Frequency converter abrupt change processing device and method for high-capacity compressed air energy storage

Abstract

The invention discloses a frequency converter abrupt change processing device and method for high-capacity compressed air energy storage, and belongs to the technical field of electric digital data processing. In each predefined time period, parameters of any type of compressor at any sampling position of a sampling time point are randomly selected as parameters of a target compressor; according to the numerical value arrangement attribute of the parameters of the compressors of the same type as the parameters of the target compressor, the reference contrast quantity of the parameters of the target compressor is obtained; obtaining an analytic value of the parameter of the target compressor according to the numerical value arrangement attribute of the parameter of the compressor of the same type at the same sampling position as the parameter of the target compressor in the current time period and the numerical value arrangement attribute of the parameter of the compressor of the same type at the same sampling time point of the parameter of the target compressor; and other methods are added, so that the accuracy of affirming the failure condition of the compressor is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric digital data processing, and in particular relates to a frequency converter mutation processing device and method for large-capacity compressed air energy storage. Background Art

[0002] Large-capacity compressed air energy storage refers to a storage method that uses electrical energy to compress air during the low-load period of the power grid, and releases the compressed air to drive the compressor to generate electricity during the peak load period of the power grid.

[0003] In terms of controlling the compressor of large-capacity compressed air energy storage, as mentioned in the prior art solution with patent publication number "CN221664966U", it includes a controller connected to the driving machine of the compressor via a frequency converter. When the compressor is operating reasonably, the controller is used to control the frequency converter to maintain the reasonable operation of the compressor via the driving machine; when the compressor fails (such as the compressor overload causes its failure), the controller is used to control the frequency converter to stop the compressor via the driving machine.

[0004] At present, the vibration value of the compressor, the flow rate of the compressor, and the speed value of the compressor are often measured based on the critical quantity. This can effectively respond to the sudden change (outlier) of the compressor parameters, so as to determine whether the compressor is in a reasonable operating condition or a failure condition. However, this method cannot identify the periodic changes of the compressor, such as the sudden change of the compressor parameters caused by the conditional factors such as the change of the compressor's operating conditions, load changes, and working fluid type. Such outlier mutation of parameters does not reflect the actual failure condition of the compressor at present, but will cause the identification of the failure condition, resulting in misidentification, thereby weakening the accuracy of the identification of the compressor failure condition. Summary of the invention

[0005] In order to solve the defects in the prior art, the present invention proposes a frequency converter mutation processing device and method for large-capacity compressed air energy storage, which randomly selects parameters of any type of compressor at any sampling position at a sampling time point in each pre-defined time period as parameters of the target compressor; obtains a benchmark control value of the parameters of the target compressor based on the numerical arrangement properties of the parameters of the same type of compressor as the parameters of the target compressor; obtains the analytical value of the parameters of the target compressor based on the numerical arrangement properties of the parameters of the same type of compressor at the same sampling position as the parameters of the target compressor in the current time period, and the numerical arrangement properties of the parameters of the same type of compressor at the same sampling time point. In addition, other methods are used to improve the accuracy of compressor failure identification.

[0006] The present invention utilizes the following technical solutions.

[0007] The inverter mutation processing method for large-capacity compressed air energy storage includes: The vibration sensor, flow sensor and speed sensor respectively sample the corresponding vibration value of the compressor, the flow value of the compressor and the speed value of the compressor and transmit them to the controller. The vibration value of the compressor, the flow value of the compressor or the speed value of the compressor is the parameter of the compressor. The controller determines whether the compressor is in a reasonable operating state or a failure state based on the compressor parameters transmitted by the sampling. When the compressor is operating properly, the controller controls the inverter to keep the compressor operating properly via the driver; when the compressor fails, the controller controls the inverter to stop the compressor via the driver; The controller determines whether the compressor is in a reasonable operating state or a failure state based on the parameters of the compressor transmitted by sampling, specifically including: Step 1, in each pre-defined time period, obtain the parameters of all types of compressors at each sampling position of the compressor at each sampling time point; Step 2, in each pre-defined time period, randomly select the parameters of any type of compressor at any sampling position at a sampling time point as the parameters of the target compressor; obtain the reference control amount of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor as the parameters of the target compressor; obtain the analytical value of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling position as the parameters of the target compressor in the current time period and the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling time point as the parameters of the target compressor; Step 3, according to the analytical value and the reference control amount of the parameter of the target compressor, the compressor mutation outlier parameter among the parameters of all compressors is obtained; according to the corresponding analytical value and the reference control amount of each compressor mutation outlier parameter and the numerical arrangement of the compressor mutation outlier parameter, the mutation outlier estimation parameter of each compressor mutation outlier parameter is obtained; Step 4: Identify the compressor based on the mutation outlier estimation parameters.

[0008] Furthermore, in Step 1, a predefined time period is set, all sampling time points in the past are divided into a number of predefined time periods, and processing and analysis are performed on the parameters of the compressor at all sampling time points in all predefined time periods.

[0009] Furthermore, in Step 1, the predefined time period is set to 2 seconds, and the sampling frequency of the compressor parameters is set to be sampled once every one millisecond.

[0010] Furthermore, in Step 2, the method for obtaining the reference control amount of the parameter of the target compressor specifically includes: Step 2-1, according to the number of numerical values ​​of the parameters of the compressors of the same type and different numerical values ​​as the parameters of the target compressor in each pre-defined time period, obtain the reasonable basic quantity of the parameters of the target compressor; Step 2-2, taking the variance of the parameters of all compressors of the same type at the same sampling time point as the parameters of the target compressor in each pre-defined time period as the interval dispersion amplitude of the parameters of the target compressor; Step 2-3, in each pre-defined time period, take the ratio of the reduction in the same interval dispersion amplitude between the parameters of the target compressor and the corresponding reasonable basic quantity as ratio one; take the average of the ratio one of all pre-defined time periods as the benchmark control quantity of the parameters of the target compressor.

[0011] Furthermore, in Step 2-1, the method for obtaining the reasonable basic amount specifically includes: In each predefined time period, the ratio of the number of parameters of the compressors with the same type and different values ​​as the parameters of the target compressor is obtained; The ratio of the number of parameters of compressors with different values ​​under the same type of parameters of the target compressor is taken as the key degree of the corresponding value, and the average of various values ​​is calculated by multiplication operation to obtain the reasonable basic quantity of the parameters of the target compressor. The corresponding reasonable basic quantity calculation equation is: ; In the equation, A sequence code representing a predefined time period. The type of the parameter representing the destination compressor; Representative A reasonable basic amount of the parameters of the target compressor in a predefined period of time; Representative The number of attributes of the parameters of the compressors with the same value among the parameters of all compressors of the same type as the parameters of the target compressor within a predefined time period; A unique serial number representing a compressor parameter of the same value; Representative In a predefined time period The number of compressor parameters of the same value belonging to the same class; Representative The number of parameters of all compressors of the same type as the parameters of the target compressor within a predefined time period; Representative In a predefined time period The numerical value of the parameter of each compressor.

