Intelligent fault detection system for high-voltage solid-state soft starting device

Abstract

The invention relates to the technical field of fault detection, in particular to an intelligent fault detection system for a high-voltage solid-state soft starting device. The first early warning module is used for determining whether the high-voltage solid-state soft starting device has a fault or not according to the fault coefficient; the fault analysis module is used for carrying out primary judgment based on the three-phase current balance degree and carrying out secondary judgment according to the maximum data correlation coefficient or the thyristor abnormality comparison degree according to the thyristor fault data correlation degree under the primary abnormal condition so as to determine whether the thyristor is abnormal or not; the abnormity identification module is used for determining an abnormal thyristor group according to the current difference degree and the temperature abnormal value; and the second early warning module is used for determining the abnormal degree of the thyristors according to the sudden damage degree or the aging degree of the thyristors and determining whether to carry out fault early warning or not based on the maximum abnormal degree of the thyristors. According to the invention, the timeliness of fault early warning can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault detection, in particular to a high-voltage solid-state soft starter fault intelligent detection system. BACKGROUND

[0002] Since the high-voltage solid-state soft starter works in a complex environment of high voltage, large current and strong electromagnetic interference for a long time, its core components are prone to failure due to aging, excessive temperature rise, overvoltage and overcurrent impact, etc. The traditional fault detection method relies on manual inspection or simple sensor alarm, which has the problems of poor real-time performance, insufficient accuracy and low intelligence, etc. Therefore, how to perform fault detection to improve the timeliness of fault warning is a problem to be solved by those skilled in the art.

[0003] Chinese patent publication No. CN117741519A discloses a motor soft starter detection method, processor and motor soft starter, comprising: obtaining the connection detection result of the voltage detection cable between the line between the power input terminal and the silicon controlled assembly and the voltage detection circuit, the connection detection result of the trigger cable between the silicon controlled assembly and the trigger circuit, and the connection detection result of the current sampling cable between the current sensor and the current sampling circuit. Without relying on manual investigation, the position of the cable connection anomaly can be quickly located. However, the above-mentioned scheme has the following problems: it cannot capture subtle abnormalities and trend changes during device operation, resulting in poor timeliness of fault warning. SUMMARY

[0004] Therefore, the present application provides a high-voltage solid-state soft starter fault intelligent detection system to overcome the problem that the prior art cannot capture subtle abnormalities and trend changes during device operation, resulting in poor timeliness of fault warning.

[0005] To achieve the above-mentioned purpose, the present application provides a high-voltage solid-state soft starter fault intelligent detection system, comprising: a data acquisition module for acquiring marker data and monitoring data of the high-voltage solid-state soft starter; a first warning module connected to the data acquisition module for determining a fault coefficient according to the abnormal reference value and fluctuation ratio value of the monitoring data, and determining whether the high-voltage solid-state soft starter has a fault according to the fault coefficient; a fault analysis module connected to the data acquisition module and the first warning module for determining based on the three-phase current balance degree under a preset fault condition, and determining based on the thyristor fault data correlation degree under a primary abnormal condition whether the thyristor has an abnormality by determining the maximum data correlation coefficient or the thyristor abnormality correlation degree for secondary determination; an abnormality identification module connected with the fault analysis module, configured to determine an abnormal thyristor group according to the current difference degree and the temperature abnormal value when the thyristor exists abnormality; a second early warning module connected with the abnormality identification module, configured to determine that the reason for the abnormal thyristor group existing abnormality includes sudden damage and aging according to the temperature difference degree, determine the thyristor abnormality degree according to the sudden damage degree or the thyristor aging degree, and determine whether to perform fault early warning based on the maximum thyristor abnormality degree.

[0006] Further, the first early warning module determines the fault coefficient according to the abnormal reference value and the fluctuation ratio value of the monitoring data. The fault coefficient has a positive correlation with the abnormal reference value and the fluctuation ratio value.

[0007] Further, the first early warning module determines that the high-voltage solid-state soft starter exists fault when the fault coefficient is greater than or equal to a first preset fault coefficient.

[0008] Further, the fault analysis module performs a first determination based on the three-phase current balance degree under a preset fault condition, including: If the three-phase current balance degree is less than a preset three-phase current balance degree, it is determined that the thyristor exists abnormality. If the three-phase current balance degree is greater than or equal to the preset three-phase current balance degree, it is determined that the thyristor does not exist abnormality. The preset fault condition is that the fault coefficient is greater than or equal to a first preset fault coefficient and less than a second preset fault coefficient.

[0009] Further, the fault analysis module performs a second determination according to the maximum data correlation coefficient or the thyristor abnormality comparison degree based on the thyristor fault data correlation degree under a first abnormality condition, including: If the thyristor fault data correlation degree is less than a preset thyristor fault data correlation degree, it is determined whether the thyristor exists abnormality according to the maximum data correlation coefficient. If the thyristor fault data correlation degree is greater than or equal to the preset thyristor fault data correlation degree, it is determined whether the thyristor exists abnormality according to the thyristor abnormality comparison degree. The first abnormality condition is that the thyristor exists abnormality in the first determination.

