Converter valve submodule energy release branch monitoring method and system of sea wind intermediate frequency power transmission system

Abstract

The invention discloses a method and a system for monitoring an energy release branch of a converter valve sub-module of a sea wind intermediate-frequency power transmission system, and relates to the technical field of converter valve sub-module monitoring. Monitoring data of an energy release branch in the converter valve sub-module are acquired, loss analysis is performed on the monitoring data, and corresponding branch loss is obtained; and performing junction temperature evaluation on the branch loss and the environment temperature of the monitoring data to obtain a corresponding branch junction temperature, performing life prediction on the branch junction temperature based on a pre-acquired historical branch junction temperature and pre-acquired historical branch damage to obtain a corresponding branch accumulated damage, and determining the branch loss according to the branch accumulated damage. And performing state evaluation according to the monitoring data, the branch junction temperature and the branch accumulated damage to obtain a state evaluation result corresponding to the energy release branch. The technical problems that a traditional energy release branch monitoring method mainly depends on regular off-line detection of an energy release branch, but the real-time health state of the energy release branch is difficult to reflect, early warning of the energy release branch cannot be achieved, and the operation reliability of a converter valve sub-module is reduced are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of converter valve sub-module monitoring, and particularly relates to a converter valve sub-module energy dissipation branch monitoring method and system for a sea wind medium frequency power transmission system. BACKGROUND

[0002] In a sea wind medium frequency power transmission system, an energy dissipation device for dealing with power surplus is a key link for realizing stable operation of the system. At present, a distributed resistance energy dissipation device based on a welded IGBT is usually integrated in a converter valve sub-module due to its advantages of smooth energy dissipation power, simple circuit structure, high cost-effectiveness and easy engineering implementation. However, as a key component frequently operated in the converter valve sub-module, the energy dissipation branch needs to repeatedly withstand the impact of high-frequency and short-time overload current. Under this extreme working condition, the energy dissipation branch is prone to failure, and it is difficult to ensure the safe and stable operation of the converter valve sub-module.

[0003] At present, the traditional energy dissipation branch monitoring method mainly relies on periodic offline detection of the energy dissipation branch, but it is difficult to reflect the real-time health status of the energy dissipation branch, and cannot realize early warning of the energy dissipation branch, thereby reducing the reliability of the operation of the sea wind medium frequency power transmission system. SUMMARY

[0004] The present application provides a converter valve sub-module energy dissipation branch monitoring method and system for a sea wind medium frequency power transmission system, which solves the technical problem that the traditional energy dissipation branch monitoring method mainly relies on periodic offline detection of the energy dissipation branch, but it is difficult to reflect the real-time health status of the energy dissipation branch, and cannot realize early warning of the energy dissipation branch, thereby reducing the reliability of the operation of the sea wind medium frequency power transmission system.

[0005] The present application provides a converter valve sub-module energy dissipation branch monitoring method and system for a sea wind medium frequency power transmission system, which solves the technical problem that the traditional energy dissipation branch monitoring method mainly relies on periodic offline detection of the energy dissipation branch, but it is difficult to reflect the real-time health status of the energy dissipation branch, and cannot realize early warning of the energy dissipation branch, thereby reducing the reliability of the operation of the sea wind medium frequency power transmission system.

[0006] The present application provides a converter valve sub-module energy dissipation branch monitoring method and system for a sea wind medium frequency power transmission system, which solves the technical problem that the traditional energy dissipation branch monitoring method mainly relies on periodic offline detection of the energy dissipation branch, but it is difficult to reflect the real-time health status of the energy dissipation branch, and cannot realize early warning of the energy dissipation branch, thereby reducing the reliability of the operation of the sea wind medium frequency power transmission system.

[0007] The present application provides a converter valve sub-module energy dissipation branch monitoring method and system for a sea wind medium frequency power transmission system, which solves the technical problem that the traditional energy dissipation branch monitoring method mainly relies on periodic offline detection of the energy dissipation branch, but it is difficult to reflect the real-time health status of the energy dissipation branch, and cannot realize early warning of the energy dissipation branch, thereby reducing the reliability of the operation of the sea wind medium frequency power transmission system.

[0008] The present application provides a converter valve sub-module energy dissipation branch monitoring method and system for a sea wind medium frequency power transmission system, which solves the technical problem that the traditional energy dissipation branch monitoring method mainly relies on periodic offline detection of the energy dissipation branch, but it is difficult to reflect the real-time health status of the energy dissipation branch, and cannot realize early warning of the energy dissipation branch, thereby reducing the reliability of the operation of the sea wind medium frequency power transmission system.

[0009] The present application provides a converter valve sub-module energy dissipation branch monitoring method and system for a sea wind medium frequency power transmission system, which solves the technical problem that the traditional energy dissipation branch monitoring method mainly relies on periodic offline detection of the energy dissipation branch, but it is difficult to reflect the real-time health status of the energy dissipation branch, and cannot realize early warning of the energy dissipation branch, thereby reducing the reliability of the operation of the sea wind medium frequency power transmission system.

[0010] Optionally, the monitoring data comprises a collector current average value, a collector current effective value, a diode current average value and a diode current effective value, and the step of performing loss analysis on the monitoring data to obtain a corresponding branch loss comprises:

[0011] inputting the collector current average value and the collector current effective value into a preset on-state loss function to obtain a corresponding on-state loss;

[0012] inputting the collector current average value and the collector current effective value into a preset on-state loss function to obtain a corresponding on-state loss;

[0013] inputting the diode current average value and the diode current effective value into a preset diode loss function to obtain a corresponding diode on-state loss and reverse recovery loss;

[0014] performing addition processing on the on-state loss, the switching loss, the diode on-state loss and the reverse recovery loss to obtain a corresponding branch loss.