[0012] Furthermore, in Step 2-3, the calculation equation of the reference control amount is: ; In the equation, A sequence code representing a predefined time period; The type of the parameter representing the destination compressor; a corresponding sampling position representing a parameter of a target compressor; A corresponding sampling time point representing a parameter of a target compressor; A reference value representing a parameter of a target compressor for a predefined period of time; Represents the number of pre-defined time periods; Representative The value of the parameter of the destination compressor within a predefined time period; Representative A reasonable basic quantity of the parameters of the target compressor within a predefined period of time; Representative The variance of the parameters of the compressors of the same type and the same sampling time point as the parameters of the target compressor within a predefined time period; Represents ratio one.

[0013] Furthermore, in Step 2, the method for obtaining the analytical value of the parameter of the target compressor includes: Step 2-4, according to the number of parameters of the same type of compressor as the parameters of the target compressor sampled before the current sampling time point in the current time period, obtain the time point reference factor corresponding to the current sampling time point; Step 2-5, calculating the average of the parameters of the compressors of the same type as the parameters of the target compressor at all sampling positions at the current sampling time point in the current period, and taking it as the average number 1; calculating the average of the parameters of the compressors of the same type as the parameters of the target compressor at all sampling time points in all sampling positions before the current sampling time point in the current period, and taking it as the average number 2; Step 2-6, obtain the compressor parameter offset based on the time point reference factor, mean 1 and mean 2. In the current period, the ratio of the target compressor parameter minus the compressor parameter offset to the interval dispersion amplitude is used as the analytical value of the target compressor parameter.

[0014] Furthermore, in Step 2-6, the method for obtaining the parameter offset of the compressor according to the time point reference factor, the mean 1 and the mean 2 includes: The amount obtained by multiplying the time point reference factor and the mean is regarded as the time point value offset; the amount obtained by performing inverse correlation on the time point reference factor and multiplying it with the mean 2 is regarded as the interval value offset; the amount obtained by adding the time point value offset and the interval value offset is regarded as the parameter offset of the compressor; The calculation equation of the analytical value of the parameter of the target compressor is: ; In the equation, Represents the current period; Represents the analytical value of the parameter of the destination compressor in the current time period; A value representing a parameter of the destination compressor in the current period; Represents the time point reference factor corresponding to the current sampling time point; represents mean 2; represents the mean of one; represents the variance of the parameters of the compressors of all sampling positions of the same type and sampling time point as the parameters of the target compressor in the current period; The compressor parameter offset representing the destination compressor parameter in the current period.

[0015] Furthermore, in Step 3, the method for obtaining the compressor mutation outlier parameter includes: When a value obtained by rounding down a reference control value of a parameter of a target compressor is different from a value obtained by rounding down an analytical value of the parameter of the target compressor, the parameter of the target compressor is regarded as a compressor mutation outlier parameter; The methods for obtaining mutation outlier estimation parameters include: Step 3-1, the amount obtained by subtracting the reference control amount from the analytical value of each compressor sudden outlier parameter is regarded as the parameter outlier mutation amplitude of the corresponding compressor sudden outlier parameter; Step 3-2, according to the difference in the numerical arrangement of each compressor sudden outlier parameter and the compressor sudden outlier parameter at the previous sampling time point in a pre-defined sampling position range, obtain the sudden outlier change amplitude of each compressor sudden outlier parameter; Step 3-3, dedimensionalize the quantity obtained by multiplying the mutation outlier change amplitude of each compressor mutation outlier parameter and the parameter outlier mutation amplitude, so as to obtain the mutation outlier estimation parameters of each compressor mutation outlier parameter.

[0016] Furthermore, in Step 3-2, the method for obtaining the mutation outlier change amplitude includes: Randomly select a compressor mutation outlier parameter as the target mutation outlier parameter; take the average of the analytical values ​​of all compressor mutation outlier parameters at the adjacent pre-defined position one of the target mutation outlier parameter at the same sampling time point as the outlier mutation attribute of the parameter of the target mutation outlier parameter; the sampling position corresponding to the position one is the mutation sampling position range where the target mutation outlier parameter is located; Obtaining the mutation outlier parameter characteristics of the target mutation outlier parameter according to the analytical value of the target mutation outlier parameter, the outlier mutation attribute of the parameter and the mutation sampling position range; The same type of compressor mutation outlier parameter at the previous sampling point of the target mutation outlier parameter at the same sampling position is regarded as the mutation outlier parameter to be analyzed; the average of the analysis values ​​of all compressor mutation outlier parameters at the predetermined adjacent predetermined position one of the mutation outlier parameter to be analyzed at the same sampling point is regarded as the outlier mutation attribute of the parameter of the mutation outlier parameter to be analyzed; the mutation sampling position range of the mutation outlier parameter to be analyzed is obtained; Obtaining the mutation outlier parameter characteristics of the mutation outlier parameter to be analyzed according to the analyzed value of the mutation outlier parameter to be analyzed, the outlier mutation attribute of the parameter and the mutation sampling position range; In Step 3-2, the characteristic operation equation of the mutation outlier parameter is: ; In the equation, Represents the outlier parameter of the target mutation; a mutation outlier parameter characteristic representing the mutation outlier parameter of interest; A predefined position one representing the outlier parameter of the mutation of interest; The mutation sampling position category representing the outlier parameter of the target mutation; Represents the analytical value of the outlier parameter of the target mutation; Outlier mutation attributes representing the parameters of the outlier parameters of the target mutation; The number of sampling positions representing the mutation sampling position range where the target mutation outlier parameter is located.

[0017] Furthermore, in Step 3-3, the operational equation for the mutation outlier estimation parameter is: ; In the equation, Represents the outlier parameter of the target mutation; A mutation outlier estimation parameter representing the target mutation outlier parameter; Represents the analytical value of the outlier parameter of the target mutation; The baseline control amount representing the outlier parameter of the mutation of interest; a mutation outlier parameter characteristic representing the mutation outlier parameter of interest; The mutation outlier parameter property representing the mutation outlier parameter to be resolved; The parameter outlier mutation amplitude representing the outlier parameter of the target mutation; Represents the use of Z-score method to Perform dedimensionalization.

[0018] Further, in Step 4, the method for identifying the compressor according to the mutation outlier estimation parameter includes: Treat all compressor sudden outlier parameters whose sudden outlier estimation parameters are higher than the pre-defined critical value as the real sudden outlier parameters; When there is a real sudden outlier parameter, the compressor is considered to be in failure condition, otherwise the compressor is considered to be in reasonable operation condition.

[0019] The inverter mutation processing device for large-capacity compressed air energy storage includes: The controller is connected to the driving machine of the compressor via the frequency converter; the controller is also connected to the vibration sensor, flow sensor and speed sensor provided on the corresponding compressor, the vibration sensor, flow sensor and speed sensor are used to sample the vibration value of the corresponding compressor, the flow value of the compressor and the speed value of the compressor respectively and transmit them to the controller, the vibration value of the compressor, the flow value of the compressor or the speed value of the compressor is the parameter of the compressor, and the controller is used to determine whether the compressor is in a reasonable operating state or a failure state according to the parameters of the compressor transmitted by the sampling; When the compressor is operating properly, the controller is used to control the inverter to keep the compressor operating properly via the driver; when the compressor fails, the controller is used to control the inverter to stop the compressor via the driver; The modules running on the controller include: A parameter module, which is used to obtain the parameters of all types of compressors at each sampling position of the compressor at each sampling time point in each pre-defined time period; The parsing module is used to randomly select the parameters of a compressor of any type at a random sampling position at a sampling time point as the parameters of the target compressor in each predefined time period; obtain the reference control amount of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor as the parameters of the target compressor; obtain the parsing value of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling position as the parameters of the target compressor in the current time period and the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling time point as the parameters of the target compressor; An estimation module, which is used to obtain the compressor mutation outlier parameters among the parameters of all compressors according to the analytical values ​​and the reference control quantities of the parameters of the target compressor; and obtain the mutation outlier estimation parameters of each compressor mutation outlier parameter according to the corresponding analytical values ​​and the reference control quantities of each compressor mutation outlier parameter and the numerical arrangement of the compressor mutation outlier parameters; The identification module is used to perform identification on the compressor according to the mutation outlier estimation parameter.

