Frequency rate of change based hybrid energy storage black start coordination control method and system

Abstract

The present application relates to the field of hybrid energy storage system control, in particular to a hybrid energy storage black start coordination control method and system based on frequency rate of change prediction; state data of a power system at the current time and operation data of a hybrid energy storage system are collected; the operation data of the hybrid energy storage system is input into an improved equivalent state of charge model to calculate the equivalent state of charge at a future time; the state data of the power system and the equivalent state of charge at the future time are input into an improved frequency rate of change prediction model to calculate the frequency rate of change at the future time; according to the frequency rate of change at the future time and the equivalent state of charge at the future time, a stability index at the future time is calculated; the present application overcomes the risk of rated frequency instability of the power system caused by load mutation and control delay, and significantly improves the frequency stability and recovery success rate during the black start process.

Description

Technical Field

[0001] This invention relates to the field of hybrid energy storage system control, specifically to a method and system for coordinated black-start control of hybrid energy storage based on frequency change rate prediction. Background Technology

[0002] Black start of a power system is a crucial process for restoring power supply independently without relying on the external power grid after a major power outage. With the technological maturity of batteries and supercapacitors, hybrid energy storage systems composed of the two have become an important technical means to improve the dynamic stability of the black start process due to their complementary power and energy characteristics.

[0003] However, sudden load changes during black start can cause drastic frequency fluctuations, and the initial characteristics of low inertia in power systems make frequency stability control particularly difficult, placing extremely high demands on the predictability, adaptability, and coordination of control strategies.

[0004] Currently, the main control strategies can be divided into three categories: First, coordination control based on voltage / frequency feedback, which generates power compensation commands through a PID (Proportional Integral Derivative) controller and distributes them between the battery and the supercapacitor using a filter; this is a passive response control. Second, coordination based on fuzzy logic or rule tables, which improves nonlinear response capabilities through a rule base designed by expert experience, but still lacks predictive capabilities. Third, optimization scheduling based on traditional model predictive control, which establishes a predictive model and solves the optimal problem in each control cycle, thus possessing a certain feedforward predictive capability.

[0005] The above schemes still have the following shortcomings: First, they all rely on current or historical measurements and lack the ability to actively predict future frequency change rates, resulting in significant control lag risks. Second, the energy storage state of charge is only used as a constraint or simple weight parameter and is not deeply coupled into the dynamic assessment of power system stability. Third, the prediction model is highly dependent and lacks robustness and adaptability in topology and parameter time-varying scenarios such as black start. Summary of the Invention

[0006] To address the problems mentioned in the prior art, this invention proposes a black-start coordinated control method and system for hybrid energy storage based on frequency change rate prediction. By combining the predicted frequency change rate and equivalent state of charge, and using a decision model to generate the final power command for coordinated control, the frequency stability and recovery success rate during the black-start process are improved.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: This invention proposes a black-start coordinated control method for hybrid energy storage based on frequency change rate prediction, comprising the following steps: S1. Collect the current status data of the power system and the operation data of the hybrid energy storage system respectively; S2. Input the operating data of the hybrid energy storage system into the improved equivalent state of charge model to calculate the equivalent state of charge at future times; S3. Input the state data of the power system and the equivalent state of charge at future times into the improved frequency change rate prediction model to calculate the frequency change rate at future times; S4. Calculate the stability index for future times based on the rate of change of frequency and the equivalent state of charge for future times. S5. Input the stability index, equivalent state of charge, and normalized rate of change of frequency at future moments into the pre-built decision model to generate the final power command; coordinate and control the hybrid energy storage system according to the final power command to perform black start.

[0008] As a further improvement of the present invention, the state data of the power system in S1 includes total load power, frequency deviation, and current frequency change rate; The operating data of the hybrid energy storage system includes the current state of charge of the battery, the current output power of the battery, and the current output power of the supercapacitor.

[0009] As a further improvement to the present invention, the expression of the improved equivalent state-of-charge model is as follows:

[0010] In the formula: for The equivalent state of charge at any given moment; This indicates the current state of charge of the battery. This represents the current output power of the battery. The battery loss function value is represented by Δt, which is the control period. The rated energy of the battery; For supercapacitor calculation coefficients; This represents the current output power of the supercapacitor. This refers to the rated energy of the supercapacitor.

