A method and system for detecting status of frequency converter in water conservancy project

Abstract

The present application relates to the technical field of inverter anomaly detection, and specifically to a method and system for detecting the state of an inverter for a water conservancy project. The present application aims to improve the accuracy of detecting abnormal data points in the time series data of the inverter performance parameters, with particular attention to the impact of environmental factors on the performance parameters. By synchronously acquiring the time series data of the performance parameters and the time series data of the environmental parameters, the performance parameters are decomposed by multi-layer discrete wavelet transform, and the approximate coefficients are mainly analyzed to gain insight into the operating status of the inverter. After preliminary screening of suspected abnormal data segments, the environmental anomaly index is calculated in combination with the environmental parameters, and the fluctuation degree value of the suspected abnormal data segment is analyzed. Based on the environmental anomaly index and the fluctuation degree value, the initial threshold of each approximate coefficient is adaptively adjusted to obtain an adaptive threshold, and finally the abnormal data is detected based on the adaptive threshold. This method can effectively eliminate abnormal data points caused by environmental factors and accurately identify abnormal data points caused by the abnormality of the inverter itself.

Description

Technical Field

[0001] The present application relates to the technical field of inverter abnormality detection, and specifically to a method and system for detecting the status of an inverter in a water conservancy project. Background Art

[0002] Frequency converters (VFDs) are frequently used electrical control devices in water conservancy projects, serving various functions. In these projects, VFDs primarily regulate the speed of equipment like pumps and fans, and, combined with sensor technology, achieve precise control of parameters like flow, pressure, and liquid level. This not only saves energy and reduces consumption, but also protects equipment through precise control and soft-start. Therefore, stable VFD operation is crucial for ensuring the safety and efficiency of water conservancy project construction. To promptly detect VFD anomalies, VFD operation must be monitored. This allows for detection of abnormalities, alerting personnel to prompt prompt repairs and ensuring construction safety.

[0003] In the existing technology, when detecting abnormalities in the working state of the inverter, wavelet transform is usually performed on the detection data of relevant parameters, and a fixed threshold is set to identify abnormal points caused by abnormal inverter status. However, due to the particularity of the water conservancy project environment, environmental factors such as temperature, humidity, dust, etc. during the operation of the inverter will cause abnormal fluctuations in the performance parameters of the inverter. Therefore, if a fixed threshold is used to detect abnormal points, the abnormal points obtained will not be accurate enough, which may easily lead to misjudgment. Summary of the Invention

[0004] In order to solve the technical problem that the detection data of the inverter during operation in water conservancy projects is affected by environmental factors, resulting in misjudgment of abnormal point detection using fixed thresholds, the purpose of this application is to provide a method and system for detecting the status of the inverter in water conservancy projects. The technical solutions adopted are as follows:

[0005] In a first aspect, the present application proposes a method for detecting the status of a frequency converter in a water conservancy project, the method comprising the following steps:

[0006] Obtaining the time series data of the performance parameters of the inverter within a preset time period and the time series data of the environmental parameters when the inverter is running;

[0007] Based on the discrete wavelet transform, the performance parameter time series data is multi-layered decomposition to obtain approximate coefficients. Based on the difference between the values of each approximate coefficient and the corresponding initial threshold, suspected abnormal data segments are determined in the performance parameter time series data. Data other than the suspected abnormal data segments are treated as pending data. In the environmental parameter time series data, the environmental anomaly index is obtained based on the change in the value corresponding to each suspected abnormal data segment at the time.

[0008] Select any suspected abnormal data segment as the target data segment and determine the corresponding comparison data segment; determine the fluctuation degree value of the target data segment based on the difference in the fluctuation of the values in the target data segment and all the comparison data segments, as well as the change difference between the values of the approximate coefficients;

[0009] Based on the fluctuation value of the target data segment and the corresponding environmental anomaly index, the initial threshold of each approximate coefficient is adjusted to obtain the adaptive threshold of the target data segment under each approximate coefficient; under each approximate coefficient, based on the difference between the corresponding numerical values of all suspected abnormal data segments and the adaptive threshold, as well as the difference between the numerical values of the pending data and the corresponding initial threshold, anomaly detection is performed to obtain abnormal data points.

[0010] Furthermore, the step of determining a suspected abnormal data segment in the performance parameter time series data based on the difference between the values of the respective approximation coefficients and the respective corresponding initial thresholds includes:

[0011] For the approximate coefficient generated by any layer of decomposition, the absolute value of each value in the approximate coefficient is compared with the corresponding initial threshold, and the value with an absolute value greater than the corresponding initial threshold is set to 0 to obtain the adjusted approximate coefficient;

[0012] Reconstruct all adjusted approximate coefficients to obtain first reconstructed time series data, subtract each value in the performance parameter time series data from the value at the corresponding moment in the first reconstructed time series data, and use data points with values other than 0 as target points;

[0013] The target points with continuous time series are grouped into suspected abnormal data segments, and all the suspected abnormal data segments in the performance parameter time series data are obtained.

[0014] Furthermore, the method for obtaining the environmental anomaly indicator includes:

[0015] In the environmental parameter time series data, the corresponding environmental parameter data segment is determined according to the time of each suspected abnormal data segment;

[0016] For any environmental parameter data segment, a first-order difference sequence of values in the environmental parameter data segment is calculated, and the values in the first-order difference sequence are averaged to obtain a first environmental parameter;

[0017] Determining a second environmental parameter of the environmental parameter data segment based on a difference between a numerical mean of the environmental parameter data segment and a numerical mean of the environmental parameter time series data;

[0018] Determining a third environmental parameter of the environmental parameter data segment based on a difference between a numerical mean and a numerical maximum in the environmental parameter data segment;

[0019] The first environmental parameter, the second environmental parameter and the third environmental parameter are integrated to obtain an environmental anomaly index of the environmental parameter data segment, and the first environmental parameter, the second environmental parameter and the third environmental parameter are all positively correlated with the environmental anomaly index.

[0020] Furthermore, the method for obtaining the fluctuation degree value includes:

[0021] Determining data anomaly indicators based on fluctuations in values in the target data segment and each comparison data segment;

[0022] Obtaining a fluctuation factor between the target data segment and each of the comparison data segments based on a difference between the data anomaly index of each comparison data segment and the data anomaly index of the target data segment;

[0023] determining an overall fluctuation index between the target data segment and each of the comparison data segments based on numerical differences between the target data segment and each of the comparison data segments in each of the approximation coefficients;

[0024] The fluctuation factors between the target data segment and all the comparison data segments as well as the overall fluctuation index are weighted and averaged to obtain the fluctuation degree value of the target data segment.