[0020] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include: The compressor is comprehensively identified, and changes in the operating conditions, load and working fluid types of the compressor are prevented, which may cause suspected failures in the compressor. Therefore, the parameters of all types of compressors at all sampling positions of the compressor are obtained; because the compressor has changes in operating conditions, load and working fluid types, in order to prevent the parameters of a small number of samples from often misidentifying the reasonable operating conditions of the compressor, several pre-defined time periods are cut out, and the parameters of the compressor at all sampling points in all pre-defined time periods are processed and analyzed; because the parameter group of the compressor in a pre-defined time period or several pre-defined time periods is often mistaken when it is compared and selected as the reasonable operating conditions, the numerical arrangement of all pre-defined time periods is analyzed to obtain the benchmark comparison value of the parameters of the target compressor; because changes such as load changes in the current time period are often different from changes such as load changes in previous pre-defined time periods Similarly, the parameters of the compressor sampled in the current period often cannot be monitored for outliers through comparison, so the numerical arrangement of the parameters of the target compressor in the current period at different sampling time points and different sampling positions is analyzed to obtain the analytical value of the parameters of the compressor at the current sampling time point; the sudden outlier parameters of the entire compressor are obtained based on the analytical values ​​and reference comparison values ​​between the parameters of the compressor; because the changes in the parameters of the compressor at different sampling positions of the compressor require some changes and take some time, it is impossible to confirm whether the sudden outlier parameters of the compressor are the parameters of the compressor under the actual failure condition, so it is necessary to combine the numerical arrangement of the sudden outlier parameters of the compressor to obtain the sudden outlier estimation parameters of the sudden outlier parameters of the compressor for confirmation; the compressor is identified through the sudden outlier estimation parameters, which avoids the misidentification of the outlier mutation of the compressor parameters caused by the periodic changes in the compressor environment, and improves the accuracy of the identification of the compressor failure condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the method for processing frequency converter mutation for large-capacity compressed air energy storage described in the present invention; Figure 2 It is a partial structural diagram of the frequency converter mutation processing device for large-capacity compressed air energy storage described in the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely express the technical solution of the present invention. The embodiments expressed in this application are only partial embodiments of the present invention, not all embodiments. According to the spirit of the present invention, other embodiments obtained by technicians in this field without making creative work are all within the protection scope of the present invention.

[0023] like Figure 1 As shown, the method for processing sudden changes of a frequency converter for large-capacity compressed air energy storage according to the present invention comprises: The vibration sensor, flow sensor and speed sensor respectively sample the corresponding vibration value of the compressor, the flow value of the compressor and the speed value of the compressor and transmit them to the controller. The vibration value of the compressor, the flow value of the compressor or the speed value of the compressor is the parameter of the compressor. The controller determines whether the compressor is in a reasonable operating state or a failure state based on the compressor parameters transmitted by the sampling. When the controller determines that the compressor is in a reasonable operating condition (that is, the compressor is in the regular sequence start, small load operation, etc. stage), the controller controls the inverter to keep the compressor in reasonable operation through the driver (keeping the compressor in reasonable operation means keeping the compressor in the regular sequence start, small load operation, etc. stage); when the controller determines that the compressor is in a failure condition (just as the compressor fails due to overload), the controller controls the inverter to stop the compressor through the driver.

[0024] The controller determines whether the compressor is in a reasonable operating state or a failure state based on the parameters of the compressor transmitted by sampling, specifically including: Step 1, in each pre-defined time period, obtain the parameters of all types of compressors at each sampling position of the compressor at each sampling time point; The present application is mainly used in the identification of the failure status of the compressor. At different time points of the compressor, all sampling positions are subjected to overall detection and control, so as to obtain the parameters of the compressor at each sampling position at each sampling time point; because the vibration value in the compressor, the flow value of the compressor and the speed of the compressor will reflect the sudden change of the compressor to map its failure status, so several types of compressor parameters are obtained to perform several types of detections on the compressor. Because the compressor has periodic changes, just like the load change, various load changes often last for a period of time. Directly using the parameters of the compressor sampled for 2s or 3s will often mistake the normal operation of the compressor for a failure status. Therefore, in a preferred but non-restrictive embodiment of the present invention, in Step 1, a predefined time period is set, and all the sampling time points in the past are divided into several predefined time periods, and the parameters of the compressor at all sampling time points in all the predefined time periods are processed and analyzed.

[0025] In a preferred but non-limiting embodiment of the present invention, in Step 1, the predefined time period is set to 2 seconds, and the sampling frequency of the compressor parameters is set to one sample per millisecond.

[0026] Here, each compressor impeller of the compressor is considered in advance as a sampling position.

[0027] Here, the same number (the number is one) of vibration sensors, flow sensors and speed sensors are set at each sampling position of the compressor to obtain multiple types of compressor parameters (that is, several types such as vibration values, flow values ​​and speed values). In addition, other types of sensors can be added to perform sampling of corresponding types of parameters on the compressor.

[0028] The present application obtains the parameters of the compressor, and analyzes the analytical values ​​of the parameters of the compressor to compare with the reference comparison value, selects the compressor sudden outlier parameters (the sudden outlier parameters are the outlier values, and the sudden outlier means the outlier) from the parameters of the compressor, and analyzes the sudden outlier estimation parameters of the sudden outlier parameters of the compressor, so as to identify the condition of the compressor. The sampling time points of each sensor sampling the corresponding parameters are the same, that is, the sampling time points of the same sampling parameters of each sensor are the same.

[0029] Step 2, in each pre-defined time period, randomly select the parameters of any type of compressor at any sampling position at a sampling time point as the parameters of the target compressor; obtain the reference control amount of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor as the parameters of the target compressor; obtain the analytical value of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling position as the parameters of the target compressor in the current time period and the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling time point as the parameters of the target compressor; The present application aims to determine whether the compressor is in a failure state, that is, whether the parameters of the compressor have an outlier mutation. Therefore, it is necessary to analyze the baseline of the reasonable operating condition of the compressor and perform a comparative selection. Because under actual conditions, the parameters of the compressor sampled in each predefined time period are large and complex. In addition, the operating conditions, loads, and working fluid types will also be different in each predefined time period. Just as the load has different conditions, there is a big difference between the load during the day and at night. Often, the parameters of the compressor with significant differences are obtained at the same sampling position of the compressor. That is, when a parameter group of the compressor in a predefined time period or several predefined time periods is used as a reasonable operating condition to perform a comparative selection, it often leads to the misunderstanding of the compressor failure state. Therefore, the parameters of all compressors in all predefined time periods are analyzed. Therefore, in the present application, the benchmark comparison amount of the parameters of the target compressor is obtained according to the numerical arrangement attributes of the parameters of the same type of compressor as the parameters of the target compressor.

