Compressed air energy storage power station power regulation detection and evaluation method and system

Abstract

The invention provides a compressed air energy storage power station power regulation detection and evaluation method and system, and belongs to the field of compressed air energy storage power. The method comprises the steps that a power instruction sequence is injected into the compressed air energy storage power station through a programmable working condition generation unit; collecting multi-source physical quantity data through a multi-dimensional sensing measurement array arranged at a key part of the power station; carrying out preprocessing and feature extraction on the collected multi-source physical quantity data to obtain a power regulation dynamic feature vector; based on the power regulation dynamic feature vector, an improved generalized frequency response function model and an energy domain index analysis model are adopted for calculation to obtain a performance index; according to a preset threshold value interval, outputting a corresponding power regulation capability grade; and generating an evaluation report based on the power regulation capability level and the performance index, and sending the evaluation report to a monitoring system through a communication interface. According to the invention, the problems of long period and single working condition of the traditional off-line test are solved, and a second-level decision basis is provided for accurate dispatching of a power grid.

Description

Technical Field

[0001] This invention belongs to the field of compressed air energy storage power technology, and in particular relates to a method and system for power regulation detection and evaluation of compressed air energy storage power stations. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In the field of large-scale energy storage technology, compressed air energy storage (CAES) power plants have become one of the key equipment supporting the stable operation of new power systems and absorbing renewable energy due to their outstanding advantages such as large power scale, high energy storage capacity and long equipment service life. They are widely used in scenarios such as grid peak shaving and valley filling, and backup power supply.

[0004] However, the power regulation capability of a CAES power plant is not constant; its dynamic response characteristics are influenced by a combination of complex factors. First, the internal pressure of the gas storage tank continuously changes during the charging and discharging process, directly determining the expander's work capacity and the compressor's power consumption. Second, the operating characteristics of multi-stage compressor units and multi-stage expander units exhibit significant nonlinearity, with their efficiency, flow rate, and power output fluctuating with changes in the operating point. Furthermore, the operating state of the heat exchange network, which is closely coupled with the compression and expansion processes, also affects the transient response performance and thermodynamic efficiency of the entire system. The combined effect of these factors results in the CAES power plant exhibiting strong nonlinear and time-varying characteristics in its power regulation limits, response speed, and regulation accuracy, making it difficult to accurately describe using a simple static model.

[0005] As the proportion of renewable energy generation continues to increase, the grid's demand for ancillary services from energy storage systems is becoming increasingly stringent. On the one hand, energy storage is required to have millisecond-level response speed and high-precision regulation capabilities when participating in primary frequency regulation to smooth out fluctuations in renewable energy output. On the other hand, in secondary frequency regulation and inertia support scenarios, energy storage systems need to stably maintain specific regulation power for a relatively long period of time to ensure grid frequency and voltage stability. However, existing testing methods for the power regulation capabilities of CAES (Centralized Energy Storage System) power plants generally rely on offline testing or static testing modes. Offline testing requires disconnecting the power plant from the grid and conducting specific operating condition tests, which not only has a testing cycle of several hours to several days, but also cannot simulate the dynamic load changes in actual grid operation. Static testing can only obtain data under fixed operating conditions (such as rated power and rated pressure), and cannot cover complex dynamic scenarios such as power plant start-up and shutdown, sudden load increases and decreases, and fault recovery. This results in a large deviation between the test results and the actual power regulation limits in operation, making it difficult to reflect the true dynamic response capabilities of the power plant. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, the present invention provides a method and system for detecting and evaluating the power regulation of compressed air energy storage power stations, which solves the problems of existing methods for detecting the power regulation capability of compressed air energy storage power stations being offline, operating under a single condition, and unable to dynamically reflect the actual regulation limits.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for detecting and evaluating the power regulation of a compressed air energy storage power station; A method for power regulation detection and evaluation of a compressed air energy storage power station includes: A preset power command sequence is injected into the compressed air energy storage power station through a programmable operating condition generation unit; By deploying a multi-dimensional sensor array at key locations in the power station, multi-source physical quantity data of the compressed air energy storage power station are collected synchronously during the power command sequence response process. The collected multi-source physical quantity data is preprocessed, and features are extracted from the preprocessed multi-source physical quantity data to obtain a power regulation dynamic feature vector. Based on the aforementioned power regulation dynamic feature vector, the improved generalized frequency response function model and energy domain index analysis model are used to calculate the performance index; and according to the preset threshold range, the corresponding power regulation capability level is output. An evaluation report is generated based on the power regulation capability level and performance indicators, and then sent to the monitoring system via a communication interface.

[0008] As a further technical solution, the power command sequence includes at least two of the following types: step, ramp, sine, and random sequence.

[0009] As a further technical solution, the multi-source physical quantity data includes compressor power, turbine power, gas storage pressure, temperature, flow rate, heat exchanger temperature difference, and valve opening.

[0010] As a further technical solution, the collected multi-source physical quantity data is preprocessed, including time alignment and noise reduction.

[0011] As a further technical solution, feature extraction is performed on the preprocessed multi-source physical quantity data to obtain a dynamic feature vector of power regulation, including: The system receives preprocessed multi-source physical quantity data and organizes it uniformly using the acquisition timestamps to form a multi-source data matrix with consistent time dimensions. The wavelet packet decomposition algorithm is called to perform wavelet packet decomposition of a specific number of layers on the normalized multi-source physical quantity data, and decompose the time domain signal of each physical quantity data into different frequency bands. The energy proportion of the coefficients of different frequency bands after the decomposition of each physical quantity data is calculated, the key frequency band components are screened out, and the noise frequency band components with low energy proportion are removed; based on the screened key frequency band components, the denoised time domain signal of each physical quantity data is obtained by wavelet packet reconstruction algorithm. For each reconstructed physical quantity in the time domain, its corresponding dynamic characteristic parameters are calculated. All the calculated dynamic characteristic parameters are arranged in a preset order to form an initial characteristic parameter set. The power regulation dynamic characteristic vector is obtained by normalizing the initial characteristic parameter set.

[0012] As a further technical solution, the performance indicators include power rise rate, power fall rate, adjustment accuracy, duration, and energy recovery efficiency.

[0013] As a further technical solution, based on the aforementioned power regulation dynamic characteristic vector, an improved generalized frequency response function model and an energy domain index analysis model are used to calculate the performance indicators, including: By using an improved generalized frequency response function calculation model, frequency domain transformation and nonlinear characteristic analysis are performed on the time domain response data in the dynamic characteristic vector of power regulation, dynamic response parameters in the power regulation process are extracted, and then the power regulation rise rate and fall rate are calculated. By using the energy domain index analysis model, the energy conversion data and power output stability data in the dynamic characteristic vector of power regulation are quantitatively analyzed, and the regulation accuracy, duration and energy recovery efficiency are calculated.