[0015] Optionally, the step of performing junction temperature evaluation on the branch loss and the environmental temperature of the monitoring data to obtain a corresponding branch junction temperature comprises:

[0016] performing addition processing on a pre-acquired first thermal resistance, a pre-acquired second thermal resistance and a pre-acquired third thermal resistance to obtain a corresponding branch thermal resistance;

[0017] performing multiplication processing on the branch thermal resistance and the branch loss to obtain a corresponding first multiplication value;

[0018] performing addition processing on the first multiplication value and the environmental temperature of the monitoring data to obtain a corresponding branch junction temperature.

[0019] Optionally, the step of performing life prediction on the branch junction temperature based on a pre-acquired historical branch junction temperature and a pre-acquired historical branch damage to obtain a corresponding branch cumulative damage comprises:

[0020] performing difference processing on the pre-acquired historical branch junction temperature and the branch junction temperature to obtain a corresponding first difference value;

[0021] inputting an absolute value of the first difference value into a preset branch damage function to obtain a corresponding branch damage value;

[0022] inputting the branch damage value and the pre-acquired historical branch damage into a preset cumulative damage function to obtain a corresponding branch cumulative damage.

[0023] Optionally, the step of performing state evaluation according to the monitoring data, the branch junction temperature and the branch cumulative damage to obtain a state evaluation result corresponding to the energy dissipation branch comprises:

[0024] inputting the branch cumulative damage into a preset residual life prediction function to obtain a corresponding branch residual life;

[0025] obtaining thermal resistance normal distribution data and pressure drop normal distribution data corresponding to the energy dissipation branch, and performing Monte Carlo analysis on the thermal resistance normal distribution data and the pressure drop normal distribution data to obtain a corresponding expected life distribution interval;

[0026] using the branch residual life to retrieve the expected life distribution interval to obtain a corresponding branch residual life probability;

[0027] performing difference processing on the turn-on pressure drop of the monitoring data and a preset standard turn-on pressure drop to obtain a corresponding second difference value;

[0028] using the absolute value of the second difference value, the branch junction temperature, the branch cumulative damage, the switching component voltage and the collector current effective value of the monitoring data to generate a corresponding target key;

[0029] using the target key to retrieve a preset state key-value pair list to match a corresponding branch state;

[0030] using the branch state, the branch residual life and the branch residual life probability as the state evaluation result corresponding to the energy dissipation branch.

[0031] Optionally, the step of performing Monte Carlo analysis on the thermal resistance normal distribution data and the pressure drop normal distribution data to obtain a corresponding expected life distribution interval comprises:

[0032] randomly selecting a thermal resistance from the thermal resistance normal distribution data as a target thermal resistance, and randomly selecting a pressure drop from the pressure drop normal distribution data as a target pressure drop;

[0033] inputting the target thermal resistance and the target pressure drop into a preset life function to obtain a corresponding expected life, and counting the number of expected lives;

[0034] when the number of expected lives is less than a preset iteration threshold, then jump to perform the step of randomly selecting a thermal resistance from the thermal resistance normal distribution data as a target thermal resistance, and randomly selecting a pressure drop from the pressure drop normal distribution data as a target pressure drop, until the number of expected lives is greater than or equal to the iteration threshold;

[0035] When the quantity value is greater than or equal to the iteration threshold value, then the Weibull distribution is used to fit each of the expected life, and an expected life distribution interval corresponding to the expected life is obtained.

[0036] The second aspect of the present application provides a sea wind medium frequency power transmission system converter valve submodule energy dissipation branch monitoring system, comprising:

[0037] The acquisition module is configured to acquire monitoring data of the energy dissipation branch in the sea wind medium frequency power transmission system converter valve submodule, perform loss analysis on the monitoring data, and obtain corresponding branch loss.

[0038] The junction temperature evaluation module is configured to perform junction temperature evaluation on the branch loss and an ambient temperature of the monitoring data, and obtain corresponding branch junction temperature.

[0039] The prediction module is configured to perform life prediction on the branch junction temperature based on a pre-acquired historical branch junction temperature and a pre-acquired historical branch damage, and obtain corresponding branch cumulative damage.

[0040] The state evaluation module is configured to perform state evaluation according to the monitoring data, the branch junction temperature and the branch cumulative damage, and obtain a state evaluation result corresponding to the energy dissipation branch.

[0041] The third aspect of the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the sea wind medium frequency power transmission system converter valve submodule energy dissipation branch monitoring method according to any one of the above aspects.

[0042] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to implement the sea wind medium frequency power transmission system converter valve submodule energy dissipation branch monitoring method according to any one of the above aspects.

[0043] The fifth aspect of the present application provides a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the sea wind medium frequency power transmission system converter valve submodule energy dissipation branch monitoring method according to any one of the above aspects.

[0044] From the above technical solutions, the present application has the following advantages:

[0045] The application obtains monitoring data of the energy dissipation branch in the converter valve sub-module of the sea wind medium frequency power transmission system, analyzes the loss of the monitoring data, obtains the corresponding branch loss, evaluates the junction temperature of the branch loss and the environmental temperature of the monitoring data, obtains the corresponding branch junction temperature, predicts the life of the branch junction temperature based on the pre-obtained historical branch junction temperature and the pre-obtained historical branch damage, obtains the corresponding branch cumulative damage, evaluates the state based on the monitoring data, the branch junction temperature and the branch cumulative damage, and obtains the state evaluation result corresponding to the energy dissipation branch. The technical problem that the traditional energy dissipation branch monitoring method mainly relies on offline detection of the energy dissipation branch at regular intervals, but it is difficult to reflect the real-time health status of the energy dissipation branch, cannot realize early warning of the energy dissipation branch, and reduces the reliability of the operation of the sea wind medium frequency power transmission system. Compared with the traditional energy dissipation branch monitoring method, the corresponding branch junction temperature is obtained by analyzing the loss of the monitoring data and evaluating the junction temperature, the corresponding branch cumulative damage is obtained by predicting the life of the branch junction temperature based on the pre-obtained historical branch junction temperature and the pre-obtained historical branch damage, and finally the state evaluation result corresponding to the energy dissipation branch is obtained by evaluating the state based on the monitoring data, the branch junction temperature and the branch cumulative damage, so as to realize accurate evaluation of the state of the energy dissipation branch and improve the reliability of the operation of the sea wind medium frequency power transmission system. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0047] Figure 1 A step flow chart of a sea wind medium frequency power transmission system converter valve sub-module energy dissipation branch monitoring method provided for the first embodiment of the present application;

