Monitoring method and system for energy dissipation branch of sub-module of converter valve of sea wind medium frequency power transmission system

Abstract

The application discloses a kind of sea wind medium frequency power transmission system converter valve submodule energy release branch monitoring method and system, it is related to converter valve submodule monitoring technical field, obtain the monitoring data of energy release branch in converter valve submodule, loss analysis is carried out to monitoring data, obtain corresponding branch loss, the ambient temperature of branch loss and monitoring data is junction temperature evaluation, obtain corresponding branch junction temperature, based on the life prediction of branch junction temperature based on pre-acquired historical branch junction temperature and pre-acquired historical branch damage, obtain corresponding branch cumulative damage, according to monitoring data, branch junction temperature and branch cumulative damage are state evaluation, obtain the state evaluation result corresponding to energy release branch.It solves the technical problem that traditional energy release branch monitoring method mainly relies on periodic 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, and the reliability of converter valve submodule operation is reduced.

Description

Technical Field

[0001] This invention relates to the field of converter valve submodule monitoring technology, and in particular to a method and system for monitoring the energy leakage branch of a converter valve submodule in a marine wind medium-frequency power transmission system. Background Technology

[0002] In offshore wind-driven medium-frequency power transmission systems, energy dissipation devices to handle power surplus are crucial for ensuring stable system operation. Currently, distributed resistive energy dissipation devices based on welded IGBTs are commonly used due to their significant advantages, such as smooth power dissipation, simple circuit structure, high cost-effectiveness, and ease of engineering implementation. The energy dissipation branch is typically integrated within the converter valve submodule. However, as a critical component within the converter valve submodule that operates frequently, the energy dissipation branch must repeatedly withstand high-frequency, short-term overload current surges. Under these extreme conditions, the energy dissipation branch is prone to failure, making it difficult to guarantee the safe and stable operation of the converter valve submodule.

[0003] Currently, traditional methods for monitoring energy leakage branches mainly rely on periodic offline testing of energy leakage branches. However, these methods are insufficient to reflect the real-time health status of energy leakage branches and cannot provide early warnings, thus reducing the reliability of the offshore wind medium-frequency power transmission system. Summary of the Invention

[0004] This invention provides a method and system for monitoring the energy leakage branch of the converter valve submodule in an offshore wind medium-frequency power transmission system. It solves the technical problem that traditional energy leakage branch monitoring methods mainly rely on periodic offline detection of the energy leakage branch, which is difficult to reflect the real-time health status of the energy leakage branch and cannot achieve early warning of the energy leakage branch, thus reducing the reliability of the offshore wind medium-frequency power transmission system.

[0005] The first aspect of this invention provides a method for monitoring the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, comprising:

[0006] 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;

[0007] The junction temperature of the branch loss and the ambient temperature of the monitoring data are evaluated to obtain the corresponding branch junction temperature.

[0008] 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.

[0009] 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.

[0010] Optionally, 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:

[0011] 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.

[0012] 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.

[0013] 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.

[0014] The corresponding branch loss is obtained by summing the conduction loss, the switching loss, the diode conduction loss, and the reverse recovery loss.

[0015] Optionally, 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:

[0016] 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.

[0017] The branch thermal resistance and the branch loss are multiplied to obtain the corresponding first multiplication value;

[0018] The first multiplier is summed with the ambient temperature of the monitoring data to obtain the corresponding branch junction temperature.

[0019] Optionally, 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 to obtain the corresponding cumulative branch damage includes:

[0020] The difference between the previously acquired historical branch junction temperature and the branch junction temperature is processed to obtain the corresponding first difference value;

[0021] The absolute value of the first difference is input into a preset branch damage function to obtain the corresponding branch damage value;

[0022] 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.

[0023] Optionally, 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-discharging branch includes:

[0024] The cumulative damage of the branch is input into a preset remaining lifetime prediction function to obtain the corresponding remaining lifetime of the branch.

[0025] 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;

[0026] The expected lifetime distribution area is retrieved using the remaining lifetime of the branch to obtain the corresponding remaining lifetime probability of the branch.

[0027] 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;

[0028] 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;

[0029] The target key is used to retrieve a preset list of state key-value pairs and match the corresponding branch state.

[0030] 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.

[0031] Optionally, 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:

[0032] 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;

[0033] 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;

[0034] 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.

[0035] 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.