[0014] The second aspect of the present invention provides a power regulation detection and evaluation system for compressed air energy storage power stations.

[0015] A power regulation detection and evaluation system for compressed air energy storage power stations includes: The programmable operating condition generation unit is configured to generate and inject a preset power command sequence into the control system of the compressed air energy storage power station; A multi-dimensional sensing and measurement array is deployed at key locations of the compressed air energy storage power station and is configured to synchronously acquire multi-source physical quantity data during the power station's response to the power command sequence. The data fusion and feature extraction module is communicatively connected to the multi-dimensional sensing and measurement array and is configured to preprocess the received multi-source physical quantity data and extract features from the preprocessed multi-source physical quantity data to obtain a power regulation dynamic feature vector. The power regulation capability grading and evaluation module, connected to the data fusion and feature extraction module, is configured to calculate the performance index based on the power regulation dynamic feature vector using an improved generalized frequency response function model and an energy domain index analysis model; and output the corresponding power regulation capability level according to a preset threshold range. The visualization reporting unit, connected to the power regulation capability grading and evaluation module, is configured to generate an evaluation report based on the power regulation capability level and performance indicators, and send it to the monitoring system through a communication interface.

[0016] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a power regulation detection and evaluation method for a compressed air energy storage power station as described in the first aspect of the present invention.

[0017] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the power regulation detection and evaluation method for a compressed air energy storage power station as described in the first aspect of the present invention.

[0018] The above one or more technical solutions have the following beneficial effects: This invention injects multiple power command sequences into the power plant through a programmable operating condition generation unit. Combined with a high-frequency sampling multi-dimensional sensor array, it can complete a full-condition power regulation capability test within 30 seconds under normal operating conditions, realizing a shift from static to dynamic evaluation methods and providing real-time, reliable data with second-level updates for grid dispatch. By employing an improved generalized frequency response function model and energy domain index analysis model, five core performance indicators—rise rate, fall rate, regulation accuracy, duration, and energy recovery efficiency—are calculated and quantitatively evaluated. This multi-dimensional, hierarchical evaluation system comprehensively and accurately characterizes the dynamic performance of the power plant, clearly defining its adaptability in different scenarios such as primary frequency regulation, secondary frequency regulation, and inertia support. Simultaneously, a visualization reporting unit transmits the hierarchical results and trend curves to the grid dispatch center in real time, providing dispatchers with intuitive and reliable technical support for formulating energy storage deployment strategies.

[0019] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 This is a flowchart of the method in the first embodiment.

[0022] Figure 2 This is a system structure diagram of the second embodiment. Detailed Implementation

[0023] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0025] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0026] The overall concept of this invention is to address the pain points of existing compressed air energy storage (CAES) power plant power regulation detection being offline, operating under a single condition, and unable to reflect the true dynamic response capability. This invention proposes a full-process solution that includes dynamic command injection, multi-source data acquisition, feature extraction, model calculation, and hierarchical evaluation. The core idea is to achieve dynamic, accurate, and real-time detection and evaluation of the power plant's power regulation capability.

[0027] First, it breaks through the limitations of traditional static testing by injecting power command sequences of various types, including step and ramp, through a programmable operating condition generation unit. This simulates various dynamic load scenarios in actual power grid operation, allowing testing to be carried out under normal operating conditions without disconnecting the power station. Second, relying on a multi-dimensional sensor measurement array deployed at key parts of the power station, it synchronously collects multi-source physical quantity data such as compressor power and gas storage pressure, ensuring the time synchronization of data and command response.

[0028] Subsequently, the collected data underwent preprocessing such as time alignment and noise reduction. Key features were extracted through wavelet packet decomposition and reconstruction to form a standardized dynamic feature vector for power regulation, providing high-quality data support for subsequent evaluation. Next, an improved generalized frequency response function model and energy domain index analysis model were used to accurately calculate five core performance indicators, including power rise rate and regulation accuracy. Then, based on preset threshold ranges, four AD capability levels were defined to achieve multi-dimensional quantitative evaluation.

[0029] Finally, a visualized evaluation report containing grade results and indicator data is generated and transmitted to the monitoring system in real time, providing a reliable basis for grid dispatch with second-level updates. This fully adapts to the stringent requirements of the grid for auxiliary services of energy storage systems under the high proportion of new energy, and promotes the transformation of CAES power plant dynamic performance evaluation from static to dynamic and from offline to online.

[0030] Example 1 This embodiment discloses a method for power regulation detection and evaluation of compressed air energy storage power stations. By injecting a preset power command sequence into the power station and simultaneously collecting multi-source physical quantity data, dynamic feature vectors are obtained through preprocessing and feature extraction. Performance indicators are calculated using an improved model, and capability levels are classified. An evaluation report is then generated and sent to the monitoring system. This method enables dynamic, real-time detection, overcomes the limitations of traditional methods, and provides accurate data for power grid dispatching.

[0031] Specifically: such as Figure 1 As shown, a method for power regulation detection and evaluation of a compressed air energy storage power station includes: Step S1: Inject a preset power command sequence into the compressed air energy storage power station through the programmable operating condition generation unit; Step S2: Through a multi-dimensional sensing and measurement array deployed at key parts of the power station, multi-source physical quantity data of the compressed air energy storage power station in response to the power command sequence are collected synchronously. Step S3: Preprocess the collected multi-source physical quantity data, extract features from the preprocessed multi-source physical quantity data, and obtain a power regulation dynamic feature vector. Step S4: Based on the power regulation dynamic feature vector, the improved generalized frequency response function model and energy domain index analysis model are used to calculate the performance index; and according to the preset threshold range, the corresponding power regulation capability level is output. Step S5: Generate an evaluation report based on the power regulation capability level and performance indicators, and send it to the monitoring system through the communication interface.

[0032] Specifically, it also includes the following: Step S1: Inject a preset power command sequence into the compressed air energy storage power station through the programmable operating condition generation unit.

[0033] First, parameter configuration preparation is required, which involves determining key parameters based on three core criteria: First, the rated power of the compressed air energy storage power station, serving as the benchmark for amplitude range setting to ensure that the command sequence is compatible with the power carrying capacity of the power station; second, the actual needs of grid dispatching. If it is necessary to test the adaptability of primary frequency regulation, the focus should be on short-duration, high-amplitude step or random sequences. If it is for secondary frequency regulation, long-duration ramp or sine sequences should be added; third, the type of power regulation operating condition to be tested, covering all scenarios such as start-stop transition, sudden load increase and decrease, steady-state maintenance, and fault recovery.

