An intelligent device for performance detection of a frequency converter

Abstract

The application relates to the technical field of frequency converters, in particular to an intelligent device for performance detection of a frequency converter. The device is provided with a pretreatment module, an abnormal influence module, a performance analysis module, a detector and a controller. A characteristic superposition coefficient is calculated based on determined environmental gradient influence characteristic values and flow disturbance characteristic values, so as to confirm a plurality of to-be-detected areas. Tags are arranged for the to-be-detected areas. In response to the arranged strong area influence tags, a mutation influence characteristic value is determined. A performance abnormality characteristic value is calculated in combination with the characteristic superposition coefficient. The detector is called to locate a detection area. The detection area is detected. The controller is used to control the start detection time and the detection frequency of the detector. The application performs regional analysis on the frequency converter. The influence of environmental data, vibration data and instantaneous voltage on the detection accuracy of the frequency converter is comprehensively analyzed. The areas needing detection are determined in a targeted manner. The detection efficiency and the detection accuracy are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of frequency converters, and particularly relates to an intelligent device for performance detection of a frequency converter. BACKGROUND

[0002] A frequency converter is a power control device for controlling an alternating current motor by changing the frequency of the motor's power supply. As the role of frequency converters in industrial automation and energy saving becomes increasingly prominent, reliable detection of their performance becomes crucial. However, traditional detection methods highly depend on connecting actual motors and mechanical loads for testing, leading to complex test environment setup, high cost, low efficiency, safety risks, and difficulty in accurately controlling and repeating load characteristics, making dynamic performance evaluation difficult, functional testing (such as protection mechanisms) incomplete, and automation level low, which severely restricts the flexibility of on-site testing. Moreover, environmental factors can also affect the performance of frequency converters, leading to errors in performance detection. Therefore, there is an urgent need to develop a new type of dedicated detection device that is safe, efficient, and accurate, and integrates automated measurement and analysis functions, to overcome the fundamental limitations of traditional methods.

[0003] Chinese Patent Publication No. CN119492947A discloses a frequency converter performance detection device, which includes a detection box with a heat-conducting sheet inside, the heat-conducting sheet forms a sealed cold-hot channel at the edge of the inner cavity of the detection box; a damping shock absorber is installed at the bottom of the detection box; a vibration generator is provided at the lower part of the detection box to provide a vibration source for the detection box; a humidifier is connected to the inner cavity of the detection box; and a cold-hot generator is connected to the cold-hot channel. The invention sets up a vibration generator, a humidifier, and a cold-hot generator, and adapts to the detection box, achieving multifunctional simulation of vibration, high humidity, high temperature, and low temperature environments in the external environment, realizing stability detection of frequency converters in extreme environments, and setting a cold-hot channel in a circulating flow state at the inner edge of the detection box, which covers the detection box with cold-hot air flowing on multiple sides, improving the uniformity of cold-hot air distribution.

[0004] Chinese patent publication No. CN112130010A discloses a static frequency converter SFC performance detection system under large current working condition, comprising: SFC device and its control system, isolation transformer, power distribution cabinet incoming line circuit breaker QF1, isolation transformer incoming line circuit breaker QF2, QF3, voltage transformer PT, current transformer CT1, CT2; the isolation transformer incoming line circuit breaker QF2, QF3 has a soft starting device; the SFC device comprises a rectifier NB, an electric reactor Ld and an inverter MB. The above detection system solves the defects that the SFC can only perform small capacity synchronous motor small current performance verification before the static frequency converter is put into operation on the engineering site, and cannot perform large current operation performance verification; only the existing conventional detection system isolation transformer, power distribution system and corresponding control strategy are used, without configuring large capacity synchronous motor, excitation system, the running, control and protection characteristics of SFC under large current working condition can be verified.

[0005] However, the prior art still has the following problems,

[0006] In actual situations, when detecting the frequency converter, full domain detection or discrete single point detection is mostly performed. Full domain detection causes excessive consumption of resources due to redundant data in low risk areas. Single point detection causes coverage blind area due to lack of spatial gradient perception, which increases the false judgment rate of multi-physical field coupling faults. The fault spreads to the monitoring point to trigger an alarm, which causes detection errors and reduces the detection accuracy. SUMMARY

[0007] Therefore, the present application provides an intelligent device for performance detection of a frequency converter to solve the problem that in actual situations, when detecting the frequency converter, full domain detection or discrete single point detection is mostly performed. Full domain detection causes excessive consumption of resources due to redundant data in low risk areas. Single point detection causes coverage blind area due to lack of spatial gradient perception, which increases the false judgment rate of multi-physical field coupling faults. The fault spreads to the monitoring point to trigger an alarm, which causes detection errors and reduces the detection accuracy.

