Method and system for controlling and adjusting network construction type energy storage equipment based on four-quadrant converter

Abstract

The invention discloses a four-quadrant converter-based network construction type energy storage equipment control regulation method and system, and belongs to the field of intelligent power grid control, and the method comprises the steps: employing a double-closed-loop control strategy to regulate the voltage of a DC bus, and dynamically adjusting the reference value of the voltage of the DC bus; coordinated optimization is performed on a global power scheduling layer and a local power control layer, active power and reactive power are adjusted in real time by using a PQ control mode, and power output is adjusted in combination with the charge state of the energy storage system; identifying a power grid fault condition, detecting an island state in real time based on a deep learning model, and adjusting inertia of a virtual synchronous machine through a four-quadrant converter; according to an instantaneous reactive power theory, reactive power requirements are calculated; data analysis is carried out on intelligent fault prediction based on edge calculation, and a distributed redundancy protection mechanism is adopted to recover faults. Through the four-quadrant converter, the power output of the energy storage equipment is adjusted, the power grid is quickly stabilized, quick response is provided when the power grid is disturbed, and the risks of instability and voltage fluctuation of the power grid are reduced.

Description

Technical Field

[0001] The present invention belongs to the field of smart grid control technology, and more specifically, relates to a control and regulation method and system for a grid-connected energy storage device based on a four-quadrant converter. Background Art

[0002] With the widespread adoption of renewable energy sources (such as solar and wind power), power systems face challenges with unstable and intermittent power supply. Energy storage systems, particularly electrochemical energy storage systems, play a key role in power grids, balancing power loads, regulating grid voltage and frequency, and mitigating peaks and valleys in power demand. Energy storage systems not only store excess energy but also release energy during peak load periods to maintain grid stability. Therefore, efficiently and reliably managing the charging and discharging processes of energy storage systems, particularly when exchanging power with the grid, has become a key focus of technical research. Four-quadrant converters are power electronic devices that enable simultaneous bidirectional flow of electrical energy. They are widely used in systems requiring bidirectional energy transfer, such as electric vehicle charging and interaction between energy storage devices and the grid. In a four-quadrant converter, the four quadrants represent different directions of power exchange: forward charging, reverse discharge, and forward discharge and reverse charging. Therefore, four-quadrant converters can flexibly control battery charging and discharging processes and provide effective regulation and compensation for grid load fluctuations.

[0003] Traditional converters may only support unidirectional power flow, but the introduction of four-quadrant converters has greatly improved the interaction between energy storage systems and the power grid, particularly in terms of grid stability, voltage regulation, and frequency regulation. With the intelligent development of modern power systems, smart grid technology has become a research focus in the power sector. Smart grids enable efficient power distribution and system stability through real-time monitoring and control of all aspects of the power grid. The integration of energy storage systems with the power grid is a key component of smart grid development. Through intelligent scheduling and control, energy storage systems can adjust power output in real time according to grid demand, ensuring load balance and optimizing power quality. By adopting advanced control algorithms and automated regulation mechanisms, smart grids can achieve efficient utilization of energy storage devices, thereby improving system reliability, stability, and flexibility. Existing energy storage systems often suffer from low control accuracy and inflexible energy regulation. Existing control methods may not be able to respond quickly enough to grid fluctuations, resulting in large fluctuations in grid frequency or voltage. Furthermore, the integration of multiple energy sources and dynamic load regulation in the grid present complex power scheduling and coordination issues between energy storage devices and the grid. Traditional energy storage systems, when faced with complex operating conditions and rapid disturbances in the power grid (such as short-term voltage drops or sudden load changes), are unable to adjust power output in a timely manner, resulting in grid instability. The control methods of energy storage systems cannot accurately track grid demand during multi-energy grid connection and dynamic load regulation. Current grid-following energy storage systems operate as slave units, passively following grid instructions, and are unable to actively maintain grid frequency and voltage stability.

[0004] In view of the above or existing problems of the control and regulation method and system of grid-type energy storage equipment based on four-quadrant converter, the present invention is proposed.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] An embodiment of the present invention provides a control and regulation method for a grid-type energy storage device based on a four-quadrant converter, including: using a dual closed-loop control strategy to regulate the DC bus voltage, and using the four-quadrant converter's rapid bidirectional energy flow to stabilize the bus voltage when the grid is disturbed, and dynamically adjusting the DC bus voltage reference value according to different working conditions; based on a hierarchical control strategy, coordinated optimization is performed at the global power scheduling layer and the local power control layer respectively, and the PQ control mode is used to adjust the active and reactive power in real time, and the power output is adjusted in combination with the charge state of the energy storage system; identifying grid fault conditions, switching to off-grid mode within milliseconds, detecting the island state in real time based on a deep learning model, and regulating the virtual synchronous machine inertia through the four-quadrant converter; according to the instantaneous reactive power theory, the reactive demand is calculated in real time, and the reactive compensation strategy is dynamically adjusted; data analysis is performed based on intelligent fault prediction of edge computing, and a distributed redundant protection mechanism is used to recover from the fault.