[0025] Furthermore, the method for obtaining the data anomaly indicator includes:

[0026] Select one of the target data segment and the corresponding comparison data segment as the data segment to be tested;

[0027] Based on the values in the data segment to be tested, a maximum variation range of the values in the data segment to be tested is determined; a first-order difference sequence of the values in the data segment to be tested is calculated, and the absolute values of the values in the first-order difference sequence are averaged to obtain a first anomaly factor; a mean of the values in the data segment to be tested is used as a mean eigenvalue, a difference between each value in the data segment to be tested and the mean eigenvalue is used as a difference factor, and the mean of all the difference factors is used as a second anomaly factor;

[0028] A data anomaly index of the data segment to be tested is determined based on the maximum variation range value, the first anomaly factor, and the second anomaly factor of the data segment to be tested, and the maximum variation range value, the first anomaly factor, and the second anomaly factor all change in the same direction as the data anomaly index.

[0029] Furthermore, the method for obtaining the adaptive threshold includes:

[0030] Determine the environmental impact index based on the fluctuation value of the target data segment and the environmental anomaly index;

[0031] When the environmental impact index is greater than or equal to the preset impact threshold, the sum of the environmental impact index and the preset constant is used as the adjustment weight; when the environmental impact index is less than the preset impact threshold, the environmental impact index is used as the adjustment weight;

[0032] The initial thresholds of the respective approximation coefficients are weightedly adjusted based on the adjustment weights to obtain adaptive thresholds of the target data segment under the respective approximation coefficients.

[0033] Furthermore, the method for obtaining the environmental impact index includes:

[0034] The value obtained by normalizing the product of the fluctuation degree value of the target data segment and the corresponding environmental anomaly index is used as the environmental impact index.

[0035] Furthermore, the method for obtaining abnormal data points includes:

[0036] Under each approximate coefficient, the absolute value of each value corresponding to each suspected abnormal data segment is compared with the adaptive threshold, and the value whose absolute value is greater than the adaptive threshold is set to 0 to obtain the adjusted approximate coefficient data segment;

[0037] Under each approximate coefficient, the absolute value of the numerical value of the pending data is compared with the corresponding initial threshold, and the value whose absolute value is greater than the initial threshold is set to 0 to obtain the adjusted pending data;

[0038] Based on the adjusted approximate coefficient data segments under each approximate coefficient and the adjusted pending data, each target approximate coefficient is obtained; all target approximate coefficients are reconstructed to obtain second reconstructed time series data, and in the performance parameter time series data, each numerical value is subtracted from the numerical value at the corresponding moment in the second reconstructed time series data, and data points with values not 0 are regarded as abnormal data points.

[0039] Furthermore, the method for obtaining the initial threshold includes:

[0040] Calculate the standard deviation of all values of each approximation coefficient;

[0041] The mean of all values of each approximation coefficient plus three times the corresponding standard deviation is used as the initial threshold of each approximation coefficient.

[0042] In the second aspect, the present application proposes a water conservancy project inverter status detection system, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it can implement any one of the steps of the water conservancy project inverter status detection method.

[0043] This application has the following beneficial effects:

[0044] The purpose of this application is to analyze the impact of environmental factors on the performance parameters of a frequency converter, thereby setting an adaptive threshold for detecting abnormal data points and avoiding the problem of low accuracy caused by misjudgment. Therefore, the time series data of the frequency converter's performance parameters are first obtained, and the time series data of the environmental parameters for the same period are also obtained. Then, the performance parameter time series data is multi-layered decomposition is performed using a discrete wavelet transform to extract information about different frequency components. Since performance parameter changes caused by environmental changes are usually time-dependent and often abnormal over a period of time, this application mainly analyzes the approximate coefficient to facilitate a deeper understanding of the frequency converter's operating status. By comparing the value of the approximate coefficient with the initial threshold, suspected abnormal data segments can be initially screened, laying the foundation for subsequent detailed abnormality detection. Then, combined with the environmental parameter time series data, an environmental anomaly index is calculated. In the subsequent process, it is possible to determine whether the abnormal data segment is related to environmental factors. Simultaneously, by analyzing the difference in fluctuation between the suspected abnormal data segment and the comparison data segment, as well as the difference in the change in the approximate coefficient value, the fluctuation level of the suspected abnormal data segment can be determined. Finally, based on the environmental anomaly indicators and fluctuation values corresponding to the suspected abnormal data segments, the initial thresholds of each approximation coefficient are adaptively adjusted to obtain the adaptive thresholds for each approximation coefficient for the suspected abnormal data segments. Since these adaptive thresholds analyze the relationship between environmental factors and performance parameter fluctuations, they can more effectively eliminate misjudged abnormal data points during subsequent anomaly detection. This effectively eliminates the situation where performance parameter changes caused by changes in environmental factors. Therefore, anomaly detection based on adaptive thresholds can produce more accurate detection results. In this case, the abnormal data points are caused by the inverter's own abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 A flow chart of a method for detecting the status of a frequency converter in a water conservancy project provided by one embodiment of the present application;

[0047] Figure 2 A schematic diagram of a 5-layer discrete wavelet decomposition provided in one embodiment of the present application;

[0048] Figure 3 A flow chart of a method for obtaining a fluctuation degree value provided in one embodiment of the present application;

[0049] Figure 4 A flow chart of a method for obtaining an adaptive threshold value provided in one embodiment of the present application;

[0050] Figure 5 A system block diagram of a water conservancy project inverter status detection system provided by one embodiment of the present application;

[0051] Figure 6 A schematic diagram of the system structure of a water conservancy project inverter status detection system provided by one embodiment of the present application;

[0052] Figure 7 A schematic diagram of a computer-readable storage medium provided for one embodiment of the present application. DETAILED DESCRIPTION

[0053] To further illustrate the technical means and effectiveness of this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method and system for detecting the status of a frequency converter in a water conservancy project, including its specific implementation, structure, features, and effectiveness. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0054] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0055] The following describes in detail a method and system for detecting the status of a frequency converter in a water conservancy project provided by the present application with reference to the accompanying drawings.

[0056] See also Figure 1 , which shows a method flow chart of a method for detecting the state of a frequency converter in a water conservancy project provided by an embodiment of the present application, the method comprising the following steps:

[0057] Step S1: Acquire the time series data of the performance parameters of the inverter within a preset time period and the time series data of the environmental parameters when the inverter is running.