[0008] To achieve the above purpose, the present application provides an intelligent device for performance detection of a frequency converter, which comprises:

[0009] A preprocessing module is used to obtain environmental data of a frequency converter operating space, analyze the environmental data to determine an environmental gradient influence representation value, obtain vibration data of the frequency converter and instantaneous voltage data of key nodes of the frequency converter;

[0010] An abnormal area module is connected with the preprocessing module and is used to analyze the vibration data to determine a flow disturbance representation value, calculate a feature superposition coefficient in combination with the environmental gradient influence representation value, confirm a plurality of to-be-detected areas, and set labels for each to-be-detected area;

[0011] a performance analysis module, connected with the abnormal area module, for analyzing the transient voltage data to determine a mutation influence characteristic value, calculating a performance abnormality characteristic value based on the mutation influence characteristic value and a characteristic superposition coefficient, and judging whether to call a detector;

[0012] a detector, connected with the performance analysis module, for locating a detection area and detecting the detection area;

[0013] a controller, connected with the performance analysis module and the detector, for controlling a start detection time and a detection frequency of the detector based on the performance abnormality characteristic value;

[0014] wherein the space environment data includes an environment temperature and an environment humidity, and the key nodes include an input side power grid voltage mutation node and an output side motor terminal voltage mutation node.

[0015] Further, the preprocessing module determines an environment gradient influence characteristic value, including,

[0016] constructing a three-dimensional coordinate system and placing the environment data in the three-dimensional coordinate system;

[0017] determining a ratio of the environment temperature to a reference environment temperature as a temperature influence factor;

[0018] determining a ratio of the environment humidity to a reference environment humidity as a humidity influence factor;

[0019] determining a mean value of a sum of the temperature influence factor and the humidity influence factor as the environment gradient influence characteristic value.

[0020] Further, the abnormal area module determines a flow disturbance characteristic value, including,

[0021] determining a vibration frequency of the frequency converter and a corresponding amplitude variance;

[0022] determining a ratio of the vibration frequency to a reference vibration frequency as a frequency influence factor;

[0023] determining a ratio of the amplitude variance to a reference amplitude variance as an amplitude influence factor;

[0024] determining a mean value of a sum of the frequency influence factor and the amplitude influence factor as the flow disturbance characteristic value.

[0025] Further, the abnormal area module calculates a characteristic superposition coefficient, including,

[0026] determining a ratio of the flow disturbance characteristic value to a reference flow disturbance characteristic value as a first influence factor;

[0027] determining a ratio of the ambient gradient influence characteristic value to a reference ambient gradient influence characteristic value as a second influence factor;

[0028] determining a weighted sum of the first influence factor and the second influence factor as a feature superposition coefficient.

[0029] Further, the abnormal region module confirms a plurality of to-be-detected regions, sets a label for each of the to-be-detected regions, and includes,

[0030] determining the feature superposition coefficient in the three-dimensional coordinate system;

[0031] If the feature superposition coefficient is greater than or equal to a feature superposition coefficient threshold value, the feature superposition coefficients corresponding to consecutive values greater than or equal to the feature superposition coefficient threshold value are divided into the same region, and a strong region influence label is set for the region;

[0032] If the feature superposition coefficient is less than the feature superposition coefficient threshold value, the feature superposition coefficients corresponding to consecutive values less than the feature superposition coefficient threshold value are divided into the same region, and a weak region influence label is set for the region.

[0033] Further, the performance analysis module analyzes the transient voltage data to determine a mutation influence characteristic value, including,

[0034] calculating a difference between the input side power grid voltage mutation node and the output side motor terminal voltage mutation node;

[0035] determining a ratio of the difference to a reference difference as a mutation influence characteristic value.

