State feedback control method for flywheel energy storage array

Abstract

The invention relates to the technical field of flywheel energy storage, in particular to a state feedback control method for a flywheel energy storage array, and the method comprises the steps: obtaining the operation parameters of the flywheel energy storage array, classifying the obtained operation parameters based on a monitoring task, and judging the state of the flywheel energy storage array through the classified parameters. A state information feedback process is optimized through a dynamic feedback control method, so that an upper system makes a quick response aiming at the state information; performing management and node control on the state of a single flywheel energy storage unit in the flywheel energy storage array based on the state information feedback; and the overall state of the flywheel energy storage array is coordinated and controlled based on the power grid demand and the state of the single flywheel energy storage unit.

Description

Technical Field

[0001] The present invention relates to the technical field of flywheel energy storage, and particularly relates to a state feedback control method for a flywheel energy storage array. Background Art

[0002] A flywheel energy storage array refers to a clustered energy storage system formed by connecting multiple flywheel energy storage units (single flywheel systems) in parallel or through coordinated control. By means of such large-scale expansion, the system capacity and power output can be increased to adapt to scenarios such as large-scale power grid services and industries that require high power output.

[0003] During the operation of a flywheel energy storage array, it is necessary to monitor its operating state parameters in real time (such as the rotational speed of the flywheel, bearing temperature, bearing vibration frequency, input and output power, etc.), determine whether the operating state is normal, and take corresponding treatment measures in case of abnormalities to ensure the safe operation of individual flywheel energy storage units and the entire flywheel energy storage matrix. That is, a complete control closed-loop is required, namely data acquisition, state judgment, state feedback, and regulation based on state feedback. Among them, how to control the state feedback efficiency and reduce control lag is the key to improving the control efficiency of the flywheel energy storage array.

[0004] For this reason, the present invention proposes a method for improving the efficiency of state feedback control of a flywheel energy storage array. Summary of the Invention

[0005] In the present invention, by dynamically adjusting the frequency of state feedback of each flywheel energy storage unit, the state feedback process is optimized to adapt to the load scenarios where the flywheel load and state are constantly changing; meanwhile, a rapid response strategy for the upper system is constructed to achieve a rapid response to the flywheel state feedback and reduce control lag.

[0006] The technical solution proposed by the present invention is: a state feedback control method for a flywheel energy storage array, the method comprising: Obtaining the operating parameters of the flywheel energy storage array, classifying the obtained operating parameters based on the monitoring tasks, and judging the state of the flywheel energy storage array by using the classified parameters; Optimizing the state information feedback process through a dynamic feedback control method to enable the upper system to make a rapid response to the state information; Based on the state information feedback, managing the state of individual flywheel energy storage units in the flywheel energy storage array and controlling the nodes; Coordinating and controlling the overall state of the flywheel energy storage array based on the grid demand and the state of individual flywheel energy storage units.

[0007] Preferably, obtaining the operating parameters of the flywheel energy storage array, classifying the obtained operating parameters based on the monitoring task, and using the classified parameters to judge the state of the flywheel energy storage array includes: According to the monitoring task, collect the physical state parameters, electrical state parameters, and system state parameters of each flywheel in the flywheel energy storage array at a preset sampling frequency, and add the identification label of the corresponding flywheel; Preprocess the physical state parameters, electrical state parameters, and system state parameters to form an original parameter set; Extract multiple parameters from the original parameter set to construct a state judgment input feature set ; where respectively represent the operating state parameter set, the health state parameter set, and the safety state parameter set; The operating state parameter set includes rotational speed, charge-discharge power, and SOC; the health state parameter set includes bearing temperature, vacuum degree, and vibration frequency; the safety state parameter set includes heating rate and vacuum leakage rate; Perform node-level state judgment on a single flywheel energy storage unit; Perform system-level state judgment on the flywheel energy storage array.