[0007] As a preferred solution of the grid-forming energy storage device control and regulation method based on a four-quadrant converter according to the present invention, the following steps are included: A dual-closed-loop control strategy is adopted to regulate the DC bus voltage. During grid disturbances, the four-quadrant converter is utilized to enable rapid bidirectional energy flow to stabilize the bus voltage. For different working conditions, the reference value of the DC bus voltage is dynamically adjusted, including: calculating the bus voltage error, using a PI controller to calculate the reference current, sending the reference current to the current inner loop for closed-loop regulation, and calculating the disturbance trend and adjusting the reference current in advance based on a neural network; calculating the current error, using a PI controller to calculate the PWM duty cycle, and sending the PWM signal to the four-quadrant converter to achieve bidirectional energy flow control: when the initial voltage is lower than the set value, the converter activates the boost mode to absorb energy from the grid and compensate the bus voltage; when the initial voltage is higher than the set value, the converter activates the buck mode to discharge to the energy storage unit and suppress voltage overshoot; if there is a short-term grid voltage drop, the grid voltage reduction is detected, and the bus voltage reference value is increased to increase the power extraction from the energy storage unit by the converter; if there is a load mutation and a sudden increase in load power is detected, the target voltage is reduced to enable the converter to absorb more energy in a short time.

[0008] As a preferred solution of the grid-forming energy storage device control and regulation method based on a four-quadrant converter according to the present invention, the following steps are included: Based on a hierarchical control strategy, coordinated optimization is carried out at the global power scheduling layer and the local power control layer respectively, including: calculating the desired bus voltage, increasing the bus voltage when the new energy output increases; reducing the target voltage when the load suddenly increases to absorb excess electrical energy, and gradually adjusting the bus voltage when the energy storage SOC approaches the upper / lower limit; calculating the desired active / reactive power, using an optimization algorithm to allocate power, and sending the power command to the local power control layer.

[0009] As a preferred solution of the grid-forming energy storage device control and regulation method based on a four-quadrant converter according to the present invention, the following steps are included: The PQ control mode is used to adjust the active and reactive power in real time, and the power output is adjusted in combination with the state of charge of the energy storage system, including: calculating a preliminary set value according to the grid power demand, dynamically adjusting it in combination with the energy storage SOC. If SOC < 20%, the discharge power is limited; if SOC is too high > 90%, the charging power is limited; if 20% < SOC < 90%, power tracking is performed according to the active power; calculating the reactive power reference value, dynamically adjusting it according to the grid voltage situation. If the voltage < 0.95 p.u., inductive reactive power Q > 0 is provided; if the voltage is too high > 1.05 p.u., capacitive reactive power Q < 0 is provided; if 0.95 < voltage < 1.05 p.u., the reactive power is maintained stable.

[0010] As a preferred solution of the control and adjustment method for grid-connected energy storage equipment based on a four-quadrant converter described in the present invention, the method includes: identifying grid fault conditions, detecting islanding status in real time based on a deep learning model, and adjusting the inertia of the virtual synchronous machine through the four-quadrant converter, including: in islanding mode, using virtual synchronous machine control to simulate the inertia and damping characteristics of the synchronous generator, calculating frequency deviation, rotational inertia compensation and power adjustment, using dynamic inertia adjustment to provide additional inertia support in the event of sudden load changes, and adjusting the VSG output in advance in combination with grid frequency prediction; when the grid returns to normal, executing grid-connected control and combining Kalman filtering to improve synchronization accuracy, and combining adaptive neural networks to optimize the grid-connected switching strategy.

[0011] As a preferred solution of the control and regulation method of the grid-type energy storage device based on the four-quadrant converter described in the present invention, wherein: according to the instantaneous reactive power theory, the reactive demand is calculated in real time, and the reactive compensation strategy is dynamically adjusted, including: real-time acquisition of the three-phase grid voltage and current, using Clarke transformation, converting the three-phase voltage and current to the α-β stationary coordinate system, combining with the variable step size LMS filter, and adjusting the Clarke transformation parameters in real time; calculating the reactive compensation amount, when the grid voltage is low Q comp >0, inject reactive power into the system. When the grid voltage is high, Q comp <0, absorb reactive power from the system; set the reactive current reference value, control the converter output current, use SVPWM to control the converter, and output compensation current.