[0011] As a further improvement to the present invention, the expression of the improved frequency change rate prediction model is as follows:

[0012] In the formula: for Rate of change of frequency at any given moment; The rated frequency of the power system; The inherent inertial time constant of the power system; The maximum virtual inertial time constant; The dynamic virtual inertial time constant of the hybrid energy storage system; Total load power; This represents the current output power of the battery. This represents the current output power of the supercapacitor. This serves as the baseline capacity for the power system. For the equivalent damping of the power system; This is for frequency deviation; This is the virtual inertial gain coefficient; The equivalent state of charge at time t+Δt is the control period; Let be the rate of change of the current frequency at time t.

[0013] As a further improvement of the present invention, step S4 calculates the stability index for future times based on the rate of change of frequency and the equivalent state of charge at future times, including: The stability index is calculated as follows:

[0014] In the formula: for The stability index at time t, where Δt is the control period; The reference value for the rate of change of frequency; γ is the rate of change of frequency at time t+Δt; γ1 and γ2 are the weighting coefficients, respectively; This is the reference value for acceleration due to frequency change; for The acceleration due to the change in frequency at time t; exp is the natural exponential function; This is the early warning threshold for the state of charge. for The equivalent state of charge at time t.

[0015] As a further improvement of the present invention, the decision model in S5 is constructed based on the fusion of random forest algorithm and fuzzy logic.

[0016] As a further improvement to the present invention, after performing S5, S6 is also included: The final power command includes the optimal power command for the battery and the optimal power command for the supercapacitor. The optimal power command for the battery and the optimal power command for the supercapacitor are respectively sent to the power converter of the battery and the power converter of the supercapacitor for execution; After waiting for one control cycle, return to S1 and repeat the execution periodically.

[0017] This invention proposes a hybrid energy storage black-start coordinated control system based on frequency change rate prediction, comprising: The data acquisition module is used to collect the current status data of the power system and the operating data of the hybrid energy storage system, respectively. The first calculation module is used to input the operating data of the hybrid energy storage system into the improved equivalent state of charge model and calculate the equivalent state of charge at future times. The second calculation module is used to input the state data of the power system and the equivalent state of charge at future times into the improved frequency change rate prediction model to calculate the frequency change rate at future times. The third calculation module is used to calculate the stability index for future times based on the rate of change of frequency and the equivalent state of charge for future times. The power command output module is used to input the stability index, the equivalent state of charge, and the normalized rate of change of frequency at future time moments into a pre-built decision model to generate the final power command; and to coordinate and control the hybrid energy storage system according to the final power command to perform black start.

[0018] This invention proposes a hybrid energy storage black-start coordinated control device based on frequency change rate prediction, comprising a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the above-described hybrid energy storage black-start coordinated control method based on frequency change rate prediction.

[0019] This invention proposes a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned black-start coordinated control method for hybrid energy storage based on frequency change rate prediction.

[0020] Compared with the prior art, the present invention achieves the following technical effects: This invention combines the active prediction of the rate of change of frequency with the equivalent state of charge (EPC) of the hybrid energy storage system, thereby achieving proactive and adaptive adjustment of frequency dynamics during black start. This effectively overcomes the risk of power system rated frequency instability caused by load surges and control delays, significantly improving frequency stability and recovery success rate during black start. The improved EPC model predicts the future EPC of the hybrid energy storage system, quantifying different parameter scales of the battery and supercapacitor, providing a foundation for subsequent stability index analysis. The improved rate of change of frequency prediction model couples the EPC to virtual inertial regulation, effectively overcoming the shortcomings of traditional methods that cannot respond promptly to load surges due to control delays, thus improving the timeliness and accuracy of control. Finally, the stability index calculated by this invention effectively reflects the instantaneous stability of the power system, thereby comprehensively and accurately predicting the stability risk level the power system will face, providing a basis for the final power command output, and avoiding improper control due to biased assessments. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0022] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0023] See Figure 1 This embodiment provides a black-start coordinated control method for hybrid energy storage based on frequency change rate prediction, including the following steps: S1. Collect the current status data of the power system and the operation data of the hybrid energy storage system respectively; S2. Input the operating data of the hybrid energy storage system into the improved equivalent state of charge model to calculate the equivalent state of charge at future times; S3. Input the state data of the power system and the equivalent state of charge at future times into the improved frequency change rate prediction model to calculate the frequency change rate at future times; S4. Calculate the stability index for future times based on the rate of change of frequency and the equivalent state of charge for future times. S5. Input the stability index, equivalent state of charge, and normalized rate of change of frequency at future moments into the pre-built decision model to generate the final power command; coordinate and control the hybrid energy storage system according to the final power command to perform black start.