[0036] Further, the performance analysis module calculates a performance anomaly characteristic value, including,

[0037] determining a ratio of the mutation influence characteristic value to a reference mutation influence characteristic value as a mutation influence factor;

[0038] determining a ratio of the feature superposition coefficient to a reference feature superposition coefficient as a feature influence factor;

[0039] determining a weighted sum of the mutation influence factor and the feature influence factor as a performance anomaly characteristic value.

[0040] Further, the performance analysis module determines whether to call a detector, wherein,

[0041] If the performance anomaly characteristic value is greater than or equal to a performance anomaly characteristic value threshold value, the detector is called;

[0042] If the performance anomaly representation value is less than the performance anomaly representation value threshold, the detector is not called, and the detection frequency is adjusted.

[0043] Further, the detection region corresponds to a region in which the performance anomaly representation value is greater than or equal to the performance anomaly representation value threshold.

[0044] Further, the controller controls the start detection time and the detection frequency of the detector based on the performance anomaly representation value, including,

[0045] determining the time at which the detector is called as the start detection time;

[0046] determining that the detection frequency is positively correlated with the performance anomaly representation value.

[0047] Compared with the prior art, the present application sets a pretreatment module, an anomaly influence module, a performance analysis module, a detector and a controller, calculates a feature superposition coefficient based on the determined environmental gradient influence representation value and the flow disturbance representation value, confirms a plurality of detection regions, sets a label for each of the detection regions, determines a mutation influence representation value in response to the set strong region influence label, calculates a performance anomaly representation value in combination with the feature superposition coefficient, determines to call the detector, locates the detection region, detects the detection region, and controls the start detection time and the detection frequency of the detector. The present application analyzes the frequency converter in a regional manner, comprehensively analyzes the influence of environmental data, vibration data and instantaneous voltage on the detection accuracy of the frequency converter, and determines the region to be detected in a targeted manner, thereby improving the detection efficiency and the detection accuracy.

[0048] In particular, the environmental gradient influence representation value is determined by analyzing the environmental temperature and the environmental humidity, which provides a data basis for subsequent calculation of the feature superposition coefficient and regional division. In actual situations, different operating units generate different degrees of heat during the operation of the frequency converter, resulting in non-uniform temperature rise of each operating unit of the frequency converter, which damages the performance of the frequency converter. At the same time, the humidity distribution of the operating space is also uneven. High humidity environment (> 60% RH) is prone to condensation short circuit, insulation deterioration and metal corrosion. Low humidity environment (< 40% RH) is prone to static electricity accumulation and acceleration of organic material embrittlement. Further, the two uneven distributions cause the internal failure risk of the frequency converter to present a spatial gradient feature. At this time, if the past global detection method is used, data redundancy will be caused due to excessive sampling of low-risk regions, resulting in excessive consumption of calculation, storage and communication resources. If the discrete single-point detection method is used, it is difficult to effectively cover the high-risk region due to insufficient spatial resolution, resulting in omission of key failure signs. Based on this, the present application considers dividing the operating space of the frequency converter into regions, analyzes the environmental data as a division condition, to improve the performance detection efficiency and accuracy of the frequency converter.

[0049] Especially, by analyzing the vibration data of the frequency converter, the characteristic superposition coefficient is calculated in combination with the environmental gradient influence characteristic value to divide the operation space into regions and set labels. In the operation process of the frequency converter, each operation unit will generate vibration spectra with different characteristics due to the effects of electromagnetic force, switching noise, mechanical resonance, and cooling fan excitation. The difference in vibration frequency affects the aerodynamic characteristics of the micro-environment inside and around the frequency converter, leading to uneven air flow field distribution in the operation space, forming a low flow rate environment in some areas, affecting the normal use of the frequency converter. Further, the vibration data in this area will further strengthen the deviation of temperature and humidity, increase the damage probability of the frequency converter, and affect the performance detection of the frequency converter. Based on this, the vibration data is also used as a regional division condition, and is coupled with the environmental data for analysis to calculate the characteristic superposition coefficient to divide the operation space into regions and set labels for the detection area, providing a data basis for subsequent detection, and improving the performance detection efficiency and accuracy of the frequency converter.