[0036] The second aspect of this invention provides a monitoring system for the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, comprising:

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] The third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the monitoring method for the leakage branch of the converter valve submodule of the offshore wind medium frequency transmission system as described in any of the preceding claims.

[0042] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements 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 of the preceding claims.

[0043] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the energy leakage branch monitoring method for converter valve submodule of offshore wind medium-frequency transmission system as described in any of the preceding claims.

[0044] As can be seen from the above technical solutions, the present invention has the following advantages:

[0045] This invention acquires monitoring data of the energy leakage branch in the converter valve submodule of the offshore wind medium-frequency power transmission system, performs loss analysis on the monitoring data to obtain the corresponding branch loss, evaluates the junction temperature of the branch loss and the ambient temperature of the monitoring data to obtain the corresponding branch junction temperature, predicts the lifetime of the branch junction temperature based on the pre-acquired historical branch junction temperature and pre-acquired historical branch damage to obtain the corresponding cumulative damage of the branch, and performs a state assessment based on the monitoring data, branch junction temperature and cumulative damage of the branch to obtain the state assessment result corresponding to the energy leakage branch. This new method overcomes the technical problems of traditional energy leakage branch monitoring methods, which mainly rely on periodic offline testing of energy leakage branches. However, this method is difficult to reflect the real-time health status of energy leakage branches and cannot provide early warnings, thus reducing the reliability of offshore wind medium-frequency transmission systems. Compared with traditional energy leakage branch monitoring methods, this new method performs loss analysis and junction temperature assessment on monitoring data to obtain the corresponding branch junction temperature. Then, based on pre-acquired historical branch junction temperatures and damage, it predicts the lifespan of the branch junction temperature to obtain the corresponding cumulative damage. Finally, based on monitoring data, branch junction temperature, and cumulative damage, it performs a status assessment to obtain the status assessment result of the energy leakage branch. This achieves accurate assessment of the energy leakage branch status and improves the reliability of offshore wind medium-frequency transmission systems. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the steps of a method for monitoring the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, as provided in Embodiment 1 of the present invention.

[0048] Figure 2 This is a flowchart illustrating the steps of a method for monitoring the energy leakage branch of a converter valve submodule in a sea-wind medium-frequency power transmission system, as provided in Embodiment 2 of the present invention.

[0049] Figure 3 This is a schematic diagram of the welded IGBT provided in Embodiment 2 of the present invention;

[0050] Figure 4 This is a structural block diagram of a monitoring system for the energy leakage branch of a converter valve submodule in a sea-wind medium-frequency power transmission system, provided in Embodiment 3 of the present invention.

[0051] Figure 5 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0052] This invention provides a method and system for monitoring the energy leakage branch of the converter valve submodule in an offshore wind medium-frequency power transmission system. It addresses the technical problem that traditional energy leakage branch monitoring methods mainly rely on periodic offline testing of the energy leakage branch, which is difficult to reflect the real-time health status of the energy leakage branch and cannot provide early warning of the energy leakage branch, thus reducing the reliability of the offshore wind medium-frequency power transmission system.

[0053] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0054] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a method for monitoring the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, as provided in Embodiment 1 of the present invention.

[0055] This invention provides a method for monitoring the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, comprising:

[0056] Step 101: Obtain 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.

[0057] Monitoring data refers to the average collector current, effective collector current, average diode current, effective diode current, switching component voltage, and ambient temperature of the energy dissipation branch.

[0058] In this embodiment of the invention, monitoring data of the energy leakage branch in the converter valve submodule is collected by voltage sensors, Hall current sensors and temperature sensors deployed in the converter valve submodule, and loss analysis is performed on the monitoring data to obtain the corresponding branch loss.

[0059] Step 102: Evaluate the junction temperature of the branch loss and the ambient temperature of the monitoring data to obtain the corresponding branch junction temperature.

[0060] In this embodiment of the invention, the branch loss and the ambient temperature of the monitoring data are input into a preset junction temperature evaluation function to obtain the corresponding branch junction temperature.

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

[0062]

[0063] in, For branch junction temperature, For ambient temperature, For branch losses, The first thermal resistance, The second thermal resistance, It is the third thermal resistance.

[0064] Step 103: Based on the pre-acquired historical branch junction temperature and pre-acquired historical branch damage, perform lifetime prediction on the branch junction temperature to obtain the corresponding cumulative branch damage.