[0024] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments: Step 1: Collect the current state data of the power system and the operational data of the hybrid energy storage system. This embodiment collects two types of data: the state data of the power system and the operational data of the hybrid energy storage system. Specifically, the state data of the power system mainly reflects the parameters of power system power balance and frequency dynamics, including the total load power P. load (t) can be obtained through a wide-area measurement system or an in-station monitoring system; frequency deviation is calculated by the difference between the actual frequency value and the rated frequency, and can be monitored in real time by a high-precision frequency measurement device (such as a synchronous phasor measurement unit); current frequency change rate The current rate of change of frequency can be obtained by calculating the frequency difference between adjacent sampling points, or by directly measuring the rate of change of frequency provided by the synchronous phasor measurement unit.

[0025] In this embodiment, the operating data of the hybrid energy storage system includes: the current state of charge (SOC) of the battery. bat and the current output P of the battery bat And the current output power P of the supercapacitor sc (t), the above data is usually provided by the battery management system (BMS) and power conversion system (PCS) inside the hybrid energy storage system.

[0026] Step 2: Based on the operational data obtained in Step 1, input it into the improved equivalent state of charge model for calculation. This step provides a basis for subsequent calculation of the frequency change rate at future times by predicting the equivalent state of charge at future times.

[0027] The improved equivalent state-of-charge model in this embodiment is derived from the Ragone curve of electrochemical energy storage and the definition of the state of charge, as shown in the following expression:

[0028] In the formula: for The equivalent state of charge at any given moment; This indicates the current state of charge of the battery. This represents the current output power of the battery. The battery loss function value is represented by Δt, which is the control period. The rated energy of the battery; The supercapacitor calculation coefficient is set to 0.5. This represents the current output power of the supercapacitor. This refers to the rated energy of the supercapacitor.

[0029] The improved equivalent state-of-charge model in this embodiment can quantify the energy consumption of two different energy storage components, namely batteries and supercapacitors, in future timeframes. Specifically, it uses a battery loss function value to represent the energy consumption and output power of the battery. The expression is as follows:

[0030] In the formula: It is a quadratic function, and its value is 0.001; It is a linear function, with a value of 0.1; For the constant term, take the value of 2.

[0031] The expression for the supercapacitor calculation coefficient is as follows:

[0032] In the formula: The supercapacitor time constant; This represents the battery time constant.

[0033] Supercapacitor calculation coefficient It can convert the power output of the supercapacitor into the consumption corresponding to that of the battery.

[0034] The above formula is calculated as follows Equivalent state of charge at time t It is a dimensionless value between 0 and 1, representing the overall energy margin of the hybrid energy storage system at future moments. It provides a reference benchmark for subsequent calculation of frequency change rate and stability index, and solves the problem of difficulty in coordinating the state of different energy storage components in traditional methods.

[0035] Step 3: This step uses an improved frequency change rate prediction model to couple the equivalent state of charge at future moments obtained in Step 2 into the virtual inertial adjustment, thereby achieving active prediction of the frequency change rate at future moments. The improved frequency change rate prediction model in this embodiment is as follows:

[0036] In the formula: for Rate of change of frequency at any given moment; The rated frequency of the power system; Let be the inherent inertial time constant of the power system, taken as 3s; The maximum virtual inertial time constant; The dynamic virtual inertial time constant of the hybrid energy storage system; Total load power; This represents the current output power of the battery. This represents the current output power of the supercapacitor. This serves as the baseline capacity for the power system. For the equivalent damping of the power system; This is for frequency deviation; This is the virtual inertial gain coefficient; The equivalent state of charge at time t+Δt; Let be the rate of change of the current frequency at time t.