[0058] Voltage is the most basic performance parameter in the operation of the frequency converter. Monitoring it can timely detect the problem of abnormal fluctuations in the grid voltage. Abnormal fluctuations in voltage will lead to increased equipment loss. Long-term use under unstable voltage conditions will affect the working state of the frequency converter and affect the life of the equipment. One of the main functions of the frequency converter is to change the frequency of the output power supply to meet the needs of different motor controls. Therefore, abnormal fluctuations in the output frequency can also reflect the operating state of the frequency converter. Of course, the performance parameters of the frequency converter also include speed, current, etc. In one embodiment of the present application, voltage is selected as the target performance parameter, and based on the voltage sensor device deployed on the frequency converter, the voltage timing data of the frequency converter within a preset time period is obtained as the performance parameter timing data of the frequency converter. It should be noted that in other embodiments of the present application, the output frequency can also be selected as the target performance parameter, and the frequency timing data can be obtained as the performance parameter timing data of the frequency converter. This is not limited or elaborated here.

[0059] When the inverter itself has an abnormality, such as power supply voltage fluctuation, load transient change, etc., its performance parameters will fluctuate abnormally. However, due to the special environment in which the inverter is located in the water conservancy project, the temperature, humidity and dust concentration in the environment cannot be effectively controlled at a more appropriate level. Therefore, the temperature, humidity and dust concentration of its working environment will change. The change of environmental factors will also affect the working performance of the inverter, which will also cause the performance parameters to fluctuate. Therefore, in the process of using a fixed threshold to detect abnormal data points, it is impossible to effectively distinguish between the two, which will cause misjudgment and it will be impossible to effectively identify whether the inverter has actually had an abnormality. Therefore, in the embodiment of the present application, by analyzing the impact of environmental factors on performance parameters, an adaptive threshold is set to distinguish different situations and improve the accuracy of identifying abnormal data points. Since the inverter contains high-power electronic components and is easily affected by the operating temperature, in the embodiment of the present application, temperature is selected as the target environmental parameter, and the temperature time series data within a preset time period is obtained based on the temperature sensors deployed around the inverter as the environmental parameter time series data. It should be noted that in other embodiments of the present application, humidity or dust concentration can also be selected as the target environmental parameter, and the corresponding environmental parameter time series data can be obtained, which is not limited or elaborated here.

[0060] At this point, the performance parameter time series data and environmental parameter time series data corresponding to the inverter can be obtained. It should be noted that the preset time period is set to one hour. The sampling interval and time period of the performance parameter time series data and the environmental parameter time series data should be consistent. The sampling interval is set to one minute. The specific time period and sampling interval can be adjusted according to the implementation scenario and are not limited or detailed here.

[0061] Step S2: Perform multi-layer decomposition on the performance parameter time series data based on discrete wavelet transform to obtain approximate coefficients; determine suspected abnormal data segments in the performance parameter time series data based on the difference between the values of each approximate coefficient and their corresponding initial thresholds; treat data other than the suspected abnormal data segments as pending data; in the environmental parameter time series data, obtain the environmental anomaly index based on the change in the value corresponding to the moment of each suspected abnormal data segment.

[0062] Since the discrete wavelet transform can decompose time series data into frequency bands of different scales, it can effectively capture multi-scale features and can be used to identify abnormal data. Abnormal data usually has relatively obvious numerical deviation characteristics. Therefore, the performance parameter time series data can be decomposed into multiple layers based on the discrete wavelet transform, and an initial threshold is set to preliminarily determine suspected abnormal data segments in the performance parameter time series data. Given that the purpose of the embodiment of the present application is to analyze the impact of environmental factors on the performance parameters of the inverter, the changes in the environmental parameters corresponding to each suspected abnormal data segment can be analyzed in the environmental parameter time series data, thereby obtaining the environmental anomaly index within the time period, providing a reference for analyzing the impact of environmental factors in the subsequent process.

[0063] Discrete wavelet transform is a method for analyzing signals or data in the time and frequency domain. It can provide the time-frequency localization characteristics of the signal and effectively extract information from the signal. By performing discrete wavelet transform on the performance parameter time series data, the approximate coefficients (approximate components) and detail coefficients (detail components) of each layer can be obtained. See Figure 2 , which shows a schematic diagram of a 5-layer decomposition of discrete wavelet decomposition provided in one embodiment of the present application.

[0064] The approximation coefficient reflects the overall smoothness or approximate representation of the performance parameter time series data, while the detail coefficient reflects the local details of the performance parameter time series data, which can reveal sudden changes in the signal. It should be noted that in one embodiment of the present application, the number of decomposition levels is set to 5. The specific number of decomposition levels can be adjusted according to the implementation scenario and is not limited here. The discrete wavelet transform method is a technical means well known to those skilled in the art and is not described in detail here. The wavelet basis used can be the Haar wavelet. The specific type of wavelet basis can also be adjusted according to the implementation scenario and is not limited here.

[0065] Given the time-dependent impact of environmental factors on performance parameters, it's necessary to analyze performance parameter fluctuations over continuous time periods. Therefore, we identify suspected abnormalities in the performance parameter time series data based on approximate coefficients. For each approximate coefficient, we compare its value with the corresponding initial threshold to determine which values within the approximate coefficient are abnormal. This facilitates identifying suspected abnormal data segments in the performance parameter time series data. These suspected abnormal data segments may be caused by changes in environmental factors or by abnormalities in the inverter itself.

[0066] Preferably, in one embodiment of the present application, the method for obtaining a suspected abnormal data segment includes:

[0067] First, for the approximate coefficients generated by any layer of decomposition, the absolute value of each value in the approximate coefficient is compared with the corresponding initial threshold, and the values with absolute values greater than the corresponding initial threshold are set to 0 to obtain the adjusted approximate coefficients.

[0068] Then all the adjusted approximate coefficients are reconstructed to obtain the first reconstructed time series data. In order to identify the data points that are different from the first reconstructed time series data and the performance parameter time series data, each value in the performance parameter time series data can be subtracted from the value at the corresponding moment in the first reconstructed time series data, and the data point with a value not equal to 0 is used as the target point, which is the preliminary abnormal data point.

[0069] Since continuous abnormal data points are more likely to be caused by environmental factors and are also targets that should be analyzed in depth, the target points that are continuous in time series are grouped into suspected abnormal data segments. Based on the above process, all suspected abnormal data segments in the performance parameter time series data can be obtained.

