A compressed air energy storage power station health state detection and evaluation method

Abstract

The present application belongs to the technical field of power station health state detection and evaluation, and specifically discloses a compressed air energy storage power station health state detection and evaluation method, which comprises the following steps: calculating the actual conversion efficiency based on the input and output total power of the current detection period, and constructing a conversion efficiency trend baseline combined with historical data; predicting the efficiency through exponential smoothing, obtaining the baseline change rate combined with the slope, then calculating the conversion efficiency deviation by weighted fusion, and comparing it with the health threshold to determine the health state; when the health state is unhealthy, the deviation indexes of each stage are calculated according to the comparison between the theoretical and actual parameters for the compression, transmission, storage and release stages; comparing the deviation indexes of each stage with the upper limit of the historical normal fluctuation range to determine the abnormal stage; correcting the deviation indexes according to the upstream abnormal relationship, and determining the maintenance execution mode and sorting execution according to the corrected value. The present application realizes the health evaluation and accurate maintenance decision of multi-link coupled power stations.

Description

Technical Field

[0001] This invention belongs to the field of power plant health status detection and assessment technology, and relates to a method for detecting and assessing the health status of a compressed air energy storage power plant. Background Technology

[0002] Compressed air energy storage power stations generate electricity by compressing air during off-peak hours and releasing it during peak hours to drive an expander. Their operation involves multiple energy conversion stages, including compression, transmission, storage, and release. These stages are closely coupled, and performance degradation in any stage directly affects the actual conversion efficiency of the power station. With increasing operating time, the compressor, heat exchanger, air storage device, and expander gradually experience performance degradation, leading to a decrease in power station efficiency. Therefore, conducting health status monitoring and assessment of compressed air energy storage power stations to promptly identify and locate performance degradation points is crucial for ensuring the safe and economical operation of the power station.

[0003] Currently, conventional methods for assessing the health status of compressed air energy storage power stations in the industry mostly focus on monitoring single devices or single parameters. For example, Chinese invention patent CN111896873A discloses a method and system for assessing the health status of energy storage power stations. This method obtains the energy storage battery's charge by collecting the power station's operating data, determines the battery's open-circuit voltage using a preset second-order RC circuit equivalent model, fits the charge and open-circuit voltage to determine the Ah-Voc relationship curve, and then determines the battery's health status based on this curve.

[0004] However, existing technologies based on monitoring a single energy storage medium have the following obvious limitations: First, the method only focuses on the performance degradation of a single energy storage medium and cannot be applied to the characteristics of compressed air energy storage power stations, which involve multiple energy conversion links such as compression, transmission, storage and release, and the links are closely coupled. When the overall efficiency of the compressed air energy storage power station decreases, it cannot identify the abnormal stage that causes the efficiency decline, thus making it difficult to achieve accurate maintenance.

[0005] Second, this method only assesses the health status by fitting curves with measured data, without establishing a mechanism to compare the performance indicators of each stage with the expected values. At the same time, it cannot calculate the deviation indicators of each stage. Consequently, it cannot determine the maintenance targets and maintenance execution methods based on the abnormal stage and its deviation indicators after determining that the power plant is unhealthy. Therefore, it cannot achieve priority ranking and differentiated maintenance execution methods when multiple stages are coupled with anomalies, resulting in a lack of targeted maintenance decisions.

[0006] Therefore, there is an urgent need for a detection and evaluation method that can assess the health status of compressed air energy storage power stations at the system level, based on the multi-link coupling characteristics of the stations, and can quickly locate abnormal stages from the overall efficiency decline, thereby providing corresponding maintenance execution methods to solve the above-mentioned technical problems. Summary of the Invention

[0007] In view of this, in order to solve the problems mentioned in the background technology, a health status detection and evaluation method for compressed air energy storage power stations is proposed.

[0008] The objective of this invention can be achieved through the following technical solution: This invention provides a method for health status detection and evaluation of a compressed air energy storage power station, including: calculating the actual conversion efficiency based on the total input and output power of the energy storage power station in the current detection period, and constructing a conversion efficiency trend baseline based on the actual conversion efficiency data of historical detection periods.

[0009] Based on the actual conversion efficiency and the conversion efficiency trend baseline of the current detection period, the conversion efficiency deviation is calculated, and the conversion efficiency deviation is compared with the health threshold to determine the health status of the energy storage power station.

[0010] When the health status is unhealthy, for the compression stage, transmission stage, storage stage and release stage respectively, the deviation index of each stage is calculated based on the deviation between the corresponding performance index and the expected value.