[0010] Further, the confirmation method of the thyristor abnormality comparison degree includes: If the current rate change degree is greater than or equal to a preset current rate change degree, the thyristor abnormality comparison degree is determined according to the rate change comparison degree. If the current rate change degree is less than the preset current rate change degree, the thyristor abnormality comparison degree is determined according to the current decay degree.

[0011] Further, the abnormal recognition module determines the abnormal thyristor group according to the current difference degree and the temperature abnormal value; The abnormal thyristor group is a thyristor group with a current difference degree greater than a preset current difference degree or a temperature abnormal value greater than a preset temperature abnormal value. The thyristor group is each thyristor in a single phase in a three-phase bridge circuit of the high-voltage solid-state soft start device.

[0012] Further, the second early warning module determines that the abnormal thyristor group has an abnormal reason of sudden damage when the temperature difference degree is greater than or equal to a preset temperature difference degree, and determines the thyristor abnormality degree according to the sudden damage degree. The sudden damage degree is determined according to the voltage drop change degree.

[0013] Further, the second early warning module determines that the abnormal thyristor group has an abnormal reason of aging when the temperature difference degree is less than a preset temperature difference degree, and determines the thyristor abnormality degree according to the thyristor aging degree. The thyristor aging degree is determined based on the current-temperature correlation degree.

[0014] Further, the second early warning module determines whether to perform fault early warning according to the abnormality degree change degree when the maximum thyristor abnormality degree is greater than or equal to a preset maximum thyristor abnormality degree. When determining whether to perform fault early warning according to the abnormality degree change degree, if the abnormality degree change degree is greater than or equal to a preset abnormality degree change degree, fault early warning is performed; if the abnormality degree change degree is less than the preset abnormality degree change degree, fault early warning is not needed.

[0015] Compared with the prior art, the beneficial effects of the present application are that, in the technical scheme of the present application, by monitoring the abnormal reference value and the fluctuation comparison value of the data, the degree of deviation of each monitoring parameter in the high-voltage solid-state soft start device from the normal operating state and the stability of the parameter change can be effectively reflected, and then the fault coefficient is determined according to the abnormal reference value and the fluctuation comparison value of the monitoring data, the abnormality severity of the high-voltage solid-state soft start device as a whole is effectively reflected through the fault coefficient, and then whether the high-voltage solid-state soft start device has a fault is determined according to the fault coefficient, which can realize accurate evaluation of the operating state of the device, avoid misjudgment or missed judgment caused by single parameter abnormality, and simultaneously realize gradient management of the fault through the hierarchical threshold, which not only ensures timely identification of early hidden dangers, but also triggers emergency early warning when the fault risk is high, and finally improves the reliability of device operation and the pertinence of maintenance.

[0016] Further, in the present application, the balance state of the current of each phase in the three-phase bridge circuit is effectively reflected by the three-phase current balance degree, and then a first determination is made according to the three-phase current balance degree, so that it can be preliminarily and quickly identified whether the thyristor is abnormal, and then under the condition of the first abnormality, a second determination is made by the thyristor fault data correlation degree according to the data correlation coefficient or the thyristor abnormality comparison degree, so that the determination basis can be flexibly selected according to the correlation strength of the fault data and the non-fault data, and the accuracy of the thyristor abnormality determination is improved.

[0017] Further, in the present application, the temperature distribution uniformity of each thyristor in the same thyristor group is effectively reflected by the temperature difference degree, and then the thyristor abnormality degree is adaptively determined according to the burst damage degree or the thyristor aging degree according to the temperature difference degree, which is beneficial to accurately distinguish the abnormal reason of the abnormal thyristor group, and assess the severity of the abnormality through the corresponding quantitative indicators burst damage degree or thyristor aging degree.

[0018] Further, in the present application, the most serious abnormality degree in each abnormal thyristor group is effectively reflected by the maximum thyristor abnormality degree, and then it is determined whether to perform fault warning based on the maximum thyristor abnormality degree, so that the most dangerous abnormal state can be focused on preferentially, which is beneficial to avoid the lag of the warning caused by the overall average abnormal level covering the local serious fault, and improve the pertinence and timeliness of the fault warning. BRIEF DESCRIPTION OF DRAWINGS

[0019] Fig. 1 It is a module connection diagram of the fault intelligent detection system of the high-voltage solid-state soft start device of the present application; Fig. 2 It is a flowchart for determining whether the high-voltage solid-state soft start device has a fault according to the fault coefficient of the present application; Fig. 3 It is a flowchart for making a first determination based on the three-phase current balance degree of the present application; Fig. 4 It is a flowchart for determining whether to perform fault warning based on the maximum thyristor abnormality degree of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose and advantages of the present application more clear and explicit, the present application will be further described below in combination with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the protection scope of the present application.