[0070] In other embodiments of the present application, the method for determining the target point may also be: in the performance parameter time series data, the difference between each value and the value at the corresponding moment in the first reconstructed time series data is calculated, and the data point whose difference value is greater than the preset deviation threshold is used as the target point, wherein the preset deviation threshold is set to 3, and the specific value can be adjusted according to the implementation scenario, which is not limited here.

[0071] It should be noted that the initial threshold for each approximation coefficient is set by calculating the standard deviation of all values in the approximation coefficient and then adding three times the corresponding standard deviation to the mean of all values in the approximation coefficient. The initial threshold setting can be adjusted based on experience or implementation scenarios and is not limited here.

[0072] At this point, all suspected abnormal data segments in the performance parameter time series data can be obtained, and the data except the suspected abnormal data segments are used as pending data, which includes normal data points and abnormal data points caused by the inverter's own abnormality.

[0073] For suspected abnormal data segments, since they may be affected by environmental factors, it is necessary to analyze the environmental parameter time series data to determine the changes in environmental parameters in the time period corresponding to the suspected abnormal data segment and obtain the environmental anomaly index.

[0074] Preferably, in one embodiment of the present application, the method for obtaining an environmental anomaly indicator includes:

[0075] In the environmental parameter time series data, the corresponding environmental parameter data segment is determined according to the moment of each suspected abnormal data segment. Through this process, the environmental parameters corresponding to the suspected abnormal data segment can be accurately obtained. For example, the suspected abnormal data segment corresponds to moment 3-moment 7, then in the environmental parameter time series data, the environmental parameter data segment corresponding to the suspected abnormal data segment is also moment 3-moment 7.

[0076] During the operation of the inverter, the temperature will gradually increase, and in a high temperature environment, the parameters of the motor windings will change, thereby affecting the calculation accuracy of the vector transformation, affecting the maximum output current, etc., and also affecting the stability of the inverter performance parameters. Therefore, in one embodiment of the present application, the increase in data values in the environmental parameter data segment is mainly analyzed.

[0077] The analysis of the increase in the values in the environmental parameter data segment can be characterized based on the difference between the environmental parameters at adjacent moments. Therefore, for any environmental parameter data segment, the first-order difference sequence of the values in the environmental parameter data segment is calculated, and the values in the first-order difference sequence are averaged to obtain the first environmental parameter. The formula model of the first environmental parameter includes:

[0078]

[0079] in, Indicates the The first environmental parameter of an environmental parameter data segment; Indicates the The total number of values in the first-order difference sequence corresponding to each environmental parameter data segment; Indicates the The first order difference sequence corresponding to the environmental parameter data segment numerical values.

[0080] In the formula model of the first environmental parameter, It represents the difference between the value of the latter moment and the value of the previous moment in the environmental parameter data segment. If the environmental parameter increases with time, then The value of will be a positive value, and the greater the degree of increase in the environmental parameter, the greater the value. Therefore, when the mean of all values in the first-order difference sequence, that is, the first environmental parameter, is larger, it means that the degree of increase in the environmental parameter is greater.

[0081] Similarly, at the overall level, the difference between the overall change trend of the environmental parameter data segment and the overall change trend of the environmental parameter time series data can be analyzed. That is, based on the difference between the numerical mean of the environmental parameter data segment and the numerical mean of the environmental parameter time series data, the second environmental parameter of the environmental parameter data segment can be determined. The formula model of the second environmental parameter includes:

[0082]

[0083] in, Indicates the The second environmental parameter of the environmental parameter data segment; Indicates the The numerical mean of the environmental parameter data segment; Represents the numerical mean of the environmental parameter time series data; Indicates the preset first parameter.

[0084] In the formula model of the second environmental parameter, by comparing the numerical mean of the environmental parameter data segment with the numerical mean of the environmental parameter time series data, the larger the ratio, the higher the value in the environmental parameter data segment is compared with the entire environmental parameter time series data. This also means that the greater the degree of increase in the environmental parameter, the higher the value of the preset first parameter. The function of is to prevent the denominator from being 0. Here, the value can be 0.001. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0085] In order to capture the difference between the overall change of the environmental parameter data segment and those representing extreme or abnormal environmental parameters, the third environmental parameter of the environmental parameter data segment can be determined based on the difference between the mean value and the maximum value in the environmental parameter data segment. The formula model of the third environmental parameter includes:

[0086]

[0087] in, Indicates the The third environmental parameter of the environmental parameter data segment; Indicates the The numerical mean of the environmental parameter data segment; Indicates the maximum value of the environmental parameter time series data; Indicates the preset second parameter.

[0088] In the formula model of the third environmental parameter, the average level of the values in the environmental parameter data segment is used as the numerator, and the maximum value in the entire environmental parameter time series data is used as the denominator. At this time, the denominator represents the maximum value of the environmental parameter. If the numerator is larger, the third environmental parameter is larger, which means that the environmental parameter in the environmental parameter data segment is at a higher level, which means that the degree of increase in the environmental parameter is greater. The second parameter is preset. The function of is to prevent the denominator from being 0. Here, the value can be 0.001. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0089] In other embodiments of the present application, the difference between the maximum value of the environmental parameter time series data and the numerical mean of the environmental parameter data segment can also be negatively correlated and normalized to form the third environmental parameter. The specific formula model is as follows:

[0090]

[0091] in, Indicates the The third environmental parameter of the environmental parameter data segment; Indicates the The numerical mean of the environmental parameter data segment; Indicates the maximum value of the environmental parameter time series data; Expressed as a natural constant An exponential function with base .

[0092] In this formula model, when the difference between the maximum value of the environmental parameter time series data and the mean value of the environmental parameter data segment The smaller the time is, the closer the average level of the environmental parameter data segment is to the maximum value of the entire environmental parameter time series data, indicating that the environmental parameters in the environmental parameter data segment are at a higher level, indicating that the degree of increase in the environmental parameters is greater, so the difference is negatively correlated and normalized to achieve logical relationship correction, thereby obtaining the third environmental parameter.

[0093] Finally, in order to provide a comprehensive, multi-angle environmental anomaly index, the first environmental parameter, the second environmental parameter, and the third environmental parameter can be integrated to obtain the environmental anomaly index of the environmental parameter data segment, and the first environmental parameter, the second environmental parameter, and the third environmental parameter are all positively correlated with the environmental anomaly index. The formula model of the environmental anomaly index includes:

[0094]

[0095] in, Indicates the Environmental anomaly indicators for each environmental parameter data segment; Indicates the The first environmental parameter of an environmental parameter data segment; Indicates the The second environmental parameter of the environmental parameter data segment; Indicates the The third environmental parameter of the environmental parameter data segment; Represents the normalization function.