[0011] The deviation indicators for each stage are compared with the historical normal fluctuation range for the corresponding stage to identify abnormal stages.

[0012] Based on the abnormal stage and its deviation index, and by correcting the deviation index of the abnormal stage based on the upstream abnormal stage in the energy flow direction, the maintenance objects and maintenance execution methods of the energy storage power station are determined.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention constructs a conversion efficiency trend baseline based on the actual conversion efficiency data of historical detection cycles, and then uses the exponential smoothing method to predict the predicted conversion efficiency of the current detection cycle. The deviation between the actual conversion efficiency and the predicted value is weighted and fused with the baseline change rate to obtain the deviation degree, thus avoiding misjudgment caused by relying only on a single point efficiency value or ignoring the efficiency change trend.

[0014] (2) This invention calculates the deviation index based on the deviation between the performance index and the expected value of each stage, namely the compression stage, transmission stage, storage stage and release stage, and realizes the phased performance quantification of the multi-link coupled system. This enables the rapid location of abnormal stages when the overall efficiency declines, and provides a clear direction for subsequent accurate maintenance.

[0015] (3) This invention determines abnormal stages by comparing the deviation indicators of each stage with the historical normal fluctuation range based on historical health period statistics, effectively distinguishing between normal performance fluctuations and performance degradation, and avoiding false detections caused by random fluctuations.

[0016] (4) This invention determines the scope of maintenance objects based on the number of abnormal stages after determining the abnormal stage, identifies the upstream abnormal stage based on the energy flow direction, and then reduces the superimposed impact of upstream abnormality on the deviation index of the current stage by introducing a correction coefficient less than 1, eliminates upstream interference, and thus determines the maintenance execution mode according to the correction deviation value and executes the maintenance execution mode in sequence according to the priority sequence, thereby realizing differentiated and prioritized maintenance decision-making when there are multiple stages of coupled abnormality. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram showing the connections between the steps of the method of the present invention; Figure 2 This is a schematic diagram showing the connection steps for calculating the conversion efficiency deviation of the present invention; Figure 3 This is a schematic diagram showing the calculation steps of the deviation index at each stage of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention achieves the detection and anomaly location of the health status of compressed air energy storage power stations through systematic decomposition and quantitative evaluation. Specifically, the method first constructs a baseline based on the historical trend of actual conversion efficiency, and determines the overall health status through exponential smoothing prediction and weighted deviation calculation. Then, for the four stages of compression, transmission, storage, and release, deviation indicators are calculated by comparing the corresponding performance indicators of each stage with expected values. Next, the deviation indicators of each stage are compared with historical normal fluctuation ranges to identify abnormal stages. Finally, based on the number, order, and upstream coupling relationship of abnormal stages, the maintenance targets and maintenance execution methods are determined. This solves the problems of existing technologies that only focus on a single energy storage medium, cannot locate abnormal stages, and lack tiered maintenance decision-making.

[0021] Please see Figure 1 As shown, the present invention provides a method for health status detection and evaluation of a compressed air energy storage power station, comprising the following steps S1 to S5.

[0022] S1. Calculation of actual conversion efficiency and construction of trend baseline.

[0023] Based on the total input and output power of the energy storage power station during the current testing period, the actual conversion efficiency is calculated, and a conversion efficiency trend baseline is constructed based on the actual conversion efficiency data of historical testing periods.

[0024] Specifically, the calculation of the actual conversion efficiency includes the following steps: S1-1, obtaining the total input power absorbed by the energy storage power station from the grid side and the total output power output to the grid side during the current detection period. Wherein, the total input power is the cumulative value of all active power consumed by the motor during the compression phase, and the total output power is the cumulative value of all active power output by the generator during the release phase.

[0025] S1-2. The ratio of the total output power to the total input power is taken as the actual conversion efficiency of the current detection cycle.

[0026] Specifically, the construction of the conversion efficiency trend baseline includes the following steps: S1-3, selecting a preset number of detection cycles adjacent to the current detection cycle from historical detection cycles, as a set of adjacent historical cycles. The preset number can be set to 5 detection cycles. This number can also be adjusted within the range of 3 to 10 detection cycles according to the operating frequency of the energy storage power station and the accuracy requirements of the health assessment.

[0027] S1-4. Obtain the actual conversion efficiency data corresponding to each detection period in the set of adjacent historical periods, and sort the actual conversion efficiency data in chronological order to form a continuous efficiency data sequence.

[0028] S1-5. Based on the continuous efficiency data sequence, the smoothed value corresponding to each period is calculated using the moving average method, and the sequence composed of the smoothed values ​​of each period is used as the conversion efficiency trend baseline.

