Flywheel energy storage system and electrical control method and system thereof

Abstract

The embodiment of the invention provides a flywheel energy storage system and an electrical control method and system thereof, and the electrical control method comprises the steps: collecting the operation state data of the flywheel energy storage system in real time, and carrying out the preprocessing of the operation state data; inputting the preprocessed operation state data into a parameter adaptive control algorithm, dynamically adjusting control parameters of the algorithm through fuzzy logic, and generating a control instruction for controlling the power converter; based on the control instruction and the real-time demand of a power grid, cooperatively controlling the output power and the direct-current voltage of the flywheel energy storage system, and when it is detected that the direct-current voltage deviates from a target range, automatically switching an electrical control mode to a voltage stabilization mode for stabilizing the direct-current voltage within the target range; and monitoring the operation state of the flywheel energy storage system in real time, and triggering a protection mechanism when at least one abnormity of overvoltage abnormity, overcurrent abnormity or mechanical fault abnormity is detected.

Description

Technical Field

[0001] This application relates to the technical field of electrical control, and in particular to flywheel energy storage systems and their electrical control methods and systems. Background Technology

[0002] Flywheel energy storage, as an advanced physical energy storage technology, boasts advantages such as high energy density, fast charging and discharging speed, and long lifespan, and has broad application prospects in fields such as power regulation and energy management. However, existing flywheel energy storage electrical control systems still have shortcomings in terms of response speed, control accuracy, and stability, making it difficult to meet complex and ever-changing power demands.

[0003] Traditional control strategies in existing technologies (such as proportional-integral-derivative (PID) control and vector control) are insufficient in handling nonlinear characteristics. Fixed-parameter controllers (such as PID controllers) have weak adaptability to environmental changes (temperature, bearing wear) and external disturbances (grid voltage fluctuations, mechanical vibration), and their dynamic response speed is limited. For example, robust control (H∞) and model predictive control (MPC) strategies rely on accurate system state-space models, but flywheel rotor dynamics (including flexible deformation), electromagnetic coupling characteristics (time-varying motor parameters), and mechanical losses (changes in bearing lubrication conditions) are difficult to fully model, leading to a gap between theoretical performance and actual results. Meanwhile, intelligent control strategies (such as fuzzy control and neural network control) rely on manually designed rule bases, which may lack completeness under complex operating conditions (multi-mode switching, fault tolerance), potentially resulting in control blind spots. Existing intelligent control strategies lack rigorous stability proofs, and under the strong nonlinearity and strong coupling characteristics of flywheel systems, they may induce limit cycle oscillations or chaotic phenomena. Furthermore, the system-level collaborative control neglects the collaborative optimization of the flywheel body, power electronic converter, and mechanical support system (such as magnetic levitation bearing), resulting in insufficient coordination across multiple time scales and weak grid adaptability and fault tolerance.

[0004] Therefore, there is a lack of existing technologies that can provide a precise and efficient control scheme for flywheel energy storage systems, thereby meeting complex and ever-changing power demands. Summary of the Invention

[0005] This application proposes a flywheel energy storage system and its electrical control method and system to address the deficiencies of the prior art.

[0006] According to a first aspect of the embodiments of this application, an electrical control method for a flywheel energy storage system is provided, the method comprising: Real-time acquisition of operating status data of the flywheel energy storage system, and preprocessing of the operating status data to eliminate noise interference; The preprocessed operating status data is input into the parameter adaptive control algorithm, and the control parameters of the algorithm are dynamically adjusted through fuzzy logic to generate control commands for controlling the power converter. Based on the control commands and the real-time requirements of the power grid, the output power and DC voltage of the flywheel energy storage system are controlled in a coordinated manner. When the DC voltage is detected to deviate from the target range, the electrical control mode is automatically switched to the voltage regulation mode to stabilize the DC voltage within the target range. The operating status of the flywheel energy storage system is monitored in real time, and a protection mechanism is triggered when at least one of the following abnormalities is detected: overvoltage abnormality, overcurrent abnormality, or mechanical fault abnormality. The protection mechanism includes at least cutting off the power supply.

[0007] In some implementations, the operating status data includes at least rotational speed, electrical power, and DC voltage, and the preprocessing includes at least filtering.