[0048] Figure 2 A step flow chart of a sea wind medium frequency power transmission system converter valve sub-module energy dissipation branch monitoring method provided for the second embodiment of the present application;

[0049] Figure 3 A structure schematic diagram of a welded IGBT provided for the second embodiment of the present application;

[0050] Figure 4 A structure block diagram of a sea wind medium frequency power transmission system converter valve sub-module energy dissipation branch monitoring system provided for the third embodiment of the present application;

[0051] Figure 5 A structure block diagram of an electronic device provided for the fourth embodiment of the present application. DETAILED DESCRIPTION

[0052] The embodiment of the present application provides a kind of sea wind mid-frequency power transmission system converter valve submodule energy release branch monitoring method and system, to solve the technical problem that the traditional energy release branch monitoring method mainly relies on periodically offline detection to energy release branch, but it is difficult to reflect the real-time health status of energy release branch, early warning to energy release branch cannot be realized, reduce the reliability of sea wind mid-frequency power transmission system operation.

[0053] To make the application purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] Please refer to Figure 1 , Figure 1 A step flow chart of a sea wind mid-frequency power transmission system converter valve submodule energy release branch monitoring method is provided for the embodiment one of the present application.

[0055] The sea wind mid-frequency power transmission system converter valve submodule energy release branch monitoring method provided by the present application comprises:

[0056] Step 101, obtain the monitoring data of energy release branch in sea wind mid-frequency power transmission system converter valve submodule, perform loss analysis on the monitoring data to obtain corresponding branch loss.

[0057] The monitoring data refers to the average value of collector current, the effective value of collector current, the average value of diode current, the effective value of diode current, switch component voltage and environmental temperature, etc.

[0058] In the embodiment of the present application, the voltage sensor, Hall current sensor and temperature sensor deployed in the converter valve submodule are used to collect the monitoring data of energy release branch in the converter valve submodule, and loss analysis is performed on the monitoring data to obtain corresponding branch loss.

[0059] Step 102, evaluate the junction temperature of branch loss and environmental temperature of monitoring data to obtain corresponding branch junction temperature.

[0060] In the embodiment of the present application, the branch loss and environmental temperature of monitoring data are input into the preset junction temperature evaluation function to obtain the corresponding branch junction temperature.

[0061] It should be noted that the junction temperature evaluation function is specifically:

[0062]

[0063] wherein, is a branch junction temperature, is an ambient temperature, is a branch loss, is a first thermal resistance, is a second thermal resistance, is a third thermal resistance.

[0064] Step 103, life prediction is performed on the branch junction temperature based on the pre-acquired historical branch junction temperature and the pre-acquired historical branch damage, to obtain corresponding branch cumulative damage.

[0065] In the embodiment of the present application, the pre-acquired historical branch junction temperature is processed by difference with the branch junction temperature to obtain a corresponding first difference. The absolute value of the first difference is input into a preset branch damage function to obtain a corresponding branch damage value. The branch damage value and the pre-acquired historical branch damage are input into a preset cumulative damage function to obtain a corresponding branch cumulative damage.

[0066] Step 104, state evaluation is performed according to the monitoring data, the branch junction temperature and the branch cumulative damage to obtain a state evaluation result corresponding to the energy releasing branch.

[0067] In the embodiment of the present application, the branch cumulative damage is input into a preset residual life prediction function to obtain a corresponding branch residual life. The thermal resistance normal distribution data and the pressure drop normal distribution data corresponding to the energy releasing branch are acquired, and Monte Carlo analysis is performed on the thermal resistance normal distribution data and the pressure drop normal distribution data to obtain a corresponding expected life distribution interval. The branch residual life is used to search the expected life distribution area to obtain a corresponding branch residual life probability. The on pressure drop of the monitoring data is processed by difference with a preset standard on pressure drop to obtain a corresponding second difference. The absolute value of the second difference, the branch junction temperature, the branch cumulative damage, the switch component voltage and the collector current effective value of the monitoring data are used to generate a corresponding target key. The target key is used to search a preset state key-value pair list to match a corresponding branch state. The branch state, the branch residual life and the branch residual life probability are taken as the state evaluation result corresponding to the energy releasing branch.

[0068] In the embodiment of the present application, by acquiring the monitoring data of the energy dissipation branch in the converter valve sub-module, the loss of the monitoring data is analyzed, the corresponding branch loss is obtained, the junction temperature of the branch is evaluated based on the environmental temperature of the monitoring data and the branch loss, the junction temperature of the branch is predicted based on the pre-acquired historical junction temperature of the branch and the pre-acquired historical damage of the branch, the corresponding cumulative damage of the branch is obtained, the state evaluation result corresponding to the energy dissipation branch is obtained through state evaluation based on the monitoring data, the junction temperature of the branch and the cumulative damage of the branch. The technical problem that the traditional energy dissipation branch monitoring method mainly relies on periodic offline detection of the energy dissipation branch, but it is difficult to reflect the real-time health status of the energy dissipation branch, and early warning of the energy dissipation branch cannot be realized, and the reliability of the operation of the sea wind medium frequency power transmission system is reduced. Compared with the traditional energy dissipation branch monitoring method, the corresponding junction temperature of the branch is obtained through loss analysis and junction temperature evaluation of the monitoring data, the cumulative damage of the corresponding branch is obtained through life prediction of the junction temperature of the branch based on the pre-acquired historical junction temperature of the branch and the pre-acquired historical damage of the branch, and the state evaluation result corresponding to the energy dissipation branch is obtained through state evaluation based on the monitoring data, the junction temperature of the branch and the cumulative damage of the branch. Therefore, the accurate evaluation of the state of the energy dissipation branch is realized, and the reliability of the operation of the sea wind medium frequency power transmission system is improved.