[0021] 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.

[0022] It should be noted that in the description of the present application, the terms indicating the direction or positional relationship of "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0023] Please refer to Figs. 1 to 4 The present application provides a high-voltage solid-state soft start device fault intelligent detection system, comprising: The data acquisition module is used to collect the marker data and the monitoring data of the high-voltage solid-state soft start device; The first early warning module is connected with the data acquisition module, and is used to determine the fault coefficient according to the abnormal reference value and the fluctuation comparison value of the monitoring data, and to determine whether the high-voltage solid-state soft start device has a fault according to the fault coefficient; The fault analysis module is connected with the data acquisition module and the first early warning module, and is used to make a first determination based on the three-phase current balance degree under a preset fault condition, and to make a second determination according to the maximum data correlation coefficient or the thyristor abnormal comparison degree to determine whether there is an abnormal thyristor under a first abnormal condition; The abnormal identification module is connected with the fault analysis module, and is used to determine the abnormal thyristor group according to the current difference degree and the temperature abnormal value when the thyristor has an abnormality; The second early warning module is connected with the abnormal identification module, and is used to determine that the reason for the abnormality of the abnormal thyristor group includes sudden damage and aging according to the temperature difference degree, to determine the thyristor abnormality degree according to the sudden damage degree or the thyristor aging degree, and to determine whether to make a fault early warning based on the maximum thyristor abnormality degree.

[0024] The application scenario of the present application is the fault detection of the high-voltage solid-state soft start device, and the high-voltage solid-state soft start device is a high-voltage solid-state soft starter; In the present application, a plurality of historical records are correspondingly provided, and each historical record records the fluctuation reference value, the abnormal reference value, the fluctuation comparison value and the three-phase current balance degree in the historical process of at least one fault detection of the high-voltage solid-state soft start device, and each historical record corresponds to a qualified mark, which records whether the process of fault detection of the high-voltage solid-state soft start device meets the user's demand, and the qualified mark can be recorded manually.

[0025] The monitoring period is continuously cycled in the application, and the length of the monitoring period can be set according to the user's demand; the greater the user's demand for fault detection accuracy, the shorter the length of the monitoring period; a value of the monitoring period is provided, and the length of the monitoring period is 5 min; The monitoring data includes monitoring parameters corresponding to each time point in the current monitoring period, a setting method of the time point is provided, and the starting time of the current monitoring period is taken as a starting point; an interval point is set every 10 s, and the starting point and each interval point are recorded as time points; the monitoring parameters include but are not limited to output voltage, output current, motor speed and maximum thyristor temperature; the output voltage is the voltage value output by the high-voltage solid-state soft starter to the motor, which is monitored by a voltage sensor; the output current is the current value flowing from the high-voltage solid-state soft starter to the motor, which is monitored by a current sensor; the motor speed refers to the rotating speed of the controlled motor, which is monitored by a speed sensor; the maximum thyristor temperature is the maximum value of the temperature of each thyristor, and the temperature of the thyristor is detected by a temperature sensor; the motor is the motor controlled by the high-voltage solid-state soft starter, and the motor includes a high-voltage asynchronous motor and a synchronous motor, which is easily understood by those skilled in the art and will not be described in detail; The application includes several marker data, and each marker data is the monitoring data of a single monitoring period of the high-voltage solid-state soft starter in the historical operation process; each marker data corresponds to a data label, and the data label is '0' or '1'; when the data label is '0', it indicates that the thyristor of the high-voltage solid-state soft starter is abnormal; when the data label is '1', it indicates that the thyristor of the high-voltage solid-state soft starter is normal; the marker data with the data label '0' is recorded as the thyristor fault data, and the marker data with the data label '1' is recorded as the non-thyristor fault data.

[0026] Specifically, the first early warning module determines the fault coefficient according to the abnormal reference value and the fluctuation ratio value of the monitoring data. The fault coefficient, the abnormal reference value and the fluctuation ratio value are all positively correlated.