[0008] In some embodiments, the parameter adaptive control algorithm is a proportional-integral-derivative (PID) control algorithm. At least one of the proportional coefficient, integral coefficient, and derivative coefficient of the parameter adaptive control algorithm is dynamically adjusted based on fuzzy logic. The step of dynamically adjusting the control parameters of the algorithm through fuzzy logic and generating control commands for controlling the power converter includes: Convert the precise values ​​of the operating status data into corresponding fuzzy sets; Based on a pre-set fuzzy rule base used to reflect the nonlinear characteristics of flywheel energy storage systems, reasoning is performed on the fuzzy set to obtain the adjustment amounts of the proportional coefficient, integral coefficient, and differential coefficient. The adjustment amounts of the proportional coefficient, integral coefficient, and derivative coefficient are respectively converted into precise proportional parameter values, integral parameter values, and derivative parameter values, and the parameters of the parameter adaptive control algorithm are updated accordingly. Control commands for controlling the power converter are generated based on the updated parameter adaptive control algorithm.

[0009] In some implementations, the coordinated control of the flywheel energy storage system's output power and DC voltage based on the control commands and the real-time demands of the power grid, and the automatic switching of the electrical control mode to voltage regulation mode when the DC voltage deviates from the target range, to stabilize the DC voltage within the target range, includes: When the real-time demand of the power grid is energy storage, the flywheel energy storage system is controlled to charge at maximum power based on the control command; When the real-time demand of the power grid is to release energy, the flywheel energy storage system is controlled to discharge at a preset target power based on the control command; During the charging and discharging processes, the DC voltage is monitored in real time. When the DC voltage deviates from the target range, the control mode is automatically switched to voltage regulation mode to stabilize the DC voltage within the target range.

[0010] In some embodiments, the real-time monitoring of the flywheel energy storage system's operating status and the triggering of a protection mechanism upon detecting at least one of the following abnormalities: overvoltage, overcurrent, or mechanical fault, includes: Real-time monitoring of the DC voltage, AC current, and bearing vibration or temperature signals of the flywheel energy storage system; The monitored DC voltage, AC current, bearing vibration, and temperature signals are compared with preset first voltage thresholds, current thresholds, bearing vibration thresholds, and bearing temperature thresholds (safety thresholds), respectively; wherein, When the DC voltage exceeds the first voltage threshold, it is determined to be an overvoltage abnormality; When the alternating current exceeds the current threshold, it is determined to be an overcurrent abnormality; When the bearing vibration signal exceeds the bearing vibration threshold or the bearing temperature signal exceeds the bearing temperature threshold, it is determined to be an abnormal mechanical fault. Based on any one of the overvoltage anomalies, the overcurrent anomalies, and the mechanical fault anomalies, the corresponding protection mechanism is triggered according to the severity level of the corresponding anomaly.

[0011] In some implementations, the severity levels of the anomaly include primary anomalies and high-level anomalies, and triggering the corresponding protection mechanism based on the severity level of the corresponding anomaly includes: When the corresponding abnormality is the primary abnormality, the operation of executing the target control strategy is triggered. The target control strategy includes reducing the charging and discharging power or switching to the voltage regulation mode. When the corresponding exception is classified as a high-level exception, the operation of cutting off the power supply is triggered.

[0012] In some embodiments, the rated power of the flywheel energy storage system is 5 MW; the parameter adaptive control algorithm is set based on the significant nonlinearity and strong disturbance characteristics generated by the 5 MW high-power flywheel during charging and discharging.

[0013] According to a second aspect of this application, an electrical control system for a flywheel energy storage system includes: The data acquisition and processing module is used to acquire the operating status data of the flywheel energy storage system in real time and preprocess the operating status data to eliminate noise interference. The control command generation module is used to input the preprocessed operating status data into the parameter adaptive control algorithm, dynamically adjust the control parameters of the algorithm through fuzzy logic, and generate control commands for controlling the power converter. The collaborative control module is used to coordinately control the output power and DC voltage of the flywheel energy storage system based on the control commands and the real-time demand of the power grid, and automatically switch the electrical control mode to the voltage regulation mode when the DC voltage deviates from the target range, so as to stabilize the DC voltage within the target range. The protection mechanism activation module is used to monitor the operating status of the flywheel energy storage system in real time, and to trigger the protection mechanism when at least one of the following abnormalities is detected: overvoltage abnormality, overcurrent abnormality, or mechanical fault abnormality. The protection mechanism includes at least cutting off the power supply.