[0069] Please refer to Figure 2 , Figure 2 The step flow chart of a sea wind medium frequency power transmission system converter valve sub-module energy dissipation branch monitoring method provided in the second embodiment of the present application is shown in the figure.

[0070] The sea wind medium frequency power transmission system converter valve sub-module energy dissipation branch monitoring method provided by the present application comprises the following steps:

[0071] Step 201, acquiring the monitoring data of the energy dissipation branch in the sea wind medium frequency power transmission system converter valve sub-module, and analyzing the loss of the monitoring data to obtain the corresponding branch loss.

[0072] Further, the monitoring data comprises the average value of the collector current, the effective value of the collector current, the average value of the diode current and the effective value of the diode current, and step 201 comprises the following sub-steps:

[0073] S11, inputting the average value of the collector current and the effective value of the collector current into a preset on-state loss function to obtain the corresponding on-state loss.

[0074] In the embodiment of the present application, the average value of the collector current and the effective value of the collector current are inputted into a preset on-state loss function to obtain the corresponding on-state loss.

[0075] It should be noted that the on-state loss function is specifically as follows:

[0076]

[0077] wherein, Pon is the on-state loss, Pon is the on-state loss, Pon is the on-state loss, Ic_avg is the average collector current, Ic_rms is the root mean square collector current.

[0078] It is worth mentioning that the first fitting parameter and the second fitting parameter can be obtained from the data manual of the IGBT.

[0079] S12, input the average collector current and the root mean square collector current into a preset switching loss function to obtain the corresponding switching loss.

[0080] In the embodiment of the present application, the average collector current and the root mean square collector current are input into the preset switching loss function to obtain the corresponding switching loss.

[0081] It should be noted that the switching loss function is specifically:

[0082]

[0083] wherein, Psw is the switching loss, fsw is the switching frequency, Psw is the switching loss, Psw is the switching loss, Psw is the switching loss.

[0084] S13, input the average diode current and the root mean square diode current into a preset diode loss function to obtain the corresponding on-state loss and reverse recovery loss of the diode.

[0085] In the embodiment of the present application, the average diode current and the root mean square diode current are input into the preset diode loss function to obtain the corresponding on-state loss and reverse recovery loss of the diode.

[0086] It should be noted that the diode loss function is specifically:

[0087]

[0088]

[0089] wherein, Pon is the on-state loss, a sixth fitting parameter used for fitting the IGBT switching loss characteristic curve, a seventh fitting parameter used for fitting the IGBT switching loss characteristic curve, an eighth fitting parameter used for fitting the IGBT switching loss characteristic curve, a diode current average value, a diode current effective value, a reverse recovery loss, a ninth fitting parameter used for fitting the reverse parallel diode conduction characteristic curve, a tenth fitting parameter used for fitting the reverse parallel diode conduction characteristic curve.

[0090] S14, summing the on-state loss, the switching loss, the diode on-state loss and the reverse recovery loss to obtain the corresponding branch loss.

[0091] In the embodiment of the present application, the sum value between the on-state loss, the switching loss, the diode on-state loss and the reverse recovery loss is calculated to obtain the corresponding branch loss.

[0092] Step 202, summing the pre-acquired first thermal resistance, the pre-acquired second thermal resistance and the pre-acquired third thermal resistance to obtain the corresponding branch thermal resistance.

[0093] The first thermal resistance refers to the thermal resistance from the soldered IGBT junction to the shell.

[0094] The second thermal resistance refers to the thermal resistance from the soldered IGBT shell to the heat sink.

[0095] The third thermal resistance refers to the thermal resistance from the soldered IGBT heat sink to the environment.

[0096] In the embodiment of the present application, the sum value between the pre-acquired first thermal resistance, the pre-acquired second thermal resistance and the pre-acquired third thermal resistance is calculated to obtain the corresponding branch thermal resistance.

[0097] Step 203, multiplying the branch thermal resistance and the branch loss to obtain the corresponding first multiplication value.

[0098] In the embodiment of the present application, the multiplication value between the branch thermal resistance and the branch loss is calculated to obtain the corresponding first multiplication value.

[0099] Step 204, summing the first multiplication value and the environmental temperature of the monitoring data to obtain the corresponding branch junction temperature.

[0100] In the embodiment of the present application, the sum value between the first multiplication value and the environmental temperature of the monitoring data is calculated to obtain the corresponding branch junction temperature.

[0101] It is worth mentioning that the welding IGBT contains multiple heat conduction paths, for example, junction to case, case to heat sink, heat sink to environment. Referring to Figure 3 As shown, each heat conduction path can be modeled as a parallel combination of a thermal resistance and a thermal capacitance. To consider the thermal coupling problem that may exist between different levels of heat transfer, a coupling thermal resistance is introduced to characterize, and finally the corresponding branch junction temperature is obtained by solving the discretized node heat balance equation (since the temperature of the internal materials of the IGBT propagates slowly, the influence of thermal fusion can be ignored).