[0034] Based on the above, key parameters are set item by item in the visualization parameter configuration interface of the programmable operating condition generation unit: the sequence type must select at least two of the following: step, ramp, sine, and random sequences to form a multi-scenario combined test scheme; the amplitude range is strictly set to ±20% to ±120% of the power plant's rated power, of which ±20% to ±50% covers normal load fluctuations, and ±80% to ±120% covers overload operation test scenarios; the duration is flexibly configured according to the operating condition type, with 10s~30s adapting to short-term sudden operating conditions, 60s-120s corresponding to normal dynamic adjustment scenarios, and 180s-300s used for long-term steady-state maintenance capability testing; the output interval is fixed at 5s to ensure the continuity of the command sequence and the synchronization of data acquisition, ensuring coverage of various typical power fluctuation scenarios in actual power plant operation.

[0035] The programmable operating condition generation unit automatically generates corresponding preset power command sequences by calling the built-in sequence generation algorithm according to the configured parameters. For step sequences, it generates instantaneous power jump commands according to the set amplitude. For ramp sequences, it calculates the power change slope according to the set amplitude and duration to generate linearly changing power commands. For sinusoidal sequences, it generates periodically fluctuating power commands according to the set amplitude and frequency. For random sequences, it generates power commands that conform to the random characteristics of the power grid load based on the preset probability distribution model, ensuring that the generated power command sequences can simulate the dynamic power demand in the actual operation of the power plant.

[0036] Finally, the programmable condition generation unit executes command injection and synchronization control. It interfaces with the power plant control system via dual interfaces, ensuring real-time and reliable command transmission through a hard-wired interface. The communication interface is adaptable to different power plant control systems, injecting the generated power command sequence into the power plant's power regulation control loop in real time, triggering the power plant's dynamic response. Simultaneously, at the instant of command injection, the unit sends a synchronization acquisition trigger signal to the multi-dimensional sensing measurement array, ensuring that the time deviation between power command injection and multi-source physical quantity data acquisition is ≤1ms. This achieves precise synchronization between the command response process and the data acquisition process, providing a high-quality data source with consistent time for subsequent data processing and feature extraction.

[0037] Step S2 involves synchronously collecting multi-source physical quantity data of the compressed air energy storage power station in response to the power command sequence through a multi-dimensional sensing and measurement array deployed at key parts of the power station.

[0038] First, precise deployment of the sensor array is required. This necessitates classifying, deploying, and fixing sensors at key monitoring points of the power plant's core equipment and systems. For compressors, high-precision power sensors are mounted near the drive shaft at their power output end via flange connections, ensuring a tight fit between the sensor and the power transmission path to accurately capture real-time power consumption data during compressor operation. Turbine power sensors, using the same high-precision model, are mounted at the coupling position at the turbine's power output end, simultaneously collecting mechanical power data output by the turbine. Monitoring of the gas storage facility must consider both the internal environment and the state of the medium. Pressure and temperature sensors are embedded into pre-set monitoring points on the inner wall of the gas storage facility through reserved mounting holes. Pressure sensors are deployed at two key heights in the middle and bottom of the gas storage facility, while temperature sensors are evenly distributed in a ring at three monitoring points to ensure comprehensive capture of the spatial distribution and dynamic changes of pressure and temperature within the gas storage facility. The temperature difference monitoring of the heat exchange network focuses on the energy exchange efficiency of hot and cold fluids. Temperature difference sensors are installed in pairs on the inlet and outlet pipes of the hot and cold fluids in the heat exchanger. The sensor probes are in direct contact with the fluid medium, and the installation position is no more than 1 meter away from the heat exchanger interface to reduce the impact of pipeline heat dissipation on temperature difference measurement. The valve opening sensors are integrated into the actuators of each key valve. They acquire the valve opening angle data in real time through mechanical linkage, covering core control components such as inlet valves, exhaust valves, and bypass valves.

[0039] After deployment, rigorous accuracy calibration was performed on all sensors. Standard calibration equipment was used to calibrate each type of sensor. Power sensors were input with a known power signal through a standard power source, and the sensor sensitivity coefficient was adjusted to ensure a measurement error ≤ ±0.5%. Pressure sensors were calibrated using a piston-type pressure gauge with a standard pressure applied, and the calibration error was controlled within ±0.1 MPa. Temperature sensors were placed in a constant temperature bath and calibrated at multiple points within a range of -20℃ to 150℃, with an error ≤ ±0.3℃. Flow sensors were calibrated using a standard flow meter, with an error ≤ ±1%. Differential temperature sensors and valve opening sensors were also calibrated using corresponding standard equipment. The calibration results of all sensors must comply with relevant national testing standards, and only those that pass calibration can be put into use.

[0040] Furthermore, synchronous control of data acquisition is a crucial step in ensuring data validity. The instant the programmable condition generator injects a power command sequence into the power plant control system, a synchronous acquisition trigger signal is sent to the central control unit of the multi-dimensional sensor array via a hard-wired signal channel. This trigger signal is a high-level pulse signal with a pulse width set to 10ms to ensure stable recognition by the control unit. Upon receiving the trigger signal, the central control unit immediately sends a start acquisition command to all sensors in the array, with a command transmission delay ≤1ms, ensuring that all sensors start data acquisition at the same time. During acquisition, each sensor continuously collects data at a preset sampling frequency and transmits the collected multi-source physical quantity data, such as compressor power, turbine power, gas storage pressure and temperature, medium flow rate, heat exchanger temperature difference, and valve opening, to the central control unit in real time via industrial Ethernet. The central control unit adds a unified timestamp to the received data, completing the initial data integration and ensuring that each set of physical quantity data accurately corresponds to a specific time node of the power command response. This achieves strict time synchronization between the power command response process and the data acquisition process, laying the foundation for subsequent analysis of the dynamic correlation between various physical quantities and power command changes.

[0041] Step S3: Preprocess the collected multi-source physical quantity data, extract features from the preprocessed multi-source physical quantity data, and obtain the power regulation dynamic feature vector.

[0042] First, the collected multi-source physical quantity data undergoes time alignment and denoising to ensure precise synchronization of all physical quantity data under the same time reference, eliminating the impact of time deviation on data correlation. Denoising eliminates data interference and preserves valid signals, providing high-quality data support for subsequent data fusion, feature extraction, and power regulation capability grading assessment.

