Bus capacitor life estimation method and device, storage medium and electronic equipment

Abstract

The invention relates to the technical field of electronics, in particular to a bus capacitor life estimation method and device, a storage medium and electronic equipment, and the method comprises the steps: collecting the operation characteristic parameters and instantaneous environment temperature of a system where a bus capacitor is located in real time according to a sampling period; judging a working condition category corresponding to the current sampling period according to the operation characteristic parameters, and obtaining a unit damage life and a correction coefficient corresponding to the working condition category; calculating to obtain the damage life increment of the current sampling period, wherein the calculation formula is shown in the specification; accumulating the damage increments of all the sampling periods to obtain a total accumulated damage life; and subtracting the total accumulated damage life from the rated life of the bus capacitor to obtain the residual life of the bus capacitor. On the premise that additional hardware sampling equipment is not added, high-precision and high-efficiency bus capacitor online service life estimation can be completed, and meanwhile the good working condition self-adaptive capacity is achieved.

Description

Technical Field

[0001] This invention relates to the field of electronic technology, and specifically to a method, apparatus, storage medium, and electronic device for estimating the lifespan of bus capacitors. Background Technology

[0002] Bus capacitors (mostly film capacitors) are core energy storage and filtering components in power electronic equipment such as motor drive systems and frequency converters. Their health status directly determines the operational reliability of the entire equipment. The reliability of the film inside the capacitor is the weakest link in the entire system. Its performance degradation can easily lead to equipment failure and affect the stable operation of the system.

[0003] Traditional methods for predicting bus capacitor lifespan often rely on single parameters such as operating temperature and internal temperature rise to build predictive models, which have significant limitations under complex and variable operating conditions. In practical applications, the capacitor's operating voltage and ripple current are both key factors affecting its lifespan, with ripple current being the root cause of increased internal temperature rise and losses. Single-parameter models cannot reflect the combined effects of multiple factors on capacitor lifespan. Some existing online lifespan prediction solutions attempt to improve the prediction model by directly measuring ripple current through hardware sampling methods such as sampling resistors, but this approach not only increases the hardware complexity of the system but also raises the research, development, and application costs of the equipment.

[0004] In summary, how to comprehensively consider the impact of multiple operating conditions and stresses on the lifespan of bus capacitors without increasing additional hardware investment, and achieve high-precision online lifespan prediction of capacitors, has become a key technical challenge for improving the reliability of power electronic equipment systems and promoting the transformation of equipment from periodic maintenance to predictive maintenance. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, storage medium, and electronic device for estimating the lifespan of bus capacitors, which can achieve high-precision and high-efficiency online estimation of bus capacitor lifespan without adding additional hardware sampling equipment, while also possessing good adaptability to operating conditions.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, the present invention discloses a method for estimating the lifespan of a bus capacitor, comprising:

[0008] With sampling period Real-time acquisition of operating characteristic parameters and instantaneous ambient temperature of the system where the bus capacitor is located;

[0009] The operating condition category corresponding to the current sampling period is determined based on the aforementioned operating characteristic parameters. Obtain the unit damage lifetime corresponding to the operating condition category. and correction factor ;

[0010] The damage lifetime increment for the current sampling period is calculated. The calculation formula is: ;

[0011] The total cumulative damage lifetime is obtained by summing the damage increments over all sampling periods.

[0012] The remaining life of the bus capacitor is obtained by subtracting the total cumulative damage life from the rated life of the bus capacitor.

[0013] In the above technical solution, by collecting system operating characteristic parameters and instantaneous ambient temperature in real time at sampling intervals, and combining this with the determination of operating condition category, real-time calculation of life damage can be achieved. This accurately captures the dynamic changes in operating conditions and ambient temperature, synchronizing life damage calculation with the actual aging process of the capacitor. This effectively improves the timeliness and accuracy of remaining life assessment and avoids assessment deviations caused by sudden changes in operating conditions and temperature fluctuations. Furthermore, by determining the current operating condition category based on the operating characteristic parameters and matching the corresponding unit damage life and correction coefficient, targeted adaptation between the operating condition and the life damage model is achieved. This fully reflects the differentiated impact of multi-factor coupling effects on capacitor life under different operating conditions, conforms to the physical aging mechanism of capacitors, and solves the technical problem that traditional single-parameter models cannot reflect multi-condition coupled damage, significantly improving the accuracy of life estimation.