[0102] Step 205, based on the pre-acquired historical branch junction temperature and the pre-acquired historical branch damage, the life prediction of the branch junction temperature is carried out, and the corresponding branch cumulative damage is obtained.

[0103] Further, step 205 includes the following sub-steps:

[0104] S21, difference processing is carried out on the pre-acquired historical branch junction temperature and the branch junction temperature, and the corresponding first difference value is obtained.

[0105] The historical branch junction temperature refers to the branch junction temperature calculated in the last monitoring period.

[0106] In the embodiment of the present application, the difference between the pre-acquired historical branch junction temperature and the branch junction temperature is calculated to obtain the corresponding first difference value.

[0107] S22, the absolute value of the first difference value is input into the preset branch damage function, and the corresponding branch damage value is obtained.

[0108] In the embodiment of the present application, the absolute value of the first difference value is input into the preset branch damage function as the temperature fluctuation amplitude to obtain the corresponding branch damage value.

[0109] It should be noted that the branch damage function is specifically:

[0110]

[0111] Wherein, The branch damage value is, The first damage fitting coefficient is, The second damage fitting coefficient is, The temperature fluctuation amplitude is.

[0112] S23, the branch damage value and the pre-acquired historical branch damage are input into the preset cumulative damage function, and the corresponding branch cumulative damage is obtained.

[0113] The historical branch damage refers to the branch damage value calculated in the historical monitoring process of the energy dissipation branch.

[0114] In the embodiment of the present application, the ratio between the cumulative loss reference value (1) and the branch damage value and the pre-acquired historical branch is calculated respectively to obtain a plurality of first ratios, and each first ratio is added to obtain the corresponding branch cumulative damage.

[0115] It should be noted that the cumulative damage function is specifically:

[0116]

[0117] Among them, is the branch cumulative damage, is the i th branch damage value, i is the index of the branch damage value, and n is the total number of branch damage values.

[0118] Step 206, according to the monitoring data, the branch junction temperature and the branch cumulative damage, the state evaluation result corresponding to the energy release branch is obtained.

[0119] Further, step 206 includes the following sub-steps:

[0120] S31, input the branch cumulative damage into the preset residual life prediction function to obtain the corresponding branch residual life.

[0121] In the embodiment of the present application, the branch cumulative damage is input into the preset residual life prediction function to obtain the corresponding branch residual life.

[0122] It should be noted that the residual life prediction function is specifically:

[0123]

[0124] Among them, is the branch residual life, is the periodic damage, is the monitoring times.

[0125] S32, obtain the thermal resistance normal distribution data and the pressure drop normal distribution data corresponding to the energy release branch, and perform Monte Carlo analysis on the thermal resistance normal distribution data and the pressure drop normal distribution data to obtain the corresponding expected life distribution interval.

[0126] Further, S32 includes the following sub-steps:

[0127] S321, randomly select a thermal resistance from the thermal resistance normal distribution data as a target thermal resistance, and randomly select a pressure drop from the pressure drop normal distribution data as a target pressure drop.

[0128] The thermal resistance normal distribution data refers to the normal distribution of the thermal resistance of the welded IGBT in the energy release branch. It can be obtained by the welded IGBT experiment manual.

[0129] The voltage drop normal distribution data refers to the normal distribution of the on-state voltage drop of the soldered IGBT in the energy dissipation branch. The data can be obtained through the soldered IGBT experiment manual.

[0130] In the embodiment of the present application, one value is randomly selected from the normal distribution of the thermal resistance and the on-state voltage drop, to obtain the target thermal resistance and the target voltage drop.

[0131] S322, input the target thermal resistance and the target voltage drop into the preset life function to obtain the corresponding expected life, and count the number of expected life values.

[0132] In the embodiment of the present application, the target thermal resistance and the target voltage drop are substituted into the preset life function to obtain the corresponding expected life, and the number of expected life values obtained by operation is counted.

[0133] It should be noted that the life function is specifically:

[0134]

[0135] Wherein, is the power loss, is the target voltage drop, is the on-state current, is the junction temperature fluctuation, is the target thermal resistance, is the expected life, is the number of failure cycles, is the annual average cycle number.

[0136] S323, when the number of values is less than the preset iteration threshold, then jump to execute the step of randomly selecting one thermal resistance from the thermal resistance normal distribution data as the target thermal resistance, and randomly selecting one voltage drop from the voltage drop normal distribution data as the target voltage drop, until the number of values is greater than or equal to the iteration threshold.

[0137] The iteration threshold refers to the upper limit value of the iteration operation.

[0138] In the embodiment of the present application, it is judged whether the number of expected life values is less than 1000, and when the number of values is less than 1000, then jump to execute S321-S322.

[0139] S324, when the number of values is greater than or equal to the iteration threshold, then the Weibull distribution is used to fit each expected life to obtain the corresponding expected life distribution interval.

[0140] In the embodiment of the present application, when the number of values is greater than or equal to 1000, then each expected life is fitted based on the preset Weibull distribution cumulative failure probability function to obtain the corresponding expected life distribution interval.

[0141] It should be noted that the Weibull distribution cumulative failure probability function is specifically:

[0142]

[0143] wherein, is a cumulative failure probability, is a first fitting coefficient, is a second fitting coefficient. The first fitting coefficient and the second fitting coefficient can be obtained by fitting each expected life.

[0144] S33, the branch residual life is searched to retrieve the expected life distribution area, and the corresponding branch residual life probability is obtained.

[0145] In the embodiment of the present application, the branch residual life is searched to retrieve the expected life distribution area, and the corresponding branch residual life probability is obtained. For example, referring to Table 1, when the branch residual life is in the interval of 0-10, the corresponding branch residual life probability is 60%. When the branch residual life is in the interval of 10-15, the corresponding branch residual life probability is 30%. When the branch residual life is in the interval of 15-20, the corresponding branch residual life probability is 10%.