[0037] Since the power system's ability to suppress frequency changes (i.e., virtual inertia) is not fixed, but dynamically adjusted according to the future energy margin of the hybrid energy storage system: when the predicted equivalent state of charge is high, the virtual inertia effect approaches the maximum set value, and the power system can suppress drastic frequency changes; when the equivalent state of charge is low, the virtual inertia effect is appropriately weakened to avoid excessive discharge of the hybrid energy storage system.

[0038] The rated frequency of the power system in the above formula The inherent inertial time constant of the power system is obtained according to power grid standards (50Hz or 60Hz). The calculation formula is as follows:

[0039] In the formula: The moment of inertia of the synchronous generator in the black-start power supply; This is the rated angular frequency of the power system.

[0040] Maximum virtual inertia time constant The calculation formula is as follows:

[0041] In the formula: The maximum virtual inertial time constant that the hybrid energy storage system can provide is taken as 2S to 8S; The inertial adjustment sensitivity coefficient is set to 5–10. It is the hyperbolic tangent function.

[0042] Baseline capacity of power system The equivalent damping is obtained by taking the rated capacity of the black-start power supply. The virtual inertial gain coefficient K is obtained by applying a small perturbation and measuring the frequency response. vir The value is determined through simulation optimization, and is taken as 0.01 to 0.03.

[0043] This embodiment combines the equivalent state of charge with frequency change rate prediction, which can calculate the frequency change rate at future moments, thereby sensing the trend of the frequency change rate in advance and effectively overcoming the defect of traditional methods that rely solely on current measurements, resulting in control lag.

[0044] Step 4: This step combines the future frequency change rate, the current frequency change rate, and the equivalent state of charge at the future time to calculate the stability index at the future time. In this embodiment, the stability index can convert the future frequency change rate, the current frequency change rate, and the equivalent state of charge at the future time into a single quantized signal, providing a basis for the subsequent final power command allocation.

[0045] The formula for calculating the stability index is as follows:

[0046] In the formula: for Stability indicators at any given time; The reference value for the rate of change of frequency is 0.5 Hz / s; γ is the rate of change of frequency at time t+Δt; γ1 and γ2 are weighting coefficients, respectively, with values ​​of 0.3 and 0.2. This is the reference value for acceleration due to frequency change; for The acceleration due to the change in frequency at time t; exp is the natural exponential function; This is the early warning threshold for the state of charge. for The equivalent state of charge at time t.

[0047] In the embodiment, the formula for calculating the stability index is obtained by improving the Lyapunov stability theory; the frequency change rate benchmark value in the above formula adopts the power system protection setting value, the frequency change acceleration benchmark value is set according to the empirical value, and the state of charge warning threshold is selected as the lower limit of safe operation of the hybrid energy storage system.

[0048] Stability index in this embodiment As a dimensionless value, its magnitude directly reflects the stability of the power system at future moments: the higher the stability index, the greater the risk of frequency instability faced by the power system; the lower the stability index, the safer the power system is. By using the quantitative evaluation method of stability index, the problem of relying on subjective experience or a single index for stability judgment in traditional methods is solved.

[0049] Step 5: In this step, the stability index, the equivalent state of charge, and the normalized rate of change of frequency at future moments are input into the pre-built decision model. The decision model then outputs the optimal power command for the battery and the optimal power command for the supercapacitor, respectively. The specific calculation formulas are as follows:

[0050]

[0051] In the formula: for The optimal power command for the battery at any given time; for The optimal power command for the supercapacitor at any given moment; is the number of decision trees in the random forest; i is the i-th decision tree in the random forest; Let represent the fuzzy rule weights corresponding to the i-th decision tree; Let be the output function of the i-th decision tree in the random forest for the battery. Let be the output function of the i-th decision tree in the random forest for the supercapacitor; For stability indicators; This is the equivalent state of charge; This represents the normalized rate of change of the predicted frequency. Indicates the weight of the fuzzy rule; For stability indicators For the membership function value of the k-th fuzzy rule; For stability indicators The membership function value for the j-th fuzzy rule; For temporary index variables; The center of the k-th fuzzy rule; Let be the width of the k-th fuzzy rule.

[0052] The decision model in this embodiment integrates the random forest algorithm and fuzzy logic. The inputs of the decision model include the stability index of future time, the equivalent state of charge of future time, and the normalized rate of change of frequency of future time. The outputs are the optimal output power command of the battery and the optimal output power command of the supercapacitor.