[0050] Especially, in response to the setting result of the strong regional influence label, the transient voltage data of the frequency converter is analyzed to determine the mutation influence characteristic value, and the performance abnormality characteristic value is calculated in combination with the characteristic superposition coefficient to call the detector. In actual situations, the performance detection of the frequency converter is mostly periodic point detection, or responsive detection based on the results of global detection and discrete single-point detection. It can be understood that both periodic detection and responsive detection have a time lag, and discrete sampling cannot continuously capture the damage process, leading to the continuous accumulation of hidden failures before the threshold alarm. More seriously, the conservative setting of the alarm threshold makes the system cross the damage inflection point when triggered, and the multi-physical field coupling effect further causes irreversible performance degradation. Further, the prior art detects the entire frequency converter to determine the abnormal position, which is low in efficiency. Based on this, the present application considers analyzing multi-source data only in the region where the strong regional influence label is set, and comprehensively analyzing the characteristic superposition coefficient and the influence of the transient voltage of the frequency converter on the performance of the region to quickly locate the abnormal region of the frequency converter, thereby improving the performance detection efficiency and accuracy of the frequency converter.

[0051] Especially, the present application sets a controller to regulate the detector according to the analysis result of the performance analysis module, so as to keep the detection of the frequency converter timely responsive and improve the performance detection efficiency and accuracy of the frequency converter. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The structural schematic diagram of the intelligent device for performance detection of the frequency converter of the present application embodiment;

[0053] Figure 2 The logic block diagram of the present application embodiment for confirming a plurality of detection areas and setting labels for each of the detection areas;

[0054] Figure 3 Logic block diagram for judging whether to call the detector for the embodiment of the application. DETAILED DESCRIPTION

[0055] In order to make the objects and advantages of the present application clearer, the present application will be further described in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0056] It should be noted that in the description of the present application, unless otherwise explicitly specified and limited, the term “connection” should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or internal communication of two elements. For those skilled in the art, the specific meaning of the above-mentioned term in the present application can be understood according to the specific circumstances.

[0057] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not used to limit the protection scope of the present application.

[0058] Please refer to Figure 1 , Figure 1 The structural schematic diagram of the intelligent device for performance detection of the frequency converter for the embodiment of the application, the intelligent device for performance detection of the frequency converter of the present application comprises:

[0059] A preprocessing module is used to acquire environmental data of a running space of the frequency converter, analyze the environmental data to determine an environmental gradient influence representation value, acquire vibration data of the frequency converter and instantaneous voltage data of key nodes of the frequency converter;

[0060] An abnormal area module is connected with the preprocessing module and is used to analyze the vibration data to determine a flow disturbance representation value, calculate a feature superposition coefficient in combination with the environmental gradient influence representation value, confirm a plurality of to-be-detected areas, and set labels for each of the to-be-detected areas;

[0061] A performance analysis module is connected with the abnormal area module, responds to a set strong area influence label, analyzes the instantaneous voltage data to determine a mutation influence representation value, calculates a performance anomaly representation value based on the mutation influence representation value and the feature superposition coefficient, and judges whether to call a detector;

[0062] The detector is connected with the performance analysis module and is used to locate a detection area and detect the detection area;

[0063] a controller connected with the performance analysis module and the detector, for controlling the start detection time and detection frequency of the detector based on the performance anomaly characterization value;

[0064] The space environment data includes environment temperature and environment humidity, and the key nodes include input side power grid voltage mutation nodes and output side motor end voltage mutation nodes.

[0065] Specifically, the acquisition method of the environment temperature is not limited, for example, an infrared camera can be arranged inside the running space to detect the temperature distribution, of course, other devices can also be used, as long as the temperature data of the running space can be detected.

[0066] Specifically, the acquisition method of the environment humidity is not limited, for example, a network based on point type sensor can be arranged to scan the environment humidity, of course, other methods can also be used by those skilled in the art, as long as the humidity data of the running space can be detected.

[0067] Specifically, the acquisition method of the vibration data is not limited, for example, the infrared camera can be used to collect the vibration data, of course, other devices can also be used, as long as the vibration data of the frequency converter can be detected.

[0068] Specifically, the acquisition method of the instantaneous voltage data is not limited, for example, an optical fiber isolated voltage sensor can be arranged at the key node, of course, other devices can also be used, as long as the instantaneous voltage data of the frequency converter can be detected.