[0146] Table 1

[0147]

[0148] S34, the on-voltage drop of the monitoring data is difference processed with the preset standard on-voltage drop, and the corresponding second difference value is obtained.

[0149] In the embodiment of the present application, the difference between the on-voltage drop of the monitoring data and the preset standard on-voltage drop is calculated, and the corresponding second difference value is obtained.

[0150] S35, the absolute value of the second difference value, the branch junction temperature, the branch cumulative damage, the switch component voltage and the collector current effective value of the monitoring data are used to generate the corresponding target key.

[0151] The switch component voltage refers to the collector-emitter voltage.

[0152] In the embodiment of the present application, the absolute value of the second difference value, the branch junction temperature, the branch cumulative damage, the switch component voltage and the collector current effective value of the monitoring data are used to generate the corresponding target key, wherein the target key includes the absolute value of the second difference value, the branch junction temperature, the branch cumulative damage, the switch component voltage and the collector current effective value of the monitoring data.

[0153] S36, the target key is used to search the preset state key value pair list, and the corresponding branch state is matched.

[0154] In the embodiment of the present application, the target key is used to retrieve the preset state key-value pair list to match the corresponding branch state. For example, referring to Table 2, the absolute value of the second difference is used to retrieve the preset state key-value pair list to obtain the corresponding first initial branch state. The branch junction temperature is used to retrieve the state key-value pair list to obtain the corresponding second initial branch state. The branch cumulative damage is used to retrieve the state key-value pair list to obtain the corresponding third initial branch state. The switch component voltage is used to retrieve the state key-value pair list to obtain the corresponding fourth initial branch state. The collector current effective value is used to retrieve the state key-value pair list to obtain the corresponding fifth initial branch state. The first initial branch state, the second initial branch state, the third initial branch state, the fourth initial branch state, and the fifth initial branch state are used as the corresponding branch state.

[0155] Table 2

[0156]

[0157] It should be noted that the collector current effective value refers to the on-current in Table 2.

[0158] S37, the branch state, the branch remaining life, and the branch remaining life probability are used as the state evaluation result corresponding to the energy dissipation branch.

[0159] In the embodiment of the present application, the branch state, the branch remaining life, and the branch remaining life probability are used to generate the state evaluation result corresponding to the energy dissipation branch.

[0160] In the embodiment of the present application, by obtaining the monitoring data of the energy dissipation branch in the converter valve sub-module of the sea wind medium frequency power transmission system, performing loss analysis on the monitoring data to obtain the corresponding branch loss, performing junction temperature evaluation on the environmental temperature of the monitoring data and the branch loss to obtain the corresponding branch junction temperature, performing life prediction on the branch junction temperature based on the pre-obtained historical branch junction temperature and the pre-obtained historical branch damage to obtain the corresponding branch cumulative damage, and performing state evaluation according to the monitoring data, the branch junction temperature, and the branch cumulative damage to obtain the state evaluation result corresponding to the energy dissipation branch. The technical problem that the traditional energy dissipation branch monitoring method mainly relies on periodic offline detection of the energy dissipation branch, but it is difficult to reflect the real-time health status of the energy dissipation branch, cannot realize early warning of the energy dissipation branch, and reduces the reliability of the operation of the sea wind medium frequency power transmission system is overcome. Compared with the traditional energy dissipation branch monitoring method, by performing loss analysis and junction temperature evaluation on the monitoring data to obtain the corresponding branch junction temperature, performing life prediction on the branch junction temperature based on the pre-obtained historical branch junction temperature and the pre-obtained historical branch damage to obtain the corresponding branch cumulative damage, and finally performing state evaluation according to the monitoring data, the branch junction temperature, and the branch cumulative damage to obtain the state evaluation result corresponding to the energy dissipation branch, the accurate evaluation of the state of the energy dissipation branch is realized, and the reliability of the operation of the sea wind medium frequency power transmission system is improved.

[0161] Please refer to Figure 4 , Figure 4 A structural block diagram of a discharge branch monitoring system of a sea wind medium frequency power transmission system converter valve sub-module is provided for embodiment three of the application.

[0162] The application provides a discharge branch monitoring system of a sea wind medium frequency power transmission system converter valve sub-module, which comprises:

[0163] The acquisition module 301 is used for acquiring monitoring data of the discharge branch in the sea wind medium frequency power transmission system converter valve sub-module, performing loss analysis on the monitoring data, and obtaining corresponding branch loss.

[0164] The junction temperature evaluation module 302 is used for evaluating the junction temperature of the branch loss and the environmental temperature of the monitoring data, and obtaining corresponding branch junction temperature.

[0165] The prediction module 303 is used for predicting the service life of the branch junction temperature based on the pre-acquired historical branch junction temperature and the pre-acquired historical branch damage, and obtaining corresponding branch cumulative damage.

[0166] The state evaluation module 304 is used for performing state evaluation according to the monitoring data, the branch junction temperature and the branch cumulative damage, and obtaining the state evaluation result corresponding to the discharge branch.

[0167] Further, the monitoring data comprises an average value of a collector current, an effective value of the collector current, an average value of a diode current and an effective value of the diode current, and the acquisition module 301 comprises:

[0168] The on-state loss submodule is used for inputting the average value of the collector current and the effective value of the collector current into a preset on-state loss function to obtain corresponding on-state loss.

[0169] The switch loss submodule is used for inputting the average value of the collector current and the effective value of the collector current into a preset switch loss function to obtain corresponding switch loss.

[0170] The diode loss submodule is used for inputting the average value of the diode current and the effective value of the diode current into a preset diode loss function to obtain corresponding diode on-state loss and reverse recovery loss.

