Power grid oscillation suppression method for compressed air energy storage system

Abstract

The invention relates to the technical field of data processing, in particular to a power grid oscillation suppression method for a compressed air energy storage system, and the method comprises the steps: collecting data; judging a motor power abnormal event; determining a power grid high oscillation risk event; the valve opening and the nozzle opening are adjusted; a preset threshold value is adjusted, and the operation process of the expansion machine is optimized. According to the method, multi-dimensional parameters are collected, motor power abnormal events are recognized according to turbulence and fan operation states, power grid high-oscillation risk events are confirmed in combination with power grid signals, finally, the valve opening degree and the nozzle opening degree are dynamically adjusted through risk indexes, the energy storage system can conduct accurate damping intervention, and meanwhile, the energy storage system can conduct accurate damping intervention. Threshold parameters are optimized in real time by means of feedback of oscillation attenuation rate and turbulence intensity, self-learning ability adapting to different operation conditions is formed, and the problems of power grid oscillation recognition lag and energy storage system response lag caused by complex and changeable wind power environments are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for suppressing grid oscillations in compressed air energy storage systems. Background Technology

[0002] In the process of building a new power system with new energy sources as the mainstay, the large-scale integration of wind power, while promoting the clean energy transition, also brings severe challenges to the stable operation of the power grid. One of the core issues is the inherent intermittency and volatility of wind energy, especially due to turbulent changes in wind speed. When wind turbine clusters encounter drastic changes, the wind power captured by the blades generates strong pulsations, causing fluctuations in voltage and power at the grid connection point, ultimately leading to grid oscillations. Under these circumstances, research on smoothing power fluctuations and preventing the propagation of oscillating energy to the main grid through tie lines using compressed air energy storage systems is particularly promising.

[0003] Chinese Patent Application Publication No. CN115733137A discloses a system and method for suppressing low-frequency oscillations in a power system using compressed air energy storage. The system includes: a regulating valve with its opening degree controlled by an opening degree controller is provided between the air input end of the compressed air energy storage system and the air storage container. The input end of the opening degree controller is respectively connected to the additional damping control signal of the additional damping controller, the reference mass flow rate signal of the compressed air energy storage, and the average mass flow rate signal fed back by the compressed air energy storage. The input end of the additional damping controller is connected to the speed difference signal between generators in the power system when low-frequency oscillations occur in the power system.

[0004] Therefore, the system for suppressing low-frequency oscillations in the power system through compressed air energy storage has the following problems: The additional damping controller of the system only connects to the speed difference signal between generators, which is a typical ex-post reactive control based on the internal state variables of the power grid. For oscillations directly injected by new energy sources such as wind farms, its perception and response are lagging and indirect; it cannot distinguish whether the oscillation originates from regular load fluctuations or wind power turbulence. The system must wait until the fluctuation has spread to the power grid and affected the generator speed difference before it can take action, thus losing the opportunity to suppress it at the source; the control objective of this method is to quell the low-frequency oscillations that have already occurred. For typical, persistent random disturbances such as wind power turbulence, the control actions are frequent and always lag behind the disturbance; when the output of the wind farm or the power grid structure changes, the fixed control parameters cannot always maintain the optimal suppression effect. Summary of the Invention

[0005] Therefore, the present invention provides a grid oscillation suppression method for compressed air energy storage systems, which overcomes the problems of grid oscillation identification lag and energy storage system response lag caused by complex and variable wind environments in the prior art through multi-dimensional data analysis and dynamic adjustment mechanisms.

[0006] To achieve the above objectives, the present invention provides a method for suppressing grid oscillations in a compressed air energy storage system, comprising: The system collects data in real time on the expansion of the compressed air energy storage system during operation based on preset valve opening and preset nozzle opening, including the turbulence intensity of wind in the wind power plant's coverage environment, the active power of the wind turbine, the blade speed, the frequency change rate of the transmission network, the oscillation amplitude of the tie line, and the oscillation attenuation rate within the preset suppression period. An abnormal motor power event is determined based on the turbulence intensity, the preset intensity threshold, the blade rotation speed, and the active power. Based on the abnormal motor power event, a high grid oscillation risk event is determined according to the active power and the oscillation amplitude; Based on the high oscillation risk event of the power grid, the preset valve opening is adjusted according to the risk index determined by the blade rotation speed, the active power and the oscillation amplitude to obtain the adjusted valve opening, and the preset nozzle opening is adjusted according to the adjusted valve opening and the frequency change rate to obtain the adjusted nozzle opening; Based on the valve opening and the nozzle opening, the preset intensity threshold is adjusted according to the oscillation attenuation rate and the turbulence intensity within the preset adjustment time. The expansion machine is controlled by adjusting the valve opening and nozzle opening based on the adjusted preset strength threshold to suppress power grid oscillations.

[0007] Furthermore, the process of determining the occurrence of a motor power anomaly event based on the turbulence intensity, the preset intensity threshold, the blade rotation speed, and the active power includes: Based on the comparison between the turbulence intensity and the preset intensity threshold, it is determined that an abnormal wind event has occurred; Based on the aforementioned wind power anomaly event, a motor power anomaly event is determined to have occurred according to the blade rotation speed and the active power.

[0008] Furthermore, the process of determining the occurrence of a motor power anomaly event based on the blade rotation speed and the active power includes: Calculate several blade speed fluctuation values ​​based on the blade speed within a preset judgment period; Calculate several power fluctuation values ​​based on the active power within the preset determination time period; An abnormal motor power event is determined based on all the blade speed fluctuation values ​​and all the power fluctuation values.

[0009] Furthermore, the process of determining the occurrence of a motor power abnormality event based on all the blade speed fluctuation values ​​and all the power fluctuation values ​​includes: The degree of variation is calculated based on all the blade speed fluctuation values ​​and all the power fluctuation values. The occurrence of an abnormal motor power event is determined based on the comparison between the change in coordination degree and the preset coordination degree threshold.

[0010] Furthermore, the process of determining the occurrence of a high grid oscillation risk event based on the active power and the oscillation amplitude includes: Calculate several power change rates based on the active power within a preset time period; Calculate several amplitude change rates based on the oscillation amplitude within the preset time period; The occurrence of a high power grid oscillation risk event is determined based on all of the power change rate and all of the amplitude change rate.

[0011] Furthermore, the process of determining the occurrence of a high-oscillation risk event in the power grid based on all the power change rates and all the amplitude change rates includes: Calculate the power-amplitude coupling degree based on all the power change rates and all the amplitude change rates; The occurrence of a high power grid oscillation risk event is determined based on the comparison between the power amplitude coupling degree and the preset coupling degree threshold.