[0027] The abnormal reference value is the maximum value of the sub-abnormal degree corresponding to each monitoring parameter; the historical record that does not need to be fault warned and can meet the user's demand is recorded as a reference historical record; for any monitoring parameter, the monitoring parameter is recorded as a target monitoring parameter; the sub-abnormal degree corresponding to the target monitoring parameter = (the abnormal value of the target monitoring parameter in the current monitoring period - the average value of the abnormal values of the target monitoring parameter in the monitoring periods corresponding to each reference historical record) / the average value of the abnormal values of the target monitoring parameter corresponding to each reference historical record; the abnormal value of the target monitoring parameter is the maximum value of the target monitoring parameter corresponding to each time point in a single monitoring period. The fluctuation ratio value is the maximum value in the sub-fluctuation ratio values corresponding to each monitoring parameter, and the sub-fluctuation ratio value corresponding to the target monitoring parameter is (a fluctuation reference value of the target monitoring parameter in a current monitoring period - an average of fluctuation reference values of the target monitoring parameter in monitoring periods corresponding to each reference historical record) / the average of the fluctuation reference values of the target monitoring parameter in the monitoring periods corresponding to each reference historical record; The fluctuation reference value of the target monitoring parameter in a single monitoring period is a standard deviation of values of the target monitoring parameter corresponding to each time point in the monitoring period; The fault coefficient is (an abnormal reference value / an average of abnormal reference values corresponding to each reference historical record) * a first weight coefficient + (a fluctuation ratio value / an average of fluctuation ratio values corresponding to each reference historical record) * a second weight coefficient, the first weight coefficient is 0.6, and the second weight coefficient is 0.4.

[0028] Specifically, the first early warning module determines that the high-voltage solid-state soft starter has a fault when the fault coefficient is greater than or equal to a first preset fault coefficient.

[0029] If the fault coefficient is greater than or equal to the first preset fault coefficient and less than a second preset fault coefficient, it is determined whether the thyristor has an abnormality. If the fault coefficient is greater than or equal to the second preset fault coefficient, fault early warning is performed. If the fault coefficient is less than the first preset fault coefficient, it is determined that the high-voltage solid-state soft starter does not have a fault. The first preset fault coefficient is less than the second preset fault coefficient, and the values of the first preset fault coefficient and the second preset fault coefficient can be determined by a user according to an actual application scenario. The greater the user's demand for early warning accuracy, the smaller the values of the first preset fault coefficient and the second preset fault coefficient. A value of the first preset fault coefficient and the second preset fault coefficient is provided, the first preset fault coefficient is 0.2, and the second preset fault coefficient is 0.4.

[0030] It can be understood that when the fault coefficient is greater than or equal to the first preset fault coefficient and less than the second preset fault coefficient, it indicates that the operating parameters of the high-voltage solid-state soft starter have deviated from the normal range, but the overall abnormality degree is at a medium level and has not reached an emergency risk level. However, since the thyristor is a core power element of the high-voltage solid-state soft starter, its performance degradation or abnormality is often the main cause of early faults, and the abnormality at this stage can be caused by slight aging or poor contact of the thyristor, so it is determined whether the thyristor has an abnormality to accurately locate potential hazards. When the fault coefficient is greater than or equal to the second preset fault coefficient, it indicates that the abnormal degree of the running parameter of the device has been relatively significant, the fault risk has been greatly increased, and therefore, the fault early warning needs to be immediately performed, the user is prompted to urgently troubleshoot and handle, and the fault is avoided from being expanded.

[0031] Specifically, the fault analysis module performs a first determination based on the three-phase current balance degree under a preset fault condition, including: If the three-phase current balance degree is less than a preset three-phase current balance degree, it is determined that the thyristor is abnormal; If the three-phase current balance degree is greater than or equal to the preset three-phase current balance degree, it is determined that the thyristor is not abnormal. The preset fault condition is that the fault coefficient is greater than or equal to a first preset fault coefficient and less than a second preset fault coefficient.

[0032] The three-phase current balance degree = 1 - (maximum phase current - minimum phase current) / three-phase average current. In the high-voltage solid-state soft start device, a three-phase bridge circuit is used in the high-voltage solid-state soft start device, the three-phase current refers to the current passing through the three phases of the three-phase bridge circuit, the maximum phase current and the minimum phase current correspond to the maximum value and the minimum value of the current passing through the three phases, respectively, the current passing through each phase is monitored by a current sensor, and the three-phase average current is the average value of the current passing through the three phases in the main circuit of the high-voltage solid-state soft start device. The value of the preset three-phase current balance degree can be determined by the user according to the actual application scene. The greater the user's demand for improving the fault detection accuracy, the smaller the value of the preset three-phase current balance degree. A value determination method of the preset three-phase current balance degree is provided. The average value of the three-phase current balance degree corresponding to the historical record that meets the user's demand is recorded as the preset three-phase current balance degree.

[0033] Specifically, the fault analysis module determines, according to the thyristor fault data correlation degree, whether to perform a second determination according to the maximum data correlation coefficient or the thyristor abnormality comparison degree under a first abnormal condition, including: If the thyristor fault data correlation degree is less than a preset thyristor fault data correlation degree, it is determined whether the thyristor is abnormal according to the maximum data correlation coefficient; If the thyristor fault data correlation degree is greater than or equal to the preset thyristor fault data correlation degree, it is determined whether the thyristor is abnormal according to the thyristor abnormality comparison degree. The first abnormal condition is that the thyristor is abnormal in the first determination.