[0012] As a preferred solution of the control and regulation method for a grid-type energy storage device based on a four-quadrant converter described in the present invention, data analysis is performed based on intelligent fault prediction based on edge computing, and a distributed redundant protection mechanism is used to recover from the fault, including:

[0013] LSTM and Transformer are used to perform short-term and long-term trend analysis, predict energy storage system faults, set health score thresholds, and determine the fault level based on the device status. The fault health score formula is as follows:

[0014]

[0015] Where N is the total number of parameters, X i is the actual value of the i-th monitoring parameter, X min is the minimum value of the i-th parameter, X maxis the maximum value of the i-th parameter; if the health status score is >85%, it is in a healthy state and the system is operating normally; if the mild abnormality score is 60% < score <85%, it is a mild abnormality and the equipment issues an early warning; if the score is <60%, it is a serious abnormality and the fault protection mechanism is triggered; when a fault is detected, the system immediately starts the intelligent fault location algorithm to determine the specific location of the fault. If the battery unit fails, the charge state, temperature and voltage changes of the energy storage unit are detected to determine whether there is an abnormal unit; if the inverter fails, the output voltage, current and frequency changes are analyzed to determine whether there is an inverter fault; if there is a grid-side fault, it switches to off-grid mode and starts the independent power supply function.

[0016] A grid-type energy storage device control and regulation system based on a four-quadrant converter includes: a voltage control module for regulating the DC bus voltage using a dual closed-loop control strategy. When the grid is disturbed, the bus voltage is stabilized by the rapid bidirectional flow of energy of the four-quadrant converter, and the DC bus voltage reference value is dynamically adjusted according to different working conditions; a power regulation module for performing coordinated optimization at the global power scheduling layer and the local power control layer based on a hierarchical control strategy, adjusting active and reactive power in real time using the PQ control mode, and regulating power output in combination with the charge state of the energy storage system; an operation control module for identifying grid fault conditions, switching to off-grid mode within milliseconds, detecting islanding status in real time based on a deep learning model, and regulating the inertia of the virtual synchronous machine through the four-quadrant converter; a reactive compensation module for calculating reactive power demand in real time according to instantaneous reactive power theory and dynamically adjusting the reactive compensation strategy; an abnormal protection module for performing data analysis based on intelligent fault prediction of edge computing and recovering from faults using a distributed redundant protection mechanism.

[0017] A computing device, comprising:

[0018] at least one processor, memory, and input-output unit;

[0019] The memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the steps of the control and regulation method of a grid-type energy storage device based on a four-quadrant converter.

[0020] A computer-readable storage medium includes instructions, which, when executed on a computer, enable the computer to execute the steps of a control and regulation method for a grid-type energy storage device based on a four-quadrant converter.

[0021] The beneficial effects of the present invention are as follows: the present invention, through a four-quadrant converter, can adjust the power output of the energy storage device in real time when emergencies such as grid load fluctuations and frequency fluctuations occur, thereby quickly stabilizing the grid. This feature enables the energy storage system to provide a rapid response when disturbances occur in the grid, reducing the risk of grid instability and voltage fluctuations. The effective regulation and optimized control of the energy storage system can provide the reactive power support required by the grid, reduce voltage fluctuations, and improve the quality of electric energy. The present invention can maintain the stability of the grid voltage and ensure the reliable operation of the grid through precise reactive power regulation. The energy storage device can flexibly adjust the power output according to the real-time needs of the grid, thereby improving the operating efficiency of the energy storage system and reducing the incidence of system failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 This is a flow chart of a control and regulation method for a grid-type energy storage device based on a four-quadrant converter provided in an embodiment of the present invention.

[0024] Figure 2 A schematic structural diagram of a grid-type energy storage device control and regulation system based on a four-quadrant converter provided in an embodiment of the present invention.

[0025] Figure 3 The figure schematically shows a structural diagram of a medium according to an embodiment of the present invention.

[0026] Figure 4 The figure schematically shows a structural diagram of a computing device according to an embodiment of the present invention.

[0027] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0030] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0031] Example

[0032] Reference below Figure 1 , Figure 1 This is a flow chart of a method for controlling and regulating a grid-type energy storage device based on a four-quadrant converter according to an embodiment of the present invention. It should be noted that the embodiments of the present invention can be applied to any applicable scenario.

[0033] Figure 1 The process of the control and adjustment method of a grid-type energy storage device based on a four-quadrant converter provided by an embodiment of the present invention includes:

[0034] S1: A dual closed-loop control strategy is used to regulate the DC bus voltage. When the grid is disturbed, the four-quadrant converter uses the rapid bidirectional flow of energy to stabilize the bus voltage. The DC bus voltage reference value is dynamically adjusted according to different operating conditions.