[0012] Furthermore, the process of adjusting the preset valve opening based on the risk index determined by the blade rotation speed, the active power, and the oscillation amplitude includes: The risk index is determined based on the blade rotation speed, the active power, and the oscillation amplitude. The preset valve opening is adjusted according to the risk index.

[0013] Furthermore, the process of adjusting the preset valve opening degree according to the risk index includes: The index deviation is calculated based on the comparison between the risk index and the preset index threshold. The preset valve opening is adjusted based on the comparison between the exponential deviation and the preset exponential deviation threshold to obtain the adjusted valve opening.

[0014] Furthermore, the process of adjusting the preset nozzle opening based on the valve opening and the frequency change rate to obtain the adjusted nozzle opening includes: Based on the valve opening, the rate of change fluctuation value is calculated according to the frequency change rate within the preset adjustment period; The preset nozzle opening is adjusted based on the comparison between the rate of change fluctuation value and the preset rate of change fluctuation threshold, thereby obtaining the adjusted nozzle opening.

[0015] Furthermore, the process of adjusting the preset intensity threshold based on the oscillation attenuation rate and the turbulence intensity within a preset adjustment period includes: The rate of change of attenuation rate is calculated based on the oscillation attenuation rate within the preset adjustment period; The preset intensity threshold is adjusted based on the first comparison result between the attenuation rate change rate and the preset attenuation rate change threshold. Based on the second comparison result of the attenuation rate change rate and the preset attenuation rate change rate threshold, the intensity deviation change rate is calculated according to several intensity deviations between the turbulence intensity and the preset intensity threshold at each adjustment time within the preset adjustment period, and the preset intensity threshold is adjusted according to the comparison result of the intensity deviation change rate and the preset intensity deviation change rate threshold.

[0016] Compared with existing technologies, the advantages of this invention lie in its ability to accurately track the oscillation evolution path through multi-parameter linkage judgment. First, it identifies the source anomaly based on turbulence and wind turbine operating status, namely, abnormal motor power events. Then, it confirms system risks, namely, high-oscillation risk events in the power grid, by combining power grid signals. Turbulence intensity, as the initial disturbance source, first triggers a coordinated anomaly in wind turbine blade speed and active power, which then manifests as tie-line oscillations and frequency changes through the power grid coupling effect. Finally, it dynamically adjusts valve and nozzle openings through a risk index, enabling the compressed air energy storage system to precisely dampen and intervene in the oscillation propagation path. Simultaneously, it utilizes feedback from oscillation attenuation rate and turbulence intensity to optimize threshold parameters in real time, forming a self-learning capability adaptable to different operating conditions. This effectively solves the problems of delayed power grid oscillation identification and delayed energy storage system response caused by complex and variable wind environments.

[0017] Furthermore, by comparing turbulence intensity with a threshold, the system first identifies wind anomalies as an external cause. Turbulence intensity is an important factor affecting the operating status of wind turbines. Excessive turbulence intensity may lead to unstable input of the wind turbine, thereby affecting the normal output of the motor power. However, not all wind anomalies will lead to unit malfunction. Therefore, the system further introduces the synergistic analysis of blade speed and active power. When the turbulence intensity exceeds the threshold, the system determines that the wind is abnormal. This not only avoids misjudgments that may be caused by a single wind condition parameter, but also accurately captures the critical state of the wind turbine transitioning from being subjected to external disturbances to internal power anomalies, providing a reliable early judgment basis for subsequent oscillation risk warning.

[0018] Furthermore, by quantifying the fluctuations in blade speed and active power, abnormal power events of the generator can be accurately identified. Blade speed and active power are key indicators reflecting the operating status of wind turbines, and their fluctuations can directly reflect the stability of generator power. Calculating the fluctuation sequence of the two and analyzing their coordination can effectively avoid misjudgments caused by instantaneous changes in a single parameter. At the same time, it can also capture the trend and amplitude of parameter changes, ensuring the accuracy and reliability of the judgment results. It can effectively distinguish between normal power fluctuations caused by natural turbulence and the precursors of unit instability caused by turbulence, providing a reliable basis for subsequent oscillation risk warning.

[0019] Furthermore, by quantitatively analyzing the dynamic coupling relationship between the wind turbine's aeromechanical system and electrical output system, accurate identification of abnormal motor power states was achieved. Under normal operating conditions, the mechanical speed fluctuations of the blades and the active power fluctuations of the generator output should maintain a high degree of dynamic coordination. When this inherent coupling relationship is disrupted, it manifests as a significant reduction in the degree of coordination. By calculating the degree of coordination between the two fluctuation sequences and comparing it with a threshold, normal turbulent fluctuations can be effectively distinguished from genuine system anomalies, thus providing an accurate trigger signal for subsequent oscillation suppression and avoiding misjudgments or omissions that may result from monitoring a single parameter.

[0020] Furthermore, by dynamically tracking the coupling relationship between power disturbances and grid response, a forward-looking assessment of oscillation risks is achieved. Based on the forced oscillation mechanism of the power system, when the rate of change of active power from wind power generation and the rate of change of the oscillation amplitude of the grid interconnection show a significant correlation in the time domain, it indicates that random power fluctuations are transforming into structural grid oscillations. By analyzing the dynamic correlation characteristics of the two rate of change sequences, the driving force of wind turbine power fluctuations on the grid can be effectively identified, thereby providing early warning of high-risk states before the oscillation fully forms, capturing early signs of system instability, and providing a key decision-making basis for the early intervention of energy storage systems.

[0021] Furthermore, by quantifying the correlation strength between power disturbances and the dynamic response of the power grid, a precise judgment on the evolution trend of oscillation risk was achieved. When the rate of change of wind power and the rate of change of tie-line oscillation amplitude show a high spatiotemporal correlation, it indicates that random power fluctuations have been transformed into structural oscillation risks through the grid impedance characteristics. This power amplitude coupling degree is a key indicator for measuring the amplification of energy disturbances in the system and the formation of positive feedback. By calculating the dynamic correlation between the two rate of change sequences and comparing them with preset thresholds, the critical state of the system transitioning from stable fluctuations to unstable oscillations can be effectively identified. This provides an accurate critical trigger signal for the early intervention of compressed air energy storage systems, solving the problem of missing the best control opportunity due to response lag in traditional methods.

[0022] Furthermore, by constructing a risk assessment model that integrates multi-source information, precise feedforward control of the damping power of the compressed air energy storage system was achieved. Based on a full-chain perspective of oscillation propagation: blade speed reflects the mechanical dynamics of the disturbance source, active power characterizes the output state of the electrical side, and oscillation amplitude reflects the grid's response level. These three together constitute a complete causal chain from disturbance input to power conversion to grid impact. By integrating these three key parameters into a unified risk index, the limitations of single-signal control are overcome, and the overall oscillation threat level currently faced by the system can be accurately quantified. This allows valve opening adjustments to no longer merely respond to existing oscillations but to proactively adjust based on the severity of the oscillation risk, significantly improving the accuracy and timeliness of damping control.