[0029] It should be noted that the specific calculation method of the moving average method is as follows: set the width of the sliding window. The window width This is a preset odd number (e.g., 3 or 5). For the first [number] in a continuous efficiency data sequence... Take a data point, and then take the data point and the data points before and after it. The nth adjacent data point (when the window width is 3, that is, the nth) , , (i.e., the first data point), calculate the arithmetic mean of the adjacent data points, as the first data point. The smoothed values ​​correspond to each period. For data points at the ends of the sequence that cannot fill the window width, a mirror filling method can be used. All the calculated smoothed values ​​are arranged in chronological order to form a smoothed value sequence of the same length as the original data sequence, which serves as the baseline for the transformation efficiency trend.

[0030] S2. Calculation of Conversion Efficiency Deviation and Determination of Health Status.

[0031] Based on the actual conversion efficiency and the conversion efficiency trend baseline of the current detection period, the conversion efficiency deviation is calculated, and the conversion efficiency deviation is compared with the health threshold to determine the health status of the energy storage power station.

[0032] Please see Figure 2 As shown, specifically, the calculation of the conversion efficiency deviation includes the following sub-steps S2-1 to S2-6.

[0033] S2-1, Calculation of predicted conversion efficiency.

[0034] The predicted conversion efficiency for the current detection period is obtained by recursively predicting based on the conversion efficiency trend baseline using the exponential smoothing method.

[0035] It should be noted that the specific steps for performing the recursive prediction are as follows: Assume the conversion efficiency trend baseline is continuously... The smoothed values ​​of each detection cycle constitute the total value, denoted as . ,in This is the smoothed value of the detection period closest to the current detection period.

[0036] The recursive forecasting method using single exponential smoothing has the following recursive formula: In the formula, For the first The predicted value of the cycle, For the first The actual smoothed value of the period, For the first The predicted value of the cycle, This is a smoothing constant, with a range of values ​​of 1. For example, Set the value to 0.3. Specifically, during the first cycle, use the actual smoothed value of the first cycle. As the predicted value for the first period Then, starting from the first cycle, the predicted values ​​for each cycle are calculated sequentially, thereby determining the predicted values ​​for the second cycle. The predicted value of the cycle is used as the predicted conversion efficiency for the current detection cycle.

[0037] S2-2, Determination of baseline change rate.

[0038] Curve fitting is performed on the conversion efficiency trend baseline to obtain the slope of the current detection cycle. If the slope is greater than or equal to 0, then 0 is taken as the baseline change rate; otherwise, the absolute value of the slope is taken as the baseline change rate. The baseline change rate is a non-negative value used to characterize the strength of the downward trend in efficiency.

[0039] For example, the slope is calculated using the three-point central difference method: let the smoothing value corresponding to the current detection cycle be... The previous cycle was The next cycle is Then the slope It is impossible to take the start of the sequence. When forward differencing is used, for sequences where the ends cannot be taken... When using backward difference, a backward difference method is employed.

[0040] S2-3, Calculation of efficiency deviation value.

[0041] Obtain the actual conversion efficiency for the current detection period, calculate the difference between the actual conversion efficiency and the predicted conversion efficiency, and obtain the efficiency deviation value.

[0042] S2-4, Weighted fusion of conversion efficiency deviation.

[0043] The conversion efficiency deviation is obtained by weighting and fusing the absolute value of the efficiency deviation with the baseline change rate.

[0044] It should be added that the weighted fusion calculation formula for the conversion efficiency deviation is as follows: In the formula The deviation of conversion efficiency. The absolute value of the efficiency deviation. The rate of change of the baseline. and These are the weights for the absolute value of the efficiency deviation and the rate of change of the baseline, respectively. For example, , .

[0045] By using weighted fusion to calculate the conversion efficiency deviation, on the one hand, the weight allocation can reflect the actual weight of the absolute value of efficiency deviation and the baseline change rate on different dimensions of the power plant's health status, reflecting the difference in contribution between the current degree of efficiency deviation and the long-term efficiency change trend to the overall deviation; on the other hand, it can directly integrate the two information to comprehensively consider the impact of instantaneous deviation and trend change on the power plant's health status, avoiding misjudgments caused by relying on only a single indicator.