[0053] Specifically, firstly, continuous stability indicators are fuzzified using fuzzy logic, which can be divided into different state levels such as stable, early warning, and emergency, and corresponding fuzzy weights are generated to dynamically adjust the importance of the output of each decision tree in the random forest algorithm. The random forest algorithm is then trained offline using a large amount of simulation or historical running data to learn the complex nonlinear mapping relationship between input features and optimal power allocation. During the training process, the goal is to minimize frequency fluctuations and maximize energy storage utilization efficiency.

[0054] When running online, the decision model combines the state weights provided by fuzzy logic with the learning results of random forest to comprehensively calculate the optimal power command under the current operating conditions. This ensures the accuracy of power allocation and improves the adaptability to complex operating conditions. It effectively solves the defects of traditional models, such as strong dependence or subjective rule setting, and makes the decision-making process both robust and flexible.

[0055] Finally, the generated optimal power command for the battery is sent to the power converter of the battery for execution, and the generated optimal power command for the supercapacitor is sent to the power converter of the supercapacitor for execution. It should be noted that before the optimal power command is executed, a power limit check is required. If the power of the optimal power command exceeds the safe operating range, it is scaled and adjusted proportionally to ensure the safety of the battery and the supercapacitor.

[0056] After the final power command is executed, there is a delay of one control cycle (usually 0.1 to 0.5 seconds). After the control cycle ends, the process of steps one to five is repeated to enter the next control cycle, so as to realize the periodic closed-loop feedback control of the black start process.

[0057] The cyclic iterative control method can track changes in the power system status in real time, continuously optimize power allocation, ensure frequency stability throughout the black start process, and significantly improve the recovery success rate of black start.

[0058] Based on the same inventive concept, this embodiment of the invention also provides a hybrid energy storage black start coordinated control system based on frequency change rate prediction. Since the principle of solving the problem by this hybrid energy storage black start coordinated control system based on frequency change rate prediction is similar to the aforementioned hybrid energy storage black start coordinated control method based on frequency change rate prediction, the implementation of this hybrid energy storage black start coordinated control system based on frequency change rate prediction can refer to the implementation of the hybrid energy storage black start coordinated control method based on frequency change rate prediction, and the repeated parts will not be described again.

[0059] In specific implementation, the hybrid energy storage black-start coordinated control system based on frequency change rate prediction provided in this embodiment of the invention specifically includes: The data acquisition module is used to collect the current status data of the power system and the operating data of the hybrid energy storage system, respectively. The first calculation module is used to input the operating data of the hybrid energy storage system into the improved equivalent state of charge model and calculate the equivalent state of charge at future times. The second calculation module is used to input the state data and equivalent state of charge of the power system into the improved frequency change rate prediction model to calculate the frequency change rate at future times. The third calculation module is used to calculate the stability index at future times based on the rate of change of frequency and the equivalent state of charge. The power command output module is used to input stability indicators, equivalent state of charge, and normalized rate of change of frequency into a pre-built decision model to generate the final power command; and to coordinate and control the hybrid energy storage system according to the final power command to perform black start.

[0060] Accordingly, this embodiment of the invention also provides a hybrid energy storage black start coordinated control device based on frequency change rate prediction, including a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the hybrid energy storage black start coordinated control method based on frequency change rate prediction provided in this embodiment of the invention.

[0061] For more detailed information on the above methods, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0062] Accordingly, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-described hybrid energy storage black-start coordinated control method based on frequency change rate prediction provided in embodiments of the present invention.

[0063] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems, devices, and storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0064] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0065] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0066] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0067] The foregoing has provided a detailed description of the black-start coordinated control method, system, equipment, and storage medium for hybrid energy storage based on frequency change rate prediction. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A black-start coordinated control method for hybrid energy storage based on frequency change rate prediction, characterized in that, Includes the following steps: S1. Collect the current status data of the power system and the operation data of the hybrid energy storage system respectively; S2. Input the operating data of the hybrid energy storage system into the improved equivalent state of charge model to calculate the equivalent state of charge at future times; S3. Input the state data of the power system and the equivalent state of charge at future times into the improved frequency change rate prediction model to calculate the frequency change rate at future times; S4. Calculate the stability index for future times based on the rate of change of frequency and the equivalent state of charge for future times. S5. Input the stability index, equivalent state of charge, and normalized rate of change of frequency at future moments into the pre-built decision model to generate the final power command; coordinate and control the hybrid energy storage system according to the final power command to perform black start.