[0030] In a preferred but non-limiting embodiment of the present invention, in Step 2, the method for obtaining the reference control amount of the parameter of the target compressor specifically includes: Step 2-1, according to the number of numerical values ​​of the parameters of the compressors of the same type and different numerical values ​​as the parameters of the target compressor in each pre-defined time period, obtain the reasonable basic quantity of the parameters of the target compressor; Among the parameters of all compressors in each pre-defined time period, the number of parameters of the compressors corresponding to the sudden outlier conditions is small. For the parameters of the target compressor, the higher the number of compressor parameters with the same numerical values ​​among the parameters of all compressors of the same type, the more reasonable the operating condition of the compressor parameter corresponding to the numerical value is, that is, the larger the credible value of the compressor parameter corresponding to the numerical value is, the more reasonable the level of the parameters of the target compressor is reflected. Therefore, in the present application, the reasonable basic quantity of the parameters of the target compressor is obtained based on the numerical arrangement properties of the parameters of all compressors of the same type as the parameters of the target compressor.

[0031] In a preferred but non-limiting embodiment of the present invention, in Step 2-1, the method for obtaining a reasonable basic amount specifically includes: In each predefined time period, the ratio of the number of parameters of the compressors with the same type and different values ​​as the parameters of the target compressor is obtained; The ratio of the number of parameters of compressors with different values ​​under the same type of parameters of the target compressor is taken as the key degree of the corresponding value, and the average of various values ​​is calculated by multiplication operation to obtain the reasonable basic quantity of the parameters of the target compressor. The corresponding reasonable basic quantity calculation equation is: ; In the equation, A sequence code representing a predefined time period (the sequence code is a code that increases one by one for each predefined time period in the order of the predefined time periods), The type of the parameter representing the destination compressor; Representative A reasonable basic amount of the parameters of the target compressor in a predefined period of time; Representative The number of compressor parameters with the same value among all compressor parameters of the same type within a predefined period (the values ​​of the parameters of the same type of compressors are the same); A serial code representing the attributes of the parameters of the compressors with the same value (the serial code representing the attributes of the parameters of the compressors with the same value is a code that increases the attributes of the parameters of the compressors with the same value one by one according to a set order); Representative In a predefined time period The number of compressor parameters of the same value belonging to the same class; Representative The number of parameters of all compressors of the same type as the parameters of the target compressor within a predefined time period; Representative In a predefined time period The value of the parameter of the compressor with the same value.

[0032] In the reasonable basic quantity operation equation, The higher the number of compressor parameters with the same value, the higher the Confidential values ​​of individual compressor parameters of the same value The larger it is, the more likely it is the parameter of the compressor under reasonable operating conditions; take the average of the corresponding numerical values ​​of the parameters of each same compressor by multiplying the reliable value, analyze the reasonable range and value of the parameters of all compressors of the same type as the parameters of the target compressor, reflect the reasonable level of the parameters of the target compressor, and obtain the reasonable basic value of the parameters of the target compressor.

[0033] Step 2-2, taking the variance of the parameters of all compressors of the same type at the same sampling time point as the parameters of the target compressor in each pre-defined time period as the interval dispersion amplitude of the parameters of the target compressor; The dispersion amplitude of the parameters of all compressors of the same type at the same sampling point as the parameters of the target compressor is analyzed. If the dispersion amplitude is lower, it means that the parameters of the compressors at all sampling positions are closer to the same value. At this time, the value will be more reasonable, that is, the parameters of the target compressor will be in a more reasonable operating condition.

[0034] In the present application, the variance of the parameters of all compressors of the same type at the same sampling point of the parameters of the target compressor is calculated, and the variance is used as the interval dispersion amplitude, so the interval dispersion amplitude represents the rationality of the parameters of the target compressor itself.

[0035] Step 2-3, in each pre-defined time period, take the ratio of the reduction in the same interval dispersion amplitude between the parameters of the target compressor and the corresponding reasonable basic quantity as ratio one; take the average of the ratio one of all pre-defined time periods as the benchmark control quantity of the parameters of the target compressor.

[0036] In order to remove the numerical changes of parameters often introduced by changes such as load changes, the present application performs digitization on the standard values ​​of the parameters of the target compressor through the comprehensive and reasonable range of the parameters of all compressors of the same type as the parameters of the target compressor in each pre-defined time period. The present application uses the idea of ​​benchmark scores to analyze the distortion of the parameters of the target compressor, so as to obtain the benchmark control amount of the parameters of the target compressor. In a preferred but non-limiting embodiment of the present invention, in Step 2-3, the calculation equation of the benchmark control amount is: ; In the equation, A sequence code representing a predefined time period; The type of the parameter representing the destination compressor; a corresponding sampling position representing a parameter of a target compressor; A corresponding sampling time point representing a parameter of a target compressor; A reference value representing a parameter of a target compressor for a predefined period of time; Represents the number of pre-defined time periods; Representative The value of the parameter of the destination compressor within a predefined time period; Representative A reasonable basic quantity of the parameters of the target compressor within a predefined period of time; Representative The variance of the parameters of the compressors of the same type and the same sampling time point as the parameters of the target compressor within a predefined time period; Represents ratio one.

[0037] In the benchmark control quantity calculation equation, the value of the parameter of the target compressor in each pre-defined time period is calculated by using the idea of ​​benchmark score. and reasonable basic amount The difference is digitized, and the ratio of the digitized To represent the to reduce the effect of frequently occurring specific load changes and other conditions on the parameters of the compressor in a predefined period or several predefined periods, take the average of the reductions in all predefined periods to obtain the benchmark comparison value of the parameters of the target compressor, that is, to obtain the benchmark comparison value of the parameters of each type of compressor at each sampling position at each sampling point in different predefined time periods.

[0038] The above method obtains a benchmark comparison value of the parameters of the target compressor through a comprehensive analysis of the parameters of the compressor in all the pre-defined time periods in the past. Based on this, it can confirm whether the parameters of the compressor at the current sampling point in a current time period, that is, the current sampling point (the sampling point in the current time period is the current sampling point) are reasonable. Because the load and other changing conditions in the current time period are different from the load and other changing conditions in all the pre-defined time periods in the past, just as the load in the current time period is different from the load in each pre-defined time period in the past, the parameters of the compressor sampled in the current time period often cannot be identified as outliers through comparison. Therefore, in this application, the parameters of the compressor at the current sampling point are converted into the difference value corresponding to the parameters of the compressor at the previous sampling point and other positions, so as to obtain the analytical value of the parameters of the compressor at the current sampling point.

[0039] In a preferred but non-limiting embodiment of the present invention, in Step 2, the method for obtaining the analytical value of the parameter of the target compressor includes: Step 2-4, based on the number of parameters of the same type of compressor as the parameters of the target compressor sampled before the current sampling point in the current time period (the current time period is the time period for sampling the parameters of the compressor, and the size of the current time period is the same as the size of the pre-defined time period), obtain the time point reference factor corresponding to the current sampling time point; the time point reference factor is proportional to the number of parameters of the same type of compressor as the parameters of the target compressor sampled before the current sampling point; the parameters of the target compressor in the current time period are the parameters of any type of compressor at any sampling position at the current sampling point in the time period as the parameters of the target compressor in the time period.