[0046] The weights can be set based on the operating characteristics of the energy storage power station, historical health period data statistics, or engineering experience, or they can be obtained through sensitivity analysis or data-driven methods. For example, the absolute value of efficiency deviation, baseline change rate, and corresponding actual health status labels during the historical health period of the power station can be collected. Principal component analysis can be used to determine the contribution of these two factors to the health status assessment. After normalization, they can be converted into weights, and the sum of the weights is 1, thereby accurately quantifying the conversion efficiency deviation.

[0047] Specifically, the determination of the health status of the energy storage power station includes: S2-5, health threshold statistics.

[0048] The health threshold is calculated based on the deviation of conversion efficiency across multiple testing cycles during the historical health period.

[0049] It should be noted that the statistical health threshold includes: assuming a total of [number] samples were collected during the historical health period. The conversion efficiency deviation data for each detection cycle is denoted as... Calculate the mean of this set of data. and standard deviation Set the health threshold to In the formula For a preset multiple, for example, .

[0050] Among them, the preset multiple The threshold can be set according to the power station's tolerance for health risks, with a typical value of 2, corresponding to approximately 95% of normal fluctuations. If there is no historical health data at the initial stage of operation of the energy storage power station, the preset percentage of the design conversion efficiency can be used as the initial health threshold. After accumulating data for no less than 10 testing cycles, the threshold can be updated according to the above statistical method.

[0051] S2-6, Health Status Comparison and Judgment.

[0052] The conversion efficiency deviation is compared with the health threshold. If the conversion efficiency deviation is less than or equal to the health threshold, the energy storage power station is determined to be in a healthy state; otherwise, the energy storage power station is determined to be in an unhealthy state.

[0053] S3. Calculation of deviation indicators for each stage.

[0054] When the health status is unhealthy, for the compression stage, transmission stage, storage stage and release stage respectively, the deviation index of each stage is calculated based on the deviation between the corresponding performance index and the expected value.

[0055] It should be noted that the performance indicators corresponding to each stage refer to the key measurable parameters of energy conversion or working fluid transmission efficiency in the corresponding stage, specifically including: compression stage: input electrical energy value, pressure and temperature before and after charging; transmission stage: compressor outlet mass flow rate, gas storage inlet mass flow rate; storage stage: working fluid mass after charging, working fluid mass before release; release stage: actual compressed air working fluid mass, temperature value after heating before entering the expander, actual power generation.

[0056] Correspondingly, the expected value refers to the theoretical output value given by the mapping relationship constructed based on historical health period data, such as the theoretical compressed air working fluid mass, theoretical power generation, or the ideal value determined based on the law of conservation of physics, such as the theoretical leakage rate of 0 in the transmission and storage stages.

[0057] Please see Figure 3 As shown, specifically, the calculation of the deviation index for each stage includes the following sub-steps S3-1 to S3-4.

[0058] S3-1 Determination of deviation index during compression stage.

[0059] Based on the correspondence between input electrical energy data and compressed air working fluid quality from historical testing cycles, the deviation index for the compression stage is determined.

[0060] Furthermore, the determination of the compression stage deviation index includes: S3-1-1, obtaining the input electrical energy value and the corresponding actual compressed air working fluid quality of multiple detection cycles during the historical health period of the energy storage power station, and constructing the electrical energy-working fluid mapping relationship between the electrical energy value and the compressed air working fluid quality.

[0061] It should be noted that the specific steps for constructing the mapping relationship using the piecewise linear interpolation method are as follows: obtain the input electrical energy values ​​and the corresponding actual compressed air working fluid mass of multiple detection cycles during the historical health period of the energy storage power station, and form the original data pair set.

[0062] Sort all data pairs according to the input electrical energy value from smallest to largest to obtain an ordered sequence.

[0063] If multiple data pairs have the same or a difference less than the preset tolerance for input electrical energy values, the arithmetic mean of the working fluid quality of these data pairs is taken, and they are merged into a single node. The sorted and deduplicated ordered sequence is then used as the piecewise linear interpolation mapping relationship between electrical energy and working fluid.

[0064] S3-1-2. Obtain the input electrical energy value of the current detection cycle, and query the theoretical compressed air working fluid mass corresponding to the input electrical energy value based on the electrical energy-working fluid mapping relationship.

[0065] It should be noted that the steps for querying the theoretical compressed air working fluid mass are as follows: For the input electrical energy value of the current detection cycle, based on its relationship with the minimum and maximum electrical energy values ​​in the piecewise linear interpolation mapping relationship between electrical energy and working fluid, the following processing rules are adopted respectively: When the input electrical energy value is less than the minimum electrical energy value, the nearest neighbor extrapolation method is adopted, and the compressed air working fluid mass corresponding to the minimum electrical energy value is taken as the theoretical compressed air working fluid mass.