[0023] Furthermore, by establishing a hierarchical threshold decision-making mechanism, precise graded control of the regulation intensity of the compressed air energy storage system is achieved. Based on the continuous changing characteristics of oscillation risk: firstly, the risk baseline level is determined by comparing the risk index with a preset threshold; then, the severity of risk changes is identified through a secondary judgment of the index deviation and the deviation threshold. This ensures both the matching of valve opening adjustment with the current oscillation threat level and avoids over-response of the control system to minor risk fluctuations. By transforming the continuous risk index into a discrete valve opening adjustment strategy, both control sensitivity and system operational stability are maintained, enabling the energy storage system to provide appropriate damping support according to the actual severity of the oscillation risk.

[0024] Furthermore, by establishing a coordinated control mechanism for valve opening and nozzle opening, the compressed air energy storage system achieves multi-timescale response capability to grid frequency changes, based on the complementary functions of power regulation and inertial support: adjusting the valve opening mainly changes the steady-state value of the output power, while adjusting the nozzle opening constitutes a rapid aerodynamic damping loop, achieving precise suppression of high-frequency oscillations by changing the expander flow characteristics. Through secondary adjustment based on the frequency change rate fluctuation value, the system can identify the urgency of grid transient stability. When the frequency changes drastically, the nozzle opening change is precisely matched with the grid instability level, and the nozzle opening is quickly adjusted to enhance the system's damping characteristics; when the frequency is relatively stable, the rotational speed is kept stable to maintain system inertia, enabling the energy storage system to provide rapid power support and achieving a coordinated suppression effect on grid oscillations.

[0025] Furthermore, by establishing a dual adaptive mechanism based on control effect and environmental situation, intelligent optimization of system sensitivity is achieved. The rate of change of oscillation decay rate reflects the actual effect of the current control strategy. If the decay rate does not improve significantly after control, it indicates that the system is not sensitive enough to the identification of disturbance sources, and the preset intensity threshold needs to be lowered to capture more subtle abnormal signs. The rate of change of intensity deviation characterizes the dynamic characteristics of environmental disturbance. By analyzing the trend of deviation between turbulence intensity and threshold, the future development direction of disturbance mode can be predicted. When control is effective but the environment continues to deteriorate, by monitoring the trend of deviation between turbulence intensity and threshold, the development of environmental situation can be predicted and the threshold can be increased accordingly to recalibrate the benchmark. By combining control effect feedback with environmental situation prediction, the system can dynamically optimize its detection sensitivity according to the actual operating conditions. This avoids the problem of missed detection caused by overly conservative threshold settings and prevents false actions caused by overly sensitive thresholds, significantly improving the adaptability and reliability of the system under different operating conditions. Attached Figure Description

[0026] Figure 1 This is a flowchart of the grid oscillation suppression method for compressed air energy storage systems in this embodiment; Figure 2 This is a logic diagram for determining the occurrence of an abnormal wind event in this embodiment; Figure 3 This is a logic diagram for determining the occurrence of a motor power abnormality event in this embodiment; Figure 4 This embodiment defines the logic diagram for determining the occurrence of a high power grid oscillation risk event. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] Please see Figure 1The diagram shows a flowchart of a grid oscillation suppression method for a compressed air energy storage system according to this embodiment. This embodiment provides a grid oscillation suppression method for a compressed air energy storage system, comprising: real-time acquisition of data on the turbulence intensity of wind in the wind farm's coverage environment, the active power of the wind turbine, the blade speed, the frequency change rate of the transmission grid, the oscillation amplitude of the tie line, and the oscillation attenuation rate within a past preset suppression period during the operation of the expander of the compressed air energy storage system based on preset valve openings and preset nozzle openings; determining a motor power anomaly event based on the turbulence intensity, a preset intensity threshold, the blade speed, and the active power; and, based on the motor power anomaly event, determining the grid oscillation suppression method according to the preset suppression period. The active power and the oscillation amplitude determine the occurrence of a high grid oscillation risk event. Based on the high grid oscillation risk event, the preset valve opening is adjusted according to the risk index determined by the blade rotation speed, the active power, and the oscillation amplitude to obtain an adjustment valve opening. The preset nozzle opening is then adjusted according to the adjustment valve opening and the frequency change rate to obtain an adjustment nozzle opening. Based on the adjustment valve opening and the adjustment nozzle opening, the preset intensity threshold is adjusted according to the oscillation attenuation rate and the turbulence intensity within a preset adjustment period. The expander is controlled to operate based on the adjusted valve opening and the adjusted nozzle opening, which are re-determined according to the adjusted preset intensity threshold, in order to suppress grid oscillations.

[0030] In this embodiment, wind turbines and compressed air energy storage systems coexist in a large coastal wind farm grid-connected system. When the leading edge of a typhoon sweeps across the wind farm area, the wind turbine group encounters drastically changing turbulent wind conditions. The wind power captured by the blades generates strong fluctuations, causing voltage and power at the grid connection point to swing, ultimately resulting in grid oscillations. At this time, by adjusting the valves and nozzle system of the expander unit of the compressed air energy storage system, the power fluctuations are smoothed, effectively preventing the oscillation energy from propagating to the main grid through the tie line, and ensuring that the power supply quality of the receiving-end load center is not affected by extreme weather.

[0031] The preset valve opening is the baseline opening of the expander inlet valve of the compressed air energy storage system during continuous grid-connected standby operation. It depends on the basic power demand of the power grid, the pressure of the air storage tank, and the system's optimal operating efficiency point under partial load, and is typically set between 30% and 70%. In this embodiment, it is set to 40%, providing sufficient bidirectional adjustment margin to cope with wind energy fluctuations. It can be quickly opened to over 70% to increase power generation, or closed to below 20% to reduce power generation, while ensuring low losses and economic efficiency of the system in standby mode.

[0032] The preset nozzle opening is the reference angle of the expander's adjustable stator vane when the compressed air energy storage system is continuously connected to the grid and on standby. It depends on the expander's design operating point and the optimal balance between airflow efficiency and adjustment margin. It is usually 60% to 90%, and in this embodiment it is set to 75%, which allows the expander to operate in the high-efficiency range while reserving sufficient bidirectional fine adjustment capability to cope with oscillations.