[0065] In this embodiment of the invention, the pre-acquired historical branch junction temperature and the branch junction temperature are processed by difference to obtain a 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 branch cumulative damage.

[0066] Step 104: Based on the monitoring data, branch junction temperature, and cumulative damage of the branch, conduct a condition assessment to obtain the condition assessment results corresponding to the energy-dissipating branch.

[0067] In this embodiment of the invention, the cumulative damage of the branch is input into a preset remaining lifetime prediction function to obtain the corresponding remaining lifetime of the branch. Normal distribution data of thermal resistance and voltage drop corresponding to the energy-discharging branch are acquired, and Monte Carlo analysis is performed on these data to obtain the corresponding expected lifetime distribution range. The expected lifetime distribution region is retrieved using the remaining lifetime of the branch to obtain the corresponding remaining lifetime probability. 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 of the monitoring data, and the effective value of the collector current are used to generate the corresponding target key. The target key is used to retrieve a preset list of state key-value pairs to 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-discharging branch.

[0068] In this embodiment of the invention, by acquiring monitoring data of the energy leakage branch in the converter valve submodule, loss analysis is performed on the monitoring data to obtain the corresponding branch loss. The branch loss and the ambient temperature of the monitoring data are used to evaluate the junction temperature to obtain the corresponding branch junction temperature. Based on the pre-acquired historical branch junction temperature and pre-acquired historical branch damage, the lifespan of the branch junction temperature is predicted to obtain the corresponding cumulative damage of the branch. Based on the monitoring data, the branch junction temperature, and the cumulative damage of the branch, a state assessment is performed to obtain the state assessment result corresponding to the energy leakage branch. This new method overcomes the technical problems of traditional energy leakage branch monitoring methods, which mainly rely on periodic offline testing of energy leakage branches. However, this method is difficult to reflect the real-time health status of energy leakage branches and cannot provide early warnings, thus reducing the reliability of offshore wind medium-frequency transmission systems. Compared with traditional energy leakage branch monitoring methods, this new method performs loss analysis and junction temperature assessment on monitoring data to obtain the corresponding branch junction temperature. Then, based on pre-acquired historical branch junction temperatures and damage, it predicts the lifespan of the branch junction temperature to obtain the corresponding cumulative damage. Finally, based on monitoring data, branch junction temperature, and cumulative damage, it performs a status assessment to obtain the status assessment result of the energy leakage branch. This achieves accurate assessment of the energy leakage branch status and improves the reliability of offshore wind medium-frequency transmission systems.

[0069] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a method for monitoring the energy leakage branch of a converter valve submodule in a marine wind medium-frequency power transmission system, as provided in Embodiment 2 of the present invention.

[0070] This invention provides a method for monitoring the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, comprising:

[0071] Step 201: Obtain the 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.

[0072] Furthermore, the monitoring data includes the average collector current, the effective collector current, the average diode current, and the effective diode current. Step 201 includes the following sub-steps:

[0073] S11. Input the average value of the collector current and the effective value of the collector current into the preset conduction loss function to obtain the corresponding conduction loss.

[0074] In this embodiment of the invention, the average value of the collector current and the effective value of the collector current are used as inputs to a preset conduction loss function to obtain the corresponding conduction loss.

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

[0076]

[0077] in, For on-state loss, The first fitting parameter used when fitting the IGBT conduction characteristic curve. The second fitting parameter is used when fitting the IGBT conduction characteristic curve. This represents the average collector current. This is the effective value of the collector current.

[0078] It is worth mentioning that the first and second fitting parameters can be obtained from the IGBT datasheet.

[0079] S12. Input the average value of the collector current and the effective value of the collector current into the preset switching loss function to obtain the corresponding switching loss.

[0080] In this embodiment of the invention, the average value of the collector current and the effective value of the collector current are used as inputs to a preset switching loss function to obtain the corresponding switching loss.

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

[0082]

[0083] in, For switching losses, For switching frequency, The third fitting parameter used when fitting the IGBT switching loss characteristic curve. The fourth fitting parameter used when fitting the IGBT switching loss characteristic curve. The fifth fitting parameter used when fitting the IGBT switching loss characteristic curve.

[0084] S13. Input the average value of the diode current and the effective value of the diode current into the preset diode loss function to obtain the corresponding diode conduction loss and reverse recovery loss.