[0035] Preferably, the bus voltage error is calculated, a PI controller is used to calculate the reference current, the reference current is sent to the current inner loop for closed-loop regulation, and the disturbance trend is calculated in advance based on the neural network and the reference current is adjusted;

[0036] The current error is calculated, and the PWM duty cycle is calculated using a PI controller. This PWM signal is then sent to the four-quadrant converter to achieve bidirectional energy flow control. When the initial voltage is lower than the set value, the converter enters boost mode, absorbing energy from the grid to compensate for the bus voltage. When the initial voltage is higher than the set value, the converter enters buck mode, discharging the energy to the energy storage unit to suppress voltage overshoot.

[0037] If the grid voltage drops briefly, the grid voltage is detected to be lowered, the bus voltage reference value is increased, and the power drawn by the converter from the energy storage unit is increased; if the load suddenly changes, a sudden increase in load power is detected, and the target voltage is lowered so that the converter can absorb more energy in a short period of time.

[0038] Furthermore, the current bus voltage V is measured. DC , and set the reference voltage V ref , voltage error e V Calculated by the following formula:

[0039] e V =V DC -V ref

[0040] Among them, if eV >0, it means the bus voltage is too high and needs to be stepped down; if eV<0, it means the bus voltage is too low and needs to be stepped up;

[0041] The voltage error e is controlled by PI controller. V Adjust and calculate the reference current I ref , using the trained neural network model, based on the historical voltage and load data of the power grid, the voltage fluctuation trend of the power grid is predicted in real time. The neural network model outputs the future power grid disturbance trend P NN (t), according to the prediction result P of the neural network NN (t), for the reference current I ref Adjustment is performed. If the grid voltage fluctuation is predicted to be negative, the reference current is increased, otherwise the reference current is reduced. Calculate the reference current I ref With the actual current I DC The current error between I , using PI controller to control the current error e I Make adjustments and calculate the PWM duty cycle D.

[0042] Furthermore, when the bus voltage V DC Lower than the set value V ref When the bus voltage V DC Higher than the set value V ref When the power grid voltage drops for a short time, the converter detects that the power grid voltage V grid Decrease and increase the bus voltage reference value V ref At this time, the reference current I ref When the system detects a sudden increase in load, the system lowers the target voltage V ref , which enables the converter to absorb more energy in a short period of time to cope with load fluctuations.

[0043] S2: Based on a hierarchical control strategy, coordinated optimization is performed at the global power dispatch layer and the local power control layer. The PQ control mode is used to adjust active and reactive power in real time, and the power output is adjusted in combination with the state of charge of the energy storage system.

[0044] Preferably, the expected bus voltage is calculated. When the output of new energy increases, the bus voltage is increased. When the load suddenly increases, the target voltage is lowered to absorb excess electric energy. When the energy storage SOC approaches the upper / lower limit, the bus voltage is gradually adjusted. The expected active / reactive power is calculated, the power is allocated using the optimization algorithm, and the power command is sent to the local power control layer.

[0045] Preferably, according to the power demand of the power grid, calculate the preliminary set value, dynamically adjust it in combination with the energy storage SOC. If SOC < 20%, limit the discharge power; if SOC is too high > 90%, limit the charging power; if 20% < SOC < 90%, perform power tracking according to the active power; calculate the reactive power reference value, dynamically adjust it according to the grid voltage situation. If the voltage < 0.95 p.u., provide inductive reactive power Q > 0; if the voltage is too high > 1.05 p.u., provide capacitive reactive power Q < 0; if 0.95 < voltage < 1.05 p.u., maintain the reactive power stability.

[0046] Furthermore, the influence of the energy storage state SOC on the bus voltage is determined by the following rules: SOC < 20%: gradually reduce the bus voltage, limit the discharge, and avoid over-discharging of the energy storage; SOC > 90%: gradually increase the bus voltage, limit the charging, and prevent overcharging; 20% < SOC < 90%: maintain the normal working voltage: calculate the preliminary set value P init , dynamically adjust it in combination with the energy storage SOC. Among them, if SOC < 20%, limit the discharge power: if SOC > 90%, limit the charging power: if 20% < SOC < 90%, execute according to the active power tracking set value;

[0047] Use the target optimization algorithm to allocate the active and reactive powers of the energy storage system:

[0048] min(∑ i w P (P i -P cmd ) 2 +w Q (Q i -Q cmd ) 2 )

[0049] Among them, w P , w Q are the active and reactive power weight factors respectively; P i , Q i are the power distribution amounts of each energy storage unit; P cmd [[ID=4,3]], Q cmd are the expected power command values.

[0050] S3: Identify the grid fault situation, switch to the off-grid mode within milliseconds, detect the islanding state in real time based on the deep learning model, and adjust the inertia of the virtual synchronous machine through the four-quadrant converter.