[0014] Furthermore, this application employs a single-sampling-cycle damage lifetime increment calculation and full-cycle incremental accumulation method, decomposing the overall lifetime loss calculation into simple basic multiplication and accumulation operations. This results in a small computational load per operation and low hardware computational burden, allowing direct execution within existing embedded controllers of power electronic equipment. It adapts to the equipment's hardware operating environment and meets the requirements for online real-time calculation. The remaining lifespan of the bus capacitor is directly obtained by subtracting the total accumulated damage lifetime from the rated lifespan. The evaluation results are intuitive and clear, providing a clear indicator of capacitor health status for equipment operation and maintenance. This facilitates the transformation of power electronic equipment from traditional periodic maintenance to condition-based predictive maintenance, enabling early prediction of capacitor failure risks and avoiding system downtime due to sudden capacitor failure, effectively improving the overall reliability and availability of the equipment.

[0015] Meanwhile, the operating characteristic parameters and instantaneous ambient temperature required by this application are all conventional parameters that can be directly collected by the power electronic equipment controller. There is no need to add additional hardware devices such as sampling resistors and current probes, nor is it necessary to modify the main circuit of the system. While achieving high-precision life estimation, it effectively controls the system transformation cost, reduces the system complexity, and is easy to integrate into existing products to achieve large-scale application, making it highly practical for engineering.

[0016] Furthermore, the correction coefficient is determined by querying a pre-established mapping relationship between the ambient temperature corresponding to the operating condition category and the correction coefficient, based on the operating condition category and the instantaneous ambient temperature; wherein, different operating condition categories correspond to independent mapping relationships.

[0017] In the above technical solution, an independent mapping relationship between ambient temperature and correction coefficient is configured for each operating condition category. This can accurately reflect the differentiated impact of ambient temperature on capacitor life damage under different operating conditions, avoid the adaptation deviation of a single mapping relationship for multiple operating conditions and multiple temperature coupling scenarios, make the calculation of damage life increment more in line with the actual physical aging law of capacitor, and further improve the accuracy of remaining life estimation.

[0018] Furthermore, the formula for calculating the unit damage life corresponding to the aforementioned operating condition category is as follows: In the formula, The unit damage life under rated conditions. Operating condition category The corresponding actual operating voltage, Operating condition category The corresponding rated operating voltage, Voltage stress coefficient, Operating condition category The corresponding rated operating temperature, The instantaneous ambient temperature Operating condition category The corresponding temperature rise value.

[0019] In the above technical solution, the calculation formula decouples the capacitor life degradation process into voltage stress. With thermal stress Two dominant factors are identified: voltage stress, which reflects the accelerated aging effect of the electric field on the dielectric through an inverse power law relationship, and thermal stress, which reflects the influence of temperature on the chemical reaction rate through an Arrhenius-type exponential term. This modeling method based on the physical mechanisms of aging avoids the simplistic assumptions of traditional single-parameter models, making the lifetime estimation results more consistent with the actual failure path of capacitors.

[0020] In the calculation formula The method adds the instantaneous ambient temperature to the temperature rise value corresponding to the operating condition category, which is equivalent to the actual internal operating temperature of the capacitor. This dynamically reflects the additional heating effect caused by ripple current under different load conditions without relying on real-time thermal simulation or additional temperature measurement. This allows for precise differentiation of the lifespan loss difference between high-ripple current and low-ripple current operating conditions under the same ambient temperature, significantly improving the prediction accuracy in complex operating scenarios. Furthermore, the key parameters in the calculation formula... All data can be directly acquired or pre-calibrated using existing sensors or control algorithms within the system, without the need for additional dedicated hardware. The controller can calculate in real time based on the current operating conditions and ambient temperature during each sampling cycle. This enables dynamic updates of lifetime damage, meeting the dual requirements of power electronic systems for real-time performance and low overhead.

[0021] Furthermore, determining the operating condition category corresponding to the current sampling period based on the aforementioned operating characteristic parameters includes:

[0022] Match the operating feature parameters of the current sampling period with the pre-established working condition category feature library;

[0023] The working condition category feature library contains multiple working condition categories, and each working condition category is associated with its corresponding feature parameter range or category center;

[0024] Based on the matching results, the operating feature parameters of the current sampling period are assigned to the operating condition category with the highest matching degree.