[0069] Specifically, the pre-processing module determines the environment gradient influence characterization value, including,

[0070] for constructing a three-dimensional coordinate system, and placing the space environment data in the three-dimensional coordinate system;

[0071] for determining the ratio of the environment temperature to the reference environment temperature as a temperature influence factor;

[0072] for determining the ratio of the environment humidity to the reference environment humidity as a humidity influence factor;

[0073] for determining the average of the sum of the temperature influence factor and the humidity influence factor as the environment gradient influence characterization value.

[0074] Specifically, in the implementation, the frequency converter running space is regarded as a cube, the three axes of the three-dimensional coordinate system are three edges of the cube, and the origin of the three-dimensional coordinate system is the intersection of the three edges of the cube.

[0075] Specifically, the reference ambient temperature is pre-calculated, and a plurality of temperature values of the frequency converter in the normal operation process are obtained in advance, and the average of the plurality of temperature values is determined as the reference ambient temperature.

[0076] Specifically, the reference ambient humidity is pre-calculated, and a plurality of humidity values of the frequency converter in the normal operation process are obtained in advance, and the average of the plurality of humidity values is determined as the reference ambient humidity.

[0077] Specifically, the environmental gradient influence representation value is determined by analyzing the ambient temperature and the ambient humidity, which provides a data basis for subsequent calculation of the feature superposition coefficient and region division. In actual situations, different operating units will generate different degrees of heat during the operation of the frequency converter, resulting in abnormal non-uniform temperature rise of each operating unit of the frequency converter, which damages the performance of the frequency converter. At the same time, the humidity distribution of the operating space is also uneven. High humidity environment (> 60% RH) is easy to cause condensation short circuit, insulation deterioration and metal corrosion; low humidity environment (< 40% RH) is easy to accumulate static electricity and accelerate the embrittlement of organic materials. Further, the two uneven distributions will cause the internal failure risk of the frequency converter to present a spatial gradient characteristic. At this time, if the past global detection method is used, data redundancy will be caused due to over-sampling of low-risk areas, resulting in excessive consumption of calculation, storage and communication resources. If the discrete single-point detection method is used, it is difficult to effectively cover the high-risk area due to insufficient spatial resolution, resulting in the omission of key failure signs. Based on this, the present application considers dividing the operating space of the frequency converter, and analyzing the environmental data as a division condition to improve the performance detection efficiency and accuracy of the frequency converter.

[0078] The abnormal region module determines a flow disturbance representation value, comprising,

[0079] to determine the vibration frequency of the frequency converter and the corresponding amplitude variance;

[0080] to determine the ratio of the vibration frequency to the reference vibration frequency as a frequency influence factor;

[0081] to determine the ratio of the amplitude variance to the reference amplitude variance as an amplitude influence factor;

[0082] to determine the average of the sum of the frequency influence factor and the amplitude influence factor as a flow disturbance representation value.

[0083] Specifically, the reference vibration frequency is pre-calculated, and a plurality of vibration frequencies of the frequency converter in the normal operation process are obtained in advance, and the average of the plurality of vibration frequencies is determined as the reference vibration frequency.

[0084] Specifically, the reference amplitude variance is pre-calculated, and a plurality of amplitude variances of the frequency converter in the normal operation process are obtained in advance, and the average of the amplitude variances is determined as the reference amplitude variance.

[0085] The abnormal area module calculates a feature superposition coefficient, including,

[0086] The ratio of the flow disturbance characteristic value to the reference flow disturbance characteristic value is determined as the first influence factor.

[0087] The ratio of the environmental gradient influence characteristic value to the reference environmental gradient influence characteristic value is determined as the second influence factor.

[0088] The weighted sum of the first influence factor and the second influence factor is determined as the feature superposition coefficient.

[0089] Specifically, the reference flow disturbance characteristic value is pre-calculated, and a plurality of flow disturbance characteristic values of the frequency converter in the normal operation process are obtained in advance, and the average of the flow disturbance characteristic values is determined as the reference flow disturbance characteristic value.

[0090] Specifically, the reference environmental gradient influence characteristic value is pre-calculated, and a plurality of environmental gradient influence characteristic values of the frequency converter in the normal operation process are obtained in advance, and the average of the environmental gradient influence characteristic values is determined as the reference environmental gradient influence characteristic value.