[0171] The branch loss submodule is used for summing the on-state loss, the switch loss, the diode on-state loss and the reverse recovery loss to obtain corresponding branch loss.

[0172] Further, the junction temperature evaluation module 302 comprises:

[0173] The branch thermal resistance submodule is used for summing the pre-acquired first thermal resistance, the pre-acquired second thermal resistance and the pre-acquired third thermal resistance to obtain corresponding branch thermal resistance.

[0174] a first multiplication submodule, configured to multiply the branch thermal resistance and the branch loss to obtain a corresponding first multiplication;

[0175] a junction temperature submodule, configured to add the first multiplication and an ambient temperature of the monitoring data to obtain a corresponding branch junction temperature.

[0176] Further, the prediction module 303 comprises:

[0177] a difference submodule, configured to subtract the pre-acquired historical branch junction temperature from the branch junction temperature to obtain a corresponding first difference;

[0178] a branch damage submodule, configured to input an absolute value of the first difference into a preset branch damage function to obtain a corresponding branch damage value;

[0179] a cumulative damage submodule, configured to input the branch damage value and the pre-acquired historical branch damage into a preset cumulative damage function to obtain a corresponding branch cumulative damage.

[0180] Further, the state evaluation module 304 comprises:

[0181] a branch residual life submodule, configured to input the branch cumulative damage into a preset residual life prediction function to obtain a corresponding branch residual life;

[0182] an expected life distribution interval submodule, configured to acquire thermal resistance normal distribution data and pressure drop normal distribution data corresponding to the energy dissipation branch, and perform Monte Carlo analysis on the thermal resistance normal distribution data and the pressure drop normal distribution data to obtain a corresponding expected life distribution interval;

[0183] a branch residual life probability submodule, configured to search the expected life distribution interval by using the branch residual life to obtain a corresponding branch residual life probability;

[0184] a state evaluation submodule, configured to subtract a preset standard conduction pressure drop from a conduction pressure drop of the monitoring data to obtain a corresponding second difference;

[0185] an absolute value of the second difference, the branch junction temperature, the branch cumulative damage, a switch component voltage of the monitoring data, and a collector current effective value are used to generate a corresponding target key;

[0186] the target key is used to search a preset state key-value pair list to match a corresponding branch state;

[0187] the branch state, the branch residual life, and the branch residual life probability are taken as a state evaluation result corresponding to the energy dissipation branch.

[0188] Further, the expected life distribution interval submodule comprises:

[0189] The selecting unit is configured to randomly select a thermal resistance from the thermal resistance normal distribution data as a target thermal resistance and randomly select a pressure drop from the pressure drop normal distribution data as a target pressure drop;

[0190] The statistical analysis unit is configured to input the target thermal resistance and the target pressure drop into a preset life function to obtain a corresponding expected life and count a quantity value of the expected life;

[0191] The jumping unit is configured to, when the quantity value is less than a preset iteration threshold, jump to execute the step of randomly selecting a thermal resistance from the thermal resistance normal distribution data as a target thermal resistance and randomly selecting a pressure drop from the pressure drop normal distribution data as a target pressure drop until the quantity value is greater than or equal to the iteration threshold.

[0192] The fitting unit is configured to, when the quantity value is greater than or equal to the iteration threshold, fit each expected life by using a Weibull distribution to obtain a corresponding expected life distribution interval.

[0193] Please refer to Figure 5 , Figure 5 a structural block diagram of an electronic device provided in Embodiment Four of the present application.

[0194] The electronic device of the embodiment of the present application comprises a memory 401 and a processor 402, the memory 401 stores a computer program; when the computer program is executed by the processor 402, the processor 402 executes the sea wind medium frequency power transmission system converter valve sub-module energy dissipation branch monitoring method of any one of the above embodiments.

[0195] The memory 401 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk, or a ROM. The memory 401 has a storage space 403 for program codes 413 for performing any of the method steps in the above-described methods. For example, the storage space 403 for program codes can include individual program codes 413 for implementing the various steps in the above-described methods, respectively. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disc (CD), a memory card, or a floppy disk. The program codes can be compressed, for example, in a suitable form. These codes, when run by a computing processing device, cause the computing processing device to perform the individual steps in the above-described methods. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disc (CD), a memory card, or a floppy disk. The program codes can be compressed, for example, in a suitable form. These codes, when run by a computing processing device, cause the computing processing device to perform the individual steps in the above-described methods of monitoring the energy dissipation branch of a sub-module of a converter valve of a sea wind medium frequency power transmission system.

[0196] The embodiment five of the present application further provides a computer readable storage medium, which has stored thereon a computer program, and the computer program is executed by a processor to implement the method for monitoring the energy dissipation branch of a sub-module of a converter valve of a sea wind medium frequency power transmission system according to any of the above-described embodiments.

[0197] The embodiment six of the present application further provides a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to perform the method for monitoring the energy dissipation branch of a sub-module of a converter valve of a sea wind medium frequency power transmission system according to any of the above-described embodiments.

[0198] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0199] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the units is only a logical function division, and there can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0200] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0201] In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0202] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or substantially, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various media that can store program codes.

[0203] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, characterized in that, include: Acquire monitoring data of the energy leakage branch in the converter valve submodule of the offshore wind medium frequency transmission system, perform loss analysis on the monitoring data, and obtain the corresponding branch loss; The junction temperature of the branch loss and the ambient temperature of the monitoring data are evaluated to obtain the corresponding branch junction temperature. Based on the pre-acquired historical branch junction temperature and pre-acquired historical branch damage, the lifetime of the branch junction temperature is predicted to obtain the corresponding cumulative branch damage. Based on the monitoring data, the branch junction temperature, and the cumulative damage of the branch, a condition assessment is performed to obtain the condition assessment result corresponding to the energy-dissipating branch.