[0008] Preferably, the performing node-level state judgment on a single flywheel energy storage unit includes: Construct a state classification rule, that is, combine the elements in the state judgment input feature set through logical combination, and define the operating state in combination with the threshold, including: The state of each flywheel in the flywheel energy storage array, that is, the node-level state , where represents the input power, represents the bearing temperature, represents the temperature rise rate of the bearing, represents the bearing vibration energy; represents the rotational speed fluctuation rate; If , judge that the flywheel is in a normal charging state; If , judge that the flywheel is in a bearing overheating state; If , judge that the flywheel is in a rotor imbalance state.

[0009] Preferably, the performing system-level state judgment on the flywheel energy storage array includes: Collect the operating parameters of all flywheels to generate a global feature parameter set ; where respectively represent the average SOC, the maximum temperature value, the number of faulty flywheels, and the power imbalance degree; where , represents the average input power; represents the number of flywheels; represents the input power of the system-level status ; If , it is determined that the flywheel energy storage array is in a normal state; If , it is determined that the flywheel energy storage array is in a partial failure state; If , it is determined that the flywheel energy storage array is in an overloaded state; If , it is determined that the flywheel energy storage array is in an emergency shutdown state.

[0010] Preferably, the state information feedback process is optimized by the dynamic feedback control method to enable the upper-level system to make a rapid response to the state information, including: Classify the node-level status by priority and assign a priority value, and preferentially feedback the status with a high priority value, including: The priority value of the normal charging state ; The priority value of the bearing overheating state ; The priority value of the rotor imbalance state ; where ; Dynamically adjust the state feedback frequency, that is, adjust the state feedback frequency according to the load and state change rate of the flywheel, including: Set the dynamic feedback frequency ; where respectively represent the basic feedback frequency, adjustment coefficient, feedback sensitivity coefficient, maximum allowable change range of the parameter, and absolute value of the parameter change.

[0011] Preferably, the state information feedback process is optimized by the dynamic feedback control method to enable the upper-level system to make a rapid response to the state information, and further includes: Set an event trigger mechanism to avoid invalid feedback, including: Set the event trigger condition ; where represents the parameter value collected last time, represents the static threshold; represents the dynamic change rate threshold; represents the parameter value collected at the current moment; Utilize pre-buffering and preloading, state machine jump prediction, and lightweight decision trees to construct a rapid response strategy for the upper-level system to achieve a rapid response to the flywheel state feedback.

[0012] Preferably, the upper system fast response strategy constructed by using pre - cache and pre - loading, state machine jump prediction and lightweight decision tree includes: Pre - store key data and model parameters, including: Define a memory area with a fixed size locally and store data cyclically; Adopt the Least Recently Used (LRU) algorithm to dynamically eliminate low - frequency accessed data; Construct a flywheel dynamics model and pre - load the flywheel dynamics model in the CPU cache L1 or L2; Pre - compile the pre - trained decision tree model and state transition matrix and store them in the memory reserved area, including: Construct a Markov chain model, train the Markov chain model using historical state statistical data obtained from the database, and calculate the state transition probability; State transition probability , where represents the number of statistics when the current moment is in state and the next space - time becomes state ; represents the number of statistics of state ; Predict the next state ; where ; According to the predicted next state, pre - load the preset control logic into the memory in advance. The control logic includes cooling, reducing the speed, and shutting down; Construct a lightweight decision tree, select the rotational speed, bearing temperature, and vibration frequency as splitting nodes, and use the maximum information gain criterion to select splitting nodes; Limit the tree depth to 3 to prevent overfitting; Convert the decision data into a bit - mask query table and achieve fast response through FPGA; The management and node control of the state of a single flywheel energy storage unit in the flywheel energy storage array based on state information feedback includes: Obtain the state feedback information of the flywheel; Deploy an edge controller for each flywheel singly. According to the state feedback information, control the flywheel system parameters, specifically: Use a PID controller to control the flywheel speed: Motor output torque ; where represents the PID gain; represents the target speed, represents the flywheel speed.