[0085] In this embodiment of the invention, the average value of diode current and the effective value of diode current are used as inputs to a preset diode loss function to obtain the corresponding diode conduction loss and reverse recovery loss.

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

[0087]

[0088]

[0089] in, For diode conduction losses, The sixth fitting parameter used when fitting the IGBT switching loss characteristic curve. The seventh fitting parameter used when fitting the IGBT switching loss characteristic curve. The eighth fitting parameter used when fitting the IGBT switching loss characteristic curve. This represents the average diode current. This is the effective value of the diode current. For reverse recovery loss, The ninth fitting parameter used when fitting the conduction characteristic curve of an anti-parallel diode. The tenth fitting parameter is used to fit the conduction characteristic curve of an anti-parallel diode.

[0090] S14. Sum the conduction loss, switching loss, diode conduction loss and reverse recovery loss to obtain the corresponding branch loss.

[0091] In this embodiment of the invention, the sum of the conduction loss, switching loss, diode conduction loss and reverse recovery loss is calculated to obtain the corresponding branch loss.

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

[0093] The first thermal resistance refers to the thermal resistance from the junction to the case of a soldered IGBT.

[0094] The second thermal resistance refers to the thermal resistance from the welded IGBT case to the heat sink.

[0095] The third thermal resistance refers to the thermal resistance between the welded IGBT heatsink and the environment.

[0096] In this embodiment of the invention, the sum of the pre-obtained first thermal resistance, the pre-obtained second thermal resistance, and the pre-obtained third thermal resistance is calculated to obtain the corresponding branch thermal resistance.

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

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

[0099] Step 204: Add the first multiplier value to the ambient temperature of the monitoring data to obtain the corresponding branch junction temperature.

[0100] In this embodiment of the invention, the sum of the first multiplier and the ambient temperature of the monitoring data is calculated to obtain the corresponding branch junction temperature.

[0101] It's worth noting that soldered IGBTs involve multiple heat conduction paths, such as junction to case to heatsink to environment. See also... Figure 3 As shown, each heat conduction path can be modeled as a parallel combination of thermal resistance and thermal capacity. For the thermal coupling problem that may exist between different levels, we consider introducing coupling thermal resistance for characterization. Finally, we solve the discretized node thermal balance equation to obtain the corresponding branch junction temperature (since the temperature of the material inside the IGBT is transferred slowly, the thermal melting effect can be ignored).

[0102] Step 205: Based on the pre-acquired historical branch junction temperature and pre-acquired historical branch damage, perform lifetime prediction on the branch junction temperature to obtain the corresponding cumulative branch damage.

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

[0104] S21. Perform difference processing on the previously obtained historical branch junction temperature and the branch junction temperature to obtain the corresponding first difference value.

[0105] Historical branch junction temperature refers to the branch junction temperature calculated in the previous monitoring cycle.

[0106] In this embodiment of the invention, the difference between the previously obtained historical branch junction temperature and the branch junction temperature is calculated to obtain the corresponding first difference.

[0107] S22. Input the absolute value of the first difference into the preset branch damage function to obtain the corresponding branch damage value.

[0108] In this embodiment of the invention, the absolute value of the first difference is used as the temperature fluctuation amplitude input into a preset branch loss function to obtain the corresponding branch damage value.

[0109] It should be noted that the branch loss function is as follows:

[0110]

[0111] in, This represents the branch damage value. The first damage fitting coefficient, The second damage fitting coefficient, This represents the amplitude of temperature fluctuation.

[0112] S23. Input the branch damage value and the pre-acquired historical branch damage into the preset cumulative damage function to obtain the corresponding branch cumulative damage.

[0113] Historical branch damage refers to the branch damage value calculated during the historical monitoring of the energy leakage branch.

[0114] In this embodiment of the invention, the ratio between the cumulative loss baseline value (with a value of 1) and the branch damage value and the pre-acquired historical branch is calculated to obtain multiple first ratios. The first ratios are then summed to obtain the corresponding cumulative branch damage.

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

[0116]

[0117] in, For the cumulative loss of the branch road, Let be the damage value of the i-th branch, where i is the index of the branch damage value and n is the total number of branch damage values.

[0118] Step 206: Based on the monitoring data, branch junction temperature, and cumulative damage of the branch, conduct a condition assessment to obtain the condition assessment results corresponding to the energy-dissipating branch.

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

[0120] S31. Input the cumulative damage of the branch into the preset remaining life prediction function to obtain the corresponding remaining life of the branch.