[0033] In this embodiment, turbulence intensity, a dimensionless parameter describing the severity of instantaneous wind speed fluctuations, is a core indicator of wind energy volatility and reflects the stability of wind energy. High turbulence intensity means rapid and disordered wind speed changes, which is the main source of wind power fluctuations. It is measured at high frequency using an ultrasonic anemometer installed on the wind farm's wind measurement tower or wind turbine. Active power is the actual power delivered from the wind turbine to the grid, directly reflecting the power injection level of the wind turbine into the grid. Its fluctuations are the excitation source causing grid oscillations. It is measured in real time by an energy metering device on the wind turbine's outlet side. Blade speed is the rotational speed of the wind turbine rotor, reflecting the mechanical state of the wind turbine capturing wind energy. Its coordination with active power is key to determining whether the wind turbine is operating normally. It is measured by a speed encoder installed on the wind turbine's main shaft.

[0034] The rate of change of frequency is the rate at which the power grid frequency changes over time. It is one of the most sensitive dynamic indicators for measuring the instantaneous balance and stability of the power grid. A larger rate of change of frequency indicates a more severe power deficit or surplus, and a more unstable system. The grid voltage waveform is synchronously sampled by a synchronous phasor measurement unit, and the voltage phase change is calculated and tracked in real time using Discrete Fourier Transform (DFT). Since frequency is the derivative of phase, the rate of change of frequency can be calculated to the millisecond level. Oscillation amplitude is the amplitude of low-frequency oscillations in power or current on tie lines in the power grid. It directly quantifies the severity of grid oscillations. After collecting power or current data from tie lines by a synchronous phasor measurement unit, the oscillation amplitude is obtained through online analysis of the data stream using a signal analysis Fourier Transform algorithm. Oscillation decay rate is a parameter describing how quickly the historical oscillation amplitude decays over time. It directly characterizes the strength of system damping. A larger decay rate indicates that the oscillation subsides faster and the system stability is better. The oscillation decay rate is obtained by curve fitting of the decay waveform obtained from the oscillation amplitude within a preset suppression period by a synchronous phasor measurement unit.

[0035] The preset suppression period is the length of the time window used to evaluate historical control effects and calculate oscillation attenuation rates. It depends on the period of the main low-frequency oscillation modes of the power grid and the shortest effective data length required to evaluate the system's damping characteristics, and is usually set between 5 and 30 minutes. In this embodiment, it is set to 10 minutes, which can cover several complete oscillation cycles for accurate attenuation rate calculation, while ensuring the timeliness of the evaluation data, enabling the system to provide rapid feedback on the control effect.

[0036] The preset intensity threshold is a benchmark value used to determine whether the turbulence intensity has reached the level of wind anomalies. It depends on the specific design of the wind power generation system, the operating environment, and its sensitivity to wind anomalies, and is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3, which can ensure the system's sensitivity to wind anomalies while avoiding frequent misjudgments due to the threshold being set too low, thus ensuring the stable operation of the system.

[0037] The preset adjustment duration is the length of time required to evaluate the control effect and environmental conditions to adjust the threshold. It depends on the period of the power grid oscillation mode and the time it takes for the control effect to manifest, and is usually set between 10 and 60 minutes. In this embodiment, it is set to 30 minutes, which can accommodate enough oscillation events for statistical evaluation, ensuring the reliability of the adjustment decision, while also keeping up with environmental changes in a timely manner.

[0038] By employing multi-parameter linkage judgment, precise tracking of the oscillation evolution path is achieved. First, the source anomaly is identified based on turbulence and wind turbine operating status, namely, abnormal motor power events. Then, system risks are confirmed by combining grid signals, namely, high grid oscillation risk events. Turbulence intensity, as the initial disturbance source, first triggers a coordinated anomaly in wind turbine blade speed and active power, which then manifests as tie-line oscillations and frequency changes through grid coupling effects. Finally, the valve and nozzle openings are dynamically adjusted using a risk index, enabling the compressed air energy storage system to precisely dampen and intervene in the oscillation propagation path. Simultaneously, by utilizing feedback from oscillation attenuation rate and turbulence intensity to optimize threshold parameters in real time, a self-learning capability adapting to different operating conditions is formed, effectively solving the problems of grid oscillation identification lag and energy storage system response lag caused by complex and variable wind environments.

[0039] Please see Figure 2 As shown, this is a logic diagram for determining the occurrence of an abnormal wind event in this embodiment. In this embodiment, the process of determining the occurrence of an abnormal motor power event based on the turbulence intensity, the preset intensity threshold, the blade rotation speed, and the active power includes: determining that an abnormal wind event has occurred when the turbulence intensity is greater than the preset intensity threshold; and determining that an abnormal motor power event has occurred based on the abnormal wind event, according to the blade rotation speed and the active power.

[0040] By comparing turbulence intensity with a threshold, the system first identifies wind anomalies as an external cause. Turbulence intensity is an important factor affecting the operating status of wind turbines. Excessive turbulence intensity may lead to unstable input of the wind turbine, thereby affecting the normal output of the motor power. However, not all wind anomalies will lead to unit malfunction. Therefore, the system further introduces the synergistic analysis of blade speed and active power. When the turbulence intensity exceeds the threshold, the system determines that the wind is abnormal. This not only avoids misjudgments that may be caused by a single wind condition parameter, but also accurately captures the critical state of the wind turbine transitioning from external disturbance to internal power anomaly, providing a reliable early judgment basis for subsequent oscillation risk warning.

[0041] Specifically, the process of determining the occurrence of a motor power abnormality event based on the blade rotation speed and the active power includes: calculating the variance of all blade rotation speeds from the initial time to each time within a preset determination period to obtain several blade rotation speed fluctuation values; calculating the variance of all active power from the initial time to each time within the preset determination period to obtain several power fluctuation values; and determining the occurrence of a motor power abnormality event based on all blade rotation speed fluctuation values ​​and all power fluctuation values.

[0042] The preset judgment duration is the length of time used to collect data to determine whether a motor power anomaly event has occurred. It depends on the dynamic response characteristics of the wind turbine, the stability requirements of the power grid, and the sensitivity to detecting motor power anomalies, and is typically set between 10 and 30 minutes. In this embodiment, it is set to 15 minutes, which ensures the sensitivity to detecting motor power anomalies while avoiding false judgments due to excessively short timeframes.

[0043] By quantifying the fluctuations in blade speed and active power, abnormal power events of the generator can be accurately identified. Blade speed and active power are key indicators reflecting the operating status of wind turbines, and their fluctuations can directly reflect the stability of generator power. Calculating the fluctuation sequence of the two and analyzing their coordination can effectively avoid misjudgments caused by instantaneous changes in a single parameter. At the same time, it can also capture the trend and amplitude of parameter changes, ensuring the accuracy and reliability of the judgment results. It can effectively distinguish between normal power fluctuations caused by natural turbulence and the precursors of unit instability caused by turbulence, providing a reliable basis for subsequent oscillation risk warning.