[0013] Preferably, coordinating and controlling the overall state of the flywheel energy storage array based on the grid demand and the state of a single flywheel energy storage unit includes: Receiving a grid command, and parsing to obtain a frequency modulation signal, a power adjustment amount, and a target output power; Obtaining the real-time output power of the flywheel energy storage array and calculating the total power to be adjusted ; where represents the power adjustment amount; Obtaining the operating parameters of each flywheel to form a node operating parameter vector where respectively represent the rotational speed, temperature, vibration frequency, and power of the th flywheel; Dynamically allocating output power to each flywheel, that is, minimizing fault diffusion and life loss while meeting the target output power , including: The available output power of each flywheel is: ; where represents the rated power of the flywheel, represents the SOC correction factor, represents the maximum output power of the battery, represents the power adjustment period; Assigning priority weights according to the health status of the flywheel and preferentially using healthy flywheels; where respectively represent the number of charge and discharge cycles completed by the flywheel and the total designed charge and discharge cycles; While meeting the grid demand, balancing the loads of each flywheel in the flywheel energy storage array to extend the service life, specifically: Constructing an optimization model: where represents the life protection weight; represents the number of flywheels working in the flywheel energy storage array; Using the alternating direction method of multipliers ADMM to solve the optimization model to obtain the finally allocated ; Sending to the controller of each flywheel; Monitoring the state of each flywheel, that is, the node-level state, and when the state changes, setting to zero and triggering power reallocation.

[0014] The present invention also provides an electronic device, including a processor, a memory connected to the processor, and a communication module, where the electronic device is configured to execute the state feedback control method for a flywheel energy storage array as described above.

[0015] The present invention also provides a computer-readable storage medium storing a computer program, and the computer program is executed by a processor to implement the state feedback control method for a flywheel energy storage array as described above.

[0016] Advantages of the present invention: 1. In the present invention, according to the real-time operation parameters, load, and state change rate of the flywheel energy storage system, the frequency of state feedback is adaptively adjusted. Meanwhile, based on the event-trigger mechanism, feedback is triggered only when a real change in the state occurs (for example, when exceeding a set threshold), which can avoid invalid reporting and reduce bandwidth occupancy. Through pre-cache, state jump prediction, and lightweight decision tree encoding, the upper system can quickly respond to the feedback state, thereby reducing the control delay of the flywheel energy storage array.

[0017] 2. In the present invention, based on the grid demand and the real-time state of each flywheel, the state of the flywheel energy storage array is adjusted, that is, the distribution of the total demand power among multiple flywheel energy storage units is adjusted, and the corresponding output power is borne according to the corresponding priority weights. It can adapt to the fluctuations of the grid demand and the changes in the state of the flywheel energy storage units, and ensure the safe operation of the flywheel energy storage array. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a state feedback control method for a flywheel energy storage array according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious deformations. The basic principles defined in the following description can be applied to other embodiments, deformation schemes, improvement schemes, equivalent schemes, and other technical schemes that do not depart from the spirit and scope of the present invention.

[0020] It can be understood that the term "one" should be understood as "at least one" or "one or more". That is, in one embodiment, the number of an element can be one, while in other embodiments, the number of the element can be multiple. The term "one" cannot be understood as a limitation on the number.

[0021] Refer to Figure 1 , the technical solution provided by the present invention is: a state feedback control method for a flywheel energy storage array, including the following steps: Step 1: Obtain the operating parameters of the flywheel energy storage array, classify the obtained operating parameters based on the monitoring task, and use the classified parameters to judge the state of the flywheel energy storage array. The steps include: Step 1.1: According to the monitoring task, collect the physical state parameters, electrical state parameters, and system state parameters of each flywheel in the flywheel energy storage array at a preset acquisition frequency, and add the identification tags of the corresponding flywheels; Step 1.2: Preprocess the physical state parameters, electrical state parameters, and system state parameters to form an original parameter set; Step 1.3: Extract multiple parameters from the original parameter set to construct a state judgment input feature set ; where respectively represent the operating state parameter set, the health state parameter set, and the safety state parameter set.