[0121] In this embodiment of the invention, the cumulative damage of the branch is used as input to a preset remaining lifetime prediction function to obtain the corresponding remaining lifetime of the branch.

[0122] It should be noted that the remaining lifetime prediction function is as follows:

[0123]

[0124] in, For the remaining life of the branch, For periodic damage, Number of monitoring sessions.

[0125] S32. Obtain the normal distribution data of thermal resistance and normal distribution data of pressure 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 pressure drop to obtain the corresponding expected lifetime distribution range.

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

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

[0128] The thermal resistance normal distribution data refers to the normal distribution of thermal resistance in the welded IGBT within the energy dissipation branch. This data can be obtained from the welded IGBT experimental manual.

[0129] The voltage drop normal distribution data refers to the normal distribution of the conduction voltage drop of the welded IGBT in the energy dissipation branch. It can be obtained from the welded IGBT experimental manual.

[0130] In this embodiment of the invention, one value is randomly selected from the normal distribution of thermal resistance and conduction voltage drop to obtain the target thermal resistance and target voltage drop.

[0131] S322. Input the target thermal resistance and target voltage drop into the preset lifetime function to obtain the corresponding expected lifetime, and count the numerical value of the expected lifetime.

[0132] In this embodiment of the invention, the target thermal resistance and target voltage drop are substituted into a preset lifetime function to obtain the corresponding expected lifetime, and the numerical value of the expected lifetime obtained is statistically calculated.

[0133] It should be noted that the lifetime function is specifically as follows:

[0134]

[0135] in, For power consumption loss, For the target pressure reduction, To conduct current, For junction temperature fluctuations, For the target thermal resistance, For life expectancy, This represents the number of failure cycles. This represents the average number of cycles per year.

[0136] S323. 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 normal distribution data of thermal resistance and randomly selecting a voltage drop as the target voltage drop from the normal distribution data of voltage drop, until the quantity value is greater than or equal to the iteration threshold.

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

[0138] In this embodiment of the invention, it is determined whether the expected lifespan is less than 1000. If the value is less than 1000, then the process jumps to execute S321-S322.

[0139] S324. When the quantity value is greater than or equal to the iteration threshold, the Weibull distribution is used to fit each expected lifetime to obtain the corresponding expected lifetime distribution interval.

[0140] In this embodiment of the invention, when the quantity value is greater than or equal to 1000, the expected lifetime is fitted based on the preset Weibull distribution cumulative failure probability function to obtain the corresponding expected lifetime distribution interval.

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

[0142]

[0143] in, For cumulative failure probability, The first fitting coefficient, The first and second fitting coefficients can be obtained by fitting various expected lifetimes.

[0144] S33. Use the branch remaining lifetime to retrieve the expected lifetime distribution area and obtain the corresponding branch remaining lifetime probability.

[0145] In this embodiment of the invention, the expected lifetime distribution area is retrieved using the remaining lifetime of the branch to obtain the corresponding remaining lifetime probability. For example, referring to Table 1, when the remaining lifetime of the branch is in the range of 0-10, the corresponding remaining lifetime probability is 60%. When the remaining lifetime of the branch is in the range of 10-15, the corresponding remaining lifetime probability is 30%. When the remaining lifetime of the branch is in the range of 15-20, the corresponding remaining lifetime probability is 10%.

[0146] Table 1

[0147]

[0148] S34. Perform difference processing on the monitoring data conduction voltage drop and the preset standard conduction voltage drop to obtain the corresponding second difference value.

[0149] In this embodiment of the invention, the difference between the on-state voltage drop of the monitoring data and the preset standard on-state voltage drop is calculated to obtain the corresponding second difference.

[0150] S35. The corresponding target bond is generated using the absolute value of the second difference, the branch junction temperature, the branch cumulative damage, the switching component voltage and collector current effective value of the monitoring data.

[0151] The voltage of a switching component refers to the collector-emitter voltage.

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

[0153] S36. Use the target key to retrieve the preset list of state key-value pairs and match the corresponding branch state.