[0096] In the formula model of the environmental anomaly index, the larger the first environmental parameter, the larger the second environmental parameter, and the larger the third environmental parameter, the more it indicates that the environmental parameters are on an upward trend, which will have a greater impact on the performance parameters of the inverter. Therefore, the product of the three indicators is normalized and the value is used as the environmental anomaly index of the environmental parameter data segment.

[0097] In other embodiments of the present application, the normalized value of the sum of the first environmental parameter, the second environmental parameter, and the third environmental parameter may be used as the environmental anomaly indicator of the environmental parameter data segment.

[0098] Step S3: Select any suspected abnormal data segment as the target data segment and determine the corresponding comparison data segment; determine the fluctuation degree value of the target data segment based on the difference in the fluctuation of the values in the target data segment and all the comparison data segments, as well as the change difference between the values of the approximate coefficients.

[0099] Before determining the impact of environmental factors on a suspected abnormal data segment in the inverter's performance parameters, the stability of the suspected abnormal data segment itself should be analyzed. To more accurately measure the stability of the suspected abnormal data segment, in this embodiment of the application, a comparison analysis is performed between the suspected abnormal data segment and the comparison data segment. Furthermore, since data fluctuations are also reflected in the approximate coefficients of each layer, to obtain a more accurate value for the degree of fluctuation, two main aspects are analyzed: the difference in the fluctuation of the values in the suspected abnormal data segment and the comparison data segment, and the difference in the values of the approximate coefficients. The degree of fluctuation of the suspected abnormal data segment is determined by analyzing the difference in the values of the approximate coefficients.

[0100] The method for determining the comparison data segment is as follows: for any suspected abnormal data segment, a preset number of data points before the time sequence of the suspected abnormal data segment are used to form a comparison data segment. Similarly, a preset number of data points after the time sequence of the suspected abnormal data segment are used to form a comparison data segment. If the suspected abnormal data segment is at the beginning of the performance parameter time series data, then only the preset number of data segments after the time sequence of the suspected abnormal data segment are obtained to form a comparison data segment. If the suspected abnormal data segment is at the end of the performance parameter time series data, then only the preset number of data segments before the time sequence of the suspected abnormal data segment are obtained to form a comparison data segment. The preset number is set as the segment length of the suspected abnormal data segment. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0101] For the sake of convenience in explanation and illustration, a suspected abnormal data segment is selected as a target data segment. Preferably, in one embodiment of the present application, a method for obtaining a fluctuation degree value includes:

[0102] See also Figure 3 , which shows a flow chart of a method for obtaining a fluctuation degree value provided by an embodiment of the present application, the method comprising the following steps:

[0103] Step S301: Calculate data anomaly indicators based on fluctuations in values in the target data segment and each corresponding comparison data segment.

[0104] Among the target data segment and the corresponding comparison data segment, any one is selected as the data segment to be tested.

[0105] Given that the fluctuation of numerical values can be characterized by the fluctuation range of numerical values, the changing trend of numerical values and the difference between numerical values, the maximum variation range value of the numerical values in the data segment to be tested is determined based on the numerical values in the data segment to be tested. The maximum variation range value helps to understand the fluctuation amplitude of the numerical values in the data segment to be tested.

[0106] In addition, the first-order difference sequence of the values in the data segment to be tested is calculated, and the absolute values of the values in the first-order difference sequence are averaged to obtain the first abnormality factor. The averaging of the first-order difference sequence can reflect the changes between the values in the data segment to be tested, which helps to capture small changes in the values and thus characterize the fluctuations of the data segment to be tested.

[0107] At the same time, the mean of the values in the data segment to be tested is taken as the mean eigenvalue, the difference between each value in the data segment to be tested and the mean eigenvalue is taken as the difference factor, and the mean of all difference factors is taken as the second abnormal factor. The second abnormal factor reflects the degree of dispersion of the values in the data segment to be tested relative to the mean, which helps to identify the discrete characteristics of the values in the data segment to be tested.

[0108] Finally, based on the maximum variation range value, the first anomaly factor, and the second anomaly factor of the data segment to be tested, the data anomaly index of the data segment to be tested is determined, and the maximum variation range value, the first anomaly factor, and the second anomaly factor all change in the same direction as the data anomaly index. The formula model of the data anomaly index can be, for example:

[0109]

[0110] in, Indicates the data segment to be tested Data anomaly indicators; Indicates the data segment to be tested The extreme difference between the elements; Indicates the data segment to be tested The mean of the absolute values of the elements in the corresponding first-order difference sequence; Indicates the data segment to be tested The total number of values in ; Indicates the data segment to be tested Middle numerical values; Indicates the data segment to be tested The numerical mean of , also known as the mean eigenvalue.

[0111] In the formula model of data anomaly index, the range of the values in the data segment to be tested represents the instability of the values in the data segment to be tested to a certain extent. Then, the absolute values of all the values in the first-order difference sequence are calculated and averaged to obtain the first anomaly factor. , which is used to illustrate the degree of fluctuation between the data values in the data segment to be tested. Then the mean of all the values in the data segment to be tested is calculated as the mean eigenvalue This value represents the average level of the values in the data segment to be tested, and the difference between each value in the data segment to be tested and the mean characteristic value is calculated and used as the difference factor The difference factor reflects the deviation between each value in the data segment to be tested and the overall average level. The larger the value, the greater the degree of deviation, that is, the stronger the fluctuation. The second abnormal factor The larger the value is, the higher the abnormality of the data segment to be tested is.

[0112] In other embodiments of the present application, the sum of the maximum variation range value, the first abnormality factor, and the second abnormality factor may also be used as the data abnormality indicator of the data segment to be tested.

[0113] Step S302: Obtain the fluctuation degree value of the target data segment according to the difference in data anomaly indicators between the target data segment and each corresponding comparison data segment, and the change difference between the values of the approximation coefficients.

[0114] According to the difference between the data anomaly index of each comparison data segment and the data anomaly index of the target data segment, the fluctuation factor between the target data segment and each comparison data segment is obtained. The fluctuation factor can be used to characterize the abnormal fluctuation characteristics between the target data segment and its corresponding comparison data segment.