[0022] Among them, the operating state parameter set includes rotational speed, charge-discharge power, and SOC; the health state parameter set includes bearing temperature, vacuum degree, and vibration frequency; the safety state parameter set includes heating rate and vacuum leakage rate.

[0023] Step 1.4: Node-level state judgment of a single flywheel energy storage unit, specifically: Construct a state classification rule, that is, combine the elements in the state judgment input feature set through logical combination, and define the operating state in combination with thresholds, including: The state of each flywheel in the flywheel energy storage array, that is, the node-level state , where represents the input power, represents the bearing temperature, represents the bearing temperature rise rate, represents the bearing vibration energy; represents the rotational speed volatility; If , it is judged that the flywheel is in a normal charging state; If , it is judged that the flywheel is in a bearing overheating state; If , it is judged that the flywheel is in a rotor imbalance state.

[0024] Step 1.5: System-level state judgment after multiple flywheels form an array, specifically: Collect the operating parameters of all flywheels to generate a global feature parameter set ; where respectively represent the average SOC, the maximum temperature value, the number of faulty flywheels, and the power imbalance degree; Among them, , represents the average input power; Indicates the number of flywheels; Indicates the input power of the nth flywheel; ; If , it is determined that the flywheel energy storage array is in a normal state; If , it is determined that the flywheel energy storage array is in a partial failure state; If , it is determined that the flywheel energy storage array is in an overloaded state; If , it is determined that the flywheel energy storage array is in an emergency shutdown state.

[0025] Step 2. Optimize the state information feedback process through a dynamic feedback control method to enable the upper-level system to respond quickly to the state information, including the following steps: Step 2.1. Classify the node-level states by priority and assign priority values, and preferentially feedback the states with high priority values, including: The priority value of the normal charging state ; The priority value of the bearing overheating state ; The priority value of the rotor imbalance state ; where ; Dynamically adjust the state feedback frequency, that is, adjust the state feedback frequency according to the load and state change rate of the flywheel, including: Set the dynamic feedback frequency ; where respectively represent the base feedback frequency, adjustment coefficient, feedback sensitivity coefficient, maximum allowable change range of the parameter, and absolute value of the parameter change.

[0026] For example, when the bearing temperature rises at a rate of 3 °C / min, the maximum allowable temperature rise rate of the system is 5 °C / min; At this time, The state feedback frequency of the priority is: .

[0027] Step 2.2. Set an event trigger mechanism to avoid invalid feedback, including: Set the event trigger condition ; where represents the parameter value collected last time, represents the static threshold; represents the dynamic change rate threshold; represents the parameter value collected at the current moment; Step 2.3: Construct a fast response strategy for the upper system using pre - cache and pre - loading, state machine jump prediction, and lightweight decision trees to achieve fast response to the flywheel state feedback, including the following steps: Pre - store key data and model parameters, including: Define a memory area of a fixed size locally and store data cyclically; Adopt the LRU (Least Recently Used) algorithm to dynamically eliminate low - frequency accessed data; Construct a flywheel dynamics model and pre - load the flywheel dynamics model into the CPU cache L1 or L2; Pre - compile the pre - trained decision tree model and the state transition matrix and store them in the memory reserved area, including: Construct a Markov chain model, train the Markov chain model using historical state statistical data obtained from the database, and calculate the state transition probability; State transition probability , where represents the current time as state and the next time - space becomes state the statistical count; represents the statistical count of state ; Predict the next state ; where ; According to the predicted next state, pre - load the preset control logic into the memory in advance. The control logic includes cooling, reducing the rotation speed, and shutting down.

[0028] For example, through historical state data, when the flywheel is in the charging state and there is an 80% probability of entering the "bearing overheating state". When it is obtained that the current time is in the charging state and the bearing temperature is 75 °C, it is predicted that the flywheel will enter the bearing overheating state, and the cooling control logic is pre - loaded into the memory in advance for quick access.