[0091] Specifically, the sum of the weight coefficients of the first influence factor and the second influence factor is 1, and when the weight is allocated, it is considered that the environmental factor has a greater influence on the performance of the frequency converter than the vibration, so the second influence factor is allocated more, and the weight coefficient of the first influence factor is set to 0.4, and the weight coefficient of the second influence factor is set to 0.6.

[0092] Specifically, by analyzing the vibration data of the frequency converter, the feature superposition coefficient is calculated in combination with the environmental gradient influence characteristic value to divide the operation space into regions and set labels, in the operation process of the frequency converter, each operation unit will generate vibration spectra with different characteristics due to the effects of electromagnetic force, switching noise, mechanical resonance and cooling fan excitation, the difference of these vibration frequencies affects the aerodynamic characteristics of the internal and peripheral micro-environment of the frequency converter, resulting in uneven air flow field distribution of the operation space, forming a low flow rate environment in some areas, affecting the normal use of the frequency converter, further, the vibration data of the region will strengthen the deviation of temperature and humidity, increase the damage probability of the frequency converter, affect the performance detection of the frequency converter, based on this, the vibration data is also used as a region division condition, and is coupled with the environmental data for analysis, to calculate the feature superposition coefficient, to divide the operation space into regions, set the label of the to-be-detected region, provide data basis for subsequent detection, improve the performance detection efficiency and accuracy of the frequency converter.

[0093] Referring to Figure 2 , Figure 2 The abnormal region module confirms a plurality of to-be-detected regions, and sets a label for each of the to-be-detected regions. Specifically, the abnormal region module confirms a plurality of to-be-detected regions, and sets a label for each of the to-be-detected regions, including,

[0094] to superimpose the feature superposition coefficient in the three-dimensional coordinate system;

[0095] If the feature superposition coefficient is greater than or equal to a feature superposition coefficient threshold value, the feature superposition coefficients corresponding to consecutive values greater than or equal to the feature superposition coefficient threshold value are divided into the same region, and a strong region influence label is set for the region;

[0096] If the feature superposition coefficient is less than the feature superposition coefficient threshold value, the feature superposition coefficients corresponding to consecutive values less than the feature superposition coefficient threshold value are divided into the same region, and a weak region influence label is set for the region.

[0097] Specifically, the feature superposition coefficient threshold value represents a boundary of a region in which the frequency converter is affected by multiple factors, and is pre-calculated data. The feature superposition coefficient threshold value is determined by multiplying the mean value of a plurality of feature superposition coefficients of the frequency converter in an abnormal running process by an influence coefficient. In actual situations, to improve the calculation accuracy, the influence coefficient is 0.9.

[0098] It can be understood that in the three-dimensional coordinate system, there are a plurality of regions with strong region influence labels and a plurality of regions with weak region influence labels, and the regions with strong region influence labels can be connected to the regions with weak region influence labels.

[0099] Specifically, the performance analysis module analyzes the instantaneous voltage data to determine a mutation influence representation value, including,

[0100] to calculate the difference between the input side power grid voltage mutation node and the output side motor terminal voltage mutation node;

[0101] to determine the ratio of the difference to a reference difference value as a mutation influence representation value.

[0102] Specifically, the reference difference value is pre-calculated, and a plurality of difference values of the frequency converter in a normal running process are pre-acquired, and the average value of each difference value is determined as the reference difference value.

[0103] Specifically, the performance analysis module calculates a performance anomaly representation value, including,

[0104] to determine the ratio of the mutation influence representation value to a reference mutation influence representation value as a mutation influence factor;

[0105] determining a ratio of the feature superposition coefficient to a reference feature superposition coefficient as a feature influence factor;

[0106] determining a weighted sum of the mutation influence factor and the feature influence factor as a performance anomaly representation value.

[0107] Specifically, the reference mutation influence representation value is pre-calculated, a plurality of mutation influence representation values of the frequency converter in the normal operation process are obtained in advance, and an average value of the plurality of mutation influence representation values is determined as the reference mutation influence representation value.

[0108] Specifically, the reference feature superposition coefficient is a feature superposition coefficient corresponding to the reference environmental gradient influence representation value and the reference flow interference representation value.