[0051] Preferably, in island mode, virtual synchronous machine control is adopted to simulate the inertia and damping characteristics of the synchronous generator, calculate the frequency deviation, rotational inertia compensation and power adjustment, adopt dynamic inertia adjustment to provide additional inertia support in the event of sudden load changes, and adjust the VSG output in advance in combination with grid frequency prediction; when the grid returns to normal, grid-connected control is executed and combined with Kalman filtering to improve synchronization accuracy, and combined with adaptive neural network to optimize the grid-connected switching strategy.

[0052] Furthermore, the VSG simulates the moment of inertia through the synchronous generator's rotational motion equation:

[0053]

[0054] Where J is the equivalent moment of inertia of VSG, ω is the angular velocity of the motor, T m is the motor mechanical power input, T e is the electromagnetic power output of the motor, D is the damping coefficient; when the load suddenly changes, the system adjusts T m Compensate to make the frequency fluctuation smooth;

[0055] When the load changes suddenly, the system dynamically adjusts the inertia J to provide additional inertia support:

[0056]

[0057] Among them, J0 is the basic moment of inertia, k J is the dynamic inertia adjustment coefficient, dP load is the load change rate;

[0058] Use Kalman filter + frequency prediction algorithm to predict the next moment grid frequency f pred The VSG output is then adjusted in advance based on the prediction results. When the grid returns to normal, the system must synchronize with the grid. Because the frequency and voltage of the VSG in island mode may differ from the grid, phase synchronization and bumpless switching are required before grid connection. A Kalman filter algorithm is used to reduce phase angle measurement noise and optimize grid synchronization. Because traditional PI control grid switching can cause transient shocks, an adaptive neural network is used to predict the optimal grid switching time. Bumpless grid connection is performed when the power error is minimized.

[0059] S4: Based on the instantaneous reactive power theory, the reactive power demand is calculated in real time and the reactive power compensation strategy is adjusted dynamically.

[0060] Preferably, the three-phase grid voltage and current are collected in real time, and the three-phase voltage and current are converted to the α-β stationary coordinate system using Clarke transformation. The Clarke transformation parameters are adjusted in real time in combination with the variable step size LMS filter. The reactive compensation amount is calculated. When the grid voltage is low, Q comp>0, inject reactive power into the system. When the grid voltage is high, Q comp <0, absorb reactive power from the system; set the reactive current reference value, control the converter output current, use SVPWM to control the converter, and output compensation current.

[0061] Furthermore, since the grid voltage and current may be affected by harmonics and noise, directly applying the Clarke transform may lead to calculation errors. Therefore, a variable step-size least mean square (LMS) adaptive filter is used to optimize the transformation parameters. The LMS filter update formula is:

[0062] W(n+1)=W(n)+μ(n)·e(n)·X(n)

[0063] Among them, W(n) is the Clarke transformation weight matrix, e(n) is the filtering error, that is, the error between the target output and the actual output, and X(n) is the input vector. Through the variable step-size LMS algorithm, the α-β axis voltage and current can still be accurately obtained under the condition of grid disturbance, thereby improving the accuracy of reactive power compensation.

[0064] SVPWM selects the optimal space vector to make the converter output current closely follow the reference value, calculates the reference voltage vector, selects the two nearest basis vectors for interpolation, and finally generates a PWM signal through SVPWM to control the IGBT, so that the converter outputs the compensation current.

[0065] S5: Perform data analysis based on intelligent fault prediction using edge computing and use distributed redundant protection mechanisms to recover from faults.

[0066] Preferably, LSTM and Transformer are used to perform short-term and long-term trend analysis to predict the failure of the energy storage system. The health score threshold is set and the fault level is determined according to the device status. The fault health score formula is as follows:

[0067]

[0068] Where N is the total number of parameters, X i is the actual value of the i-th monitoring parameter, X min is the minimum value of the i-th parameter, X max is the maximum value of the i-th parameter;

[0069] If the health status score is >85%, it is in a healthy state and the system is operating normally. If the mild abnormality score is 60% < score <85%, it is a mild abnormality and the device issues an early warning. If the score is <60%, it is a severe abnormality and the fault protection mechanism is triggered.

[0070] When a fault is detected, the system immediately activates the intelligent fault location algorithm to determine the specific location of the fault. If the battery unit fails, the charge state, temperature and voltage changes of the energy storage unit are detected to determine whether there is an abnormal unit; if the inverter fails, the output voltage, current and frequency changes are analyzed to determine whether there is an inverter failure; if there is a grid-side fault, it switches to off-grid mode and starts the independent power supply function.

[0071] Furthermore, in a certain energy storage system, sensors collect the following key parameters in real time, recording data every 1 second:

[0072]

[0073] Train the LSTM model, using the past 5 seconds of data as input to predict the next second of data:

[0074]

[0075] It was observed that the error increased at 00:05 and there was an abnormal drop in battery voltage, prompting a battery failure warning.