2. The method for monitoring the energy leakage branch of the converter valve submodule in a marine wind-driven medium-frequency power transmission system according to claim 1, characterized in that, The monitoring data includes the average collector current, the effective collector current, the average diode current, and the effective diode current. The step of performing loss analysis on the monitoring data to obtain the corresponding branch loss includes: The average value of the collector current and the effective value of the collector current are input into a preset conduction loss function to obtain the corresponding conduction loss. The average value of the collector current and the effective value of the collector current are input into a preset switching loss function to obtain the corresponding switching loss. The average value of the diode current and the effective value of the diode current are input into a preset diode loss function to obtain the corresponding diode conduction loss and reverse recovery loss. The corresponding branch loss is obtained by summing the conduction loss, the switching loss, the diode conduction loss, and the reverse recovery loss.

3. The method for monitoring the energy leakage branch of the converter valve submodule in a marine wind-driven medium-frequency power transmission system according to claim 1, characterized in that, The step of evaluating the junction temperature of the branch loss and the ambient temperature of the monitoring data to obtain the corresponding branch junction temperature includes: The first thermal resistance, the second thermal resistance, and the third thermal resistance obtained in advance are summed to obtain the corresponding branch thermal resistance. The branch thermal resistance and the branch loss are multiplied to obtain the corresponding first multiplication value; The first multiplier is summed with the ambient temperature of the monitoring data to obtain the corresponding branch junction temperature.

4. The method for monitoring the energy leakage branch of the converter valve submodule in a marine wind-driven medium-frequency power transmission system according to claim 1, characterized in that, The step of predicting the lifetime of the branch junction temperature based on the pre-acquired historical branch junction temperature and pre-acquired historical branch damage, and obtaining the corresponding cumulative branch damage, includes: The difference between the previously acquired historical branch junction temperature and the branch junction temperature is processed to obtain the corresponding first difference value; The absolute value of the first difference is input into a preset branch damage function to obtain the corresponding branch damage value; The branch damage value and the pre-acquired historical branch damage are input into a preset cumulative damage function to obtain the corresponding cumulative branch damage.

5. The method for monitoring the energy leakage branch of the converter valve submodule in a marine wind-driven medium-frequency power transmission system according to claim 1, characterized in that, The step of performing a condition assessment based on the monitoring data, the branch junction temperature, and the cumulative damage of the branch to obtain the condition assessment result corresponding to the energy-dissipating branch includes: The cumulative damage of the branch is input into a preset remaining lifetime prediction function to obtain the corresponding remaining lifetime of the branch. Obtain the normal distribution data of thermal resistance and normal distribution data of voltage drop corresponding to the energy leakage branch, and perform Monte Carlo analysis on the normal distribution data of thermal resistance and normal distribution data of voltage drop to obtain the corresponding expected lifetime distribution range; The expected lifetime distribution area is retrieved using the remaining lifetime of the branch to obtain the corresponding remaining lifetime probability of the branch. The difference between the on-state voltage drop of the monitoring data and the preset standard on-state voltage drop is processed to obtain the corresponding second difference value; The absolute value of the second difference, the branch junction temperature, the cumulative damage of the branch, the switching component voltage and collector current RMS value of the monitoring data are used to generate the corresponding target bond; The target key is used to retrieve a preset list of state key-value pairs and match the corresponding branch state. The branch state, the remaining lifetime of the branch, and the remaining lifetime probability of the branch are used as the state evaluation results corresponding to the energy leakage branch.

6. The method for monitoring the energy leakage branch of the converter valve submodule in a marine wind-driven medium-frequency power transmission system according to claim 5, characterized in that, The step of performing Monte Carlo analysis on the thermal resistance normal distribution data and the voltage drop normal distribution data to obtain the corresponding expected lifetime distribution range includes: A thermal resistance is randomly selected from the normal distribution data of thermal resistance as the target thermal resistance, and a voltage drop is randomly selected from the normal distribution data of voltage drop as the target voltage drop; Input the target thermal resistance and the target voltage drop into a preset lifetime function to obtain the corresponding expected lifetime, and count the numerical value of the expected lifetime; When the quantity value is less than the preset iteration threshold, the process jumps to the step of randomly selecting a thermal resistance as the target thermal resistance from the thermal resistance normal distribution data and randomly selecting a voltage drop as the target voltage drop from the voltage drop normal distribution data, until the quantity value is greater than or equal to the iteration threshold. When the quantity value is greater than or equal to the iteration threshold, the Weibull distribution is used to fit each of the expected lifetimes to obtain the corresponding expected lifetime distribution interval.

7. A monitoring system for the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, characterized in that, include: The acquisition module is used to acquire monitoring data of the energy leakage branch in the converter valve submodule of the offshore wind medium frequency transmission system, and to perform loss analysis on the monitoring data to obtain the corresponding branch loss. The junction temperature assessment module is used to assess the junction temperature of the branch loss and the ambient temperature of the monitoring data to obtain the corresponding branch junction temperature. The prediction module is used to predict the lifetime of the branch junction temperature based on the pre-acquired historical branch junction temperature and pre-acquired historical branch damage, and obtain the corresponding cumulative branch damage. The condition assessment module is used to perform condition assessment based on the monitoring data, the branch junction temperature, and the cumulative damage of the branch, and obtain the condition assessment result corresponding to the energy leakage branch.

8. An electronic device, characterized in that, The system includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the monitoring method for the energy leakage branch of the converter valve submodule in the offshore wind medium-frequency transmission system as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the method for monitoring the energy leakage branch of the converter valve submodule in the offshore wind medium frequency transmission system as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the method for monitoring the energy leakage branch of the converter valve submodule of the offshore wind medium-frequency transmission system as described in any one of claims 1-6.

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