[0034] The thyristor fault data correlation degree is an average value of the correlation reference values corresponding to each thyristor fault data, and the correlation reference value corresponding to a single thyristor fault data is a maximum value in the correlation coefficients corresponding to the thyristor fault data and each non-thyristor fault data; The correlation coefficient corresponding to a single thyristor fault data and a single non-thyristor fault data is an average value of the matching coefficients corresponding to each monitoring parameter; The calculation formula of the matching coefficient r corresponding to a single monitoring parameter is: ; Wherein, m is the number of time points in a single monitoring period; And are values of the monitoring parameter corresponding to the kth time point in a single thyristor fault data and a single non-thyristor fault data, is an average value of the values of the monitoring parameter corresponding to each time point in a single thyristor fault data, is an average value of the values of the monitoring parameter corresponding to each time point in a single non-thyristor fault data, k = 1, 2, 3, …, m; The value of the preset thyristor fault data correlation degree can be determined by the user according to the actual application scene. It can be understood that the smaller the value of the preset thyristor fault data correlation degree, the greater the user's demand for the accuracy of identifying potential faults caused by non-strongly associated factors. A method for determining the value of the preset thyristor fault data correlation degree is provided. The user determines whether there is an abnormal history record of the thyristor according to the thyristor abnormal comparison degree, and the average value of the thyristor fault data correlation degree corresponding to the history record that meets the user's demand is recorded as the preset thyristor fault data correlation degree; Determine whether there is an abnormal thyristor according to the maximum data correlation coefficient, comprising: If the maximum data correlation coefficient is greater than or equal to the preset maximum data correlation coefficient, it is determined that the thyristor is abnormal; If the maximum data correlation coefficient is less than the preset maximum data correlation coefficient, it is determined that the thyristor is not abnormal.

[0035] Determine whether there is an abnormal thyristor according to the thyristor abnormal comparison degree, comprising: If the thyristor abnormal comparison degree is greater than or equal to the preset thyristor abnormal comparison degree, it is determined that the thyristor is abnormal; If the thyristor abnormal comparison degree is less than the preset thyristor abnormal comparison degree, it is determined that the thyristor is not abnormal.

[0036] The maximum data correlation coefficient is the maximum value of the correlation coefficients corresponding to the monitoring data and each thyristor fault data. It should be noted that the calculation method of the correlation coefficient corresponding to the monitoring data and a single thyristor fault data is the same as that of a single thyristor fault data and a single non-thyristor fault data, and details are not described herein; The values of the preset maximum data correlation coefficient and the preset thyristor abnormality comparison degree can be determined by the user according to the actual application scene. The greater the user's demand for improving the timeliness and accuracy of fault warning, the smaller the values of the preset maximum data correlation coefficient and the preset thyristor abnormality comparison degree. A method for determining the values of the preset maximum data correlation coefficient and the preset thyristor abnormality comparison degree is provided. The average value of the maximum data correlation coefficient corresponding to the historical record in which the thyristor exists abnormality and meets the user's demand is recorded as the preset maximum data correlation coefficient, and the average value of the thyristor abnormality comparison degree corresponding to the historical record in which the thyristor exists abnormality and meets the user's demand is recorded as the preset thyristor abnormality comparison degree.

[0037] It can be understood that the thyristor fault data correlation degree effectively reflects the overall correlation strength between the thyristor fault data and the non-thyristor fault data. When the thyristor fault data correlation degree is less than the preset thyristor fault data correlation degree, it indicates that the overall correlation between the thyristor fault data and the non-thyristor fault data is weak, and the monitoring parameter characteristics of the two types of data are significantly different. At this time, by comparing the direct correlation between the current monitoring data and the thyristor fault data, it can be more clearly identified whether the thyristor is abnormal. Therefore, whether the thyristor exists abnormality is determined according to the maximum data correlation coefficient. When the thyristor fault data correlation degree is greater than or equal to the preset thyristor fault data correlation degree, it indicates that the overall correlation between the thyristor fault data and the non-thyristor fault data is strong. When the correlation degree of data characteristics is high, the direct correlation comparison effect is limited, and focusing on the abnormality degree of the core operating parameters of the thyristor can more accurately locate whether the thyristor exists abnormality.

[0038] Specifically, the confirmation method of the thyristor abnormality comparison degree comprises: If the current rate change degree is greater than or equal to the preset current rate change degree, the thyristor abnormality comparison degree is determined according to the rate change comparison degree; If the current rate change degree is less than the preset current rate change degree, the thyristor abnormality comparison degree is determined according to the current decay degree.