[0029] Construct a lightweight decision tree, select the rotation speed, bearing temperature, and vibration frequency as splitting nodes, and use the maximum information gain criterion to select splitting nodes; Limit the tree depth to 3 to prevent overfitting; Convert the decision data into a bit - mask query table and achieve fast response through FPGA. For example, compile common fault response strategies into a binary instruction set and pre - store them in the memory to reduce the overhead of instruction interpretation and execution.

[0030] For example, during charging, the sudden vibration frequency of the flywheel exceeds the standard. The vibration frequency is written into the memory buffer, and based on historical data, it is predicted that the next state will enter the rotor imbalance state, and the "vibration suppression" strategy is pre-loaded; The decision tree triggers the "vibration suppression" strategy, obtains the instruction of "reduce load by 30% and start active damping", and the instruction is sent to the magnetic bearing through the FPGA.

[0031] The upper system here refers to the core control and management system of the flywheel energy storage matrix, including the edge control system, cloud server, local control system, etc.

[0032] Step 3: Based on the state information feedback, manage the state and node control of a single flywheel energy storage unit in the flywheel energy storage array, including the following steps: Obtain the state feedback information of the flywheel; Deploy an edge controller for each flywheel alone. According to the state feedback information, control the flywheel system parameters, specifically: Use a PID controller to control the flywheel speed: Motor output torque ; where represents the PID gain; represents the target speed, represents the flywheel speed.

[0033] Step 4: Based on the grid demand and the state of a single flywheel energy storage unit, coordinately control the overall state of the flywheel energy storage array, including the following steps: Receive the grid instruction and parse it to obtain the frequency modulation signal, power adjustment amount, and target output power; Obtain the real-time output power of the flywheel energy storage array , and calculate the total power to be adjusted ; where represents the power adjustment amount; Obtain the operating parameters of each flywheel to form a node operating parameter vector , where respectively represent the th flywheel's speed, temperature, vibration frequency, and power; Dynamically allocate the output power for each flywheel, that is, while meeting the target output power , minimize the fault spread and life loss, including: The available output power of each flywheel is: ; where represents the flywheel rated power, represents the SOC correction factor, represents the maximum output power of the battery, Indicates the power adjustment period; Allocate priority weights according to the health status of the flywheels and preferentially use healthy flywheels; wherein, respectively represent the number of charge and discharge cycles completed by the flywheel and the total designed number of charge and discharge cycles; While meeting the grid demand, balance the loads of each flywheel in the flywheel energy storage array to extend the service life, specifically: Construct an optimization model: wherein, represents the life protection weight; represents the number of flywheels working in the flywheel energy storage array; Use the Alternating Direction Method of Multipliers (ADMM) to solve the optimization model and obtain the finally allocated ; Send to the controllers of each flywheel; Monitor the status of each flywheel, i.e., the node-level status. When the status changes, set to zero and trigger power reallocation.

[0034] For example, the grid demand is , and there are 100 flywheels in the flywheel energy storage array, 5 of which are faulty and the remaining 95 are normal; among the 95 normal flywheels, 10% are aging flywheels ( small), and 90% are healthy flywheels ( large). The rated output power of each flywheel is 1 MW and the SOC = 81%.

[0035] Node-level fault , system-level status (partial fault), clear the available output power of the faulty node (faulty flywheel), and the remaining nodes undertake the power output.

[0036] At this time, the available power is MW. After allocation by ADMM, the healthy flywheels undertake more power (0.4 to 0.58 MW), and the aging flywheels undertake less power (0 to 0.4 MW).

[0037] The present invention also provides an electronic device, including a processor, a memory connected to the processor, and a communication module. The electronic device is used to execute the above-mentioned state feedback control method for a flywheel energy storage array.

[0038] The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned state feedback control method for a flywheel energy storage array.

[0039] In the embodiments disclosed by the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. The embodiments disclosed by the present invention include a computer program product, which includes a computer program carried on a computer-readable medium. The computer program contains program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the methods of the present application are performed. It should be noted that the above-mentioned computer-readable medium in the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or combined with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: a wireless segment, a wire segment, an optical cable, RF, etc., or any suitable combination of the above.