[0115] Then, based on the overall numerical difference between the target data segment and each comparison data segment in each approximation coefficient, the overall fluctuation index between the target data segment and each comparison data segment is determined. Since the approximation coefficient reflects the characteristics of the data at different scales, the overall fluctuation index can comprehensively evaluate the fluctuation difference between the target data segment and the comparison data segment at different levels, thereby more accurately determining the fluctuation of the target data segment. The formula model of the overall fluctuation index includes:

[0116]

[0117] in, Indicates the target data segment The corresponding The overall volatility index between the comparison data segments; Represents the total number of approximate coefficients, that is, the number of decomposition levels; Indicates in Among the approximate coefficients, the target data segment The corresponding numerical mean; Indicates in Among the approximate coefficients, the target data segment No. The numerical mean corresponding to the comparison data segments.

[0118] In the formula model of the overall volatility index, since the target data segment is a suspected abnormal data segment that has been screened out, if there is a relatively obvious difference in the numerical values of the target data segment and its corresponding comparison data segment at different decomposition levels, then it can be considered that the volatility of the target data segment is relatively large, that is, the instability is relatively high.

[0119] Finally, the fluctuation factors between the target data segment and all the comparison data segments and the overall fluctuation index are weighted and averaged to obtain the fluctuation degree value of the target data segment. The formula model of the fluctuation degree value can be specifically as follows:

[0120]

[0121] in, Indicates the target data segment The fluctuation value of Indicates the target data segment The number of comparison data segments; Indicates the target data segment The corresponding The overall volatility index between the comparison data segments; Indicates the target data segment Data anomaly indicators; Indicates the target data segment The corresponding The data anomaly indicators of the comparison data segments.

[0122] In the formula model of the fluctuation degree value, the larger the overall fluctuation index between the target data segment and the comparison data segment, the greater the fluctuation degree of the target data segment and the higher the instability of the data.

[0123] Step S4: Based on the fluctuation value of the target data segment and the corresponding environmental anomaly index, the initial threshold of each approximation coefficient is adjusted to obtain the adaptive threshold of the target data segment under each approximation coefficient; under each approximation coefficient, based on the difference between the numerical value corresponding to all suspected abnormal data segments and the adaptive threshold, as well as the difference between the numerical value of the pending data and the corresponding initial threshold, anomaly detection is performed to obtain abnormal data points.

[0124] In step S2, the environmental anomaly index is analyzed and obtained, and in step S3, the fluctuation degree value of the suspected abnormal data segment is analyzed and obtained. Therefore, based on these two indicators, the influence of environmental factors on the suspected abnormal data segment can be determined, thereby adjusting the initial threshold of each approximate coefficient to obtain an adaptive threshold, which is used to eliminate the influence of environmental factors and facilitate the use of the adaptive threshold in the subsequent process to more accurately identify abnormal data points caused by inverter abnormalities.

[0125] Preferably, in one embodiment of the present application, the method for obtaining the adaptive threshold includes:

[0126] See also Figure 4 , which shows a flow chart of a method for obtaining an adaptive threshold provided by an embodiment of the present application, the method comprising the following steps:

[0127] Step S401: Determine an environmental impact index based on the fluctuation degree value of the target data segment and the corresponding environmental anomaly index.

[0128] The product of the fluctuation degree value of the target data segment and the environmental anomaly index is normalized to obtain the value as the environmental impact index. The formula model of the environmental impact index can be, for example:

[0129]

[0130] in, Indicates the target data segment Environmental impact indicators; Indicates the target data segment Environmental anomaly indicators of the corresponding environmental parameter data segment; Indicates the target data segment The fluctuation value of Represents the normalization function.

[0131] In the formula model for the environmental impact index, environmental factors can affect the inverter's performance parameters, causing abnormal fluctuations. Abnormalities in the inverter itself can also cause abnormal fluctuations in performance parameters. Therefore, to reduce misjudgments and ensure that the resulting abnormal data points are caused by inverter abnormalities rather than environmental factors, the environmental impact index is calculated based on the target data segment's fluctuation value and the environmental anomaly index. A larger environmental anomaly index indicates changes in environmental factors during that period. If, correspondingly, the target data segment's instability during that period increases, then the instability of the performance parameters during that period is caused by changes in environmental factors. Conversely, a smaller environmental anomaly index and a larger fluctuation value indicate that the instability of the performance parameters during that period is caused by inverter abnormalities, and the likelihood of being affected by environmental factors is lower. Based on the above logic, the environmental anomaly index is multiplied by the fluctuation degree value and the product is normalized to obtain the environmental impact index of the target data segment. The larger the value, the more it indicates that the instability of the target data segment is affected by environmental factors. In the subsequent process, we should focus on eliminating this part of the influence to obtain more accurate abnormal data points.

[0132] Step S402: adjusting the initial thresholds of the respective approximation coefficients based on the environmental impact index of the target data segment to obtain adaptive thresholds of the target data segment under the respective approximation coefficients.

[0133] When the environmental impact index is greater than or equal to the preset impact threshold, the sum of the environmental impact index and the preset constant is used as the adjustment weight; when the environmental impact index is less than the preset impact threshold, the environmental impact index is used as the adjustment weight;

[0134] Based on the adjustment weight, the initial threshold of each approximation coefficient is weightedly adjusted to obtain the adaptive threshold of the target data segment under each approximation coefficient. The formula model of the adaptive threshold includes:

[0135]

[0136] in, Indicates the target data segment In the The corresponding adaptive threshold in the approximation coefficient; Indicates the The initial threshold corresponding to the approximate coefficient; Indicates the target data segment Environmental impact indicators; Indicates a preset constant; Indicates the preset impact threshold.

[0137] In the formula model of the adaptive threshold, based on the analysis in step S401, it can be seen that the larger the environmental impact index, the more likely it is that the fluctuations in the inverter's performance parameters during that period are caused by changes in environmental factors. The method for determining suspected abnormal data segments in step S2 is to set the values greater than the initial threshold in each approximate coefficient to 0, and then reconstruct and compare them with the performance parameter time series data. Therefore, in order to eliminate misjudgments caused by environmental factors, the initial threshold corresponding to the period with a large environmental impact index should be increased. Conversely, when the environmental impact index is small, it means that the fluctuations in the performance parameters at this time are caused by the inverter's own abnormality. In order to more accurately identify these abnormal data points, the initial threshold corresponding to that period can be reduced. Therefore, based on the aforementioned logic, the formula for the above-mentioned adaptive threshold is constructed.