2. The black-start coordinated control method for hybrid energy storage based on frequency change rate prediction according to claim 1, characterized in that, The power system status data in S1 includes total load power, frequency deviation, and current frequency change rate. The operating data of the hybrid energy storage system includes the current state of charge of the battery, the current output power of the battery, and the current output power of the supercapacitor.

3. The black-start coordinated control method for hybrid energy storage based on frequency change rate prediction according to claim 1, characterized in that, The expression for the improved equivalent state-of-charge model is as follows: In the formula: for The equivalent state of charge at any given moment; This indicates the current state of charge of the battery. This represents the current output power of the battery. The battery loss function value is represented by Δt, which is the control period. The rated energy of the battery; For supercapacitor calculation coefficients; This represents the current output power of the supercapacitor. This refers to the rated energy of the supercapacitor.

4. The black-start coordinated control method for hybrid energy storage based on frequency change rate prediction according to claim 1, characterized in that, The expression for the improved frequency change rate prediction model is as follows: In the formula: for Rate of change of frequency at any given moment; The rated frequency of the power system; The inherent inertial time constant of the power system; The maximum virtual inertial time constant; The dynamic virtual inertial time constant of the hybrid energy storage system; Total load power; This represents the current output power of the battery. This represents the current output power of the supercapacitor. This serves as the baseline capacity for the power system. For the equivalent damping of the power system; This is for frequency deviation; This is the virtual inertial gain coefficient; The equivalent state of charge at time t+Δt is the control period; Let be the rate of change of the current frequency at time t.

5. The black-start coordinated control method for hybrid energy storage based on frequency change rate prediction according to claim 1, characterized in that, In step S4, the stability index for future times is calculated based on the rate of change of frequency and the equivalent state of charge at future times, including: The stability index is calculated as follows: In the formula: for The stability index at time t, where Δt is the control period; The reference value for the rate of change of frequency; γ is the rate of change of frequency at time t+Δt; γ1 and γ2 are the weighting coefficients, respectively; This is the reference value for acceleration due to frequency change; for The acceleration due to the change in frequency at time t; exp is the natural exponential function; This is the early warning threshold for the state of charge. for The equivalent state of charge at time t.

6. The black-start coordinated control method for hybrid energy storage based on frequency change rate prediction according to claim 1, characterized in that, The decision model in S5 is constructed based on a fusion of random forest algorithm and fuzzy logic.

7. The black-start coordinated control method for hybrid energy storage based on frequency change rate prediction according to claim 1, characterized in that, Following S5, S6 is also included: The final power command includes the optimal power command for the battery and the optimal power command for the supercapacitor. The optimal power command for the battery and the optimal power command for the supercapacitor are respectively sent to the power converter of the battery and the power converter of the supercapacitor for execution; After waiting for one control cycle, return to S1 and repeat the execution periodically.

8. A hybrid energy storage black-start coordinated control system based on frequency change rate prediction, characterized in that, include: The data acquisition module is used to collect the current status data of the power system and the operating data of the hybrid energy storage system, respectively. The first calculation module is used to input the operating data of the hybrid energy storage system into the improved equivalent state of charge model and calculate the equivalent state of charge at future times. The second calculation module is used to input the state data of the power system and the equivalent state of charge at future times into the improved frequency change rate prediction model to calculate the frequency change rate at future times. The third calculation module is used to calculate the stability index for future time based on the rate of change of frequency and the equivalent state of charge at future time. The power command output module is used to input the stability index, the equivalent state of charge, and the normalized rate of change of frequency at future time moments into a pre-built decision model to generate the final power command; and to coordinate and control the hybrid energy storage system according to the final power command to perform black start.

9. A hybrid energy storage black-start coordinated control device based on frequency change rate prediction, characterized in that, It includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the black-start coordinated control method for hybrid energy storage based on frequency change rate prediction as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the frequency change rate prediction-based hybrid energy storage black start coordinated control method as described in any one of claims 1 to 7.

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