[0040] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0041] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the said principles, any changes or modifications may be made to the embodiments of the present invention.

Claims

1. A state feedback control method for a flywheel energy storage array, characterized in that The method includes: Obtaining the operation parameters of the flywheel energy storage array, classifying the obtained operation parameters based on the monitoring tasks, and judging the state of the flywheel energy storage array by using the classified parameters; Optimizing the state information feedback process through a dynamic feedback control method to enable the upper-level system to make a quick response to the state information; Managing and node controlling the state of a single flywheel energy storage unit in the flywheel energy storage array based on the state information feedback; Coordinating and controlling the overall state of the flywheel energy storage array based on the grid demand and the state of a single flywheel energy storage unit.

2. The state feedback control method for a flywheel energy storage array according to claim 1, characterized in that, The obtaining the operation parameters of the flywheel energy storage array, classifying the obtained operation parameters based on the monitoring tasks, and judging the state of the flywheel energy storage array by using the classified parameters includes: According to the monitoring tasks, collecting the physical state parameters, electrical state parameters and system state parameters of each flywheel in the flywheel energy storage array at a preset sampling frequency, and adding the identification tags of the corresponding flywheels; Preprocessing the physical state parameters, electrical state parameters and system state parameters to form an original parameter set; Extract multiple parameters from the original parameter set to construct a state judgment input feature set ; among them, respectively represent the operating state parameter set, the health state parameter set, and the safety state parameter set; The operation state parameter set includes rotational speed, charge and discharge power, and SOC; the health state parameter set includes bearing temperature, vacuum degree, and vibration frequency; the safety state parameter set includes heating rate and vacuum leakage rate; Performing node-level state judgment on a single flywheel energy storage unit; Performing system-level state judgment on the flywheel energy storage array.

3. A state feedback control method for a flywheel energy storage array according to claim 2, characterized in that, The performing node-level state judgment on a single flywheel energy storage unit includes: Constructing a state classification rule, that is, combining elements in the state judgment input feature set through logical combination and defining the operation state in combination with thresholds, including: The state of each flywheel in the flywheel energy storage array, i.e., the node-level state , where represents the input power, represents the bearing temperature, represents the temperature rise rate of the bearing, represents the bearing vibration energy; represents the rotational speed fluctuation rate; If , it is determined that the flywheel is in a normal charging state; If , it is determined that the flywheel is in a state of overheating of the bearing; If , it is determined that the flywheel is in a state of rotor imbalance.

4. A state feedback control method for a flywheel energy storage array according to claim 3, characterized in that The performing system-level state judgment on the flywheel energy storage array includes: Collect the operating parameters of all flywheels to generate a global characteristic parameter set ; among them, respectively represent the average SOC, the maximum temperature value, the number of faulty flywheels, and the power imbalance degree; Among them, , represents the average input power; represents the number of flywheels; represents the input power of the System-level status ; If , it is determined that the flywheel energy storage array is in a normal state; If , it is determined that the flywheel energy storage array is in a partial failure state; If , it is determined that the flywheel energy storage array is in an overload state; If , it is determined that the flywheel energy storage array is in an emergency shutdown state.

5. A state feedback control method for a flywheel energy storage array according to claim 4, characterized in that, The optimizing the state information feedback process through a dynamic feedback control method to enable the upper-level system to make a quick response to the state information includes: Classifying the node-level states by priority and assigning priority values, and preferentially feedbacking the states with high priority values, including: Priority value in normal charging state ; Priority value for bearing overheat status ; Priority value of rotor imbalance state ; where ; Dynamically adjusting the state feedback frequency, that is, adjusting the state feedback frequency according to the load and state change rate of the flywheel, including: Set the dynamic feedback frequency ; where respectively represent the basic feedback frequency, adjustment coefficient, feedback sensitivity coefficient, maximum allowable change range of the parameter, and absolute value of the parameter change.