[0043] Secondly, the preprocessed multi-source physical quantity data is received and uniformly organized by the acquisition timestamp to form a multi-source data matrix with consistent time dimension. The wavelet packet decomposition algorithm is applied to the normalized multi-source physical quantity data, performing 3-5 levels of wavelet packet decomposition to decompose the time-domain signal of each physical quantity data into different frequency bands. Specifically, for high-frequency fluctuating data such as compressor power and turbine power, the decomposition coefficients of the high-frequency bands are retained; for slowly varying data such as gas storage pressure and temperature, the decomposition coefficients of the low-frequency bands are retained, achieving differentiated extraction of frequency bands for physical quantities with different characteristics. The energy proportion of the different frequency band coefficients after decomposition of each physical quantity data is calculated, key frequency band components are selected, and noise frequency band components with low energy proportions are removed. Based on the selected key frequency band components, the wavelet packet reconstruction algorithm is used to obtain the denoised time-domain signal of each physical quantity data, eliminating random interference and measurement errors in the multi-source data and improving the data signal-to-noise ratio.

[0044] Wavelet packet decomposition uses wavelet basis functions (such as Daubechies wavelets) to recursively decompose the signal. Let the original signal be x(t). After n levels of decomposition, 2n frequency band coefficients are obtained. Each frequency band corresponds to a subspace, and the coefficients are represented as... Where j is the decomposition level and k is the frequency band index. Then, the energy percentage is calculated; for each frequency band, the energy... Total energy Energy percentage By setting a threshold, low-energy frequency bands are eliminated. During the reconstruction process, only the coefficients of the key frequency bands are used for inverse wavelet packet transform to obtain the denoised signal. This process achieves data denoising and feature enhancement, providing high-quality input for subsequent model calculations.

[0045] For each reconstructed physical quantity in the time domain, its corresponding dynamic characteristic parameters are calculated. For compressor power and turbine power signals, the maximum power change per unit time (i.e., power change rate), power stability fluctuation amplitude, and delay time for power to reach the target value are calculated. For gas storage pressure signals, the pressure rise and fall rates, pressure stability interval width, and pressure fluctuation peak value are calculated. For gas storage temperature signals, the temperature change rate and temperature-power coupling coefficient are calculated. For heat exchanger temperature difference signals, the stable temperature difference value and temperature difference fluctuation range are calculated. For valve opening signals, the valve action response time and opening adjustment amplitude are calculated. All calculated dynamic characteristic parameters are arranged in the order of power-related parameters, pressure-related parameters, temperature-related parameters, temperature difference-related parameters, and valve-related parameters to form an initial characteristic parameter set. The initial characteristic parameter set is then normalized to eliminate the influence of different dimensions on the characteristic vector. Finally, a power regulation dynamic characteristic vector with fixed dimensions and standardized values ​​is obtained, which is used for the index calculation of the subsequent power regulation capability classification and evaluation module.

[0046] Step S4: Based on the power regulation dynamic feature vector, the improved generalized frequency response function model and energy domain index analysis model are used to calculate the performance index; and according to the preset threshold range, the corresponding power regulation capability level is output.

[0047] An improved generalized frequency response function (GFRF) calculation model is used to perform frequency domain transformation and nonlinear characteristic analysis on the time-domain response data in the dynamic characteristic vector of power regulation, extracting dynamic response parameters during the power regulation process, and then calculating the power regulation rise rate and fall rate. The rise rate is determined by analyzing the maximum power change per unit time during the process of power rising from the initial value to the target setpoint, and the fall rate is determined by analyzing the maximum power change per unit time during the process of power falling from the target setpoint to the initial value. The GFRF is an extension of the linear frequency response function (FRF) and is used to describe the frequency response of nonlinear systems. The improved GFRF model may combine time-frequency analysis techniques (such as wavelet transform or short-time Fourier transform) and nonlinear system identification methods to handle the nonlinear time-varying characteristics of compressed air energy storage power stations. The improved GFRF model is used to perform frequency domain transformation and nonlinear characteristic analysis on the dynamic characteristic vector of power regulation to calculate the power rise rate and fall rate. Its specific architecture includes the following components: (1) Time-domain to frequency-domain conversion module: Converts the time-domain response data (such as power signal) in the power regulation dynamic feature vector into a frequency-domain representation. This may be achieved through Fourier transform or wavelet transform, but given that wavelet packet decomposition is already used in the front end, the model may directly utilize wavelet coefficients or perform additional frequency domain analysis.

[0048] (2) Nonlinear Characteristic Analysis Module: Uses high-order frequency response functions to capture the nonlinear behavior of the system. For example, for a nonlinear system, the output frequency response can be represented as a multivariate function of the input frequency, modeled using Volterra series or similar methods.

[0049] (3) Dynamic response parameter extraction module: Extract key parameters from the frequency response, such as resonant frequency, bandwidth, phase delay, etc. These parameters are related to the dynamic performance of power regulation.

[0050] In the specific analytical calculation process, the frequency domain transformation is performed first. Let the power time-domain signal be P(t), and the frequency domain representation is obtained through Fourier transform. .

[0051] The improved GFRF model uses second-order or higher-order frequency response functions for nonlinear analysis. For example, for a nonlinear system, the relationship between the output y(t) and the input u(t) can be described using Volterra series: in, It is the nth order Volterra kernel; Let be the time delay variable; in the frequency domain, the nth order GFRF is... .

[0052] Features related to power regulation, such as the amplitude and phase of the dominant frequency components, are extracted from the GFRF. Then, the time-domain dynamic response parameters are obtained through inverse transformation or model fitting.

[0053] Power regulation is performed based on dynamic response parameters, and the rise and fall rates of power regulation are calculated as follows: in, The rate of increase; The rate of descent; It is the change in power. These are time intervals. The model uses frequency response analysis to help determine the maximum values ​​of these changes, for example, by identifying the time-domain slope corresponding to the frequency components.

[0054] Using an energy domain index analysis model, the energy conversion data and power output stability data in the dynamic characteristic vector of power regulation are quantitatively analyzed to calculate the regulation accuracy, duration, and energy recovery efficiency. The regulation accuracy is determined by the maximum percentage of the deviation between the actual output power and the target set power during the power regulation process, as shown below: Adjustment accuracy is defined as the accuracy during the entire power adjustment process. Maximum value: in, For at any time t The relative deviation between actual power and target power; For at any time t The actual output power; For at any time t Target power setting; To adjust the precision.

[0055] The duration of operation is determined by recording the continuous duration for which the power output remains within the target power setting accuracy range; the energy recovery efficiency is determined by calculating the ratio of the actual output energy to the theoretical input energy during power regulation. Specifically, the actual output energy... The actual output power is obtained by integrating: , These are the start and end times of power regulation, respectively. Theoretical input energy. This may be based on a power command sequence or a system theoretical model. In this embodiment, the theoretical input energy references the energy consumed during the compression process or the ideal cycle energy, using the energy corresponding to the power command: Therefore, energy recovery efficiency for: This measure assesses the efficiency of the energy conversion process; a higher value indicates less energy loss. Therefore, the energy domain index analysis model, through the aforementioned process and formulas, quantitatively evaluates power regulation performance, providing key indicators for power grid dispatch.