[0025] In the above technical solution, matching and judgment based on an offline-built feature library effectively avoids interference from instantaneous noise, sensor fluctuations, or short-term anomalies during real-time operation on the judgment of operating conditions. By using the "highest matching degree" criterion, the system can still make reasonable assignments when feature parameters are at the category boundary, significantly improving the recognition stability during dynamic operating condition switching and avoiding jumps in lifespan calculations due to misjudgments of operating conditions. Furthermore, by completely transferring complex operating condition clustering analysis to the offline stage, retaining only lightweight matching query operations for online judgment, online calculations only need to perform low-complexity operations such as vector distance calculations or threshold comparisons, greatly reducing the real-time computational burden on the embedded controller. This ensures that there are no performance conflicts between the lifespan estimation module and the main control system during collaborative operation, perfectly meeting the stringent real-time requirements of industrial control scenarios.

[0026] Furthermore, the construction of the working condition category feature library includes:

[0027] Collect historical operational data and extract multi-dimensional operational feature parameters corresponding to each set of data to form a feature vector;

[0028] The feature vectors were clustered offline using the K-means clustering algorithm, and the actual operating conditions were divided into 5 to 12 categories.

[0029] The cluster center vector corresponding to each working condition category is used as the category center of that working condition category and stored in the working condition category feature library.

[0030] In the above technical solution, unsupervised clustering is performed based on massive historical operating data. The classification of operating conditions fully follows the inherent distribution law of the actual operating characteristics of the equipment, effectively avoiding the subjective bias of manual experience settings. The clustering process automatically identifies the natural clustering pattern of operating parameters, ensuring that the electro-thermal stress characteristics of operating conditions within the same category are highly similar. This provides a solid physical basis for sharing correction parameters for similar operating conditions in the future, fundamentally ensuring the applicability and reliability of the life correction model. In addition, limiting the number of clusters to a reasonable range of 5 to 12 is a scientific decision that comprehensively considers the completeness of operating condition coverage and the efficiency of system implementation: too few categories may lead to the incorrect merging of operating conditions with significant differences in physical characteristics, weakening the pertinence of life correction; too many categories will cause storage redundancy, increase the complexity of matching calculations, and may reduce the statistical significance of parameter calibration due to the sparsity of single-category samples.

[0031] Furthermore, the operating characteristic parameters include average operating voltage, load current characteristic value, modulation ratio, and power factor.

[0032] In the above technical solution, the average operating voltage directly quantifies the electric field stress intensity and is a key input to the voltage aging model; the load current characteristic value and modulation ratio implicitly characterize the amplitude and spectral characteristics of the ripple current; and the power factor reflects the load phase characteristics, further refining the waveform differences of the ripple current. The integration of these four factors allows for the complete extraction of electro-thermal stress information from existing system control parameters without the need for additional ripple current sampling, thoroughly resolving the contradiction between hardware increments and stress perception. Furthermore, all operating characteristic parameters are inherent computational or monitoring quantities in the digital control loops of systems such as frequency converters and motor drives, allowing for complete reliance on existing software architecture without the need for additional sensors, signal conditioning circuits, or communication interfaces, significantly improving the engineering feasibility of the solution in retrofitting existing equipment and deploying new platforms.

[0033] In a second aspect, the present invention discloses a bus capacitor life estimation device, comprising:

[0034] The data acquisition module is configured to use a sampling period. Real-time acquisition of operating characteristic parameters and instantaneous ambient temperature of the system where the bus capacitor is located;

[0035] The operating condition determination module is configured to determine the operating condition category corresponding to the current sampling period based on the operating characteristic parameters. ;

[0036] The parameter query module is configured to determine the operating condition category corresponding to the current sampling period based on the operating characteristic parameters. Obtain the unit damage lifetime corresponding to the operating condition category. and correction factor ;

[0037] The damage increment calculation module is configured to calculate based on the formula. Calculate the damage lifetime increment for the current sampling period. ;

[0038] The accumulation module is configured to accumulate the damage lifetime increments for all sampling periods to obtain the total cumulative damage lifetime.

[0039] The remaining life calculation module is configured to subtract the total cumulative damage life from the rated life of the bus capacitor and output the remaining life of the bus capacitor.

[0040] Thirdly, the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for estimating the lifespan of bus capacitors.

[0041] Fourthly, the present invention discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described bus capacitor lifetime estimation method. Attached Figure Description

[0042] Figure 1 A flowchart illustrating the bus capacitor lifetime estimation method provided in Embodiment 1 of this application is shown.