6. A state feedback control method for a flywheel energy storage array according to claim 5, characterized in that The optimizing the state information feedback process through a dynamic feedback control method to enable the upper-level system to make a quick response to the state information further includes: Setting an event trigger mechanism to avoid invalid feedback, including: Set event trigger conditions ; where represents the parameter value collected last time, represents the static threshold value; represents the dynamic change rate threshold value; represents the parameter value collected at the current moment; Using pre-cache and preloading, state machine jump prediction and lightweight decision tree to construct a quick response strategy for the upper-level system to achieve a quick response to the flywheel state feedback.

7. A state feedback control method for a flywheel energy storage array according to claim 6, characterized in that, The using pre-cache and preloading, state machine jump prediction and lightweight decision tree to construct a quick response strategy for the upper-level system includes: Pre-storing key data and model parameters, including: Defining a memory area with a fixed size locally and circularly storing data; Adopting the least recently used algorithm LRU to dynamically eliminate low-frequency accessed data; Constructing a flywheel dynamics model and preloading the flywheel dynamics model in the CPU cache L1 or L2; Pre-compiling the pre-trained decision tree model and state transition matrix and storing them in the memory reserved area, including: Constructing a Markov chain model, training the Markov chain model by using the historical state statistical data obtained from the database, and calculating the state transition probability; State transition probability , where represents the current time as state The next space-time becomes state The statistical count of; represents state The statistical count of; Predict the next state ; wherein ; Load the preset control logic into the memory in advance according to the predicted next state, and the control logic includes cooling, reducing the rotation speed, and shutting down. Construct a lightweight decision tree, select the rotation speed, bearing temperature, and vibration frequency as splitting nodes, and use the maximum information gain criterion to select splitting nodes. Limit the tree depth to 3 to prevent overfitting. Convert the decision data into a bitmask query table to achieve fast response through FPGA. Based on the state information feedback, manage the state and node control of a single flywheel energy storage unit in the flywheel energy storage array, including: Obtain the state feedback information of the flywheel. Deploy an edge controller for each flywheel individually, and control the flywheel system parameters according to the state feedback information, specifically: Control the flywheel rotation speed using a PID controller: Motor output torque ; wherein, represents the PID gain; represents the target speed, represents the flywheel speed.

8. A state feedback control method for a flywheel energy storage array according to claim 7, characterized in that Based on the grid demand and the state of a single flywheel energy storage unit, coordinately control the overall state of the flywheel energy storage array, including: Receive grid commands and parse to obtain frequency modulation signals, power adjustment amounts, and target output powers. Obtain the real-time output power of the flywheel energy storage array , and calculate the total power to be adjusted ; among them, represents the power adjustment amount; Obtain the operating parameters of each flywheel to form a node operating parameter vector , where respectively represent the rotational speed, temperature, vibration frequency, and power of the th flywheel; Dynamically allocate the output power for each flywheel, i.e., minimize the fault spread and life loss while meeting the target output power including: The available output power of each flywheel is: ； Among them, represents the rated power of the flywheel, represents the SOC correction factor, represents the maximum output power of the battery, represents the power adjustment period; Assign priority weights according to the health status of the flywheel and preferentially use healthy flywheels; wherein, respectively represent the number of charge-discharge cycles completed by the flywheel and the total designed charge-discharge cycles; While meeting the grid demand, balance the loads of each flywheel in the flywheel energy storage array to extend the service life, specifically: Construct an optimization model: , where represents the life protection weight; represents the number of flywheels operating in the flywheel energy storage array; The alternating direction method of multipliers (ADMM) is used to solve the optimization model to obtain the finally allocated ; Send to the controllers of each flywheel; Monitor the status of each flywheel, i.e., the node-level status. When the status changes, set it to zero and trigger power reallocation.

9. An electronic device, comprising a processor, a memory and a communication module connected to the processor, characterized in that The electronic device is used to execute a state feedback control method for a flywheel energy storage array according to any one of claims 1-8 above.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement a state feedback control method for a flywheel energy storage array according to any one of claims 1-8 above.

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