[0056] Based on the preset power regulation capability level threshold range, which is divided into four levels: A, B, C, and D, the A-level threshold range is defined as a rise rate ≥ 8 MW / s and regulation accuracy ≤ ±1%. The B, C, and D-level threshold ranges correspond to rise rate and regulation accuracy requirements that are lower than A-level and decrease sequentially, as well as matching ranges for duration and energy recovery efficiency. The calculated rise rate, fall rate, regulation accuracy, duration, and energy recovery efficiency (five performance indicators in total) are compared one by one with the preset A, B, C, and D-level threshold ranges to determine the threshold range level to which each performance indicator belongs. Finally, based on the threshold range matching results of the five performance indicators, and according to the preset level determination rules, in this embodiment, the determination rule is: if the core indicator rise rate and adjustment accuracy meet a certain level threshold, and at least three of the other three indicators meet the same or higher level threshold, then it is determined to be at that level. The power regulation capability level of the compressed air energy storage power station is determined according to the above rules, and then the level result is synchronously sent to the report generation unit for generating a test report.

[0057] Step S5: Based on the power regulation capability level and performance indicators, an evaluation report is generated and sent to the monitoring system via the communication interface. These steps transform scattered detection data into a structured and easily interpretable information carrier, intuitively showcasing the core characteristics of the power plant's power regulation capability. This allows monitoring system operators to quickly grasp the overall picture of the power plant's regulation capability, avoiding time-consuming information interpretation due to data fragmentation and significantly improving information transmission efficiency.

[0058] Through the above steps, various types of power command sequences are injected using a programmable operating condition generation unit. Combined with multi-dimensional sensing and measurement arrays to synchronously collect multi-source physical quantity data, dynamic detection of compressed air energy storage power stations under normal operating conditions is achieved. This allows for coverage of various complex operating scenarios without the need to decouple the power station, completing full-condition detection within 30 seconds, thus solving the pain points of long cycles and single operating conditions in traditional offline testing. Data preprocessing and feature extraction are performed using wavelet packet decomposition and reconstruction techniques. Combined with an improved generalized frequency response function model and energy domain index analysis model, five core performance indicators, including power rise rate and regulation accuracy, are accurately calculated, constructing a multi-dimensional quantitative evaluation system that effectively characterizes the dynamic response characteristics of the power station. Through a four-level classification and visualized report output, real-time reliable data with second-level updates is provided for grid dispatch, clearly defining the adaptability of the power station under different ancillary service scenarios. This significantly improves the grid's dispatch accuracy for energy storage systems, strongly supporting the stable operation of new power systems and the consumption of renewable energy.

[0059] Example 2 This embodiment discloses a power regulation detection and evaluation system for a compressed air energy storage power station; like Figure 2 As shown, a power regulation detection and evaluation system for a compressed air energy storage power station includes: The programmable operating condition generation unit is configured to generate and inject a preset power command sequence into the control system of the compressed air energy storage power station, specifically: Based on the rated power of the compressed air energy storage power station, the grid dispatch requirements, and the type of power regulation operating condition to be detected, key parameters of the power command sequence are set in the parameter configuration interface of the programmable operating condition generation unit. The key parameters include sequence type, amplitude range, duration, and output interval. The sequence type can be selected from at least one of step, ramp, sine, and random. The amplitude range is set to ±20% to ±120%. The rated power of the compressed air energy storage power station is set, the duration is set to 10s–300s, and the output interval is set to 5s to cover typical power fluctuation scenarios in actual operation of the power station.

[0060] The programmable operating condition generation unit automatically generates corresponding preset power command sequences by calling the built-in sequence generation algorithm according to the configured parameters. For step sequences, it generates instantaneous power jump commands according to the set amplitude. For ramp sequences, it calculates the power change slope according to the set amplitude and duration to generate linearly changing power commands. For sinusoidal sequences, it generates periodically fluctuating power commands according to the set amplitude and frequency. For random sequences, it generates power commands that conform to the random characteristics of the power grid load based on the preset probability distribution model, ensuring that the generated power command sequences can simulate the dynamic power demand in the actual operation of the power plant.

[0061] A multi-dimensional sensing and measurement array, deployed at key locations in the compressed air energy storage power station, is configured to synchronously acquire multi-source physical quantity data during the power station's response to the power command sequence. Specifically: A multi-dimensional sensor array is deployed at key equipment and system components of the compressed air energy storage power station, including compressors, turbines, air storage tanks, heat exchange networks, and control valves. This array collects multi-source physical quantity data such as compressor power, turbine power, air storage tank pressure, temperature, flow rate, heat exchanger temperature difference, and valve opening. Specifically, the compressor power sensor is mounted at the compressor's power output end, the turbine power sensor at the turbine's work output end, the air storage tank pressure and temperature sensors are embedded at pre-set monitoring points on the inner wall of the air storage tank, the heat exchanger temperature difference sensor is installed on the inlet and outlet pipes of the heat exchanger for both hot and cold fluids, and the valve opening sensors are integrated into the actuators of each control valve. After deployment, each sensor is calibrated to ensure that the collected data errors meet preset detection standards.

[0062] Specifically, when the programmable operating condition generation unit injects a power command sequence into the compressed air energy storage power station, it simultaneously sends a data acquisition trigger signal to the control unit of the multi-dimensional sensing measurement array to ensure that each sensor starts data acquisition at the same time node, thereby achieving time synchronization between the power command response process and the data acquisition process.

[0063] The data fusion and feature extraction module, communicatively connected to the multi-dimensional sensing and measurement array, is configured to preprocess the received multi-source physical quantity data, and then extract features from the preprocessed multi-source physical quantity data to obtain a power regulation dynamic feature vector, specifically: First, the collected multi-source physical quantity data undergoes time alignment and denoising to ensure precise synchronization of all physical quantity data under the same time reference, eliminating the impact of time deviation on data correlation. Denoising eliminates data interference and preserves valid signals, providing high-quality data support for subsequent data fusion, feature extraction, and power regulation capability grading assessment.

[0064] Secondly, the preprocessed multi-source physical quantity data is received and uniformly organized by the acquisition timestamp to form a multi-source data matrix with consistent time dimension. The wavelet packet decomposition algorithm is applied to the normalized multi-source physical quantity data, performing 3-5 levels of wavelet packet decomposition to decompose the time-domain signal of each physical quantity data into different frequency bands. Specifically, for high-frequency fluctuating data such as compressor power and turbine power, the decomposition coefficients of the high-frequency bands are retained; for slowly varying data such as gas storage pressure and temperature, the decomposition coefficients of the low-frequency bands are retained, achieving differentiated extraction of frequency bands for physical quantities with different characteristics. The energy proportion of the different frequency band coefficients after decomposition of each physical quantity data is calculated, key frequency band components are selected, and noise frequency band components with low energy proportions are removed. Based on the selected key frequency band components, the wavelet packet reconstruction algorithm is used to obtain the denoised time-domain signal of each physical quantity data, eliminating random interference and measurement errors in the multi-source data and improving the data signal-to-noise ratio.