[0109] Specifically, the sum of the weight coefficients of the mutation influence factor and the feature influence factor is 1. When the weight coefficients are allocated, it is considered that the pressure difference can more quickly cause the performance of the frequency converter to decrease, so the weight coefficient of the mutation influence factor is set to 0.6, and the weight coefficient of the feature influence factor is set to 0.4.

[0110] Specifically, in response to the setting result of the strong area influence label, the instantaneous voltage data of the frequency converter is analyzed to determine the mutation influence representation value, the performance anomaly representation value is calculated in combination with the feature superposition coefficient, the detector is called, and in actual situations, the performance detection of the frequency converter is mostly periodic point detection, or responsive detection according to the results of global detection and discrete single-point detection. It can be understood that both the periodic detection and the responsive detection have time lag, the discrete sampling cannot continuously capture the damage process, leading to the continuous accumulation of implicit failure before the threshold alarm, and more seriously, the conservative setting of the alarm threshold makes the system cross the damage inflection point when triggered, and the multi-physical field coupling effect further causes irreversible performance degradation. Further, the prior art detects the frequency converter as a whole, determines the abnormal position in the detection, and has low efficiency. Based on this, the present application considers that only the region where the strong area influence label is set is analyzed for multi-source data, the influence of the feature superposition coefficient and the instantaneous voltage of the frequency converter on the performance of the region is comprehensively analyzed, the abnormal region of the frequency converter is quickly located, and the performance detection efficiency and accuracy of the frequency converter are improved.

[0111] Please refer to Figure 3 , Figure 3 is a logic block diagram for judging whether to call the detector of the embodiment of the application. Specifically, the performance analysis module judges whether to call the detector, wherein,

[0112] if the performance anomaly representation value is greater than or equal to a performance anomaly representation value threshold, the detector is called;

[0113] If the performance abnormality representation value is less than the performance abnormality representation value threshold, the detector is not called, and the detection frequency is adjusted.

[0114] Specifically, the performance abnormality representation value threshold represents a boundary of the abnormality of the frequency converter, and is pre-calculated data.

[0115] Specifically, the detection region is a region corresponding to the performance abnormality representation value greater than or equal to the performance abnormality representation value threshold.

[0116] Specifically, the controller controls the start detection time and the detection frequency of the detector based on the performance abnormality representation value, including,

[0117] The time for calling the detector is determined as the start detection time.

[0118] The detection frequency is positively correlated with the performance abnormality representation value.

[0119] It can be understood that there is a benchmark detection frequency, which is an engineering set frequency, and can be obtained in the related file of the installed frequency converter.

[0120] Specifically,

[0121] If the performance abnormality representation value is less than the performance abnormality representation value threshold and greater than 0.7 times the performance abnormality representation value threshold, the detection frequency is determined as 2 times the benchmark detection frequency.

[0122] If the performance abnormality representation value is less than or equal to 0.7 times the performance abnormality representation value threshold and greater than or equal to 0.5 times the performance abnormality representation value threshold, the detection frequency is determined as 1.5 times the benchmark detection frequency.

[0123] If the performance abnormality representation value is less than 0.5 times the performance abnormality representation value threshold, the benchmark detection frequency is kept unchanged.

[0124] It can be understood that the detection frequency is a positive integer, and there is a decimal number in the calculation, which is rounded up.

[0125] Specifically, the controller is arranged to regulate the detector according to the analysis result of the performance analysis module, so that the detection of the frequency converter is timely responded, and the performance detection efficiency and accuracy of the frequency converter are improved.

[0126] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will all fall within the protection scope of the present application.

[0127] The above only describes the preferred embodiments of the present application and is not intended to limit the present application; the present application can have various changes and variations for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent device for performance detection of a frequency converter, characterized in that, The application relates to a variable frequency drive (VFD) performance anomaly detection system, comprising: a preprocessing module configured to acquire environmental data of a VFD operating space, analyze the environmental data to determine an environmental gradient influence characterization value, acquire vibration data of the VFD, and acquire instantaneous voltage data of key nodes of the VFD; an abnormal area module connected to the preprocessing module, configured to analyze the vibration data to determine a flow disturbance characterization value, calculate a feature superposition coefficient based on the environmental gradient influence characterization value, and identify a plurality of detection areas and set a label for each of the detection areas; a performance analysis module connected to the abnormal area module, configured to analyze the instantaneous voltage data to determine a mutation influence characterization value in response to a strong area influence label being set, calculate a performance anomaly characterization value based on the mutation influence characterization value and the feature superposition coefficient, and determine whether to call a detector; a detector connected to the performance analysis module, configured to locate a detection area and detect the detection area; a controller connected to the performance analysis module and the detector, configured to control a start detection time and a detection frequency of the detector based on the performance anomaly characterization value; wherein the space environmental data comprises environmental temperature and environmental humidity, and the key nodes comprise an input side power grid voltage mutation node and an output side motor terminal voltage mutation node.