[0039] The current rate change degree is the maximum value in the rate reference values corresponding to each time point, for a single time point, the time point is denoted as a target time point, the rate reference value corresponding to the target time point is = | the output current corresponding to the target time point - the output current corresponding to the time point adjacent to the target time point and earlier than the target time point | / the time length between the two adjacent time points, and the unit of the rate reference value is A / s; The user can determine the preset current rate change degree value according to the actual application scene, and the smaller the preset current rate change degree value is, the greater the user's demand for determining the thyristor abnormality comparison degree according to the rate change comparison degree is, a method for determining the preset current rate change degree value is provided, and a history record of determining the thyristor abnormality comparison degree according to the rate change comparison degree is detected, and the average value of the current rate change degree corresponding to the history record that can meet the user's demand is denoted as the preset current rate change degree. When the rate change comparison degree is used to determine the thyristor abnormality comparison degree, the thyristor abnormality comparison degree = the rate change comparison degree / (the average value of the rate change comparison degrees corresponding to each reference history record + 1), and the rate change comparison degree = the current rate change degree - the preset current rate change degree. When the current decay degree is used to determine the thyristor abnormality comparison degree, the thyristor abnormality comparison degree = the current decay degree / (the average value of the current decay degrees corresponding to each reference history record + 1). The current decay degree = the average value of the output currents corresponding to each time point in the current monitoring period - the average value of the output currents corresponding to each time point in the monitoring period of each reference history record when the current rate change degree is less than the preset current rate change degree. It can be understood that the current rate change degree effectively reflects the instantaneous change degree of the output current of the high-voltage solid-state soft start device in the monitoring period, when the current rate change degree is greater than or equal to the preset current rate change degree, it indicates that the change rate exceeds the normal threshold, and therefore the abnormality caused by the current mutation needs to be focused on, and therefore the rate change comparison degree is used to determine the thyristor abnormality comparison degree. When the current rate change degree is less than the preset current rate change degree, it indicates that the output current changes gently in the monitoring period, and there is no obvious abnormality, at this time, the overall decay trend of the current should be paid more attention to, the current decay degree can reflect the decay difference between the current average level and the historical normal gentle scene, and therefore the current decay degree is used to determine the thyristor abnormality comparison degree.

[0040] Specifically, the abnormal identification module determines the abnormal thyristor group according to the current difference degree and the temperature abnormal value; The abnormal thyristor group is a thyristor group with a current difference degree greater than a preset current difference degree or a temperature abnormal value greater than a preset temperature abnormal value. The thyristor group is each thyristor in a single phase in a three-phase bridge circuit of the high-voltage solid-state soft start device.

[0041] In the high-voltage solid-state soft starter, a three-phase bridge circuit is used, and each phase contains several thyristors. Each thyristor in a single phase is referred to as a thyristor group. The current difference of a single thyristor group = |current through the phase corresponding to the thyristor group - three-phase average current| / three-phase average current. The abnormal temperature value corresponding to a single thyristor group is the average of the maximum temperature values ​​of that thyristor group at each time point in the current monitoring cycle, and the maximum temperature value of that thyristor group at a single time point is the maximum temperature value of each thyristor in that thyristor group. The preset current difference and preset temperature anomaly values ​​can be determined by the user based on the actual application scenario. The greater the user's requirement for improving the timeliness of fault warnings, the smaller the preset current difference and preset temperature anomaly values ​​should be. A method for determining the preset current difference and preset temperature anomaly values ​​is provided, which detects the average value of the current difference and the average value of the temperature anomaly corresponding to each anomaly in the historical records that can meet the user's needs, and records them as the preset current difference and preset temperature anomaly values, respectively.

[0042] Specifically, when the temperature difference is greater than or equal to the preset temperature difference, the second early warning module determines that the abnormal thyristor group is abnormal due to sudden damage, and determines the degree of thyristor abnormality based on the degree of sudden damage. The degree of sudden damage is determined based on the degree of pressure drop change.