[0065] Wavelet packet decomposition uses wavelet basis functions (such as Daubechies wavelets) to recursively decompose the signal. Let the original signal be x(t). After n levels of decomposition, 2n frequency band coefficients are obtained. Each frequency band corresponds to a subspace, and the coefficients are represented as... Where j is the decomposition level and k is the frequency band index. Then, the energy percentage is calculated; for each frequency band, the energy... Total energy Energy percentage By setting a threshold, low-energy frequency bands are eliminated. During the reconstruction process, only the coefficients of the key frequency bands are used for inverse wavelet packet transform to obtain the denoised signal. This process achieves data denoising and feature enhancement, providing high-quality input for subsequent model calculations.

[0066] For each reconstructed physical quantity in the time domain, its corresponding dynamic characteristic parameters are calculated. For compressor power and turbine power signals, the maximum power change per unit time (i.e., power change rate), power stability fluctuation amplitude, and delay time for power to reach the target value are calculated. For gas storage pressure signals, the pressure rise and fall rates, pressure stability interval width, and pressure fluctuation peak value are calculated. For gas storage temperature signals, the temperature change rate and temperature-power coupling coefficient are calculated. For heat exchanger temperature difference signals, the stable temperature difference value and temperature difference fluctuation range are calculated. For valve opening signals, the valve action response time and opening adjustment amplitude are calculated. All calculated dynamic characteristic parameters are arranged in the order of power-related parameters, pressure-related parameters, temperature-related parameters, temperature difference-related parameters, and valve-related parameters to form an initial characteristic parameter set. The initial characteristic parameter set is then normalized to eliminate the influence of different dimensions on the characteristic vector. Finally, a power regulation dynamic characteristic vector with fixed dimensions and standardized values ​​is obtained, which is used for the index calculation of the subsequent power regulation capability classification and evaluation module.

[0067] The power regulation capability grading and evaluation module, connected to the data fusion and feature extraction module, is configured to calculate performance indicators based on the power regulation dynamic feature vector using an improved generalized frequency response function model and an energy domain index analysis model; and output the corresponding power regulation capability level according to a preset threshold range. Specifically: An improved generalized frequency response function (GFRF) calculation model is used to perform frequency domain transformation and nonlinear characteristic analysis on the time-domain response data in the dynamic characteristic vector of power regulation, extracting dynamic response parameters during the power regulation process, and then calculating the power regulation rise rate and fall rate. The rise rate is determined by analyzing the maximum power change per unit time during the process of power rising from the initial value to the target setpoint, and the fall rate is determined by analyzing the maximum power change per unit time during the process of power falling from the target setpoint to the initial value. The GFRF is an extension of the linear frequency response function (FRF) and is used to describe the frequency response of nonlinear systems. The improved GFRF model may combine time-frequency analysis techniques (such as wavelet transform or short-time Fourier transform) and nonlinear system identification methods to handle the nonlinear time-varying characteristics of compressed air energy storage power stations. The improved GFRF model is used to perform frequency domain transformation and nonlinear characteristic analysis on the dynamic characteristic vector of power regulation to calculate the power rise rate and fall rate. Its specific architecture includes the following components: (1) Time-domain to frequency-domain conversion module: Converts the time-domain response data (such as power signal) in the power regulation dynamic feature vector into a frequency-domain representation. This may be achieved through Fourier transform or wavelet transform, but given that wavelet packet decomposition is already used in the front end, the model may directly utilize wavelet coefficients or perform additional frequency domain analysis.

[0068] (2) Nonlinear Characteristic Analysis Module: Uses high-order frequency response functions to capture the nonlinear behavior of the system. For example, for a nonlinear system, the output frequency response can be represented as a multivariate function of the input frequency, modeled using Volterra series or similar methods.

[0069] (3) Dynamic response parameter extraction module: Extract key parameters from the frequency response, such as resonant frequency, bandwidth, phase delay, etc. These parameters are related to the dynamic performance of power regulation.

[0070] In the specific analytical calculation process, the frequency domain transformation is performed first. Let the power time-domain signal be P(t), and the frequency domain representation is obtained through Fourier transform. .

[0071] The improved GFRF model uses second-order or higher-order frequency response functions for nonlinear analysis. For example, for a nonlinear system, the relationship between the output y(t) and the input u(t) can be described using Volterra series: in, It is the nth order Volterra kernel; Let be the time delay variable; in the frequency domain, the nth order GFRF is... .

[0072] Features related to power regulation, such as the amplitude and phase of the dominant frequency components, are extracted from the GFRF. Then, the time-domain dynamic response parameters are obtained through inverse transformation or model fitting.

[0073] Power regulation is performed based on dynamic response parameters, and the rise and fall rates of power regulation are calculated as follows: in, The rate of increase; The rate of descent; It is the change in power. These are time intervals. The model uses frequency response analysis to help determine the maximum values ​​of these changes, for example, by identifying the time-domain slope corresponding to the frequency components.

[0074] Using an energy domain index analysis model, the energy conversion data and power output stability data in the dynamic characteristic vector of power regulation are quantitatively analyzed to calculate the regulation accuracy, duration, and energy recovery efficiency. The regulation accuracy is determined by the maximum percentage of the deviation between the actual output power and the target set power during the power regulation process, as shown below: Adjustment accuracy is defined as the accuracy during the entire power adjustment process. Maximum value: in, For at any time t The relative deviation between actual power and target power; For at any time t The actual output power; For at any time t Target power setting; To adjust the precision.

[0075] The duration of operation is determined by recording the continuous duration for which the power output remains within the target power setting accuracy range; the energy recovery efficiency is determined by calculating the ratio of the actual output energy to the theoretical input energy during power regulation. Specifically, the actual output energy... The actual output power is obtained by integrating: , These are the start and end times of power regulation, respectively. Theoretical input energy. This may be based on a power command sequence or a system theoretical model. In this embodiment, the theoretical input energy references the energy consumed during the compression process or the ideal cycle energy, using the energy corresponding to the power command: Therefore, energy recovery efficiency for: This measure assesses the efficiency of the energy conversion process; a higher value indicates less energy loss. Therefore, the energy domain index analysis model, through the aforementioned process and formulas, quantitatively evaluates power regulation performance, providing key indicators for power grid dispatch.