[0040] For the parameters of the compressor in the current time period, if the number of compressor parameters sampled at the current sampling point is higher, the reference of the current sampling point is higher, and at this time, the criticality of the parameters of all compressors at the same position is higher. Therefore, as the sampling point increases, the time point reference factor continues to increase, and the position of this application is the sampling position.

[0041] Just as the time point reference factor corresponding to the first sampling time point in the current time period is set to one ten-thousandth and continues to increase as the sampling time points in the current time period move backward, the time point reference factor increases the value of the time point reference factor corresponding to the current sampling time point by one ten-thousandth as the sampling time point moves backward, thereby achieving the goal of obtaining the time point reference factor corresponding to the current sampling time point based on the number of parameters of the same type of compressor as the parameters of the target compressor sampled before the current sampling time point in the current time period, and maintaining a proportional correlation between the two.

[0042] Step 2-5, calculating the average of the parameters of the compressors of the same type as the parameters of the target compressor at all sampling positions at the current sampling time point in the current period, and taking it as the average number 1; calculating the average of the parameters of the compressors of the same type as the parameters of the target compressor at all sampling time points in all sampling positions before the current sampling time point in the current period, and taking it as the average number 2; Step 2-5 uses mean 1 to represent the mean of the parameters of the compressors at all sampling positions at the current sampling time point in the current period. The higher mean 1 is, the higher the overall value of the parameters of the compressors at all sampling positions at the current sampling time point is, which reflects the overall value arrangement in the interval; mean 2 is the mean operation performed on the parameters of all compressors at the same sampling position before the current sampling time point. The analytical methods of mean 2 and mean 1 are similar, and mean 2 reflects the overall value arrangement at the time point.

[0043] Step 2-6, obtain the compressor parameter offset based on the time point reference factor, mean 1 and mean 2. In the current period, the ratio of the target compressor parameter minus the compressor parameter offset to the interval dispersion amplitude is used as the analytical value of the target compressor parameter.

[0044] In a preferred but non-limiting embodiment of the present invention, in Step 2-6, the method for obtaining the parameter offset of the compressor according to the time point reference factor, the mean 1 and the mean 2 includes: The amount obtained by multiplying the time point reference factor and the mean is regarded as the time point value offset; the amount obtained by performing inverse correlation on the time point reference factor and multiplying it with the mean 2 is regarded as the interval value offset; the amount obtained by adding the time point value offset and the interval value offset is regarded as the parameter offset of the compressor; The calculation equation of the analytical value of the parameter of the target compressor is: ; In the equation, Represents the current period; Represents the analytical value of the parameter of the destination compressor in the current time period; A value representing a parameter of the destination compressor in the current period; Represents the time point reference factor corresponding to the current sampling time point; represents mean 2; represents the mean of one; represents the variance of the parameters of the compressors of all sampling positions of the same type and sampling time point as the parameters of the target compressor in the current period; The compressor parameter offset representing the destination compressor parameter in the current period.

[0045] In the calculation equation of the analytical value, the idea of ​​the benchmark score is used to analyze The offset of the compressor parameters corresponding to the previous sampling time point and another position, where the higher the number of compressor parameters sampled before the current sampling time point, the higher the average of the compressor parameters at the previous sampling time point. The better the reference, the greater the The criticality of The criticality of The offset of the compressor parameters corresponding to the previous sampling time point and another position To reflect The distortion amount, and as The parsed value of .

[0046] Step 3, according to the analytical value and the reference control amount of the parameter of the target compressor, the compressor mutation outlier parameter among the parameters of all compressors is obtained; according to the corresponding analytical value and the reference control amount of each compressor mutation outlier parameter and the numerical arrangement of the compressor mutation outlier parameter, the mutation outlier estimation parameter of each compressor mutation outlier parameter is obtained; According to the above method, the analytical value and the benchmark comparison value of the parameters of the target compressor are obtained, that is, the analytical value and the benchmark comparison value of the parameters of each compressor are obtained. The difference between the analytical value and the benchmark comparison value can be used to obtain the difference between the values ​​of the parameters of each compressor corresponding to the reasonable operating conditions. Therefore, the present application obtains the compressor mutation outlier parameters within the parameters of all compressors based on the analytical value and the benchmark comparison value of the parameters of the target compressor.

[0047] In a preferred but non-limiting embodiment of the present invention, in Step 3, the method for obtaining the compressor mutation outlier parameter includes: When the amount obtained by rounding down the reference control amount of the parameter of the target compressor is different from the amount obtained by rounding down the analytical value of the parameter of the target compressor, the parameter of the target compressor is regarded as the compressor mutation outlier parameter; thereby obtaining the overall compressor mutation outlier parameter; Because different pre-defined time periods often present changes in the operating conditions, loads, and working fluid types of the compressor, the vibration value of the compressor, the flow value of the compressor, the speed value of the compressor, and other types of compressor parameters will undergo significant changes. Due to the driven nature of the compressor operation, changes in the compressor parameters at different sampling positions of the compressor will take some time to change. At this time, it is impossible to confirm whether the compressor sudden outlier parameters are the parameters of the compressor under a true failure condition. Therefore, in this application, based on the corresponding analytical values ​​and benchmark comparison quantities of each compressor sudden outlier parameter, as well as the numerical arrangement of the compressor sudden outlier parameters, the sudden outlier estimation parameters of each compressor sudden outlier parameter are obtained.

[0048] The methods for obtaining mutation outlier estimation parameters include: Step 3-1, the amount obtained by subtracting the reference control amount from the analytical value of each compressor sudden outlier parameter is regarded as the parameter outlier mutation amplitude of the corresponding compressor sudden outlier parameter; The higher the value obtained by subtracting the reference control value from the analytical value of the compressor sudden change outlier parameter, the greater the probability that the compressor sudden change outlier parameter is attributed to a failure condition.

[0049] Step 3-2, according to the difference in the numerical arrangement of each compressor sudden outlier parameter and the compressor sudden outlier parameter at the previous sampling time point in a pre-defined sampling position range, obtain the sudden outlier change amplitude of each compressor sudden outlier parameter; In a preferred but non-limiting embodiment of the present invention, in Step 3-2, the method for obtaining the mutation outlier variation amplitude includes: The sampling positions of adjacent compressor mutation outlier parameters of the same type at the same sampling time point form the mutation sampling position range.

[0050] Randomly select a compressor mutation outlier parameter as the target mutation outlier parameter; take the average of the analytical values ​​of all compressor mutation outlier parameters at the adjacent pre-defined position one of the target mutation outlier parameter at the same sampling time point as the outlier mutation attribute of the parameter of the target mutation outlier parameter; the sampling position corresponding to the position one is the mutation sampling position range where the target mutation outlier parameter is located; Just as around each compressor mutation outlier parameter, the parameters of the compressor at the eight adjacent sampling positions of the same type and the same sampling time point as each compressor mutation outlier parameter are regarded as the pre-defined position one, the eight sampling positions are the mutation sampling position range where the compressor mutation outlier parameter is located.