2. The intelligent device for performance detection of a frequency converter according to claim 1, characterized in that, The preprocessing module determines the environmental gradient influence characterization value by: constructing a three-dimensional coordinate system and placing the environmental data in the three-dimensional coordinate system; determining a temperature influence factor as a ratio of the environmental temperature to a reference environmental temperature; determining a humidity influence factor as a ratio of the environmental humidity to a reference environmental humidity; and determining an environmental gradient influence characterization value as an average of a sum of the temperature influence factor and the humidity influence factor.

3. The smart device for performance detection of a frequency converter according to claim 1, characterized in that, The abnormal area module determines the flow disturbance characterization value by: determining a vibration frequency of the VFD and a corresponding amplitude variance; determining a frequency influence factor as a ratio of the vibration frequency to a reference vibration frequency; determining an amplitude influence factor as a ratio of the amplitude variance to a reference amplitude variance; and determining a flow disturbance characterization value as an average of a sum of the frequency influence factor and the amplitude influence factor.

4. The smart device for performance detection of a frequency converter according to claim 1, characterized in that, The abnormal area module calculates the feature superposition coefficient by: determining a first influence factor as a ratio of the flow disturbance characterization value to a reference flow disturbance characterization value; determining a second influence factor as a ratio of the environmental gradient influence characterization value to a reference environmental gradient influence characterization value; and determining a feature superposition coefficient as a weighted sum of the first influence factor and the second influence factor.

5. The smart device for performance detection of a frequency converter according to claim 2, characterized in that, The abnormal area module identifies a plurality of detection areas and sets a label for each of the detection areas by: placing the feature superposition coefficient in the three-dimensional coordinate system; if the feature superposition coefficient is greater than or equal to a feature superposition coefficient threshold value, dividing the feature superposition coefficients corresponding to the continuous feature superposition coefficient threshold values into the same area, and setting a strong area influence label for the area. If the feature superposition coefficient is less than a feature superposition coefficient threshold, then the continuous feature superposition coefficients less than the feature superposition coefficient threshold are divided into the same region, and a weak region influence label is set for the region.

6. The smart device for performance detection of a frequency converter according to claim 1, characterized in that, The performance analysis module analyzes the transient voltage data to determine a mutation influence representation value, including, calculating the difference between the input side grid voltage mutation node and the output side motor terminal voltage mutation node; determining the ratio of the difference to a reference difference as the mutation influence representation value.

7. The smart device for performance detection of a frequency converter according to claim 1, characterized in that, The performance analysis module calculates a performance anomaly representation value, including, determining the ratio of the mutation influence representation value to a reference mutation influence representation value as a mutation influence factor; determining the ratio of the feature superposition coefficient to a reference feature superposition coefficient as a feature influence factor; determining the weighted sum of the mutation influence factor and the feature influence factor as the performance anomaly representation value.

8. The smart device for performance detection of a frequency converter according to claim 1, characterized in that, The performance analysis module determines whether to call a detector, including, if the performance anomaly representation value is greater than or equal to a performance anomaly representation value threshold, then calling the detector; if the performance anomaly representation value is less than the performance anomaly representation value threshold, then not calling the detector and adjusting the detection frequency.

9. The smart device for performance detection of a frequency converter according to claim 1, characterized in that, The detection region is the region corresponding to the performance anomaly representation value greater than or equal to the performance anomaly representation value threshold.

10. The smart device for performance detection of a frequency converter according to claim 1, characterized in that, The controller controls the start detection time and the detection frequency of the detector based on the performance anomaly representation value, including, determining the time of calling the detector as the start detection time; determining the detection frequency to be positively correlated with the performance anomaly representation value.

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