[0154] In this embodiment of the invention, a preset list of state key-value pairs is retrieved using a target key 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 list of state key-value pairs to obtain the corresponding first initial branch state. The branch junction temperature is used to retrieve the list of state key-value pairs to obtain the corresponding second initial branch state. The cumulative damage of the branch is used to retrieve the list of state key-value pairs to obtain the corresponding third initial branch state. The switching component voltage is used to retrieve the list of state key-value pairs to obtain the corresponding fourth initial branch state. The effective value of the collector current is used to retrieve the list of state key-value pairs to obtain the corresponding fifth initial branch state. The first, second, third, fourth, and fifth initial branch states are taken as the corresponding branch states.

[0155] Table 2

[0156]

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

[0158] S37. The branch status, branch remaining life, and branch remaining life probability are used as the status assessment results corresponding to the energy leakage branch.

[0159] In this embodiment of the invention, the state assessment results corresponding to the energy leakage branch are generated by using the branch state, the branch remaining lifetime, and the branch remaining lifetime probability.

[0160] In this embodiment of the invention, monitoring data of the energy leakage branch in the converter valve submodule of the offshore wind medium-frequency power transmission system is acquired, loss analysis is performed on the monitoring data to obtain the corresponding branch loss, junction temperature is evaluated by combining the branch loss and the ambient temperature of the monitoring data to obtain the corresponding branch junction temperature, lifetime prediction is performed on the branch junction temperature based on the pre-acquired historical branch junction temperature and pre-acquired historical branch damage to obtain the corresponding cumulative branch damage, and state evaluation is performed based on the monitoring data, branch junction temperature and cumulative branch damage to obtain the state evaluation result corresponding to the energy leakage branch. This new method overcomes the technical problems of traditional energy leakage branch monitoring methods, which mainly rely on periodic offline testing of energy leakage branches. However, this method is difficult to reflect the real-time health status of energy leakage branches and cannot provide early warnings, thus reducing the reliability of offshore wind medium-frequency transmission systems. Compared with traditional energy leakage branch monitoring methods, this new method performs loss analysis and junction temperature assessment on monitoring data to obtain the corresponding branch junction temperature. Then, based on pre-acquired historical branch junction temperatures and damage, it predicts the lifespan of the branch junction temperature to obtain the corresponding cumulative damage. Finally, based on monitoring data, branch junction temperature, and cumulative damage, it performs a status assessment to obtain the status assessment result of the energy leakage branch. This achieves accurate assessment of the energy leakage branch status and improves the reliability of offshore wind medium-frequency transmission systems.

[0161] Please see Figure 4 , Figure 4 This is a structural block diagram of a power leakage branch monitoring system for a converter valve submodule of a sea-wind medium-frequency power transmission system, provided in Embodiment 3 of the present invention.

[0162] This invention provides a monitoring system for the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, comprising:

[0163] The acquisition module 301 is used to 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.

[0164] Junction temperature assessment module 302 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.

[0165] Prediction module 303 is used to predict the lifetime of branch junction temperature based on pre-acquired historical branch junction temperature and pre-acquired historical branch damage, and obtain the corresponding cumulative branch damage.

[0166] The condition assessment module 304 is used to perform condition assessment based on monitoring data, branch junction temperature, and cumulative damage of the branch, and obtain the condition assessment result corresponding to the energy leakage branch.

[0167] Furthermore, the monitoring data includes the average collector current, the effective collector current, the average diode current, and the effective diode current. The acquisition module 301 includes:

[0168] The conduction loss submodule is used to input the average value of the collector current and the effective value of the collector current into a preset conduction loss function to obtain the corresponding conduction loss.

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

[0170] The diode loss submodule is used to input the average value of the diode current and the effective value of the diode current into a preset diode loss function to obtain the corresponding diode conduction loss and reverse recovery loss.

[0171] The branch loss submodule is used to sum the conduction loss, switching loss, diode conduction loss, and reverse recovery loss to obtain the corresponding branch loss.

[0172] Furthermore, the junction temperature assessment module 302 includes:

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

[0174] The first multiplication submodule is used to multiply the branch thermal resistance and the branch loss to obtain the corresponding first multiplication value.

[0175] The junction temperature submodule is used to sum the first multiplier with the ambient temperature of the monitoring data to obtain the corresponding branch junction temperature.

[0176] Furthermore, the prediction module 303 includes:

[0177] The difference submodule is used to perform difference processing on the pre-acquired historical branch junction temperature and the branch junction temperature to obtain the corresponding first difference value;

[0178] The branch damage submodule is used to input the absolute value of the first difference into a preset branch damage function to obtain the corresponding branch damage value.