[0044] Please see Figure 3As shown, this is the logic diagram for determining the occurrence of a motor power abnormality event in this embodiment. In this embodiment, the process of determining the occurrence of a motor power abnormality event based on all the blade speed fluctuation values ​​and all the power fluctuation values ​​includes: performing maximum-minimum normalization processing on all the blade speed fluctuation values ​​to obtain several blade speed normalization values; performing maximum-minimum normalization processing on all the power fluctuation values ​​to obtain several power normalization values; calculating the cosine similarity between all the blade speed normalization values ​​and all the power normalization values ​​to obtain the change coordination degree; and determining that a motor power abnormality event has occurred when the change coordination degree is less than a preset coordination degree threshold.

[0045] The preset coordination threshold is a benchmark value used to determine whether the changes between blade speed fluctuations and active power fluctuations are abnormal. It depends on the operating characteristics of the wind turbine, the stability requirements of the power grid, and the sensitivity to detecting abnormal power events, and is typically set between 0.5 and 0.8. In this embodiment, it is set to 0.6, which effectively balances detection sensitivity and accuracy, ensuring stable system operation.

[0046] By quantitatively analyzing the dynamic coupling relationship between the wind turbine's aeromechanical system and electrical output system, accurate identification of abnormal motor power states was achieved. Under normal operating conditions, the mechanical speed fluctuations of the blades and the active power fluctuations of the generator output should maintain a high degree of dynamic coordination. When this inherent coupling relationship is disrupted, it manifests as a significant reduction in the degree of coordination. By calculating the degree of coordination between the two fluctuation sequences and comparing it with a threshold, normal turbulent fluctuations can be effectively distinguished from genuine system anomalies. This provides an accurate trigger signal for subsequent oscillation suppression, avoiding misjudgments or omissions that may result from monitoring a single parameter.

[0047] Specifically, the process of determining the occurrence of a high grid oscillation risk event based on the active power and the oscillation amplitude includes: calculating the rate of change of all active power from the initial time to each time within a preset predetermined time period to obtain several power change rates; calculating the rate of change of all oscillation amplitudes from the initial time to each time within the preset predetermined time period to obtain several amplitude change rates; and determining the occurrence of a high grid oscillation risk event based on all the power change rates and all the amplitude change rates.

[0048] The preset duration is the time length used to assess the changing trends of active power and oscillation amplitude. It depends on the dynamic response characteristics of the power grid, the output characteristics of the wind turbine, and the sensitivity to detecting high oscillation risk events in the power grid, and is set between 1 minute and 10 minutes. In this embodiment, it is set to 5 minutes, which can effectively detect high oscillation risk events in the power grid, avoid false alarms, and ensure the stable operation of the system.

[0049] By dynamically tracking the coupling relationship between power disturbances and grid response, a forward-looking assessment of oscillation risks is achieved. Based on the forced oscillation mechanism of the power system, when the rate of change of active power from wind power generation and the rate of change of the oscillation amplitude of the grid interconnection show a significant correlation in the time domain, it indicates that random power fluctuations are transforming into structural grid oscillations. By analyzing the dynamic correlation characteristics of the two rate of change sequences, the driving force of wind turbine power fluctuations on the grid can be effectively identified, thereby providing early warning of high-risk states before the oscillation fully forms, capturing early signs of system instability, and providing a key decision-making basis for the early intervention of energy storage systems.

[0050] Please see Figure 4 As shown, this is the logic diagram for determining the occurrence of a high grid oscillation risk event in this embodiment. In this embodiment, the process of determining the occurrence of a high grid oscillation risk event based on all the power change rates and all the amplitude change rates includes: performing maximum-minimum normalization on all the power change rates to obtain several power change normalization values; performing maximum-minimum normalization on all the amplitude change rates to obtain several amplitude change normalization values; calculating the Pearson correlation coefficient of all the power change normalization values ​​and all the amplitude change normalization values ​​to obtain the power-amplitude coupling degree; and determining that a high grid oscillation risk event has occurred when the power-amplitude coupling degree is greater than a preset coupling degree threshold.

[0051] The preset coupling threshold is a benchmark value used to determine whether the power amplitude coupling is abnormal. It depends on the operating characteristics of the power grid, the output characteristics of the wind turbine, and the sensitivity to detecting high oscillation risk events in the power grid, and is usually set between 0.7 and 0.9. In this embodiment, it is set to 0.8, which can ensure the sensitivity to detecting high oscillation risk events in the power grid while avoiding false judgments caused by setting the threshold too low.

[0052] By quantifying the correlation between power disturbances and the dynamic response of the power grid, a precise judgment on the evolution trend of oscillation risk is achieved. When the rate of change of wind power and the rate of change of tie-line oscillation amplitude show a high spatiotemporal correlation, it indicates that random power fluctuations have been transformed into structural oscillation risks through the grid impedance characteristics. This power amplitude coupling degree is a key indicator for measuring the amplification of energy disturbances in the system and the formation of positive feedback. By calculating the dynamic correlation between the two rate of change sequences and comparing them with preset thresholds, the critical state of the system transitioning from stable fluctuations to unstable oscillations can be effectively identified. This provides an accurate critical trigger signal for the early intervention of compressed air energy storage systems, solving the problem of missing the best control opportunity due to response lag in traditional methods.

[0053] Specifically, the process of adjusting the preset valve opening based on the risk index determined by the blade rotation speed, the active power, and the oscillation amplitude includes: calculating a standard rotation speed value based on the blade rotation speed, a preset reference rotation speed, and a preset rotation speed deviation threshold; calculating a standard power value based on the active power, a preset reference power, and a preset power deviation threshold; calculating a standard amplitude value based on the oscillation amplitude, a preset reference amplitude, and a preset amplitude deviation threshold; performing a weighted summation and maximum-minimum normalization on the standard rotation speed value, the standard power value, the standard amplitude value, the preset rotation speed weight, the preset power weight, and the preset amplitude weight to obtain the risk index; and adjusting the preset valve opening based on the risk index.

[0054] Standard value calculation formulas: A=|a-a'| / (Q+|a-a'|;B=|b-b'| / (W+|b-b'|;C=|c-c'| / (E+|c-c'|; Where A is the standard value of rotational speed, a is the blade rotational speed, a' is the preset reference rotational speed, and Q is the preset rotational speed deviation threshold; B is the standard value of power, b is the active power, b' is the preset reference power, and W is the preset power deviation threshold; C is the standard value of amplitude, c is the oscillation amplitude, c' is the preset reference amplitude, and E is the preset amplitude deviation threshold.