[0014] In some embodiments, the rated power of the flywheel energy storage system is 5 MW; the parameter adaptive control algorithm is set based on the significant nonlinearity and strong disturbance characteristics generated by the 5 MW high-power flywheel during charging and discharging.

[0015] According to a third aspect of this application, a flywheel energy storage system is provided, including a memory and a processor. The memory stores an electrical control system of the flywheel energy storage system as described above, and the processor is used to implement the electrical control method of the flywheel energy storage system as described above when the electrical control system of the flywheel energy storage system is executed.

[0016] The beneficial effects of the flywheel energy storage system and its electrical control method, as well as the system, according to the embodiments of this application, include at least the following: This application firstly provides an accurate and reliable data foundation for the entire control system by real-time acquisition and preprocessing of operational status data. It effectively eliminates the impact of sensor noise and on-site electromagnetic interference on data quality, ensuring the authenticity and accuracy of the information upon which subsequent control decisions rely, thereby improving control reliability from the source. Secondly, it employs a parameter adaptive control algorithm based on fuzzy logic, significantly enhancing the controller's intelligence and adaptability. It can dynamically adjust control parameters according to system nonlinearity and varying operating conditions, overcoming the poor adaptability of traditional fixed-parameter controllers, thus maintaining a fast and stable dynamic response under various operating conditions. Furthermore, it performs coordinated power and voltage control and automatic mode switching based on grid demand, ensuring the system's precise response to external grid requirements and the safe and stable operation of internal critical states. This mechanism not only enables rapid response to grid dispatch commands but also prioritizes maintaining DC bus voltage stability during voltage anomalies, effectively preventing system shutdowns or equipment damage caused by voltage runaway. Finally, the integrated real-time monitoring and hierarchical protection mechanism builds a comprehensive safety defense for the system. This feature can detect abnormal conditions such as overvoltage, overcurrent and mechanical failure in a timely manner, and take corresponding measures from adjustment strategies to power cut-off, minimizing the risk of failure and ensuring the safe operation of the high-value flywheel energy storage unit. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the electrical control method of the flywheel energy storage system according to an embodiment of this application. Figure 2 This is a schematic diagram of the electrical control system of the flywheel energy storage system according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the flywheel energy storage system and its electrical control method and system will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of this application. Obviously, the described embodiments are only some embodiments of the embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed embodiments of the present application, but merely to illustrate selected embodiments of the present application. Other embodiments obtained by those skilled in the art based on the embodiments of the present application without inventive effort are all within the scope of protection of the embodiments of the present application.

[0020] It can be noted that similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it will not be further defined and explained in subsequent figures according to the embodiments of this application.

[0021] This application discloses a flywheel energy storage system and its electrical control method and system. The electrical control method of the flywheel energy storage system is executed based on the electrical control system of the flywheel energy storage system. The purpose is to provide a precise and efficient control scheme for the flywheel energy storage system, thereby meeting complex and ever-changing power demands.

[0022] See attached document Figure 1 As shown, the electrical control method of the flywheel energy storage system includes the following steps 110-140.

[0023] Step 110: Real-time acquisition of the operating status data of the flywheel energy storage system, and preprocessing of the operating status data to eliminate noise interference.

[0024] For example, the rated power of the flywheel energy storage system is 5 megawatts.

[0025] For example, the operating status data includes at least rotational speed, electrical power, and DC voltage.

[0026] For example, the preprocessing includes at least filtering.

[0027] For example, the parameter adaptive control algorithm is a proportional-integral-derivative (PID) control algorithm, in which at least one of the proportional coefficient, integral coefficient and derivative coefficient is dynamically adjusted based on fuzzy logic.