[0066] When the input electrical energy value is greater than the maximum electrical energy value, the nearest neighbor extrapolation method is also used to take the compressed air working fluid mass corresponding to the maximum electrical energy value as the theoretical compressed air working fluid mass.

[0067] When the input electrical energy value is between the minimum and maximum values, the adjacent node interval where the input electrical energy value is located is found in the mapping relationship, and the theoretical compressed air working fluid mass is calculated by linear interpolation.

[0068] S3-1-3. Obtain the initial pressure and initial temperature of the gas storage device before filling, the final pressure and final temperature after filling, and the volume of the gas storage device.

[0069] S3-1-4. Calculate the mass of the working medium before inflation based on the initial pressure, initial temperature and volume of the gas storage device before inflation.

[0070] S3-1-5. Calculate the mass of the working fluid after inflation based on the termination pressure, termination temperature and volume of the air storage device after inflation, and subtract the mass of the working fluid before inflation from the mass of the working fluid after inflation to obtain the actual mass of the compressed air working fluid.

[0071] It should be noted that the mass of the working fluid before inflation is calculated by multiplying the volume of the gas storage device by the initial pressure before inflation, and then dividing by the product of the air gas constant and the initial temperature.

[0072] The method for calculating the mass of the working fluid after inflation is as follows: multiply the volume of the gas storage device by the final pressure after inflation, and then divide by the product of the air gas constant and the final temperature.

[0073] The gas constant for air is the gas constant for dry air, approximately 287 J / (kg·K). For gas storage devices such as salt caverns where the volume varies with pressure, a volume-pressure curve is used instead of a fixed volume. The effective volume is retrieved based on the current pressure before and after filling and then substituted into the calculation. Under high-pressure conditions, a compressibility factor can be introduced into the above calculation for correction.

[0074] S3-1-6. Calculate the difference between the theoretical compressed air working fluid mass and the actual compressed air working fluid mass, divide it by the theoretical compressed air working fluid mass, and obtain the compression stage deviation index.

[0075] S3-2, Determination of deviation indicators during transmission phase.

[0076] The mass flow rates at the compressor outlet and the gas storage inlet are obtained. The difference between the mass flow rates at the compressor outlet and the gas storage inlet is calculated and divided by the mass flow rate at the compressor outlet to obtain the leakage rate during the transmission stage, which serves as the deviation index for the transmission stage. The mass flow rates at the compressor outlet and the gas storage inlet are the cumulative mass flow rates at corresponding locations within the current detection cycle, obtained by integrating the instantaneous mass flow rate over the detection cycle.

[0077] S3-3, Determination of deviation indicators during the storage stage.

[0078] Obtain the mass of the working air before release and the mass of the working air after inflation. Calculate the difference between the mass of the working air before release and the mass of the working air after inflation, divide it by the mass of the working air after inflation, and obtain the leakage rate during the storage stage as a deviation index during the storage stage.

[0079] S3-4. Determination of deviation indicators during the release phase.

[0080] Based on the actual compressed air working fluid quality and the corresponding relationship between the temperature value after heating before entering the expander and the power generation in the historical testing cycle, the deviation index of the release stage is determined.

[0081] Furthermore, the determination of the deviation index for the release stage includes: S3-4-1, obtaining the actual compressed air working fluid quality, the temperature value after heating before entering the expander, and the corresponding actual power generation during multiple detection cycles in the historical health period of the energy storage power station.

[0082] S3-4-2. Using the actual mass of compressed air working fluid and the temperature after heating as input variables, and the actual power generation as output variable, construct the working fluid-electrical energy mapping relationship.

[0083] It should be noted that the working fluid-electricity mapping relationship is constructed using a multiple linear regression method. The specific steps are as follows: using the actual compressed air working fluid mass and heated temperature values ​​from multiple testing cycles within the historical healthy period as input variables, and the corresponding actual power generation as output variables, a multiple linear regression equation is obtained using the least squares method. This equation represents the working fluid-electricity mapping relationship. For the actual working fluid mass and temperature of the current testing cycle, substituting them into this equation yields the theoretical power generation.

[0084] S3-4-3. Obtain the actual compressed air working fluid mass and the temperature value after heating in the current detection cycle, and substitute them into the working fluid-electrical energy mapping relationship to obtain the theoretical power generation.

[0085] S3-4-4. Obtain the actual power generation of the current detection cycle, calculate the difference between the theoretical power generation and the actual power generation, divide by the theoretical power generation, and obtain the release stage deviation index.

[0086] S4. Identification of abnormal phases.