[0076] According to the health score below 60%, intelligent fault location is activated, battery fault detection: SOC drop rate is abnormal (drop 5% per second), battery temperature rises too fast (increases 15℃ within 5 seconds), battery voltage drops below 3.4V, there may be battery overheating or single cell abnormality, prompting overtemperature protection. Inverter fault detection: frequency drops abnormally to 47Hz, inverter output current increases by 10A, there is overload or overheating fault, prompting current limiting protection. Grid side fault detection: grid voltage drops to 205V (lower than 220V), reactive power compensation is insufficient, Q comp If the SOC is less than 0, abnormal grid voltage fluctuations will trigger a switch to island mode. If the SOC is below 40%, discharge power will be limited, load power demand will be reduced, and the air cooling system will be activated to reduce battery temperature. If the temperature continues to rise, the abnormal battery cell will be disconnected and the backup module will be switched. If the grid voltage remains below 210V, island mode will be entered, and local power supply will be enabled. Virtual synchronous machine control is used to simulate the inertia characteristics of synchronous generators and provide short-term power support. After the grid power supply is restored, Kalman filtering is used to correct the frequency to improve grid connection accuracy.

[0077] This example demonstrates how to use LSTM for short-term predictions, Transformer for long-term trend analysis, and health score calculation to accurately predict energy storage system failures. A minor anomaly at 00:03 triggers an alert; at 00:05, the health score falls below 60%, triggering fault protection. Battery overheating, inverter overload, and grid fluctuations are successfully identified; and intelligent adjustment strategies are used to restore normal system operation.

[0078] After introducing the method of the exemplary embodiment of the present invention, next, reference is made to Figure 2 The grid-type energy storage device control and regulation system based on a four-quadrant converter according to an exemplary embodiment of the present invention is described. The system includes: a voltage control module, which is used to adjust the DC bus voltage using a dual closed-loop control strategy. When the grid is disturbed, the bus voltage is stabilized by the rapid bidirectional flow of energy of the four-quadrant converter, and the DC bus voltage reference value is dynamically adjusted according to different working conditions; a power regulation module, which is used to coordinate and optimize the global power scheduling layer and the local power control layer based on a hierarchical control strategy, adjust the active and reactive power in real time using the PQ control mode, and adjust the power output in combination with the charge state of the energy storage system; an operation control module, which is used to identify grid fault conditions, switch to off-grid mode within milliseconds, detect the island state in real time based on a deep learning model, and adjust the virtual synchronous machine inertia through the four-quadrant converter; a reactive compensation module, which is used to calculate the reactive demand in real time according to the instantaneous reactive power theory and dynamically adjust the reactive compensation strategy; an abnormal protection module, which is used to perform data analysis based on intelligent fault prediction of edge computing and recover from faults using a distributed redundant protection mechanism.

[0079] After introducing the method and system of the exemplary embodiment of the present invention, the following Figure 3 For a description of a computer-readable storage medium according to an exemplary embodiment of the present invention, please refer to Figure 3 , the computer-readable storage medium shown is a CD 30, on which a computer program (i.e., a program product) is stored. When the computer program is executed by the processor, it will implement the steps described in the above method implementation method, for example, using a dual closed-loop control strategy to adjust the DC bus voltage, using the four-quadrant converter to quickly flow energy in both directions to stabilize the bus voltage when the grid is disturbed, and dynamically adjusting the DC bus voltage reference value according to different working conditions; based on the hierarchical control strategy, coordinated optimization is performed at the global power scheduling layer and the local power control layer respectively, using the PQ control mode to adjust the active and reactive power in real time, and adjusting the power output in combination with the charge state of the energy storage system; identifying grid fault conditions, switching to off-grid mode within milliseconds, detecting the island state in real time based on the deep learning model, and adjusting the virtual synchronous machine inertia through the four-quadrant converter; calculating the reactive power demand in real time according to the instantaneous reactive power theory, and dynamically adjusting the reactive compensation strategy; performing data analysis based on intelligent fault prediction of edge computing, and using a distributed redundant protection mechanism to recover from the fault; the specific implementation methods of each step are not repeated here.

[0080] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0081] After introducing the method, apparatus and medium of the exemplary embodiment of the present invention, the following is a reference to Figure 4 A control and regulation device for a grid-type energy storage device based on a four-quadrant converter according to an exemplary embodiment of the present invention.

[0082] Figure 4 A block diagram is shown of an exemplary computing device 40 , which may be a computer system or server, suitable for implementing embodiments of the present invention. Figure 4 The computing device 40 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present invention.

[0083] like Figure 4 As shown, the components of computing device 40 may include, but are not limited to, one or more processors or processing units 401 , a system memory 402 , and a bus 403 connecting various system components (including system memory 402 and processing unit 401 ).