[0028] In some implementations, the control parameters of the algorithm are dynamically adjusted using fuzzy logic to generate control commands for the power converter. This includes: converting the precise values ​​of the operating state data into corresponding fuzzy sets; reasoning about the fuzzy sets based on a preset fuzzy rule base that reflects the nonlinear characteristics of the flywheel energy storage system to obtain adjustment amounts for the proportional coefficient, integral coefficient, and derivative coefficient; converting the adjustment amounts of the proportional coefficient, integral coefficient, and derivative coefficient into precise proportional parameter values, integral parameter values, and derivative parameter values ​​respectively, and updating the parameters of the parameter adaptive control algorithm; and generating control commands for the power converter based on the updated parameter adaptive control algorithm.

[0029] Preferably, in this embodiment, high-precision sensors are used to collect the operating status data of the flywheel energy storage system in real time, and noise cancellation and interference filtering are performed to ensure that the data input to the control algorithm is accurate and reliable, providing a basis for subsequent control decisions.

[0030] In this embodiment of the application, step 110 can improve the accuracy and reliability of the collected operating status data.

[0031] Step 120: Input the preprocessed operating status data into the parameter adaptive control algorithm, dynamically adjust the control parameters of the algorithm through fuzzy logic, and generate control commands for controlling the power converter.

[0032] For example, the parameter adaptive control algorithm is a proportional-integral-derivative (PID) control algorithm, in which at least one of the proportional coefficient, integral coefficient, and derivative coefficient is dynamically adjusted based on fuzzy logic.

[0033] In some implementations, the control parameters of the algorithm are dynamically adjusted using fuzzy logic to generate control commands for the power converter. This includes: converting the precise values ​​of the operating state data into corresponding fuzzy sets; reasoning about the fuzzy sets based on a preset fuzzy rule base that reflects the nonlinear characteristics of the flywheel energy storage system to obtain adjustment amounts for the proportional coefficient, integral coefficient, and derivative coefficient; converting the adjustment amounts of the proportional coefficient, integral coefficient, and derivative coefficient into precise proportional parameter values, integral parameter values, and derivative parameter values ​​respectively, and updating the parameters of the parameter adaptive control algorithm; and generating control commands for the power converter based on the updated parameter adaptive control algorithm.

[0034] In some implementations, the parameter adaptive control algorithm is set based on the significant nonlinear and strong disturbance characteristics generated by the 5 MW high-power flywheel during charging and discharging.

[0035] In this embodiment of the application, step 120 can dynamically adjust the control parameters according to the actual operating state of the flywheel energy storage system, thereby achieving precise control of the flywheel energy storage system.

[0036] Step 130: Based on the control command and the real-time demand of the power grid, the output power and DC voltage of the flywheel energy storage system are controlled in a coordinated manner. When the DC voltage is detected to deviate from the target range, the electrical control mode is automatically switched to the voltage regulation mode to stabilize the DC voltage within the target range.

[0037] In some implementations, the output power and DC voltage of the flywheel energy storage system are coordinated and controlled based on the control command and the real-time demand of the power grid. When the DC voltage deviates from the target range, the electrical control mode is automatically switched to voltage regulation mode to stabilize the DC voltage within the target range. This includes: when the real-time demand of the power grid is energy storage, controlling the flywheel energy storage system to charge at maximum power based on the control command; when the real-time demand of the power grid is energy release, controlling the flywheel energy storage system to discharge at a preset target power based on the control command; during the charging and discharging processes, the DC voltage is monitored in real time, and when the DC voltage deviates from the target range, the control mode is automatically switched to voltage regulation mode to stabilize the DC voltage within the target range.

[0038] In this embodiment of the application, step 130 adjusts the parameter adjustment amount (such as proportional coefficient, integral coefficient, and derivative coefficient) in the parameter adaptive control algorithm in real time through fuzzy inference to adapt to the nonlinear characteristics of the flywheel energy storage system and external disturbances (such as temperature changes and grid fluctuations), thereby improving control accuracy and response speed. As a result, the output power and voltage of the flywheel energy storage system can be precisely controlled according to the real-time needs of the grid during the operation of the flywheel energy storage system.

[0039] Step 140: Monitor the operating status of the flywheel energy storage system in real time, and trigger a protection mechanism when at least one of the following abnormalities is detected: overvoltage abnormality, overcurrent abnormality, or mechanical fault abnormality. The protection mechanism includes at least cutting off the power supply.