[0055] Risk index calculation formula: R = d × A 2 +e×B 2 +f×C 2 ; Where R is the risk index, d is the preset speed weight, e is the preset power weight, and f is the preset amplitude weight.

[0056] The preset reference speed is a value used to assess whether the blade speed deviates from the normal operating range. It depends on the design parameters and rated operating speed of the wind turbine, and is usually set between 90% and 110% of the rated speed of the wind turbine. In this embodiment, it is set to 1000 RPM (the rated speed of the wind turbine in this embodiment is 1000 RPM), which ensures that the blade speed is within the normal operating range, while providing a certain buffer zone for possible fluctuations, ensuring the stable operation of the system.

[0057] The preset speed deviation threshold is a reference value used to assess the degree to which the blade speed deviates from the preset reference speed. It depends on the operating characteristics of the wind turbine and its tolerance for speed fluctuations, and is usually set between 50 RPM and 150 RPM. In this embodiment, it is set to 100 RPM, which can ensure the sensitivity of speed anomaly detection while avoiding false judgments caused by setting the threshold too low, thus ensuring the stable operation of the system.

[0058] The preset reference power is a key parameter used to assess whether the active power deviates from the normal operating range. It depends on the design parameters and rated output power of the wind turbine and is typically set between 80% and 120% of the wind turbine's rated power. In this embodiment, it is set to 1000kW (the rated power of the wind turbine in this embodiment is 1000kW), which ensures that the active power is within the normal operating range and provides a buffer zone for possible fluctuations, ensuring the stable operation of the system.

[0059] The preset power deviation threshold is a key parameter used to assess the degree to which active power deviates from a preset reference power. It depends on the operating characteristics of the wind turbine and its tolerance to power fluctuations, and is typically set between 100kW and 200kW. In this embodiment, it is set to 150kW, which can ensure the sensitivity of power anomaly detection while avoiding false alarms and ensuring the stable operation of the system.

[0060] The preset reference amplitude is a key parameter used to assess whether the oscillation amplitude deviates from the normal operating range. It depends on the operating characteristics of the power grid and its tolerance for oscillation amplitude, and is usually set between 50% and 70% of the oscillation amplitude when the power grid is operating normally. In this embodiment, it is set to 0.5 pu (the oscillation amplitude is 0.5 pu when the power grid is operating normally), which ensures that the oscillation amplitude is within the normal operating range, while providing a certain buffer for possible fluctuations and ensuring the stable operation of the system.

[0061] The preset amplitude deviation threshold is a key parameter used to assess the degree to which the oscillation amplitude deviates from the preset reference amplitude. It depends on the operating characteristics of the power grid and its tolerance for oscillation amplitude fluctuations, and is usually set between 0.05 pu and 0.1 pu. In this embodiment, it is set to 0.075 pu, which can ensure the sensitivity of detecting abnormal oscillation amplitudes while avoiding false judgments and ensuring the stable operation of the system.

[0062] The preset rotational speed weight is a key parameter used to assess the contribution of the standard rotational speed value to risk when calculating the risk index. It depends on the operating characteristics of the wind turbine and the impact of rotational speed changes on grid stability, and is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3, which can reasonably reflect the impact of rotational speed changes on grid stability, ensure more accurate calculation of the risk index, and provide a reliable basis for adjusting the valve opening.

[0063] The preset power weight is a key parameter used to assess the contribution of the power standard value to risk when calculating the risk index. It depends on the operating characteristics of the wind turbine and the impact of power changes on grid stability, and is usually set between 0.2 and 0.6. In this embodiment, it is set to 0.4, which can reasonably reflect the impact of power changes on grid stability, ensure more accurate calculation of the risk index, and provide a reliable basis for adjusting the valve opening.

[0064] The preset amplitude weight is a key parameter used to assess the contribution of the amplitude standard value to risk when calculating the risk index. It depends on the operating characteristics of the power grid and the impact of oscillation amplitude changes on power grid stability, and is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3, which can reasonably reflect the impact of oscillation amplitude changes on power grid stability, ensure more accurate calculation of the risk index, and provide a reliable basis for adjusting the valve opening.

[0065] By constructing a risk assessment model that integrates multi-source information, precise feedforward control of the damping power of the compressed air energy storage system was achieved. Based on a full-chain perspective of oscillation propagation: blade speed reflects the mechanical dynamics of the disturbance source, active power characterizes the output state of the electrical side, and oscillation amplitude reflects the grid's response level. These three factors together constitute a complete causal chain from disturbance input to power conversion to grid impact. By integrating these three key parameters into a unified risk index, the limitations of single-signal control are overcome, and the overall oscillation threat level faced by the system can be accurately quantified. This allows valve opening adjustments to no longer merely respond to existing oscillations but to proactively adjust based on the severity of the oscillation risk, significantly improving the accuracy and timeliness of damping control.

[0066] Specifically, the process of adjusting the preset valve opening according to the risk index includes: when the risk index is greater than a preset index threshold, calculating the relative deviation between the risk index and the preset index threshold to obtain the index deviation; when the index deviation is greater than a preset index deviation threshold, increasing the preset valve opening according to the relative deviation between the index deviation and the preset index deviation threshold and a preset opening adjustment coefficient to obtain the adjusted valve opening, where F'=F×(1+p×︱G-G'︱ / G'), F' is the adjusted valve opening, F is the preset valve opening, p is the preset valve adjustment coefficient, G is the index deviation, and G' is the preset index deviation threshold.

[0067] The preset index threshold is a benchmark value for triggering intervention in the compressed air energy storage system. It depends on the grid's tolerance for oscillation risks and the requirements for avoiding malfunctions in the control system, and is usually set between 0.3 and 0.6. In this embodiment, it is set to 0.4, which enables the suppression action to be initiated when the system risk first appears, achieving early intervention, while avoiding false responses to minor fluctuations such as background noise due to an excessively low threshold.

[0068] The preset exponential deviation threshold is a benchmark value used to judge the severity of risk. It depends on the desired response granularity of the control system and is usually set between 0.2 and 0.4. In this embodiment, it is set to 0.3, which can effectively distinguish high-risk states and provide a clear basis for judgment of suppression strategies.

[0069] The preset valve adjustment coefficient is a gain coefficient used to proportionally convert the calculated exponential deviation into the adjustment range of the valve opening. It depends on the power regulation capability, response speed, and safe operating range of the valve's mechanical structure in the compressed air energy storage system, and is typically set between 0.1 and 0.5. In this embodiment, it is set to 0.3 to ensure that the valve opening adjustment is both effective and stable, avoiding secondary power surges caused by excessively large opening changes or weak control and poor suppression effects due to an excessively small coefficient.