[0043] Among them, the temperature difference of a single thyristor group is the standard deviation of the temperature reference value of each thyristor in the thyristor group, and the temperature reference value of a single thyristor is the average temperature of the thyristor at each time point in the current monitoring cycle. The user can determine the preset temperature difference value according to the actual application scenario. The larger the preset temperature difference value, the greater the user's need to determine the thyristor abnormality based on the thyristor aging degree. A method for determining the preset temperature difference value is provided, which detects the historical records of the user's determination of the thyristor abnormality based on the thyristor aging degree, and records the average temperature difference value corresponding to the historical records that can meet the user's needs as the preset temperature difference value. The point in time when the anode current is greater than the anode current threshold is recorded as the conduction time point; the anode current threshold is 5mA, and the anode current is the current at the anode of the thyristor, which is detected by a current sensor; The average value of the voltage difference corresponding to each conduction time point in the current monitoring cycle is recorded as the voltage drop reference value; the input terminal of the voltage sensor is connected in parallel to the anode and cathode of the thyristor to directly collect the voltage difference between the two ends; The pressure drop change degree corresponding to the single abnormal thyristor group is the maximum value in the maximum value in the maximum value of the sub-pressure drop change degrees corresponding to each thyristor in the abnormal thyristor group, and the sub-pressure drop change degree corresponding to a single thyristor is | the average value of the pressure drop reference values corresponding to each thyristor in each reference history record - the average value of the pressure drop reference values corresponding to each thyristor in each reference history record | / the average value of the pressure drop reference values corresponding to each thyristor in each reference history record; The burst damage degree corresponding to the single thyristor group is the pressure drop change degree / the average value of the pressure drop change degrees corresponding to each thyristor group in each reference history record; The thyristor abnormality degree corresponding to the single thyristor group is the burst damage degree x the damage influence coefficient, and the damage influence coefficient is 1.2; It can be understood that the temperature difference degree effectively reflects the temperature distribution uniformity of each thyristor in the same thyristor group, and when the temperature difference degree is greater than or equal to the preset temperature difference degree, it indicates that the temperature distribution difference of each thyristor in the thyristor group is significant. Such uneven temperature distribution is usually caused by abnormal heating of individual thyristors, indicating that individual thyristors have burst damage such as local breakdown, lead virtual connection, etc. Therefore, the abnormal thyristor group is determined to have an abnormal reason of burst damage, and the thyristor abnormality degree is determined according to the burst damage degree; When the temperature difference degree is less than the preset temperature difference degree, it indicates that the temperature distribution of each thyristor in the thyristor group is relatively uniform. At this time, the abnormality is more likely to be caused by the overall performance degradation due to long-term operation. It is determined that the abnormal thyristor group has an abnormal reason of aging, and the thyristor abnormality degree is determined according to the thyristor aging degree.

[0044] Specifically, the second warning module determines that the abnormal thyristor group has an abnormal reason of aging when the temperature difference degree is less than the preset temperature difference degree, and determines the thyristor abnormality degree according to the thyristor aging degree; The thyristor aging degree is determined based on the current-temperature correlation degree.

[0045] The current-temperature correlation degree corresponding to the single abnormal thyristor group is the minimum value in the minimum value in the minimum value of the sub-current-temperature correlation degrees corresponding to each thyristor in the abnormal thyristor group; The calculation formula of the sub-current-temperature correlation degree w is: ; Wherein, m is the number of time points in a single monitoring period; And Anode current and temperature of a single thyristor at the jth time point in the current monitoring period, And The average value of the anode current and the average value of the temperature of the single thyristor at each time point in the current monitoring period, j = 1, 2, 3, …, m; The thyristor aging degree corresponding to the single thyristor group = the current temperature correlation degree / the average of the current temperature correlation degrees of each thyristor group in each reference history record; The thyristor abnormality degree corresponding to the single thyristor group = the thyristor aging degree * the aging influence coefficient, and the aging influence coefficient is 1.0.

[0046] Specifically, the second early warning module determines whether to perform fault early warning according to the abnormality degree change degree when the maximum thyristor abnormality degree is greater than or equal to the preset maximum thyristor abnormality degree; When determining whether to perform fault early warning according to the abnormality degree change degree, if the abnormality degree change degree is greater than or equal to the preset abnormality degree change degree, fault early warning is performed; if the abnormality degree change degree is less than the preset abnormality degree change degree, fault early warning is not needed.

[0047] The second early warning module does not need to perform fault early warning when the maximum thyristor abnormality degree is less than the preset maximum thyristor abnormality degree; The maximum thyristor abnormality degree is the maximum value in the thyristor abnormality degrees corresponding to each abnormal thyristor group, and the value of the preset maximum thyristor abnormality degree can be determined by a user according to an actual application scenario; the greater the accuracy requirement of the user for improving the timeliness of fault early warning, the smaller the value of the preset maximum thyristor abnormality degree; a value of the preset maximum thyristor abnormality degree is provided, the history records of the user determining whether to perform fault early warning according to the abnormality degree change degree are detected, and the average of the maximum thyristor abnormality degrees corresponding to the history records that can meet the requirement of the user is recorded as the preset maximum thyristor abnormality degree. The abnormality degree change degree = the maximum thyristor abnormality degree corresponding to the current monitoring period - the maximum thyristor abnormality degree corresponding to the monitoring period adjacent to and earlier than the current monitoring period; The value of the preset abnormality degree change degree can be determined by the user according to the actual application scenario; the greater the accuracy requirement of the user for improving the timeliness of fault early warning, the smaller the value of the preset abnormality degree change degree; a method for determining the value of the preset abnormality degree change degree is provided, the history records of the user determining whether to perform fault early warning according to the abnormality degree change degree are detected, and the average of the abnormality change degrees corresponding to the history records that can meet the requirement of the user and perform fault early warning is recorded as the preset abnormality degree change degree.

[0048] So far, the technical scheme of the present application has been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily 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 scheme after the changes or replacements will fall within the protection scope of the present application.