[0040] In some embodiments, the real-time monitoring of the flywheel energy storage system's operating status and the triggering of a protection mechanism upon detecting at least one of the following abnormalities: overvoltage, overcurrent, or mechanical fault, includes: real-time monitoring of the flywheel energy storage system's DC voltage, AC current, and bearing vibration or temperature signals; comparing the monitored DC voltage, AC current, bearing vibration, and temperature signals with preset first voltage thresholds, current thresholds, bearing vibration thresholds, and bearing temperature thresholds, respectively. Specifically, if the DC voltage exceeds the first voltage threshold, it is determined to be an overvoltage abnormality; if the AC current exceeds the current threshold, it is determined to be an overcurrent abnormality; if the bearing vibration signal exceeds the bearing vibration threshold or the bearing temperature signal exceeds the bearing temperature threshold, it is determined to be a mechanical fault abnormality; and based on any one of the overvoltage, overcurrent, or mechanical fault abnormalities, a corresponding protection mechanism is triggered according to the severity level of the corresponding abnormality.

[0041] For example, the severity level of the anomaly includes primary anomalies and high-level anomalies.

[0042] In some implementations, the corresponding protection mechanism is triggered based on the severity level of the corresponding anomaly, including: when the corresponding anomaly is a primary anomaly, triggering the execution of a target control strategy, which includes reducing the charging and discharging power or switching to a voltage regulation mode; and when the corresponding anomaly is a high-level anomaly, triggering the execution of a power cut-off operation.

[0043] In this embodiment, step 140 can monitor the operating status of the flywheel energy storage system in real time. When a fault or abnormal situation is detected, the protection mechanism is immediately triggered to cut off the power supply or adjust the control strategy. That is, according to the real-time demand of the power grid, the operating mode (charging / discharging) of the flywheel energy storage system is dynamically switched, and the control mode is automatically switched when the DC voltage is abnormal to ensure that the voltage is stable within the target range, so as to protect the safe operation of the flywheel energy storage system and the power grid.

[0044] This application firstly provides an accurate and reliable data foundation for the entire control system by real-time acquisition and preprocessing of operational status data. It effectively eliminates the impact of sensor noise and on-site electromagnetic interference on data quality, ensuring the authenticity and accuracy of the information upon which subsequent control decisions rely, thereby improving control reliability from the source. Secondly, it employs a parameter adaptive control algorithm based on fuzzy logic, significantly enhancing the controller's intelligence and adaptability. It can dynamically adjust control parameters according to system nonlinearity and varying operating conditions, overcoming the poor adaptability of traditional fixed-parameter controllers, thus maintaining a fast and stable dynamic response under various operating conditions. Furthermore, it performs coordinated power and voltage control and automatic mode switching based on grid demand, ensuring the system's precise response to external grid requirements and the safe and stable operation of internal critical states. This mechanism not only enables rapid response to grid dispatch commands but also prioritizes maintaining DC bus voltage stability during voltage anomalies, effectively preventing system shutdowns or equipment damage caused by voltage runaway. Finally, the integrated real-time monitoring and hierarchical protection mechanism builds a comprehensive safety defense for the system. This feature can detect abnormal conditions such as overvoltage, overcurrent and mechanical failure in a timely manner, and take corresponding measures from adjustment strategies to power cut-off, minimizing the risk of failure and ensuring the safe operation of the high-value flywheel energy storage unit.

[0045] See attached document Figure 2 As shown, this application also discloses an electrical control system for a flywheel energy storage system, used to implement the above-described electrical control method for the flywheel energy storage system. The electrical control system for the flywheel energy storage system includes: an electrical pulse frequency signal conversion module 210, a power weight allocation module 220, a power regulation module 230, and a biological anti-saturation mechanism triggering module 240.

[0046] For example, the data acquisition and processing module 210 is used to acquire the operating status data of the flywheel energy storage system in real time and preprocess the operating status data to eliminate noise interference.

[0047] For example, the control command generation module 220 is used to input the preprocessed operating status data into the parameter adaptive control algorithm, dynamically adjust the control parameters of the algorithm through fuzzy logic, and generate control commands for controlling the power converter.