[0087] The deviation indicators for each stage are compared with the historical normal fluctuation range for the corresponding stage to identify abnormal stages.

[0088] For example, determining the abnormal stage includes: acquiring deviation index data for each stage of multiple detection cycles within the historical health period of the energy storage power station; calculating the mean and standard deviation of the deviation index for each stage; and using the mean as the center and a preset multiple of the standard deviation as the historical normal fluctuation range for the corresponding stage. The preset multiple is used to control the width of the historical normal fluctuation range. For example, the preset multiple is set to 2, in which case the historical normal fluctuation range is the mean plus or minus two standard deviations, covering approximately 95% of the normal fluctuation data; those skilled in the art can select the corresponding preset multiple according to the power station's sensitivity requirements for health status determination.

[0089] The deviation index of each stage is compared with the upper limit of its historical normal fluctuation range, and the stage where the deviation index exceeds the upper limit is recorded as an abnormal stage.

[0090] It should be noted that since the deviation indicators at each stage are defined as loss rates, i.e., positive values ​​indicate performance degradation and negative values ​​indicate performance better than expected, and health assessments only focus on performance degradation, deviation indicators below the lower limit of the historical normal fluctuation range are not considered abnormal stages. They are usually attributed to measurement errors or occasional factors and are not included in subsequent maintenance decisions.

[0091] If no abnormal phase is identified, it is determined that the specific abnormality cannot be located, and the message "Overall performance degradation but cause unknown" is output, and a full detection is recommended.

[0092] S5. Determine the inspection targets and maintenance execution methods.

[0093] Based on the abnormal stage and its deviation index, and by correcting the deviation index of the abnormal stage based on the upstream abnormal stage in the energy flow direction, the maintenance objects and maintenance execution methods of the energy storage power station are determined.

[0094] Specifically, determining the maintenance targets and maintenance execution methods of the energy storage power station includes the following steps: S5-1, counting the total number of abnormal stages. If the total number of abnormal stages is one, the maintenance target is the equipment system corresponding to that abnormal stage. If the total number of abnormal stages is multiple, the maintenance target includes the equipment systems corresponding to all abnormal stages.

[0095] S5-2. For each abnormal stage, identify all upstream abnormal stages in the energy flow direction.

[0096] For example, the compression stage is upstream of the transmission stage, the transmission stage is upstream of the storage stage, and the storage stage is upstream of the release stage. If both the compression stage and the storage stage fail simultaneously, then for the storage stage, the compression stage is its upstream failure stage.

[0097] S5-3. If there is no abnormal upstream stage, the absolute value of the deviation index of the abnormal stage shall be used as the correction deviation value.

[0098] S5-4. If there is an upstream anomaly stage, then determine a correction coefficient less than 1.

[0099] Both upstream anomalies and current anomalies are positive deviations. Upstream anomalies will be superimposed on the current deviation index. The current measured deviation index overestimates the degree of degradation in the current stage. Therefore, the correction coefficient is taken as a value less than 1, such as 0.8, to eliminate upstream interference. If there is no upstream anomaly stage, the correction coefficient is taken as 1.

[0100] When there are multiple upstream abnormal stages, the correction coefficient is determined based on the upstream abnormal stage closest to the abnormal stage, and the other upstream abnormalities are not considered cumulatively.

[0101] S5-5. Multiply the absolute value of the deviation index in the abnormal stage by the correction coefficient to obtain the corrected deviation value.

[0102] S5-6. Based on the preset range where the corrected deviation value falls, determine the maintenance execution method corresponding to the abnormal stage. The maintenance execution method includes at least: encrypted monitoring, online diagnosis, special testing, and shutdown maintenance. Different corrected deviation value ranges correspond to different maintenance execution methods.

[0103] For example, the preset intervals can be set as follows: when the correction deviation value is less than 0.05, the corresponding maintenance execution mode is encrypted monitoring; when the correction deviation value is greater than or equal to 0.05 and less than 0.10, the corresponding mode is online diagnosis; when the correction deviation value is greater than or equal to 0.10 and less than 0.20, the corresponding mode is special inspection; and when the correction deviation value is greater than or equal to 0.20, the corresponding mode is shutdown for maintenance. The above interval thresholds can be adjusted according to the actual operating characteristics and safety requirements of the power plant, and can be determined by those skilled in the art through a limited number of experiments.