[0043] Figure 2 A schematic diagram of the bus capacitor life estimation device provided in Embodiment 2 of this application is shown. Detailed Implementation

[0044] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0045] In one embodiment, see Figure 1 As shown, this application provides a method for estimating the lifespan of bus capacitors, applicable to online lifespan monitoring of bus film capacitors (such as polypropylene film capacitors) in power electronic equipment such as motor drive systems and frequency converters. The method specifically includes the following steps:

[0046] Step S101: Real-time data acquisition, with sampling period t to collect the operating characteristic parameters and instantaneous ambient temperature of the system where the bus capacitor is located.

[0047] The operating characteristic parameters include: average operating voltage. Load current characteristic value, modulation ratio and power factor The average operating voltage is the average value of the bus voltage over one PWM cycle, and the load current characteristic value can be selected as the effective value of the load current. Alternatively, the load factor percentage, the modulation ratio (the ratio of the fundamental amplitude of the output voltage to the bus voltage), and the power factor (the ratio of the system's active power to its apparent power) can be obtained directly from the digital control loop of the existing motor controller without the need for additional sensors.

[0048] Instantaneous ambient temperature is monitored in real time by a temperature sensor (such as an NTC thermistor) installed near the bus capacitor.

[0049] Step S102, Operating condition category determination and parameter acquisition: Determine the operating condition category corresponding to the current sampling period based on the operating characteristic parameters. Obtain the unit damage lifetime corresponding to the operating condition category. and correction factor .

[0050] Specifically, the operating characteristic parameters of the current sampling period, i.e., the average operating voltage. Load current characteristic value, modulation ratio and power factor The system matches the data with a pre-established feature library of operating conditions. This feature library contains seven operating condition categories, each associated with its corresponding feature parameter range or category center. Based on the matching results, the operating feature parameters of the current sampling period are assigned to the operating condition category with the highest matching degree.

[0051] For example, the operating condition category corresponding to the current sampling period is determined based on the collected operating characteristic parameters. Specifically, it includes:

[0052] The four-dimensional feature vector of the current sampling period Perform Euclidean distance matching with a pre-established feature library of working conditions.

[0053] The working condition category feature library contains 7 working condition categories, and each category is associated with its cluster center vector. .

[0054] calculate With each Euclidean distance: .

[0055] Will The smallest category is determined as the current operating condition category. .

[0056] In one optional implementation, the construction of the operating condition category feature library includes: collecting 100,000 sets of historical operating data from the system, extracting multi-dimensional operating feature parameters corresponding to each set of data to form a feature vector, and using the K-means clustering algorithm to perform offline clustering on the feature vectors, dividing the actual operating conditions into 5 to 12 operating condition categories. The cluster center vector corresponding to each operating condition category is used as the category center for that operating condition category and stored in the operating condition category feature library.

[0057] The optimal number of clusters was determined using the elbow rule, including: calculating the sum of squared errors (SSE) for cluster number k from 2 to 15, and plotting the SSE-k variation curve. The curve characteristics were then analyzed. When k is less than the actual cluster structure, increasing the k value significantly improves the cluster cohesion, and the SSE shows a steep downward trend. When k reaches a critical value matching the actual operating condition distribution, the SSE decreases sharply (an inflection point appears), and further increasing the k value brings only a slight gain in cohesion, causing the curve to flatten. The k value corresponding to this inflection point was selected as the optimal number of clusters. After multiple rounds of experimental verification, when k=7, the SSE curve shows a significant inflection point, and the clustering results achieve the optimal balance between operating condition coverage completeness and computational efficiency. Therefore, the number of clusters was determined to be k=7. The seven operating condition categories were defined as: OC1 (light load steady state), OC2 (medium load constant speed), OC3 (heavy load start), OC4 (periodic fluctuation), OC5 (high modulation ratio), OC6 (low power factor), and OC7 (extreme overload). Each cluster center vector and its corresponding capacitor temperature rise (obtained through finite element thermal simulation or calibration experiment) are stored in the controller's non-volatile memory.

[0058] In one alternative implementation, unit damage lifetime The calculation is performed using an improved Arrhenius-inverse power-law coupling model, and the formula is as follows: In the formula, The unit damage life under rated conditions. Operating condition category The corresponding actual operating voltage, Operating condition category The corresponding rated operating voltage, This is the voltage stress coefficient, typically taken as 8. Operating condition category The corresponding rated operating temperature, The instantaneous ambient temperature Operating condition category The corresponding temperature rise value.