[0070] By establishing a hierarchical threshold decision-making mechanism, precise graded control of the regulation intensity of the compressed air energy storage system is achieved. Based on the continuous changing characteristics of oscillation risk: firstly, the risk baseline level is determined by comparing the risk index with a preset threshold; then, the severity of risk changes is identified through a secondary judgment of the index deviation and the deviation threshold. This ensures that the valve opening adjustment matches the current oscillation threat level while avoiding over-response of the control system to minor risk fluctuations. By transforming the continuous risk index into a discrete valve opening adjustment strategy, both control sensitivity and system stability are maintained, enabling the energy storage system to provide appropriate damping support based on the actual severity of the oscillation risk.

[0071] Specifically, the process of adjusting the preset nozzle opening based on the valve opening and the frequency change rate to obtain the adjusted nozzle opening includes: calculating the standard deviation of all frequency change rates within a preset adjustment period based on the valve opening to obtain the rate of change fluctuation value; when the rate of change fluctuation value is greater than a preset rate of change fluctuation threshold, increasing the preset nozzle opening based on the relative deviation between the rate of change fluctuation value and the preset rate of change fluctuation threshold and a preset nozzle adjustment coefficient to obtain the adjusted nozzle opening, where H'=H×(1+k×︱L-L'︱ / L'), H' is the adjusted nozzle opening, H is the preset nozzle opening, k is the preset speed adjustment coefficient, L is the rate of change fluctuation value, and L' is the preset rate of change fluctuation threshold.

[0072] The preset adjustment period is the length of the time window for calculating the frequency change rate fluctuation value. It depends on the grid inertial time constant and the system's response speed requirements to frequency changes, and is usually set between 10 and 120 seconds. In this embodiment, it is set to 60 seconds, which can effectively capture short- to medium-term frequency fluctuation trends, avoiding signal noise interference caused by too short a period and preventing response lag caused by too long a period.

[0073] The preset frequency fluctuation threshold is a critical value used to determine whether the frequency has entered an unstable state. It depends on the frequency quality standard of the power grid and the frequency fluctuation range during normal operation, and is usually set between 0.01 Hz / s and 0.1 Hz / s. In this embodiment, it is set to 0.05 Hz / s, which can effectively distinguish between normal frequency fluctuations of the power grid and abnormally severe fluctuations that characterize the risk of instability, providing an accurate trigger signal for nozzle opening adjustment.

[0074] The preset nozzle adjustment coefficient is a proportional gain coefficient that controls the degree of nozzle opening adjustment with frequency fluctuations. It depends on the dynamic response characteristics of the nozzle adjustment system and the expander's sensitivity to airflow changes, and is usually set between 0.2 and 0.5. In this embodiment, it is set to 0.3 to ensure rapid and smooth speed adjustment response, providing effective inertial support while avoiding speed overshoot due to an excessively large coefficient or weak adjustment effect due to an excessively small coefficient.

[0075] By establishing a coordinated control mechanism for valve opening and nozzle opening, the compressed air energy storage system achieves multi-timescale response capability to grid frequency changes. This is based on the complementary functions of power regulation and inertial support: adjusting the valve opening primarily alters the steady-state value of the output power, while adjusting the nozzle opening constitutes a rapid aerodynamic damping loop, precisely suppressing high-frequency oscillations by changing the expander's flow characteristics. Through secondary adjustment based on the frequency change rate fluctuation, the system can identify the urgency of grid transient stability. When frequency changes drastically, the nozzle opening change is precisely matched to the grid instability level, rapidly adjusting the nozzle opening to enhance the system's damping characteristics. When the frequency is relatively stable, the rotational speed is kept stable to maintain system inertia, enabling the energy storage system to provide rapid power support and achieving a coordinated suppression effect on grid oscillations.

[0076] Specifically, the process of adjusting the preset intensity threshold based on the oscillation attenuation rate and the turbulence intensity within a preset adjustment period includes: calculating the rate of change of all the oscillation attenuation rates within the preset adjustment period to obtain the attenuation rate change rate; when the attenuation rate change rate is less than the preset attenuation rate change rate threshold, increasing the preset intensity threshold based on the relative deviation between the attenuation rate change rate and the preset attenuation rate change rate threshold and a preset first adjustment coefficient, where N'=N×(1+α×︱S-S'︱ / S'), N' adjusts the intensity threshold, N is the preset intensity threshold, α is the preset first adjustment coefficient, S is the attenuation rate change rate, and S' is the preset attenuation rate change rate threshold; when the attenuation rate change rate is greater than... When the preset attenuation change rate threshold is reached, the relative deviation between the turbulence intensity and the preset intensity threshold at each adjustment time within the preset adjustment period is calculated to obtain several intensity deviations. The change rate of all intensity deviations is then calculated to obtain the intensity deviation change rate. When the intensity deviation change rate is greater than the preset intensity deviation change rate threshold, the preset intensity threshold is increased based on the relative deviation between the intensity deviation change rate and the preset intensity deviation change rate threshold, as well as the preset second adjustment coefficient. Here, T'=T×(1+β×︱Y-Y'︱ / Y'), T' is the intensity threshold adjustment, T is the preset intensity threshold, β is the preset second adjustment coefficient, Y is the intensity deviation change rate, and Y' is the preset intensity deviation change rate threshold.

[0077] The preset decay rate threshold is a standard value for determining whether the oscillation suppression effect meets the standard. It depends on the minimum requirement for the system damping strength and the expected oscillation convergence speed, and is usually set between 0.05 and 0.2. In this embodiment, it is set to 0.1, which can effectively identify situations where the control effect is not good, thereby triggering threshold optimization.

[0078] The preset first adjustment coefficient is the proportional gain coefficient for adjusting the intensity threshold. It depends on the system's tolerance for false alarms and the stability requirements of threshold adjustment, and is usually set between 0.1 and 0.3. In this embodiment, it is set to 0.2 to ensure that the threshold is adjusted gradually, avoiding excessive relaxation of monitoring standards based on a single evaluation result, and maintaining the incremental optimization of system sensitivity.

[0079] The preset threshold for the rate of change of intensity deviation is a standard value for judging whether the intensity of environmental turbulence has shown a trend of deterioration. It depends on the fluctuating characteristics of the climate in the wind farm area and the rate of environmental change that the system needs to adapt to, and is usually set between 0.1 and 0.3. In this embodiment, it is set to 0.15, which can sensitively capture the deteriorating trend of turbulence intensity continuously exceeding the current threshold, and provide a trigger signal for the system to adapt to the new environmental benchmark in advance.

[0080] The preset second adjustment coefficient is a proportional gain coefficient for adjusting the intensity threshold when the environment trend deteriorates. It depends on the severity of the environmental change and the urgency of the system needing to recalibrate its baseline, and is usually set between 0.2 and 0.4. In this embodiment, it is set to 0.3, which enables the system to more proactively adapt to the continuously deteriorating operating environment and prevents the energy storage system from being continuously overloaded due to overly stringent standards.