[0138] It should be noted that the preset impact threshold is set to 0.5, and the specific value can be adjusted according to the implementation scenario and is not limited here. In order to prevent the adjustment degree from being too large when the initial threshold is increased, in one embodiment of the present application, the preset constant Set to 1. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0139] In other embodiments of the present application, for suspected abnormal data segments whose environmental impact indicators are less than the preset environmental threshold, the initial thresholds of such suspected abnormal data segments under different approximation coefficients can also be directly used as adaptive thresholds. The reason is that under the initial thresholds, the data points in such suspected abnormal data segments can already be detected, so they do not need to be adjusted.

[0140] Based on the above process, the adaptive threshold of each suspected abnormal data segment under different approximation coefficients can be obtained. Then, under each approximation coefficient, outlier detection can be performed based on the difference between the corresponding values of all suspected abnormal data segments and their respective adaptive thresholds, as well as the difference between the values of the pending data and the corresponding initial thresholds, thereby obtaining abnormal data points.

[0141] Preferably, in one embodiment of the present application, the method for obtaining abnormal data points includes:

[0142] Under each approximate coefficient, the absolute value of each numerical value corresponding to each suspected abnormal data segment is compared with the adaptive threshold, and the numerical value whose absolute value is greater than the adaptive threshold is set to 0 to obtain the adjusted approximate coefficient data segment.

[0143] Under each approximate coefficient, the absolute value of the numerical value of the pending data is compared with the corresponding initial threshold, and the value whose absolute value is greater than the initial threshold is set to 0 to obtain the adjusted pending data.

[0144] Based on the adjusted approximate coefficient data segments and the adjusted pending data under each approximate coefficient, each target approximate coefficient is obtained; at this time, each target approximate coefficient is composed of the adjusted approximate coefficient data segments and the adjusted pending data, so all target approximate coefficients are reconstructed to obtain the second reconstructed time series data. In the performance parameter time series data, each numerical value is subtracted from the numerical value at the corresponding moment in the second reconstructed time series data, and the data points with values not 0 are regarded as abnormal data points.

[0145] In other embodiments of the present application, the method for determining abnormal data points may also be: in the performance parameter time series data, after subtracting each numerical value from the numerical value at the corresponding moment in the second reconstructed time series data, the data point whose value is greater than the preset deviation threshold is regarded as an abnormal data point, wherein the preset deviation threshold is set to 3, and the specific numerical value can be adjusted according to the implementation scenario and is not limited here.

[0146] It should be noted that the methods for obtaining approximate coefficients based on discrete wavelet transform and reconstructing the target approximate coefficients are both well-known technical means to those skilled in the art and are not described in detail here. At the same time, to facilitate calculations, all indicator data involved in the calculations in the embodiments of this application are preprocessed to eliminate dimensional effects. The specific means for eliminating dimensional effects are well-known technical means to those skilled in the art and are not limited here.

[0147] In summary, the purpose of the embodiments of the present application is to analyze the impact of environmental factors on the performance parameters of the frequency converter, thereby setting an adaptive threshold for detecting abnormal data points and avoiding the problem of low accuracy of abnormal data point detection caused by misjudgment. Therefore, the performance parameter time series data of the frequency converter is first obtained, and the environmental parameter time series data of the same period is obtained at the same time. Then, the performance parameter time series data is multi-layered decomposition is performed using discrete wavelet transform to extract information of different frequency components. Since performance changes caused by environmental changes are usually time-persistent and often abnormal within a period of time, the present application mainly analyzes the approximate coefficient to facilitate a deeper understanding of the operating status of the frequency converter. By comparing the difference between the value of the approximate coefficient and the initial threshold, suspected abnormal data segments can be preliminarily screened out, laying the foundation for subsequent detailed abnormality detection. Then, combined with the environmental parameter time series data, the environmental anomaly index is calculated. In the subsequent process, it can be determined whether the abnormal data segment is related to environmental factors. At the same time, by analyzing the difference in the fluctuation of the suspected abnormal data segment and the comparison data segment, as well as the difference in the change of the approximate coefficient, the fluctuation degree value of the suspected abnormal data segment can be determined. Finally, based on the environmental anomaly indicators and fluctuation values corresponding to the suspected abnormal data segments, the initial thresholds of each approximation coefficient are adaptively adjusted to obtain the adaptive thresholds for each approximation coefficient for the suspected abnormal data segments. Because these adaptive thresholds analyze the relationship between environmental factors and performance parameter fluctuations, they can more effectively eliminate misjudged abnormal data points. This effectively eliminates the situation where performance parameter changes caused by changes in environmental factors. Therefore, anomaly detection based on adaptive thresholds can produce more accurate detection results. In this case, the abnormal data points are caused by the inverter itself.

[0148] This embodiment also provides a water conservancy project inverter status detection system, which includes a processor, a memory and a computer program, wherein the memory is used to store the corresponding computer program, and the processor is used to run the corresponding computer program. When the computer program runs on the processor, it can implement any one of the steps of a water conservancy project inverter status detection method.

[0149] See also Figure 5 , which shows a system block diagram of a water conservancy project inverter status detection system provided by an embodiment of the present application, including: a data acquisition module 501: used to implement step S1 in the above method, an environmental impact factor analysis module 502: used to implement step S2 in the above method, an inverter performance parameter fluctuation degree analysis module 503: used to implement step S3 in the above method, and an abnormal data point detection module 504: used to implement step S4 in the above method.

[0150] See also Figure 6, which shows a system structure diagram of a water conservancy project inverter status detection system provided by an embodiment of the present application, including a processor 600, a memory 601, a bus 602 and a communication interface 603, wherein the processor 600, the communication interface 603 and the memory 601 are connected via the bus 602; wherein the memory 601 may include a high-speed random access memory, the bus 602 may be an ISA bus, a PCI bus or an EISA bus, etc., and the processor 600 may be an integrated circuit chip with signal processing capabilities.

[0151] The present application also provides a computer-readable storage medium corresponding to the method provided in the above embodiment. Figure 7 , the storage medium shown is a CD, on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, it will execute the method provided by any of the aforementioned embodiments.

[0152] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), read-only memory (ROM), and other optical and magnetic storage media, which are not listed here one by one.