[0048] For example, the coordinated control module 230 is used to coordinately control the output power and DC voltage of the flywheel energy storage system based on the control command and the real-time demand of the power grid, and automatically switch the electrical control mode to the voltage regulation mode when the DC voltage is detected to deviate from the target range, so as to stabilize the DC voltage within the target range.

[0049] For example, the protection mechanism activation module 240 is used to monitor the operating status of the flywheel energy storage system in real time, and to trigger the protection mechanism when at least one of the abnormalities of overvoltage, overcurrent or mechanical failure is detected. The protection mechanism includes at least cutting off the power supply.

[0050] In some implementations, the rated power of the flywheel energy storage system is 5 megawatts; the parameter adaptive control algorithm is set based on the significant nonlinear and strong disturbance characteristics generated by the 5-megawatt high-power flywheel during charging and discharging.

[0051] In this embodiment of the application, the modules transmit data and issue commands through a communication interface to achieve precise control of the flywheel energy storage system.

[0052] This application also discloses a flywheel energy storage system, including a memory and a processor. The memory stores the electrical control system of the flywheel energy storage system as described above, and the processor is used to implement the electrical control method of the flywheel energy storage system as described above when the electrical control system of the flywheel energy storage system is executed.

[0053] The embodiments of this application can be applied to the fields of power regulation and energy management, especially in scenarios such as power grid frequency support and peak shaving.

[0054] The systems described in this application, through the design of a control strategy based on a fuzzy PID control algorithm, achieve rapid response and precise control of the flywheel energy storage system, thereby improving the system's response speed. This application also improves the accuracy and reliability of control parameters by utilizing fuzzification technology, fuzzy inference, and declarative processing, thus enhancing the control precision of the flywheel energy storage system. Furthermore, this application ensures stable operation and safety by real-time monitoring of the flywheel energy storage system's operating status and fault conditions, triggering protection mechanisms promptly. Finally, this application is applicable to different types of flywheel energy storage systems, demonstrating broad applicability.

[0055] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. An electrical control method for a flywheel energy storage system, characterized in that, The method includes: Real-time acquisition of operating status data of the flywheel energy storage system, and preprocessing of the operating status data to eliminate noise interference; The preprocessed operating status data is input into the parameter adaptive control algorithm, and the control parameters of the algorithm are dynamically adjusted through fuzzy logic to generate control commands for controlling the power converter. Based on the control commands and the real-time requirements of the power grid, the output power and DC voltage of the flywheel energy storage system are controlled in a coordinated manner. When the DC voltage is detected to deviate from the target range, the electrical control mode is automatically switched to the voltage regulation mode to stabilize the DC voltage within the target range. The operating status of the flywheel energy storage system is monitored in real time, and a protection mechanism is triggered when at least one of the following abnormalities is detected: overvoltage abnormality, overcurrent abnormality, or mechanical fault abnormality. The protection mechanism includes at least cutting off the power supply.

2. The method according to claim 1, characterized in that, The operating status data includes at least rotational speed, electric power, and DC voltage, and the preprocessing includes at least filtering.

3. The method according to claim 1, wherein the parameter adaptive control algorithm is a proportional-integral-derivative (PID) control algorithm, and at least one of the proportional coefficient, integral coefficient, and derivative coefficient of the parameter adaptive control algorithm is dynamically adjusted based on fuzzy logic, characterized in that... The step of dynamically adjusting the control parameters of the algorithm through fuzzy logic and generating control commands for controlling the power converter includes: Convert the precise values ​​of the operating status data into corresponding fuzzy sets; Based on a pre-set fuzzy rule base used to reflect the nonlinear characteristics of flywheel energy storage systems, reasoning is performed on the fuzzy set to obtain the adjustment amounts of the proportional coefficient, integral coefficient, and differential coefficient. The adjustment amounts of the proportional coefficient, integral coefficient, and derivative coefficient are respectively converted into precise proportional parameter values, integral parameter values, and derivative parameter values, and the parameters of the parameter adaptive control algorithm are updated accordingly. Control commands for controlling the power converter are generated based on the updated parameter adaptive control algorithm.