[0076] Based on the preset power regulation capability level threshold range, which is divided into four levels: A, B, C, and D, the A-level threshold range is defined as a rise rate ≥ 8 MW / s and regulation accuracy ≤ ±1%. The B, C, and D-level threshold ranges correspond to rise rate and regulation accuracy requirements that are lower than A-level and decrease sequentially, as well as matching ranges for duration and energy recovery efficiency. The calculated rise rate, fall rate, regulation accuracy, duration, and energy recovery efficiency (five performance indicators in total) are compared one by one with the preset A, B, C, and D-level threshold ranges to determine the threshold range level to which each performance indicator belongs. Finally, based on the threshold range matching results of the five performance indicators, and according to the preset level determination rules, in this embodiment, the determination rule is: if the core indicator rise rate and adjustment accuracy meet a certain level threshold, and at least three of the other three indicators meet the same or higher level threshold, then it is determined to be at that level. The power regulation capability level of the compressed air energy storage power station is determined according to the above rules, and then the level result is synchronously sent to the report generation unit for generating a test report.

[0077] The visualization reporting unit, connected to the power regulation capability grading and evaluation module, is configured to generate an evaluation report based on the power regulation capability level and performance indicators, and send it to the monitoring system via a communication interface. Through these steps, scattered detection data is transformed into a structured and easily interpretable information carrier, intuitively displaying the core characteristics of the power plant's power regulation capability. This allows monitoring system operators to quickly grasp the overall picture of the power plant's regulation capability, avoiding time-consuming information interpretation due to data fragmentation and significantly improving information transmission efficiency.

[0078] The system utilizes a programmable operating condition generation unit to generate multiple types of power command sequences, enabling dynamic monitoring of the power station under normal operating conditions without the need for decoupling. Full operating condition coverage is achieved within 30 seconds, overcoming the pain points of long cycles and limited operating conditions in traditional offline testing. A multi-dimensional sensing array enables simultaneous acquisition of multiple physical quantities from various sources. Combined with wavelet packet decomposition and normalization processing by data fusion and feature extraction modules, data accuracy and effectiveness are ensured. An improved generalized frequency response function model and energy domain index analysis model accurately quantify five core indicators, including power rise rate and regulation accuracy, and, with a four-level classification, comprehensively characterize the dynamic response characteristics of the power station. The visualization reporting unit outputs structured evaluation reports in real time and transmits them to the monitoring system, providing reliable, second-level updates for grid dispatch. This clearly defines the power station's adaptability to different ancillary service scenarios, significantly improving dispatch accuracy and strongly supporting the stable operation of the new power system and the consumption of renewable energy.

[0079] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0080] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a power regulation detection and evaluation method for a compressed air energy storage power station as described in Example 1.

[0081] When the computer program stored on this computer-readable storage medium is executed by the processor, it will fully realize the entire process of power regulation detection and evaluation of compressed air energy storage power stations, ensuring dynamic detection, accurate data processing, and visualized evaluation results.

[0082] At the start of program execution, the processor calls the programmable operating condition generation unit control logic to automatically configure power command sequence parameters based on the rated power of the compressed air energy storage power station, grid dispatch requirements, and target detection conditions. The sequence type must select at least two of the following: step, ramp, sine, and random sequences. The amplitude range is set to ±20% to ±120% of the power station's rated power, the duration is adjusted between 10s and 300s, and the output interval is fixed at 5s. Subsequently, a dynamic power command sequence adapted to the actual operating scenario is generated through a built-in algorithm and injected into the power station control system.

[0083] Following this, the processor triggers the synchronous acquisition mechanism of the multi-dimensional sensor measurement array, which is deployed in key components such as the power plant compressor, turbine, gas storage tank, heat exchange network, and control valves. At the same time node as the power command is injected, data acquisition from each sensor is initiated, synchronously acquiring multi-source physical quantity data such as compressor power, turbine power, gas storage tank pressure and temperature, medium flow rate, heat exchanger temperature difference, and valve opening. The acquisition process and the command response process are strictly synchronized in time to ensure data correlation.

[0084] After data acquisition, the processor executes a data preprocessing process. First, it uses timestamp alignment technology to calibrate the multi-source physical quantity data to the same time base, eliminating time deviations. Then, it uses a denoising algorithm to filter out measurement interference signals. Next, it enters the feature extraction stage. The preprocessed data is first organized into a multi-source data matrix of uniform dimension according to timestamps. A wavelet packet decomposition algorithm is then used to decompose the data into 3-5 levels. Key frequency band components are selected according to energy proportions, and the denoised time-domain signal is reconstructed. Finally, the dynamic characteristic parameters of each physical quantity are calculated, and after normalization, a power regulation dynamic feature vector is formed.

[0085] Based on this feature vector, the processor loads an improved generalized frequency response function model and an energy domain index analysis model. The former performs frequency domain transformation and nonlinear analysis on the time-domain response data to extract dynamic response parameters and calculate the power rise and fall rates. The latter quantifies and analyzes energy conversion and power stability data to obtain five performance indicators: regulation accuracy, duration, and energy recovery efficiency. These indicators are then compared with preset four-level threshold ranges (A, B, C, and D). Based on the rule that the core indicators meet the standards and at least three auxiliary indicators match, the power regulation capability level is determined.

[0086] Finally, the processor drives the visualization reporting unit to integrate performance indicators and level evaluation results, generate a structured evaluation report, including data trend curves, quantitative values ​​of indicators and level determination criteria, and send it to the power grid monitoring system in real time through a standard communication interface, providing reliable data support for scheduling decisions with second-level updates. The entire process requires no manual intervention, realizing the automation and efficiency of detection and evaluation.

[0087] Example 4 The purpose of this embodiment is to provide an electronic device.

[0088] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the power regulation detection and evaluation method for a compressed air energy storage power station as described in Example 1.

[0089] The memory of this electronic device stores a power regulation detection and evaluation program for compressed air energy storage power stations. When the processor executes the program, it will perform full-process detection and evaluation step by step according to preset logic to ensure dynamic, accurate and efficient completion of the power station power regulation capability evaluation.

[0090] After the program starts, the processor first calls the programmable operating condition generation unit control module, and automatically configures the power command sequence parameters based on the power plant's rated power, grid dispatch requirements, and target detection conditions. The sequence type is selected from at least two of step, ramp, sine, and random sequences, with the amplitude range set to ±20% to ±120% of the power plant's rated power, the duration flexibly adjusted between 10s and 300s, and the output interval fixed at 5s. Subsequently, a dynamic power command sequence adapted to the actual operating scenario is generated through the built-in algorithm and injected into the power plant control system.