Claims

1. A fault intelligent detection system for a high-voltage solid-state soft starter, characterized in that, include: The data acquisition module is used to collect marker data and monitoring data from the high-voltage solid-state soft starter. The first early warning module is connected to the data acquisition module and is used to determine the fault coefficient based on the abnormal reference value and fluctuation comparison value of the monitoring data, and to determine whether the high-voltage solid-state soft starter has a fault based on the fault coefficient. The fault analysis module is connected to the data acquisition module and the first early warning module respectively. It is used to make a first judgment based on the three-phase current balance under preset fault conditions, and under a first abnormal condition, to make a second judgment based on the correlation of thyristor fault data, or the maximum data correlation coefficient or thyristor abnormality comparison degree to determine whether there is an abnormality in the thyristor. An anomaly identification module, which is connected to the fault analysis module, is used to identify the abnormal thyristor group based on the current difference and temperature anomaly value when there is an anomaly in the thyristor. The second early warning module is connected to the anomaly identification module. It is used to determine the cause of the abnormality of the abnormal thyristor group based on the temperature difference, including sudden damage and aging. It also determines the degree of thyristor abnormality based on the degree of sudden damage or thyristor aging, and determines whether to issue a fault warning based on the maximum degree of thyristor abnormality.

2. The intelligent fault detection system for high-voltage solid-state soft starter according to claim 1, characterized in that, The first early warning module determines the fault coefficient based on the abnormal reference value and fluctuation comparison value of the monitoring data; The fault coefficient is positively correlated with both the abnormal reference value and the fluctuation comparison value.

3. The intelligent fault detection system for high-voltage solid-state soft starter according to claim 2, characterized in that, The first early warning module determines that the high-voltage solid-state soft starter has a fault when the fault coefficient is greater than or equal to the first preset fault coefficient.

4. The intelligent fault detection system for high-voltage solid-state soft starter according to claim 3, characterized in that, The fault analysis module performs a judgment based on the three-phase current balance under preset fault conditions, including: If the three-phase current balance is less than the preset three-phase current balance, the thyristor is determined to be abnormal. If the three-phase current balance is greater than or equal to the preset three-phase current balance, then the thyristor is determined to be normal. The preset fault condition is that the fault coefficient is greater than or equal to the first preset fault coefficient and less than the second preset fault coefficient.

5. The intelligent fault detection system for high-voltage solid-state soft starter according to claim 4, characterized in that, The fault analysis module, under a single abnormal condition, determines a secondary judgment based on the correlation degree of thyristor fault data, or the maximum data correlation coefficient or thyristor anomaly comparison degree, including: If the correlation of thyristor fault data is less than the preset correlation of thyristor fault data, then the maximum correlation coefficient is used to determine whether there is an abnormality in the thyristor. If the correlation degree of thyristor fault data is greater than or equal to the preset correlation degree of thyristor fault data, then it is determined whether there is an abnormality in the thyristor based on the thyristor anomaly comparison degree. The aforementioned abnormal condition is that the thyristor is abnormal during a single determination.

6. The intelligent fault detection system for high-voltage solid-state soft starter according to claim 5, characterized in that, The method for confirming the anomaly comparison of the thyristor includes: If the current rate change is greater than or equal to the preset current rate change, the thyristor anomaly comparison degree is determined based on the rate change comparison degree. If the change in current rate is less than the preset change in current rate, the thyristor anomaly comparison degree is determined based on the current attenuation degree.

7. The intelligent fault detection system for high-voltage solid-state soft starter according to claim 5, characterized in that, The anomaly identification module determines the abnormal thyristor group based on the current difference and temperature anomaly value. The abnormal thyristor group is a thyristor group whose current difference is greater than a preset current difference or whose temperature abnormality value is greater than a preset temperature abnormality value. The thyristor group refers to each thyristor in a single phase of a three-phase bridge circuit in a high-voltage solid-state soft starter.

8. The intelligent fault detection system for high-voltage solid-state soft starter according to claim 7, characterized in that, When the temperature difference is greater than or equal to the preset temperature difference, the second early warning module determines that the abnormal thyristor group is due to sudden damage, and determines the degree of thyristor abnormality based on the degree of sudden damage. The degree of sudden damage is determined based on the degree of pressure drop change.

9. The intelligent fault detection system for high-voltage solid-state soft starter according to claim 8, characterized in that, When the temperature difference is less than the preset temperature difference, the second early warning module determines that the abnormal thyristor group is aging, and determines the degree of thyristor abnormality based on the degree of aging. The aging degree of the thyristor is determined based on the correlation between current and temperature.

10. The intelligent fault detection system for high-voltage solid-state soft starter according to claim 9, characterized in that, When the maximum thyristor anomaly degree is greater than or equal to the preset maximum thyristor anomaly degree, the second early warning module determines whether to issue a fault warning based on the degree of change in anomaly degree. When determining whether to issue a fault warning based on the degree of anomaly change, if the degree of anomaly change is greater than or equal to the preset degree of anomaly change, then a fault warning is issued. If the degree of anomaly change is less than the preset degree of anomaly change, then no fault warning is required.

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