[0059] The bus capacitor life model uses the Arrhenius equation, which is specifically determined by the cell temperature and the actual operating voltage. The expression is as follows: , This refers to the operating temperature. Since the operating characteristics are highly consistent within the same cluster OCi, the effective value of the ripple current determined by the load and the resulting steady-state temperature rise inside the capacitor are... This can be considered a constant inherent to this cluster. This means that, for the OCi operating condition, the actual operating temperature T of the capacitor can be simplified as: ,in The instantaneous ambient temperature Operating condition category The corresponding temperature rise value.

[0060] Substituting this relationship into the lifetime model, the cell lifetime under the OCi condition is... It can be expressed as only relating to ambient temperature The function, that is, we get: This cleverly avoids the challenge of accurately measuring ripple current or internal temperature rise in real time online, which is precisely why many existing solutions are complex or costly to implement.

[0061] The lifespan can be obtained through a limited number of simulations or calibration experiments for this typical operating condition. and temperature sensitivity coefficient It can be pre-calculated and stored in the controller lookup table for quick retrieval during online calculations.

[0062] In one optional implementation, the working condition category and the instantaneous ambient temperature are determined by querying a pre-established mapping relationship between the ambient temperature corresponding to the working condition category and the correction coefficient; wherein, different working condition categories correspond to independent mapping relationships.

[0063] Specifically, the correction factor According to the type of work condition and instantaneous ambient temperature This is determined by looking up a table. For example, the controller internally stores seven mapping tables, one for each operating condition category, and each mapping table defines the ambient temperature range. With correction factor The corresponding relationship. For example, for OC3 (heavy load start) operating condition, in hour For OC1 (light load steady state) operating condition, at the same temperature... This reflects the differences in heat accumulation effects under different operating conditions.

[0064] Step S103: Calculate the damage lifetime increment for the current sampling period. The calculation formula is: .

[0065] in, The unit of measurement is "damage per hour per minute of operation", indicating the condition under operating conditions. And the number of hours of lifespan loss caused by each minute of operation under the current ambient temperature. This is a dimensionless correction factor, with a value range of [value range missing]. This is used to compensate for secondary aging factors that are not fully captured by the model.

[0066] Step S104: Accumulate the damage increments from all sampling periods to obtain the total cumulative damage lifetime. .

[0067] The calculation formula is: .in This represents the total number of sampling periods. For the first Damage lifetime increment within each sampling period .

[0068] The controller maintains a non-volatile counter to continuously record... Even if the system loses power, the accumulated data will not be lost.

[0069] Step S105: Subtract the total accumulated damage life from the rated life of the bus capacitor to obtain the remaining life of the bus capacitor.

[0070] The calculation formula is: The rated life of the bus capacitor Provided by the manufacturer, such as 100,000 hours.

[0071] when When the value falls below a preset threshold (e.g., 20% of the rated lifespan), a warning signal is triggered and output to the human-machine interface via the CAN bus or I / O interface, prompting maintenance personnel to arrange a replacement plan. Simultaneously, The data is uploaded to the device health management cloud platform for full lifecycle data analysis.

[0072] Example 2, see Figure 2 The structural block diagram shown illustrates that this application provides a bus capacitor life estimation device, which can be integrated into the DSP or MCU of a motor controller, and includes the following functional modules:

[0073] Data acquisition module 10 is configured to use a sampling period Real-time acquisition of operating characteristic parameters and instantaneous ambient temperature of the system where the bus capacitor is located.

[0074] The operating condition determination module 20 is configured to determine the operating condition category corresponding to the current sampling period based on the operating characteristic parameters. The operating condition determination module 20 can implement the K-means clustering matching algorithm.

[0075] The parameter query module 30 is configured to determine the operating condition category corresponding to the current sampling period based on the operating characteristic parameters. Obtain the unit damage lifetime corresponding to the operating condition category. and correction factor .

[0076] Damage increment calculation module 40, configured to calculate based on formula Calculate the damage lifetime increment for the current sampling period. .

[0077] The accumulation module 50 is configured to accumulate the damage lifetime increments for all sampling periods to obtain the total cumulative damage lifetime.

[0078] The remaining life calculation module 60 is configured to subtract the total cumulative damage life from the rated life of the bus capacitor and output the remaining life of the bus capacitor.