[0081] By establishing a dual adaptive mechanism based on control effect and environmental situation, intelligent optimization of system sensitivity is achieved. The rate of change of oscillation decay rate reflects the actual effect of the current control strategy. If the decay rate does not improve significantly after control, it indicates that the system is not sensitive enough to the identification of disturbance sources, and the preset intensity threshold needs to be lowered to capture more subtle abnormal signs. The rate of change of intensity deviation characterizes the dynamic characteristics of environmental disturbance. By analyzing the trend of deviation between turbulence intensity and threshold, the future development direction of disturbance mode can be predicted. When control is effective but the environment continues to deteriorate, by monitoring the trend of deviation between turbulence intensity and threshold, the development of environmental situation can be predicted and the threshold can be increased accordingly to recalibrate the benchmark. By combining control effect feedback with environmental situation prediction, the system can dynamically optimize its detection sensitivity according to the actual operating conditions. This avoids the problem of missed detection caused by overly conservative threshold settings and prevents false actions caused by overly sensitive thresholds, significantly improving the adaptability and reliability of the system under different operating conditions.

[0082] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for suppressing grid oscillations in a compressed air energy storage system, characterized in that, include: The system collects data in real time on the expansion of the compressed air energy storage system during operation based on preset valve opening and preset nozzle opening, including the turbulence intensity of wind in the wind power plant's coverage environment, the active power of the wind turbine, the blade speed, the frequency change rate of the transmission network, the oscillation amplitude of the tie line, and the oscillation attenuation rate within the preset suppression period. An abnormal motor power event is determined based on the turbulence intensity, the preset intensity threshold, the blade rotation speed, and the active power. Based on the abnormal motor power event, a high grid oscillation risk event is determined according to the active power and the oscillation amplitude; Based on the high oscillation risk event of the power grid, the preset valve opening is adjusted according to the risk index determined by the blade rotation speed, the active power and the oscillation amplitude to obtain the adjusted valve opening, and the preset nozzle opening is adjusted according to the adjusted valve opening and the frequency change rate to obtain the adjusted nozzle opening; Based on the valve opening and the nozzle opening, the preset intensity threshold is adjusted according to the oscillation attenuation rate and the turbulence intensity within the preset adjustment time. The expansion machine is controlled by adjusting the valve opening and nozzle opening based on the adjusted preset strength threshold to suppress power grid oscillations.

2. The grid oscillation suppression method for compressed air energy storage systems according to claim 1, characterized in that, The process of determining the occurrence of a motor power anomaly event based on the turbulence intensity, a preset intensity threshold, the blade rotation speed, and the active power includes: Based on the comparison between the turbulence intensity and the preset intensity threshold, it is determined that an abnormal wind event has occurred; Based on the aforementioned wind power anomaly event, a motor power anomaly event is determined to have occurred according to the blade rotation speed and the active power.

3. The grid oscillation suppression method for compressed air energy storage systems according to claim 2, characterized in that, The process of determining the occurrence of a motor power anomaly event based on the blade rotation speed and the active power includes: Calculate several blade speed fluctuation values ​​based on the blade speed within a preset judgment period; Calculate several power fluctuation values ​​based on the active power within the preset determination time period; An abnormal motor power event is determined based on all the blade speed fluctuation values ​​and all the power fluctuation values.

4. The grid oscillation suppression method for compressed air energy storage systems according to claim 3, characterized in that, The process of determining the occurrence of a motor power abnormality event based on all the blade speed fluctuation values ​​and all the power fluctuation values ​​includes: The degree of variation is calculated based on all the blade speed fluctuation values ​​and all the power fluctuation values. The occurrence of an abnormal motor power event is determined based on the comparison between the change in coordination degree and the preset coordination degree threshold.

5. The grid oscillation suppression method for compressed air energy storage systems according to claim 4, characterized in that, The process of determining the risk event of a high power grid oscillation based on the active power and the oscillation amplitude includes: Calculate several power change rates based on the active power within a preset time period; Calculate several amplitude change rates based on the oscillation amplitude within the preset time period; The occurrence of a high power grid oscillation risk event is determined based on all of the power change rate and all of the amplitude change rate.

6. The grid oscillation suppression method for compressed air energy storage systems according to claim 5, characterized in that, The process of determining the risk event of a high power oscillation based on all of the power change rates and all of the amplitude change rates includes: Calculate the power-amplitude coupling degree based on all the power change rates and all the amplitude change rates; The occurrence of a high power grid oscillation risk event is determined based on the comparison between the power amplitude coupling degree and the preset coupling degree threshold.

7. The grid oscillation suppression method for compressed air energy storage systems according to claim 6, characterized in that, The process of adjusting the preset valve opening based on the risk index determined by the blade rotation speed, the active power, and the oscillation amplitude includes: The risk index is determined based on the blade rotation speed, the active power, and the oscillation amplitude. The preset valve opening is adjusted according to the risk index.

8. The grid oscillation suppression method for compressed air energy storage systems according to claim 7, characterized in that, The process of adjusting the preset valve opening based on the risk index includes: The index deviation is calculated based on the comparison between the risk index and the preset index threshold. The preset valve opening is adjusted based on the comparison between the exponential deviation and the preset exponential deviation threshold to obtain the adjusted valve opening.

9. The grid oscillation suppression method for compressed air energy storage systems according to claim 8, characterized in that, The process of adjusting the preset nozzle opening based on the valve opening and the frequency change rate, to obtain the adjusted nozzle opening, includes: Based on the valve opening, the rate of change fluctuation value is calculated according to the frequency change rate within the preset adjustment period; The preset nozzle opening is adjusted based on the comparison between the rate of change fluctuation value and the preset rate of change fluctuation threshold, thereby obtaining the adjusted nozzle opening.

10. The grid oscillation suppression method for a compressed air energy storage system according to claim 9, characterized in that, The process of adjusting the preset intensity threshold based on the oscillation attenuation rate and the turbulence intensity within a preset adjustment period includes: The rate of change of attenuation rate is calculated based on the oscillation attenuation rate within the preset adjustment period; The preset intensity threshold is adjusted based on the first comparison result between the attenuation rate change rate and the preset attenuation rate change threshold. Based on the second comparison result of the attenuation rate change rate and the preset attenuation rate change rate threshold, the intensity deviation change rate is calculated according to several intensity deviations between the turbulence intensity and the preset intensity threshold at each adjustment time within the preset adjustment period, and the preset intensity threshold is adjusted according to the comparison result of the intensity deviation change rate and the preset intensity deviation change rate threshold.

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