[0104] S5-7. Sort all abnormal stages according to their correction deviation values ​​from largest to smallest to obtain an abnormal stage priority sequence. Execute the maintenance execution method corresponding to each abnormal stage in sequence according to the order of the abnormal stage priority sequence. For abnormal stages with maintenance execution methods of encrypted monitoring or online diagnosis, only record the status and continue to the next stage without performing physical repairs; for special inspections or shutdown maintenance, arrange the corresponding operations according to priority.

[0105] Through the steps S1 to S5 described above, this invention constructs a complete health status detection and assessment process for compressed air energy storage power stations. Starting with overall efficiency trend analysis, it gradually delves into deviation calculations and anomaly location at each stage, ultimately outputting tiered maintenance decisions, thus achieving system-level health assessment and precise operation and maintenance guidance.

[0106] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0107] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0109] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0110] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for health status detection and evaluation of a compressed air energy storage power station, characterized in that: The method includes: Based on the total input and output power of the energy storage power station during the current testing period, the actual conversion efficiency is calculated, and based on the actual conversion efficiency data of historical testing periods, a conversion efficiency trend baseline is constructed. Based on the actual conversion efficiency and the conversion efficiency trend baseline of the current detection period, the conversion efficiency deviation is calculated, and the conversion efficiency deviation is compared with the health threshold to determine the health status of the energy storage power station. When the health status is unhealthy, for the compression stage, transmission stage, storage stage and release stage respectively, the deviation index of each stage is calculated based on the deviation between the corresponding performance index and the expected value. The deviation indicators for each stage are compared with the historical normal fluctuation range for the corresponding stage to identify abnormal stages; Based on the abnormal stage and its deviation index, and by correcting the deviation index of the abnormal stage based on the upstream abnormal stage in the energy flow direction, the maintenance objects and maintenance execution methods of the energy storage power station are determined. The calculated conversion efficiency deviation includes: The predicted conversion efficiency for the current detection period is obtained by recursively predicting based on the conversion efficiency trend baseline using the exponential smoothing method. Curve fitting is performed on the conversion efficiency trend baseline to obtain the slope of the current detection cycle. If the slope is greater than or equal to 0, then 0 is taken as the baseline change rate; otherwise, the absolute value of the slope is taken as the baseline change rate. Obtain the actual conversion efficiency for the current detection period, calculate the difference between the actual conversion efficiency and the predicted conversion efficiency, and obtain the efficiency deviation value; The conversion efficiency deviation is obtained by weighting and fusing the absolute value of the efficiency deviation with the baseline change rate. The weighted fusion calculation formula for the conversion efficiency deviation is as follows: In the formula The deviation of conversion efficiency. The absolute value of the efficiency deviation. The rate of change of the baseline. and These are the weights for the absolute value of the efficiency deviation and the rate of change of the baseline, respectively. ; The performance indicators corresponding to each stage refer to the key measurable parameters of energy conversion or working fluid transmission efficiency at the corresponding stage, specifically including: Compression stage: input electrical energy value, pressure and temperature before and after charging; Transmission stage: compressor outlet mass flow rate, gas storage inlet mass flow rate; Storage stage: working fluid mass after charging, working fluid mass before release; Release stage: actual compressed air working fluid mass, temperature value after heating before entering the expander, actual power generation; The indicators for determining the deviation during the compression stage include: The input electrical energy values ​​and corresponding actual compressed air working fluid quality were obtained from multiple detection cycles during the historical health period of the energy storage power station, and the electrical energy-working fluid mapping relationship between electrical energy values ​​and compressed air working fluid quality was constructed. Obtain the input electrical energy value for the current detection cycle, and query the theoretical compressed air working fluid mass corresponding to the input electrical energy value based on the electrical energy-working fluid mapping relationship; Obtain the initial pressure and initial temperature of the gas storage device before inflation, the final pressure and final temperature after inflation, and the volume of the gas storage device. Calculate the mass of the working fluid before inflation based on the initial pressure, initial temperature, and volume of the gas storage device before inflation. The mass of the working fluid after inflation is calculated based on the termination pressure, termination temperature and volume of the air storage device after inflation. The actual mass of compressed air working fluid is obtained by subtracting the mass of the working fluid before inflation from the mass of the working fluid after inflation. The difference between the theoretical compressed air working fluid mass and the actual compressed air working fluid mass is calculated and divided by the theoretical compressed air working fluid mass to obtain the compression stage deviation index. The indicators for determining the deviation during the release phase include: Obtain the actual compressed air working fluid quality, the temperature value after heating before entering the expander, and the corresponding actual power generation during multiple testing cycles in the historical health period of the energy storage power station; Using the actual mass of compressed air working fluid and the temperature after heating as input variables, and the actual power generation as output variable, a working fluid-electric energy mapping relationship is constructed. Obtain the actual compressed air working fluid mass and the temperature value after heating in the current detection cycle, substitute them into the working fluid-electrical energy mapping relationship, and obtain the theoretical power generation; Obtain the actual power generation of the current detection period, calculate the difference between the theoretical power generation and the actual power generation, divide by the theoretical power generation, and obtain the deviation index of the release stage.