[0051] According to the analytical value of the target mutation outlier parameter, the outlier mutation attribute of the parameter and the mutation sampling position range, the mutation outlier parameter characteristic of the target mutation outlier parameter is obtained; the target mutation outlier parameter characteristic is proportional to the analytical value of the target mutation outlier parameter, the outlier mutation attribute of the parameter and the mutation sampling position range; The same type of compressor mutation outlier parameter at the previous sampling point of the target mutation outlier parameter at the same sampling position is regarded as the mutation outlier parameter to be analyzed; the average of the analysis values ​​of all compressor mutation outlier parameters at the predetermined adjacent predetermined position one of the mutation outlier parameter to be analyzed at the same sampling point is regarded as the outlier mutation attribute of the parameter of the mutation outlier parameter to be analyzed; the mutation sampling position range of the mutation outlier parameter to be analyzed is obtained; According to the analytical value of the mutation outlier parameter to be analyzed, the outlier mutation attribute of the parameter and the mutation sampling position range, the mutation outlier parameter characteristics of the mutation outlier parameter to be analyzed are obtained; the mutation outlier parameter characteristics to be analyzed are proportional to the analytical value of the mutation outlier parameter to be analyzed, the outlier mutation attribute of the parameter and the mutation sampling position range; the mutation outlier parameter to be analyzed is also a type of mutation outlier parameter.

[0052] In a preferred but non-limiting embodiment of the present invention, in Step 3-2, the mutation outlier parameter characteristic operation equation is: ; In the equation, Represents the outlier parameter of the target mutation; a mutation outlier parameter characteristic representing the mutation outlier parameter of interest; A predefined position one representing the outlier parameter of the mutation of interest; The mutation sampling position category representing the outlier parameter of the target mutation; Represents the analytical value of the outlier parameter of the target mutation; Outlier mutation attributes representing the parameters of the outlier parameters of the target mutation; The number of sampling positions representing the mutation sampling position range where the target mutation outlier parameter is located.

[0053] In the mutation outlier parameter characteristic calculation equation, the analytical value of the target mutation outlier parameter The higher the value, the more likely the target mutation outlier parameter is an outlier mutation. The higher the value, the higher the compressor mutation outlier parameter in the position 1 defined in advance by the target mutation outlier parameter. At this time, the target mutation outlier parameter is more likely to be an outlier mutation state; the number of sampling positions in the mutation sampling position range where the target mutation outlier parameter is located The higher it is, the higher the mutation sampling position range of the target mutation outlier parameter is. At this time, the higher the mutation expansion amplitude is, and the more likely the target mutation outlier parameter will show failure.

[0054] The amount obtained by dividing the mutation outlier parameter characteristic of the target mutation outlier parameter by the mutation outlier parameter characteristic of the mutation outlier parameter to be analyzed is recorded as ,Bundle It is taken as the mutation outlier change amplitude of the target mutation outlier parameter; here, the higher the mutation outlier parameter characteristic of the target mutation outlier parameter corresponds to the mutation outlier parameter characteristic of the mutation outlier parameter to be analyzed, which means that the target mutation outlier parameter corresponds to the mutation outlier parameter to be analyzed at the previous sampling point. The higher the span of the outlier mutation range expansion is, the more likely the target mutation outlier parameter will reflect the failure state.

[0055] Step 3-3, dedimensionalize the quantity obtained by multiplying the mutation outlier change amplitude of each compressor mutation outlier parameter and the parameter outlier mutation amplitude, so as to obtain the mutation outlier estimation parameters of each compressor mutation outlier parameter.

[0056] In a preferred but non-limiting embodiment of the present invention, in Step 3-3, the operational equation of the mutation outlier estimation parameter is: ; In the equation, Represents the outlier parameter of the target mutation; A mutation outlier estimation parameter representing the target mutation outlier parameter; Represents the analytical value of the outlier parameter of the target mutation; The baseline control amount representing the outlier parameter of the mutation of interest; a mutation outlier parameter characteristic representing the mutation outlier parameter of interest; The mutation outlier parameter property representing the mutation outlier parameter to be resolved; The parameter outlier mutation amplitude representing the outlier parameter of the target mutation; Represents the use of Z-score method to Perform dedimensionalization.

[0057] In the mutation outlier estimation parameter calculation equation, the parameter outlier mutation amplitude of the target mutation outlier parameter The higher the value, the higher the difference between the analyzed value of the target mutation outlier parameter and the reference value, and the more the target mutation outlier parameter will reflect the failure of the compressor. The mutation outlier parameter characteristics of the target mutation outlier parameter correspond to the mutation outlier parameter characteristics of the mutation outlier parameter to be analyzed. The higher it is, the higher the fluctuation of the target mutation outlier parameter corresponding to the mutation outlier parameter to be analyzed at the previous sampling point, the larger the span of the outlier mutation range expansion, and the more the target mutation outlier parameter will reflect the failure status of the compressor.

[0058] Similarly, the sudden outlier estimation parameters of the sudden outlier parameters of each compressor are obtained.

[0059] Step 4: Identify the compressor based on the mutation outlier estimation parameters.

[0060] In a preferred but non-limiting embodiment of the present invention, in Step 4, the method for identifying the compressor according to the mutation outlier estimation parameter includes: Treat all compressor sudden outlier parameters whose sudden outlier estimation parameters are higher than the pre-defined critical value as the real sudden outlier parameters; When there is a real sudden outlier parameter, the compressor is considered to be in failure condition, otherwise the compressor is considered to be in reasonable operation condition.

[0061] The predefined critical volume can be .

[0062] Thus, the compressor was identified.

[0063] like Figure 2 As shown, the present invention provides a frequency converter mutation processing device for large-capacity compressed air energy storage, comprising: The controller is connected to the driving machine of the compressor via the frequency converter. The controller is also connected to the vibration sensor, flow sensor and speed sensor provided on the corresponding compressor. The vibration sensor, flow sensor and speed sensor are used to sample the vibration value, flow value and speed value of the corresponding compressor respectively and transmit them to the controller. The vibration value, flow value or speed value of the compressor are the parameters of the compressor. The controller is used to determine whether the compressor is in a reasonable operating state or a failure state according to the parameters of the compressor transmitted by the sampling. When the compressor is operating properly, the controller is used to control the inverter to keep the compressor in proper operation via the driver; when the compressor fails (such as when the compressor is overloaded and causes its failure), the controller is used to control the inverter to stop the compressor via the driver; The modules running on the controller include: A parameter module, which is used to obtain the parameters of all types of compressors at each sampling position of the compressor at each sampling time point in each pre-defined time period; The parsing module is used to randomly select the parameters of a compressor of any type at a random sampling position at a sampling time point as the parameters of the target compressor in each predefined time period; obtain the reference control amount of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor as the parameters of the target compressor; obtain the parsing value of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling position as the parameters of the target compressor in the current time period and the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling time point as the parameters of the target compressor; An estimation module, which is used to obtain the compressor mutation outlier parameters among the parameters of all compressors according to the analytical values ​​and the reference control quantities of the parameters of the target compressor; and obtain the mutation outlier estimation parameters of each compressor mutation outlier parameter according to the corresponding analytical values ​​and the reference control quantities of each compressor mutation outlier parameter and the numerical arrangement of the compressor mutation outlier parameters; The identification module is used to identify the compressor according to the mutation outlier estimation parameter. The controller can be a terminal unit indicator controller, and the air compressor can be a terminal compressor.