4. The method according to claim 1, characterized in that, Based on the control commands and the real-time demands of the power grid, the flywheel energy storage system's output power and DC voltage are coordinated for control. When the DC voltage deviates from the target range, the electrical control mode is automatically switched to voltage regulation mode to stabilize the DC voltage within the target range. This includes: When the real-time demand of the power grid is energy storage, the flywheel energy storage system is controlled to charge at maximum power based on the control command; When the real-time demand of the power grid is to release energy, the flywheel energy storage system is controlled to discharge at a preset target power based on the control command; During the charging and discharging processes, the DC voltage is monitored in real time. When the DC voltage deviates from the target range, the control mode is automatically switched to voltage regulation mode to stabilize the DC voltage within the target range.

5. The method according to claim 1, characterized in that, The system monitors the operating status of the flywheel energy storage system in real time and triggers a protection mechanism when at least one of the following abnormalities is detected: overvoltage abnormality, overcurrent abnormality, or mechanical fault abnormality, including: Real-time monitoring of the DC voltage, AC current, and bearing vibration or temperature signals of the flywheel energy storage system; The monitored DC voltage, AC current, bearing vibration, and temperature signals are compared with preset first voltage thresholds, current thresholds, bearing vibration thresholds, and bearing temperature thresholds (safety thresholds), respectively; wherein, When the DC voltage exceeds the first voltage threshold, it is determined to be an overvoltage abnormality; When the alternating current exceeds the current threshold, it is determined to be an overcurrent abnormality; When the bearing vibration signal exceeds the bearing vibration threshold or the bearing temperature signal exceeds the bearing temperature threshold, it is determined to be an abnormal mechanical fault. Based on any one of the overvoltage anomalies, the overcurrent anomalies, and the mechanical fault anomalies, the corresponding protection mechanism is triggered according to the severity level of the corresponding anomaly.

6. The method according to claim 5, characterized in that, The severity levels of the anomalies include primary anomalies and high-level anomalies. The step of triggering corresponding protection mechanisms based on the severity level of the anomaly includes: When the corresponding abnormality is the primary abnormality, the operation of executing the target control strategy is triggered. The target control strategy includes reducing the charging and discharging power or switching to the voltage regulation mode. When the corresponding exception is classified as a high-level exception, the operation of cutting off the power supply is triggered.

7. The method according to claim 1, characterized in that, The rated power of the flywheel energy storage system is 5 MW; the parameter adaptive control algorithm is set based on the significant nonlinearity and strong disturbance characteristics generated by the 5 MW high-power flywheel during charging and discharging.

8. An electrical control system for a flywheel energy storage system, characterized in that, include: The data acquisition and processing module is used to acquire the operating status data of the flywheel energy storage system in real time and preprocess the operating status data to eliminate noise interference. The control command generation module is used to input the preprocessed operating status data into the parameter adaptive control algorithm, dynamically adjust the control parameters of the algorithm through fuzzy logic, and generate control commands for controlling the power converter. The collaborative control module is used to coordinately control the output power and DC voltage of the flywheel energy storage system based on the control commands and the real-time demand of the power grid, and automatically switch the electrical control mode to the voltage regulation mode when the DC voltage deviates from the target range, so as to stabilize the DC voltage within the target range. The protection mechanism activation module is used to monitor the operating status of the flywheel energy storage system in real time, and to trigger the protection mechanism when at least one of the following abnormalities is detected: overvoltage abnormality, overcurrent abnormality, or mechanical fault abnormality. The protection mechanism includes at least cutting off the power supply.

9. The electrical control system of the flywheel energy storage system according to claim 8, characterized in that, The rated power of the flywheel energy storage system is 5 MW; the parameter adaptive control algorithm is set based on the significant nonlinearity and strong disturbance characteristics generated by the 5 MW high-power flywheel during charging and discharging.

10. A flywheel energy storage system, characterized in that, The system includes a memory and a processor, wherein the memory stores an electrical control system for a flywheel energy storage system as described in any one of claims 8 to 9, and the processor is used to implement an electrical control method for a flywheel energy storage system as described in any one of claims 1 to 7 when the electrical control system of the flywheel energy storage system is executed.

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