2. The method for health status detection and evaluation of a compressed air energy storage power station according to claim 1, characterized in that: The calculation of the actual conversion efficiency includes: Obtain the total input electricity absorbed by the energy storage power station from the grid side and the total output electricity output to the grid side during the current detection period; The ratio of total output power to total input power is used as the actual conversion efficiency for the current detection cycle.

3. The method for health status detection and evaluation of a compressed air energy storage power station according to claim 1, characterized in that: The baseline for constructing conversion efficiency trends includes: Select a preset number of detection cycles that are adjacent to the current detection cycle from the historical detection cycles, and use them as the set of neighboring historical cycles; Obtain the actual conversion efficiency data corresponding to each detection period in the set of adjacent historical periods, and sort the actual conversion efficiency data in chronological order to form a continuous efficiency data sequence; Based on the continuous efficiency data sequence, the smoothed value corresponding to each period is calculated using the moving average method, and the sequence composed of the smoothed values ​​of each period is used as the conversion efficiency trend baseline.

4. The method for health status detection and evaluation of a compressed air energy storage power station according to claim 1, characterized in that: The determination of the health status of the energy storage power station includes: Based on the deviation of conversion efficiency across multiple testing cycles during the historical health period, a health threshold is calculated. The conversion efficiency deviation is compared with the health threshold. If the conversion efficiency deviation is less than or equal to the health threshold, the energy storage power station is determined to be in a healthy state; otherwise, the energy storage power station is determined to be in an unhealthy state.

5. The method for health status detection and evaluation of a compressed air energy storage power station according to claim 1, characterized in that: The deviation indicators for each stage include: Based on the correspondence between input electrical energy data and compressed air working fluid quality in historical testing cycles, the deviation index of the compression stage is determined. Obtain the mass flow rate at the compressor outlet and the gas storage inlet, calculate the difference between the mass flow rate at the compressor outlet and the gas storage inlet, divide it by the mass flow rate at the compressor outlet, and obtain the leakage rate in the transmission stage as a deviation index in the transmission stage. Obtain the mass of the working air before release and the mass of the working air after inflation. Calculate the difference between the mass of the working air before release and the mass of the working air after inflation, divide it by the mass of the working air after inflation, and obtain the leakage rate during the storage stage as a deviation index during the storage stage. Based on the actual compressed air working fluid quality and the corresponding relationship between the temperature value after heating before entering the expander and the power generation in the historical testing cycle, the deviation index of the release stage is determined.

6. The method for health status detection and evaluation of a compressed air energy storage power station according to claim 1, characterized in that: The anomaly detection phase includes: Obtain deviation index data for each stage of multiple testing cycles during the historical health period of the energy storage power station, calculate the mean and standard deviation of the deviation index for each stage, and use the mean as the center and the preset multiple of the standard deviation as the historical normal fluctuation range for the corresponding stage. The deviation index of each stage is compared with the upper limit of its historical normal fluctuation range, and the stage where the deviation index exceeds the upper limit is recorded as an abnormal stage.

7. The method for health status detection and evaluation of a compressed air energy storage power station according to claim 1, characterized in that: The determination of the maintenance targets and maintenance methods for the energy storage power station includes: The total number of abnormal stages is counted. If the total number of abnormal stages is one, the maintenance object is the equipment system corresponding to that abnormal stage. If the total number of abnormal stages is multiple, the maintenance object includes the equipment systems corresponding to all abnormal stages. For each abnormal stage, identify all upstream abnormal stages in the energy flow direction; If there is no abnormal upstream stage, the absolute value of the deviation index of the abnormal stage shall be used as the correction deviation value. If an upstream anomaly phase exists, a correction coefficient less than 1 is determined. Multiply the absolute value of the deviation index during the abnormal phase by the correction coefficient to obtain the corrected deviation value; Based on the preset range in which the correction deviation value is located, determine the maintenance execution method corresponding to this abnormal stage; All abnormal stages are sorted in descending order of their correction deviation values ​​to obtain an abnormal stage priority sequence. The maintenance execution method corresponding to each abnormal stage is executed in sequence according to the order of the abnormal stage priority sequence.

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