[0079] In embodiment three, this application provides a computer-readable storage medium, such as a Flash memory, EEPROM, or SD card, on which a computer program is stored. When executed by a processor, this program implements the aforementioned bus capacitor life estimation method. The program code adopts a modular design and includes three core function libraries: data acquisition, operating condition identification, and life calculation. It occupies less than 8KB of storage space and is suitable for resource-constrained embedded platforms.

[0080] Example 4: This application provides an electronic device, including:

[0081] The memory stores the computer program described in Embodiment 3;

[0082] Processor, 16-bit or 32-bit MCU;

[0083] Communication interface, CAN bus controller, used to output remaining lifetime data;

[0084] The power management unit ensures that accumulated data is saved in the event of a system power failure.

[0085] When the processor executes the program, it implements all the method steps of Embodiment 1 and updates the life status of the bus capacitor in real time.

[0086] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention. Furthermore, the embodiments of the present invention and the features thereof can be combined with each other unless otherwise specified.

Claims

1. A method for estimating the lifespan of bus capacitors, characterized in that, include: With sampling period Real-time acquisition of operating characteristic parameters and instantaneous ambient temperature of the system where the bus capacitor is located; The operating condition category corresponding to the current sampling period is determined based on the aforementioned operating characteristic parameters. Obtain the unit damage lifetime corresponding to the operating condition category. and correction factor ; The damage lifetime increment for the current sampling period is calculated. The calculation formula is: ; The total cumulative damage lifetime is obtained by summing the damage increments over all sampling periods. The remaining life of the bus capacitor is obtained by subtracting the total cumulative damage life from the rated life of the bus capacitor.

2. The method for estimating the lifespan of bus capacitors according to claim 1, characterized in that: The correction coefficient is determined by querying a pre-established mapping relationship between the ambient temperature corresponding to the operating condition category and the correction coefficient, based on the operating condition category and the instantaneous ambient temperature; wherein, different operating condition categories correspond to independent mapping relationships.

3. The method for estimating the lifespan of bus capacitors according to claim 1, characterized in that: The formula for calculating the unit damage life corresponding to the aforementioned operating condition category is as follows: In the formula, The unit damage life under rated conditions. Operating condition category The corresponding actual operating voltage, Operating condition category The corresponding rated operating voltage, Voltage stress coefficient, Operating condition category The corresponding rated operating temperature, The instantaneous ambient temperature Operating condition category The corresponding temperature rise value.

4. The method for estimating the lifespan of bus capacitors according to claim 1, characterized in that, The operating condition category corresponding to the current sampling period is determined based on the aforementioned operating characteristic parameters, including: Match the operating feature parameters of the current sampling period with the pre-established working condition category feature library; The working condition category feature library contains multiple working condition categories, and each working condition category is associated with its corresponding feature parameter range or category center; Based on the matching results, the operating feature parameters of the current sampling period are assigned to the operating condition category with the highest matching degree.

5. The method for estimating the lifespan of bus capacitors according to claim 4, characterized in that, The construction of the working condition category feature library includes: Collect historical operational data and extract multi-dimensional operational feature parameters corresponding to each set of data to form a feature vector; The feature vectors were clustered offline using the K-means clustering algorithm, and the actual operating conditions were divided into 5 to 12 categories. The cluster center vector corresponding to each working condition category is used as the category center of that working condition category and stored in the working condition category feature library.

6. The method for estimating bus capacitor life according to claim 1, characterized in that: The operating characteristic parameters include average operating voltage, load current characteristic value, modulation ratio, and power factor.

7. A bus capacitor life estimation device, characterized in that, include: The data acquisition module is configured to use a sampling period. Real-time acquisition of operating characteristic parameters and instantaneous ambient temperature of the system where the bus capacitor is located; The operating condition determination module is configured to determine the operating condition category corresponding to the current sampling period based on the operating characteristic parameters. ; The parameter query module is configured to determine the operating condition category corresponding to the current sampling period based on the operating characteristic parameters. Obtain the unit damage lifetime corresponding to the operating condition category. and correction factor ; The damage increment calculation module is configured to calculate based on the formula. Calculate the damage lifetime increment for the current sampling period. ; The accumulation module is configured to accumulate the damage lifetime increments for all sampling periods to obtain the total cumulative damage lifetime. The remaining life calculation module is configured to subtract the total cumulative damage life from the rated life of the bus capacitor and output the remaining life of the bus capacitor.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the bus capacitor lifetime estimation method as described in any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the bus capacitor lifetime estimation method as described in any one of claims 1 to 6.

Images