[0064] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include: The compressor is comprehensively identified, and changes in the operating conditions, load and working fluid types of the compressor are prevented, which may cause suspected failures in the compressor. Therefore, the parameters of all types of compressors at all sampling positions of the compressor are obtained; because the compressor has changes in operating conditions, load and working fluid types, in order to prevent the parameters of a small number of samples from often misidentifying the reasonable operating conditions of the compressor, several pre-defined time periods are cut out, and the parameters of the compressor at all sampling points in all pre-defined time periods are processed and analyzed; because the parameter group of the compressor in a pre-defined time period or several pre-defined time periods is often mistaken when it is compared and selected as the reasonable operating conditions, the numerical arrangement of all pre-defined time periods is analyzed to obtain the benchmark comparison value of the parameters of the target compressor; because changes such as load changes in the current time period are often different from changes such as load changes in previous pre-defined time periods Similarly, the parameters of the compressor sampled in the current period often cannot be monitored for outliers through comparison, so the numerical arrangement of the parameters of the target compressor in the current period at different sampling time points and different sampling positions is analyzed to obtain the analytical value of the parameters of the compressor at the current sampling time point; the sudden outlier parameters of the entire compressor are obtained based on the analytical values ​​and reference comparison values ​​between the parameters of the compressor; because the changes in the parameters of the compressor at different sampling positions of the compressor require some changes and take some time, it is impossible to confirm whether the sudden outlier parameters of the compressor are the parameters of the compressor under the actual failure condition, so it is necessary to combine the numerical arrangement of the sudden outlier parameters of the compressor to obtain the sudden outlier estimation parameters of the sudden outlier parameters of the compressor for confirmation; the compressor is identified through the sudden outlier estimation parameters, which avoids the misidentification of the outlier mutation of the compressor parameters caused by the periodic changes in the compressor environment, and improves the accuracy of the identification of the compressor failure condition.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not deviate from the spirit and scope of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for processing sudden changes in frequency converters for large-capacity compressed air energy storage, characterized in that: include: The vibration sensor, flow sensor and speed sensor respectively sample the corresponding vibration value of the compressor, the flow value of the compressor and the speed value of the compressor and transmit them to the controller. The vibration value of the compressor, the flow value of the compressor or the speed value of the compressor is the parameter of the compressor. The controller determines whether the compressor is in a reasonable operating state or a failure state based on the compressor parameters transmitted by the sampling. When the compressor is operating properly, the controller is used to control the inverter to keep the compressor operating properly via the driver; when the compressor fails, the controller is used to control the inverter to stop the compressor via the driver; The controller determines whether the compressor is in a reasonable operating state or a failure state based on the parameters of the compressor transmitted by sampling, specifically including: Step 1, in each pre-defined time period, obtain the parameters of all types of compressors at each sampling position of the compressor at each sampling time point; Step 2, in each pre-defined time period, randomly select the parameters of any type of compressor at any sampling position at a sampling time point as the parameters of the target compressor; obtain the reference control amount of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor as the parameters of the target compressor; obtain the analytical value of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling position as the parameters of the target compressor in the current time period and the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling time point as the parameters of the target compressor; Step 3, according to the analytical value and the reference control amount of the parameter of the target compressor, the compressor mutation outlier parameter among the parameters of all compressors is obtained; according to the corresponding analytical value and the reference control amount of each compressor mutation outlier parameter and the numerical arrangement of the compressor mutation outlier parameter, the mutation outlier estimation parameter of each compressor mutation outlier parameter is obtained; Step 4: Identify the compressor based on the mutation outlier estimation parameters.

2. The method for processing frequency converter mutation for large-capacity compressed air energy storage according to claim 1, characterized in that: In Step 1, a predefined time period is set, all sampling time points in the past are divided into a number of predefined time periods, and the parameters of the compressor at all sampling time points in all predefined time periods are processed and analyzed.

3. The method for processing frequency converter mutation for large-capacity compressed air energy storage according to claim 2, characterized in that: In Step 1, the predefined time period is set to 2 seconds, and the sampling frequency of the compressor parameters is set to one sample per millisecond.

4. The method for processing frequency converter mutation for large-capacity compressed air energy storage according to claim 3 is characterized in that: In Step 2, the method for obtaining the reference control amount of the parameter of the target compressor specifically includes: Step 2-1, according to the number of numerical values ​​of the parameters of the compressors of the same type and different numerical values ​​as the parameters of the target compressor in each pre-defined time period, obtain the reasonable basic quantity of the parameters of the target compressor; Step 2-2, taking the variance of the parameters of all compressors of the same type at the same sampling time point as the parameters of the target compressor in each pre-defined time period as the interval dispersion amplitude of the parameters of the target compressor; Step 2-3, in each pre-defined time period, the ratio of the reduction in the same interval dispersion amplitude between the parameter of the target compressor and the corresponding reasonable basic amount is regarded as ratio 1; the average of the ratio 1 of all pre-defined time periods is regarded as the reference comparison amount of the parameter of the target compressor; In Step 2-1, the method for obtaining a reasonable basic amount specifically includes: In each predefined time period, the ratio of the number of parameters of the compressors with the same type and different values ​​as the parameters of the target compressor is obtained; The ratio of the number of parameters of compressors with different values ​​under the same type of parameters of the target compressor is taken as the key degree of the corresponding value, and the average of various values ​​is calculated by multiplication operation to obtain the reasonable basic quantity of the parameters of the target compressor. The corresponding reasonable basic quantity calculation equation is: ； In the equation, A sequence code representing a predefined time period, The type of the parameter representing the destination compressor; Representative A reasonable basic amount of the parameters of the target compressor in a predefined period of time; Representative The number of attributes of the parameters of the compressors with the same value among the parameters of all compressors of the same type as the parameters of the target compressor within a predefined time period; A unique serial number representing a compressor parameter of the same value; Representative In a predefined time period The number of compressor parameters of the same value belonging to the same class; Representative The number of parameters of all compressors of the same type as the parameters of the target compressor within a predefined time period; Representative In a predefined time period The numerical value of the parameter of each compressor.

5. The method for processing frequency converter sudden changes for large-capacity compressed air energy storage according to claim 4 is characterized in that: In Step 2-3, the calculation equation for the reference control amount is: ； In the equation, A sequence code representing a predefined time period; The type of the parameter representing the destination compressor; a corresponding sampling position representing a parameter of a target compressor; A corresponding sampling time point representing a parameter of a target compressor; A reference value representing a parameter of a target compressor for a predefined period of time; Represents the number of pre-defined time periods; Representative The value of the parameter of the destination compressor within a predefined time period; Representative A reasonable basic quantity of the parameters of the target compressor within a predefined period of time; Representative The variance of the parameters of the compressors of the same type and the same sampling time point as the parameters of the target compressor within a predefined time period; Represents ratio one.