[0084] The computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 40, including volatile and non-volatile media, removable and non-removable media.

[0085] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 is not shown in the , usually referred to as "hard drive"). Although not in Figure 4As shown in FIG403 , a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) can be provided. In these cases, each drive can be connected to bus 403 via one or more data media interfaces. System memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0086] A program / utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402. Such program modules 4024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 4024 generally implement the functions and / or methods of the embodiments described herein.

[0087] The computing device 40 may also communicate with one or more external devices 404 (e.g., a keyboard, a pointing device, a display, etc.). Such communication may be performed via an input / output (I / O) interface 405. Furthermore, the computing device 40 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 406. Figure 4 As shown, the network adapter 406 communicates with other modules (such as the processing unit 401, etc.) of the computing device 40 via the bus 403. Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with computing device 40 .

[0088] The processing unit 401 executes various functional applications and data processing by running the programs stored in the system memory 402. For example, a dual closed-loop control strategy is used to adjust the DC bus voltage. When the power grid is disturbed, the bus voltage is stabilized by the rapid bidirectional flow of energy of the four-quadrant converter, and the DC bus voltage reference value is dynamically adjusted according to different working conditions. Based on the hierarchical control strategy, coordinated optimization is performed at the global power scheduling layer and the local power control layer respectively, and the active and reactive power are adjusted in real time using the PQ control mode, and the power output is adjusted in combination with the charge state of the energy storage system. The power grid fault situation is identified and switched to the off-grid mode within milliseconds. The island state is detected in real time based on the deep learning model, and the inertia of the virtual synchronous machine is adjusted through the four-quadrant converter. According to the instantaneous reactive power theory, the reactive demand is calculated in real time, and the reactive compensation strategy is dynamically adjusted. Data analysis is performed based on the intelligent fault prediction of edge computing, and the distributed redundant protection mechanism is used to recover from the fault.

[0089] The specific implementation of each step will not be repeated here. It should be noted that while the detailed description above mentions several units / modules or sub-units / sub-modules of the synchronous escape wiring device based on multiple commodity flows, this division is merely exemplary and not mandatory. In practice, according to embodiments of the present invention, the features and functions of two or more units / modules described above may be embodied in a single unit / module. Conversely, the features and functions of a single unit / module described above may be further divided to be embodied by multiple units / modules.

[0090] In the description of the present invention, it should be noted that the terms "first", "second" and "third" are only used for descriptive purposes and should not be understood as indicating or implying relative importance.

[0091] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0093] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0094] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0095] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0096] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

[0097] Furthermore, although the operations of the method of the present invention are described in a particular order in the accompanying drawings, this does not require or imply that these operations must be performed in this particular order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

Claims

1. A control and regulation method for a grid-type energy storage device based on a four-quadrant converter, characterized in that: It includes: Adopt a double - closed - loop control strategy to regulate the DC bus voltage. When there are grid disturbances, utilize the fast bi - directional energy flow of the four - quadrant converter to stabilize the bus voltage, and dynamically adjust the DC bus voltage reference value for different working conditions; Based on a hierarchical control strategy, conduct coordinated optimization at the global power scheduling layer and the local power control layer respectively. Use the PQ control mode to adjust the active and reactive powers in real - time, and adjust the power output in combination with the state of charge of the energy storage system; Identify the grid fault situation, switch to the off - grid mode within milliseconds, detect the islanding state in real - time based on a deep - learning model, and adjust the inertia of the virtual synchronous machine through the four - quadrant converter; According to the instantaneous reactive power theory, calculate the reactive power demand in real - time and dynamically adjust the reactive power compensation strategy; Perform data analysis based on intelligent fault prediction with edge computing, and adopt a distributed redundant protection mechanism to recover from faults.

2. The control and regulation method for a grid-type energy storage device based on a four-quadrant converter according to claim 1, characterized in that: The method of adopting a double - closed - loop control strategy to regulate the DC bus voltage, using the fast bi - directional energy flow of the four - quadrant converter to stabilize the bus voltage when there are grid disturbances, and dynamically adjusting the DC bus voltage reference value for different working conditions includes: Calculate the bus voltage error, use a PI controller to calculate the reference current, send the reference current to the current inner loop for closed - loop regulation, and calculate the disturbance trend and adjust the reference current in advance based on a neural network; Calculate the current error, use a PI controller to calculate the PWM duty cycle, send the PWM signal to the four - quadrant converter to achieve bi - directional energy flow control: when the initial voltage is lower than the set value, the converter starts the boost mode, absorbs energy from the grid to compensate the bus voltage; when the initial voltage is higher than the set value, the converter starts the buck mode, discharges to the energy storage unit to suppress voltage overshoot; If the grid voltage drops suddenly for a short time, detect the decrease in grid voltage, increase the bus voltage reference value, and increase the power taken from the energy storage unit by the converter; if the load changes suddenly and the load power suddenly increases, detect it, reduce the target voltage, and make the converter absorb more energy for a short time.