[0091] Immediately afterwards, the processor triggers the synchronous acquisition function of the multi-dimensional sensing measurement array. This array is deployed in key parts of the power plant, such as compressors, turbines, and gas storage tanks. At the moment the power command is injected, each sensor is activated synchronously to collect multi-source physical quantity data such as compressor power, turbine power, gas storage tank pressure and temperature, medium flow rate, heat exchanger temperature difference, and valve opening. The acquisition process and command response are strictly synchronized in time to ensure data correlation and integrity.

[0092] After data acquisition, the processor executes a data preprocessing process. First, it uses timestamp alignment technology to calibrate the multi-source data to the same time base, eliminating time deviations. Then, it uses a denoising algorithm to filter out measurement interference signals. Next, it enters the feature extraction stage, where the preprocessed data is organized into a multi-source data matrix of uniform dimension according to timestamps. A wavelet packet decomposition algorithm is then used to decompose the data into 3-5 levels. Key frequency band components are selected based on energy proportions, and the denoised time-domain signal is reconstructed. Finally, the dynamic characteristic parameters of each physical quantity are calculated, and after normalization, a power regulation dynamic feature vector is formed.

[0093] Based on this feature vector, the processor loads an improved generalized frequency response function model and an energy domain index analysis model. The former performs frequency domain transformation and nonlinear analysis on the time-domain response data to extract dynamic response parameters and calculate the power rise and fall rates. The latter quantitatively analyzes energy conversion and power stability data to derive five core performance indicators: regulation accuracy, duration, and energy recovery efficiency. Subsequently, the processor compares these indicators with preset four-level threshold ranges (A, B, C, and D) one by one. Based on the rule that the core indicators meet the standards and at least three auxiliary indicators match, the power plant's power regulation capability level is determined.

[0094] Finally, the processor-driven visualization report module integrates performance indicators and level evaluation results to generate a structured evaluation report containing data trend curves, quantitative values, and level determination criteria. This report is sent to the power grid monitoring system in real time through a standard communication interface, providing dispatchers with real-time and reliable technical support for formulating energy storage dispatch strategies. The entire process is automated and requires no manual intervention.

[0095] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0096] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0097] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for detecting and evaluating the power regulation of a compressed air energy storage power station, characterized in that, include: A preset power command sequence is injected into the compressed air energy storage power station through a programmable operating condition generation unit; By deploying a multi-dimensional sensor array at key locations in the power station, multi-source physical quantity data of the compressed air energy storage power station are collected synchronously during the power command sequence response process. The collected multi-source physical quantity data is preprocessed, and features are extracted from the preprocessed multi-source physical quantity data to obtain a power regulation dynamic feature vector. Based on the aforementioned power regulation dynamic feature vector, the improved generalized frequency response function model and energy domain index analysis model are used to calculate the performance index; and according to the preset threshold range, the corresponding power regulation capability level is output. An evaluation report is generated based on the power regulation capability level and performance indicators, and then sent to the monitoring system via a communication interface.

2. The method for power regulation detection and evaluation of a compressed air energy storage power station as described in claim 1, characterized in that, The power command sequence includes at least two of the following types: step, ramp, sine, and random sequence.

3. The method for power regulation detection and evaluation of a compressed air energy storage power station as described in claim 1, characterized in that, The multi-source physical quantity data includes compressor power, turbine power, gas storage pressure, temperature, flow rate, heat exchanger temperature difference, and valve opening.

4. The method for power regulation detection and evaluation of a compressed air energy storage power station as described in claim 1, characterized in that, The collected multi-source physical quantity data is preprocessed, including time alignment and noise reduction.

5. The method for power regulation detection and evaluation of a compressed air energy storage power station as described in claim 1, characterized in that, Feature extraction is performed on the preprocessed multi-source physical quantity data to obtain the power regulation dynamic feature vector, including: The system receives preprocessed multi-source physical quantity data and organizes it uniformly using the acquisition timestamps to form a multi-source data matrix with consistent time dimensions. The wavelet packet decomposition algorithm is called to perform wavelet packet decomposition of a specific number of layers on the normalized multi-source physical quantity data, and decompose the time domain signal of each physical quantity data into different frequency bands. The energy proportion of the coefficients of different frequency bands after the decomposition of each physical quantity data is calculated, the key frequency band components are screened out, and the noise frequency band components with low energy proportion are removed; based on the screened key frequency band components, the denoised time domain signal of each physical quantity data is obtained by wavelet packet reconstruction algorithm. For each reconstructed physical quantity in the time domain, its corresponding dynamic characteristic parameters are calculated. All the calculated dynamic characteristic parameters are arranged in a preset order to form an initial characteristic parameter set. The power regulation dynamic characteristic vector is obtained by normalizing the initial characteristic parameter set.

6. The method for power regulation detection and evaluation of a compressed air energy storage power station as described in claim 1, characterized in that, The performance indicators include power rise rate, power fall rate, regulation accuracy, duration of operation, and energy recovery efficiency.

7. The method for power regulation detection and evaluation of a compressed air energy storage power station as described in claim 1, characterized in that, Based on the aforementioned power regulation dynamic characteristic vector, performance indicators are obtained by using an improved generalized frequency response function model and an energy domain index analysis model, including: By using an improved generalized frequency response function calculation model, frequency domain transformation and nonlinear characteristic analysis are performed on the time domain response data in the dynamic characteristic vector of power regulation, dynamic response parameters in the power regulation process are extracted, and then the power regulation rise rate and fall rate are calculated. By using the energy domain index analysis model, the energy conversion data and power output stability data in the dynamic characteristic vector of power regulation are quantitatively analyzed, and the regulation accuracy, duration and energy recovery efficiency are calculated.

8. A power regulation detection and evaluation system for a compressed air energy storage power station, characterized in that: include: The programmable operating condition generation unit is configured to generate and inject a preset power command sequence into the control system of the compressed air energy storage power station; A multi-dimensional sensing and measurement array is deployed at key locations of the compressed air energy storage power station and is configured to synchronously acquire multi-source physical quantity data during the power station's response to the power command sequence. The data fusion and feature extraction module is communicatively connected to the multi-dimensional sensing and measurement array and is configured to preprocess the received multi-source physical quantity data and extract features from the preprocessed multi-source physical quantity data to obtain a power regulation dynamic feature vector. The power regulation capability grading and evaluation module, connected to the data fusion and feature extraction module, is configured to calculate the performance index based on the power regulation dynamic feature vector using an improved generalized frequency response function model and an energy domain index analysis model; and output the corresponding power regulation capability level according to a preset threshold range. The visualization reporting unit, connected to the power regulation capability grading and evaluation module, is configured to generate an evaluation report based on the power regulation capability level and performance indicators, and send it to the monitoring system through a communication interface.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the power regulation detection and evaluation method for a compressed air energy storage power station as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the power regulation detection and evaluation method for a compressed air energy storage power station as described in any one of claims 1-7.

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