6. The method for processing frequency converter sudden changes for large-capacity compressed air energy storage according to claim 5, characterized in that: In Step 2, the method for obtaining the analytical value of the parameter of the target compressor includes: Step 2-4, according to the number of parameters of the same type of compressor as the parameters of the target compressor sampled before the current sampling time point in the current time period, obtain the time point reference factor corresponding to the current sampling time point; Step 2-5, calculating the average of the parameters of the compressors of the same type as the parameters of the target compressor at all sampling positions at the current sampling time point in the current period, and taking it as the average number 1; calculating the average of the parameters of the compressors of the same type as the parameters of the target compressor at all sampling time points in all sampling positions before the current sampling time point in the current period, and taking it as the average number 2; Step 2-6, obtaining the parameter offset of the compressor according to the time point reference factor, mean 1 and mean 2, and taking the ratio of the parameter of the target compressor minus the parameter offset of the compressor to the interval dispersion amplitude as the analytical value of the parameter of the target compressor in the current period; In Step 2-6, the method for obtaining the parameter offset of the compressor according to the time point reference factor, the mean 1 and the mean 2 includes: The amount obtained by multiplying the time point reference factor and the mean is regarded as the time point value offset; the amount obtained by performing inverse correlation on the time point reference factor and multiplying it with the mean 2 is regarded as the interval value offset; the amount obtained by adding the time point value offset and the interval value offset is regarded as the parameter offset of the compressor; The calculation equation of the analytical value of the parameter of the target compressor is: ； In the equation, Represents the current period; Represents the analytical value of the parameter of the destination compressor in the current time period; A value representing a parameter of the destination compressor in the current period; Represents the time point reference factor corresponding to the current sampling time point; represents mean 2; represents the mean of one; represents the variance of the parameters of the compressors of all sampling positions of the same type and sampling time point as the parameters of the target compressor in the current period; The compressor parameter offset representing the destination compressor parameter in the current period.

7. The method for processing frequency converter sudden changes for large-capacity compressed air energy storage according to claim 6, characterized in that: In Step 3, the method for obtaining the compressor mutation outlier parameters includes: When a value obtained by rounding down a reference control value of a parameter of a target compressor is different from a value obtained by rounding down an analytical value of the parameter of the target compressor, the parameter of the target compressor is regarded as a compressor mutation outlier parameter; The methods for obtaining mutation outlier estimation parameters include: Step 3-1, the amount obtained by subtracting the reference control amount from the analytical value of each compressor sudden outlier parameter is regarded as the parameter outlier mutation amplitude of the corresponding compressor sudden outlier parameter; Step 3-2, according to the difference in the numerical arrangement of each compressor sudden outlier parameter and the compressor sudden outlier parameter at the previous sampling time point in a pre-defined sampling position range, obtain the sudden outlier change amplitude of each compressor sudden outlier parameter; Step 3-3, dedimensionalize the amount obtained by multiplying the sudden outlier change amplitude of each compressor sudden outlier parameter and the parameter outlier sudden change amplitude, so as to obtain the sudden outlier estimation parameter of each compressor sudden outlier parameter; In Step 3-2, the method for obtaining the amplitude of the mutation outlier change includes: Randomly select a compressor mutation outlier parameter as the target mutation outlier parameter; take the average of the analytical values ​​of all compressor mutation outlier parameters at the adjacent pre-defined position one of the target mutation outlier parameter at the same sampling time point as the outlier mutation attribute of the parameter of the target mutation outlier parameter; the sampling position corresponding to the position one is the mutation sampling position range where the target mutation outlier parameter is located; Obtaining the mutation outlier parameter characteristics of the target mutation outlier parameter according to the analytical value of the target mutation outlier parameter, the outlier mutation attribute of the parameter and the mutation sampling position range; The same type of compressor mutation outlier parameter at the previous sampling point of the target mutation outlier parameter at the same sampling position is regarded as the mutation outlier parameter to be analyzed; the average of the analysis values ​​of all compressor mutation outlier parameters at the predetermined adjacent predetermined position one of the mutation outlier parameter to be analyzed at the same sampling point is regarded as the outlier mutation attribute of the parameter of the mutation outlier parameter to be analyzed; the mutation sampling position range of the mutation outlier parameter to be analyzed is obtained; Obtaining the mutation outlier parameter characteristics of the mutation outlier parameter to be analyzed according to the analyzed value of the mutation outlier parameter to be analyzed, the outlier mutation attribute of the parameter and the mutation sampling position range; In Step 3-2, the characteristic operation equation of the mutation outlier parameter is: ； In the equation, Represents the outlier parameter of the target mutation; a mutation outlier parameter characteristic representing the mutation outlier parameter of interest; A predefined position one representing the outlier parameter of the mutation of interest; The mutation sampling position category representing the outlier parameter of the target mutation; Represents the analytical value of the outlier parameter of the target mutation; Outlier mutation attributes representing the parameters of the outlier parameters of the target mutation; The number of sampling positions representing the mutation sampling position range where the target mutation outlier parameter is located.

8. The method for processing frequency converter sudden changes for large-capacity compressed air energy storage according to claim 7, characterized in that: In Step 3-3, the operational equation for the mutation outlier estimation parameter is: ； In the equation, Represents the outlier parameter of the target mutation; A mutation outlier estimation parameter representing the target mutation outlier parameter; Represents the analytical value of the outlier parameter of the target mutation; The baseline control amount representing the outlier parameter of the mutation of interest; a mutation outlier parameter characteristic representing the mutation outlier parameter of interest; The mutation outlier parameter property representing the mutation outlier parameter to be resolved; The parameter outlier mutation amplitude representing the outlier parameter of the target mutation; Represents the use of Z-score method to Perform dedimensionalization.

9. The method for processing frequency converter sudden changes for large-capacity compressed air energy storage according to claim 8, characterized in that: In Step 4, the method for identifying the compressor based on the mutation outlier estimation parameter includes: Treat all compressor sudden outlier parameters whose sudden outlier estimation parameters are higher than the pre-defined critical value as the real sudden outlier parameters; When there is a real sudden outlier parameter, the compressor is considered to be in failure condition, otherwise the compressor is considered to be in reasonable operation condition.

10. A frequency converter sudden change processing device for large-capacity compressed air energy storage, characterized in that: include: The controller is connected to the driving machine of the compressor via the frequency converter; the controller is also connected to the vibration sensor, flow sensor and speed sensor provided on the corresponding compressor, the vibration sensor, flow sensor and speed sensor are used to sample the vibration value of the corresponding compressor, the flow value of the compressor and the speed value of the compressor respectively and transmit them to the controller, the vibration value of the compressor, the flow value of the compressor or the speed value of the compressor is the parameter of the compressor, and the controller is used to determine whether the compressor is in a reasonable operating state or a failure state according to the parameters of the compressor transmitted by the sampling; When the compressor is operating properly, the controller is used to control the inverter to keep the compressor operating properly through the driver; When the compressor fails, the controller is used to control the inverter to stop the compressor via the driver; The modules running on the controller include: A parameter module, which is used to obtain the parameters of all types of compressors at each sampling position of the compressor at each sampling time point in each pre-defined time period; The parsing module is used to randomly select the parameters of a compressor of any type at a random sampling position at a sampling time point as the parameters of the target compressor in each predefined time period; obtain the reference control amount of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor as the parameters of the target compressor; obtain the parsing value of the parameters of the target compressor according to the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling position as the parameters of the target compressor in the current time period and the numerical arrangement attributes of the parameters of the same type of compressor at the same sampling time point as the parameters of the target compressor; An estimation module, which is used to obtain the compressor mutation outlier parameters among the parameters of all compressors according to the analytical values ​​and the reference control quantities of the parameters of the target compressor; and obtain the mutation outlier estimation parameters of each compressor mutation outlier parameter according to the corresponding analytical values ​​and the reference control quantities of each compressor mutation outlier parameter and the numerical arrangement of the compressor mutation outlier parameters; The identification module is used to perform identification on the compressor according to the mutation outlier estimation parameter.

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