3. The control and regulation method for a grid-type energy storage device based on a four-quadrant converter according to claim 1, characterized in that: The method of conducting coordinated optimization at the global power scheduling layer and the local power control layer respectively based on a hierarchical control strategy includes: Calculate the expected bus voltage. When the new - energy output increases, increase the bus voltage; when the load suddenly increases, reduce the target voltage to absorb the excess electrical energy. When the SOC of the energy storage is close to the upper / lower limit, gradually adjust the bus voltage; ​ 4. The control and regulation method for a grid-type energy storage device based on a four-quadrant converter according to claim 1, characterized in that: ​ ​ 5. The control and regulation method for a grid-type energy storage device based on a four-quadrant converter according to claim 1, characterized in that: The method of identifying a grid fault, detecting an islanding state in real time based on a deep learning model, and adjusting the inertia of a virtual synchronous machine through a four-quadrant converter includes: In island mode, virtual synchronous machine control is used to simulate the inertia and damping characteristics of the synchronous generator, calculate frequency deviation, rotational inertia compensation and power adjustment, and adopt dynamic inertia adjustment to provide additional inertia support in the event of sudden load changes. Combined with grid frequency prediction, VSG output is adjusted in advance. When the power grid returns to normal, grid-connected control is executed, and Kalman filtering is combined to improve synchronization accuracy, and adaptive neural network is combined to optimize the grid-connected switching strategy.

6. The control and regulation method for a grid-type energy storage device based on a four-quadrant converter according to claim 1, characterized in that: The method of calculating reactive power demand in real time and dynamically adjusting reactive power compensation strategy based on instantaneous reactive power theory includes: Real-time acquisition of three-phase grid voltage and current, using Clarke transformation to convert the three-phase voltage and current to the α-β stationary coordinate system, combined with variable step-size LMS filter, to adjust Clarke transformation parameters in real time; Calculate the reactive compensation amount. When the grid voltage is low, Q comp >0, inject reactive power into the system. When the grid voltage is high, Q comp <0, absorb reactive power from the system; set the reactive current reference value, control the converter output current, use SVPWM to control the converter, and output compensation current.

7. The control and regulation method for a grid-type energy storage device based on a four-quadrant converter according to claim 1, characterized in that: The intelligent fault prediction based on edge computing performs data analysis and adopts a distributed redundant protection mechanism to recover from the fault, including: LSTM and Transformer are used to perform short-term and long-term trend analysis, predict energy storage system faults, set health score thresholds, and determine the fault level based on the device status. The fault health score formula is as follows: Where N is the total number of parameters, X i is the actual value of the i-th monitoring parameter, X min is the minimum value of the i-th parameter, X max is the maximum value of the i-th parameter; If the health status score is >85%, it is in a healthy state and the system is operating normally. If the mild abnormality score is 60% < score <85%, it is a mild abnormality and the device issues an early warning. If the score is <60%, it is a severe abnormality and the fault protection mechanism is triggered. When a fault is detected, the system immediately activates the intelligent fault location algorithm to determine the specific location of the fault. If the battery unit fails, the charge state, temperature and voltage changes of the energy storage unit are detected to determine whether there is an abnormal unit; if the inverter fails, the output voltage, current and frequency changes are analyzed to determine whether there is an inverter failure; if there is a grid-side fault, it switches to off-grid mode and starts the independent power supply function.

8. A grid-type energy storage device control and regulation system based on a four-quadrant converter, characterized in that: include: The voltage control module is used to regulate the DC bus voltage using a dual closed-loop control strategy. During grid disturbances, it uses the rapid bidirectional flow of energy from the four-quadrant converter to stabilize the bus voltage. It also dynamically adjusts the DC bus voltage reference value for different operating conditions. The power regulation module is used to coordinate and optimize the global power scheduling layer and the local power control layer based on a hierarchical control strategy. It uses the PQ control mode to adjust active and reactive power in real time and adjusts power output in combination with the state of charge of the energy storage system. The operation and control module is used to identify grid fault conditions, switch to off-grid mode within milliseconds, detect islanding conditions in real time based on a deep learning model, and adjust the inertia of the virtual synchronous machine through a four-quadrant converter; Reactive power compensation module, used to calculate reactive power demand in real time and dynamically adjust reactive power compensation strategy based on instantaneous reactive power theory; The abnormal protection module is used for data analysis based on intelligent fault prediction of edge computing and uses a distributed redundant protection mechanism to recover from faults.

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