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

[0180] Furthermore, the state assessment module 304 includes:

[0181] The branch remaining life submodule is used to input the cumulative damage of the branch into a preset remaining life prediction function to obtain the corresponding branch remaining life.

[0182] The expected lifetime distribution interval submodule is used to acquire the thermal resistance normal distribution data and pressure drop normal distribution data corresponding to the energy leakage branch, and to perform Monte Carlo analysis on the thermal resistance normal distribution data and pressure drop normal distribution data to obtain the corresponding expected lifetime distribution interval.

[0183] The branch remaining lifetime probability submodule is used to retrieve the expected lifetime distribution area using the branch remaining lifetime and obtain the corresponding branch remaining lifetime probability.

[0184] The status assessment submodule is used to perform difference processing between the on-state voltage drop of the monitoring data and the preset standard on-state voltage drop to obtain the corresponding second difference value;

[0185] The corresponding target bond is generated using the absolute value of the second difference, the branch junction temperature, the branch cumulative damage, the switching component voltage and the effective value of the collector current from the monitoring data.

[0186] The target key is used to retrieve a pre-defined list of state key-value pairs and match the corresponding branch state.

[0187] The branch status, remaining lifespan, and remaining lifespan probability are used as the status assessment results for the energy leakage branch.

[0188] Furthermore, the expected lifespan distribution interval submodule includes:

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

[0190] The statistical analysis unit is used to input the target thermal resistance and target voltage drop into a preset lifetime function to obtain the corresponding expected lifetime and to statistically analyze the expected lifetime value.

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

[0192] The fitting unit is used to fit each expected lifetime with a Weibull distribution when the quantity value is greater than or equal to the iteration threshold, so as to obtain the corresponding expected lifetime distribution interval.

[0193] Please see Figure 5 , Figure 5 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0194] An electronic device according to an embodiment of the present invention includes: 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 energy leakage branch monitoring method of the converter valve submodule of the offshore wind medium frequency transmission system as described in any of the above embodiments.

[0195] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When these codes are run by a computing device, the computing device causes the computing device to perform the various steps in the energy leakage branch monitoring method of the converter valve submodule of the offshore wind medium frequency transmission system described above.

[0196] Embodiment 5 of the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the energy leakage branch monitoring method of the converter valve submodule of the offshore wind medium-frequency transmission system as described in any of the above embodiments.

[0197] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the energy leakage branch monitoring method of the converter valve submodule of the offshore wind medium-frequency transmission system as described in any of the above embodiments.

[0198] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0199] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0200] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0201] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0202] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0203] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

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; Input the branch loss and the ambient temperature of the monitoring data into a preset junction temperature evaluation function 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. 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. The junction temperature evaluation function is specifically as follows: ； in, For branch junction temperature, For ambient temperature, For branch losses, The first thermal resistance, The second thermal resistance, The third thermal resistance; The specific flux loss function is as follows: ； in, For on-state losses, The first fitting parameter used when fitting the IGBT conduction characteristic curve. The second fitting parameter is used when fitting the IGBT conduction characteristic curve. This represents the average collector current. This is the effective value of the collector current; The switching loss function is specifically as follows: ； in, For switching losses, For switching frequency, The third fitting parameter used when fitting the IGBT switching loss characteristic curve. The fourth fitting parameter used when fitting the IGBT switching loss characteristic curve. The fifth fitting parameter used when fitting the IGBT switching loss characteristic curve; The diode loss function is specifically as follows: ； in, For diode conduction losses, The sixth fitting parameter used when fitting the IGBT switching loss characteristic curve. The seventh fitting parameter used when fitting the IGBT switching loss characteristic curve. The eighth fitting parameter used when fitting the IGBT switching loss characteristic curve. This represents the average diode current. This is the effective value of the diode current. For reverse recovery loss, The ninth fitting parameter used when fitting the conduction characteristic curve of an anti-parallel diode. The tenth fitting parameter is used to fit the conduction characteristic curve of an anti-parallel diode.

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 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.

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 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.

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 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.

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 4, 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.

6. A monitoring system for the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system, used to implement the monitoring method for the energy leakage branch of a converter valve submodule in a marine wind-driven medium-frequency power transmission system as described in any one of claims 1-5, 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.

7. 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 power transmission system as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the energy leakage branch monitoring method of the converter valve submodule of the offshore wind medium frequency transmission system as described in any one of claims 1-5.

9. 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 1-5.

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