[0153] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

[0154] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for detecting the state of a frequency converter in a water conservancy project, characterized in that: The method comprises: Obtaining the time series data of the performance parameters of the inverter within a preset time period and the time series data of the environmental parameters when the inverter is running; Based on the discrete wavelet transform, the performance parameter time series data is decomposed into multiple layers to obtain approximate coefficients. Based on the difference between the values of each approximate coefficient and the corresponding initial threshold, suspected abnormal data segments are determined in the performance parameter time series data. Data other than the suspected abnormal data segments are treated as pending data. In the environmental parameter time series data, the environmental anomaly index is obtained based on the change in the value corresponding to each suspected abnormal data segment at the time. Select any suspected abnormal data segment as the target data segment and determine the corresponding comparison data segment; determine the fluctuation degree value of the target data segment based on the difference in the fluctuation of the values in the target data segment and all the comparison data segments, as well as the change difference between the values of the approximate coefficients; Based on the fluctuation value of the target data segment and the corresponding environmental anomaly index, the initial threshold of each approximation coefficient is adjusted to obtain the adaptive threshold of the target data segment under each approximation coefficient; under each approximation coefficient, anomaly detection is performed based on the difference between the corresponding values of all suspected abnormal data segments and the adaptive threshold, as well as the difference between the values of the pending data and the corresponding initial threshold, to obtain abnormal data points; The method for obtaining the fluctuation degree value includes: Determining data anomaly indicators based on fluctuations in values in the target data segment and each comparison data segment; Obtaining a fluctuation factor between the target data segment and each of the comparison data segments based on a difference between the data anomaly index of each comparison data segment and the data anomaly index of the target data segment; determining an overall fluctuation index between the target data segment and each of the comparison data segments based on numerical differences between the target data segment and each of the comparison data segments in each of the approximation coefficients; The fluctuation factors between the target data segment and all the comparison data segments as well as the overall fluctuation index are weighted and averaged to obtain the fluctuation degree value of the target data segment.

2. A method for detecting the state of a frequency converter in a water conservancy project according to claim 1, characterized in that: The step of determining a suspected abnormal data segment in the performance parameter time series data based on the difference between the values of the respective approximation coefficients and the respective corresponding initial thresholds includes: For the approximate coefficient generated by any layer of decomposition, the absolute value of each value in the approximate coefficient is compared with the corresponding initial threshold, and the value with an absolute value greater than the corresponding initial threshold is set to 0 to obtain the adjusted approximate coefficient; Reconstruct all adjusted approximate coefficients to obtain first reconstructed time series data, subtract each value in the performance parameter time series data from the value at the corresponding moment in the first reconstructed time series data, and use data points with values other than 0 as target points; The target points with continuous time series are grouped into suspected abnormal data segments, and all the suspected abnormal data segments in the performance parameter time series data are obtained.

3. A method for detecting the state of a frequency converter in a water conservancy project according to claim 1, characterized in that: The method for obtaining the environmental anomaly indicator includes: In the environmental parameter time series data, the corresponding environmental parameter data segment is determined according to the time of each suspected abnormal data segment; For any environmental parameter data segment, a first-order difference sequence of values in the environmental parameter data segment is calculated, and the values in the first-order difference sequence are averaged to obtain a first environmental parameter; Determining a second environmental parameter of the environmental parameter data segment based on a difference between a numerical mean of the environmental parameter data segment and a numerical mean of the environmental parameter time series data; Determining a third environmental parameter of the environmental parameter data segment based on a difference between a numerical mean and a numerical maximum in the environmental parameter data segment; The first environmental parameter, the second environmental parameter and the third environmental parameter are integrated to obtain an environmental anomaly index of the environmental parameter data segment, and the first environmental parameter, the second environmental parameter and the third environmental parameter are all positively correlated with the environmental anomaly index.

4. A method for detecting the state of a frequency converter in a water conservancy project according to claim 1, characterized in that: The method for obtaining the data anomaly indicator includes: Select one of the target data segment and the corresponding comparison data segment as the data segment to be tested; Based on the values in the data segment to be tested, a maximum variation range of the values in the data segment to be tested is determined; a first-order difference sequence of the values in the data segment to be tested is calculated, and the absolute values of the values in the first-order difference sequence are averaged to obtain a first anomaly factor; a mean of the values in the data segment to be tested is used as a mean eigenvalue, a difference between each value in the data segment to be tested and the mean eigenvalue is used as a difference factor, and the mean of all the difference factors is used as a second anomaly factor; A data anomaly index of the data segment to be tested is determined based on the maximum variation range value, the first anomaly factor, and the second anomaly factor of the data segment to be tested, and the maximum variation range value, the first anomaly factor, and the second anomaly factor all change in the same direction as the data anomaly index.

5. A method for detecting the state of a frequency converter in a water conservancy project according to claim 1, characterized in that: The method for obtaining the adaptive threshold includes: Determine the environmental impact index based on the fluctuation value of the target data segment and the environmental anomaly index; When the environmental impact index is greater than or equal to the preset impact threshold, the sum of the environmental impact index and the preset constant is used as the adjustment weight; when the environmental impact index is less than the preset impact threshold, the environmental impact index is used as the adjustment weight; The initial thresholds of the respective approximation coefficients are weightedly adjusted based on the adjustment weights to obtain adaptive thresholds of the target data segment under the respective approximation coefficients.

6. A method for detecting the state of a frequency converter in a water conservancy project according to claim 5, characterized in that: The method for obtaining the environmental impact index includes: The value obtained by normalizing the product of the fluctuation degree value of the target data segment and the corresponding environmental anomaly index is used as the environmental impact index.

7. A method for detecting the state of a frequency converter in a water conservancy project according to claim 1, characterized in that: The method for obtaining abnormal data points includes: Under each approximate coefficient, the absolute value of each value corresponding to each suspected abnormal data segment is compared with the adaptive threshold, and the value whose absolute value is greater than the adaptive threshold is set to 0 to obtain the adjusted approximate coefficient data segment; Under each approximate coefficient, the absolute value of the numerical value of the pending data is compared with the corresponding initial threshold, and the value whose absolute value is greater than the initial threshold is set to 0 to obtain the adjusted pending data; Based on the adjusted approximate coefficient data segments under each approximate coefficient and the adjusted pending data, each target approximate coefficient is obtained; all target approximate coefficients are reconstructed to obtain second reconstructed time series data, and in the performance parameter time series data, each numerical value is subtracted from the numerical value at the corresponding moment in the second reconstructed time series data, and data points with values not 0 are regarded as abnormal data points.

8. A method for detecting the state of a frequency converter in a water conservancy project according to claim 1, characterized in that: The method for obtaining the initial threshold includes: Calculate the standard deviation of all values of each approximation coefficient; The mean of all values of each approximation coefficient plus three times the corresponding standard deviation is used as the initial threshold of each approximation coefficient.

9. A water conservancy project inverter status detection system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for detecting the state of a frequency converter for a water conservancy project as described in any one of claims 1 to 8 are implemented.

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