlled according to the voltage deviation and / or the current deviation to reduce the voltage deviation and / or the current deviation.

5. The control method for the energy storage converter according to any one of claims 1-4, characterized in that, The method further includes: If the deviation between the simulated output and the actual output of the energy storage converter acquired by a pre-set sensor is within the preset range: Based on the change in the midpoint voltage of the energy storage converter in the simulated output and the first capacitor voltage at the previous moment, the first capacitor voltage at the current moment is determined, wherein the midpoint voltage is the voltage of the neutral point of the energy storage converter relative to ground, and the first capacitor voltage is the capacitor voltage between the neutral point of the energy storage converter and the first bus. Based on the change in the midpoint voltage of the energy storage converter in the simulated output and the second capacitor voltage at the previous moment, the second capacitor voltage at the current moment is determined, wherein the second capacitor voltage is the capacitor voltage between the neutral point and the second bus of the energy storage converter; The parameters related to capacitor voltage in the state estimation model are updated based on the current capacitor voltage and the second capacitor voltage. Furthermore, based on the deviation value, a correction value is added to the state estimation model to make the simulated output consistent with the actual output.

6. The control method for the energy storage converter according to claim 5, characterized in that, The method further includes: If the deviation between the simulated output and the actual output of the energy storage converter acquired by the pre-set sensor is within a preset range, the bus capacitance at the current moment is determined based on the bus capacitance of the energy storage converter at the previous moment acquired by the sensor, the output current of the energy storage converter, and the bus capacitance of the energy storage converter at the previous moment in the simulated output, wherein the bus capacitance is the capacitance on the DC side of the energy storage converter; Based on the determined bus capacitance at each moment, the variation characteristics of the bus capacitance are statistically determined. Based on the changing characteristics of the bus capacitance, the parameters related to the bus capacitance in the state estimation model are corrected.

7. A control device for an energy storage converter, characterized in that, The device includes: The signal acquisition module is used to acquire the input signals received by the energy storage converter, wherein the input signals include the operating status of each switch of the energy storage converter, the operating parameters of the energy storage converter, and the parameters of the connected power grid; The output determination module is used to control the switching state of each switch of the energy storage converter according to the input signal to adjust the output of the energy storage converter, and to input the input signal into a pre-established state estimation model of the energy storage converter to obtain the simulated output of the energy storage converter output by the state estimation model. The control module is used to determine that the sensor is faulty when the deviation between the simulated output and the actual output of the energy storage converter collected by the preset sensor exceeds a preset range, and to adjust the switching state of each switch of the energy storage converter based on the feedback of the simulated output to control the operating state of the energy storage converter.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 1 to 6.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method of any one of claims 1 to 6 through the computer program.

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

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