A Fault Current Suppression Method and System for DC Ice-Melting Device

Abstract

The present invention relates to the field of information technology, and discloses a fault current suppression method and system for a DC ice melting device. The method comprises the following steps: analyzing fault current data of the DC ice melting device by fast Fourier transform, and obtaining DC bus voltage drop data based on the analysis result, processing the data using a support vector regression algorithm, obtaining a voltage stress surge trend, and then determining stress distribution data of switching devices in the device; processing the stress distribution data using a Kalman filter algorithm, obtaining a processing result to determine a dynamic correction coefficient of a bridge arm switching timing, and processing the result and the device's component response delay data using a particle swarm optimization algorithm to generate a bridge arm switching timing sequence to determine a real-time adjustment parameter affecting the discharge rate of a buffer capacitor in the device, so as to perform impedance matching on a capacitor discharge circuit, and comparing voltage oscillation characteristics before and after matching to determine an impedance compensation value for a commutation path for execution, thereby effectively suppressing current oscillations in the DC ice melting device.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a fault current suppression method and system for a DC ice melting device. Background Art

[0002] With climate change and the frequent occurrence of extreme weather, the role of DC de-icing technology in suppressing fault currents and improving system reliability has become increasingly prominent. As a key technology for combating line icing in power systems, research on DC de-icing devices is crucial for ensuring the safe and stable operation of power grids. However, traditional DC de-icing devices mostly rely on a single topology or fixed control strategy, making it difficult to effectively address the sudden voltage stress and current oscillations encountered during bridge arm reconstruction.

[0003] Therefore, how to deal with the voltage stress mutation and current oscillation of the existing DC ice melting device during the bridge arm reconstruction process has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0004] The present invention provides a fault current suppression method and system for a DC ice melting device, which solves the problem of how to deal with voltage stress mutation and current oscillation in the bridge arm reconstruction process of the existing DC ice melting device.

[0005] To solve the above technical problems, the first aspect of the present invention provides a fault current suppression method for a DC ice melting device, comprising:

[0006] Real-time acquisition of commutation path impedance and fault current data from the DC de-icing device. Fast Fourier transform analysis is performed to obtain current oscillation data, which is then fed into a pre-built impedance and current relationship model for processing, generating DC bus voltage drop data.

[0007] The DC bus voltage drop data is processed using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching device in the DC ice melting device, and the stress distribution data is processed using a Kalman filter algorithm to generate comprehensive stress distribution data to determine a dynamic correction coefficient for the bridge arm switching timing;

[0008] Obtaining component response delay data of the DC ice melting device and combining it with the dynamic correction coefficient, processing the data using a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence to determine a real-time adjustment parameter that affects the discharge rate of the buffer capacitor in the DC ice melting device;

[0009] Real-time data of the DC bus voltage in the DC ice-melting device is extracted and combined with the real-time adjustment parameters, adaptive impedance matching processing is performed on the capacitor discharge circuit, and an impedance compensation value for the commutation path is determined by comparing voltage oscillation characteristics before and after impedance matching to achieve suppression of current oscillation in the DC ice-melting device.

[0010] A second aspect of the present invention provides a fault current suppression system for a DC ice melting device, comprising:

[0011] The current data processing module is used to collect the commutation path impedance and fault current data of the DC ice melting device in real time, and analyze them through fast Fourier transform to obtain current oscillation data. The data is then input into a pre-built impedance and current relationship model for processing to generate DC bus voltage drop data.

[0012] a correction coefficient generation module, configured to process the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching devices in the DC ice melting device, and process the stress distribution data using a Kalman filter algorithm to generate comprehensive stress distribution data to determine a dynamic correction coefficient for the bridge arm switching timing;

[0013] an adjustment parameter determination module, configured to obtain component response delay data of the DC ice-melting device, combine the data with the dynamic correction coefficient, and process the data using a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence to determine a real-time adjustment parameter affecting the discharge rate of the buffer capacitor in the DC ice-melting device;

[0014] A current oscillation suppression module is configured to extract real-time data of the DC bus voltage in the DC ice-melting device and combine it with the real-time adjustment parameters to perform adaptive impedance matching on the capacitor discharge circuit, and determine an impedance compensation value for the commutation path by comparing the voltage oscillation characteristics before and after impedance matching, thereby suppressing current oscillations in the DC ice-melting device.

[0015] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0016] (1) By collecting commutation path impedance and fault current data in real time and extracting current oscillation characteristics using fast Fourier transform, high-frequency harmonic components can be dynamically identified and oscillations of specific frequencies can be suppressed in a targeted manner. The support vector regression algorithm is used to perform nonlinear modeling on bus voltage drop data to predict the sudden increase trend of voltage stress and effectively avoid overvoltage damage to switching devices. Based on the bridge arm switching timing sequence generated by particle swarm optimization and combined with the component response delay data, the buffer capacitor discharge rate is dynamically adjusted to reduce the voltage oscillation amplitude.

[0017] (2) Through adaptive impedance compensation, the impedance of the commutation path is matched with the capacitor discharge circuit to reduce the voltage spikes caused by the reflected wave; multiple algorithms such as fast Fourier transform, support vector regression, Kalman filtering, and particle swarm optimization are used to coordinate and cover the entire chain from signal processing to control decision-making, and the impedance compensation value is dynamically adjusted according to the real-time feedback of the bus voltage to form a "perception-decision-execution" closed loop. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a flow chart of a fault current suppression method for a DC ice melting device provided by one embodiment of the present invention;

[0020] Figure 2 This is a structural diagram of an electronic device provided by one embodiment of the present invention;

[0021] Reference numerals:

[0022] Among them, 10, current data processing module; 20, correction coefficient generation module; 30, adjustment parameter determination module; 40, current oscillation suppression module. DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings and embodiments. Obviously, the embodiments described are only some embodiments of the present invention, rather than all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] In the description of this application, the terms "first," "second," "third," etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first," "second," "third," etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0025] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the system or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0026] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meanings as those commonly understood by those skilled in the art. The terms used in this specification are only for describing specific embodiments and are not intended to limit the present invention. Those skilled in the art will understand the specific meanings of the above terms in this application according to specific circumstances.

[0027] At present, bridge arm reconstruction technology is usually used to suppress fault current on the DC side of DC ice melting devices. However, when the bridge arm topology switches from a full-bridge structure to a half-bridge structure, the impedance mutation of the commutation circuit in the DC ice melting device will cause the current oscillation amplitude to increase, the DC bus voltage to drop instantaneously, and the voltage stress borne by the switching devices to increase instantaneously. Therefore, how to dynamically adjust the bridge arm switching timing and the buffer capacitor discharge rate to effectively deal with the voltage stress mutation and current oscillation of the existing DC ice melting device during the bridge arm reconstruction process has become an urgent problem to be solved.

[0028] Based on this, in one embodiment, if Figure 1 As shown, the first aspect of the present invention provides a fault current suppression method for a DC ice melting device, comprising:

[0029] S1. Real-time acquisition of commutation path impedance and fault current data of the DC ice melting device, and analysis through fast Fourier transform to obtain current oscillation data, which is input into a pre-built impedance and current relationship model for processing to generate DC bus voltage drop data;

[0030] In one embodiment, step S1 includes:

[0031] collecting commutation path impedance and fault current data of the DC ice melting device in real time to construct an original data set, and processing the original data set using a fast Fourier transform to obtain current oscillation data of the DC ice melting device;

[0032] When the current oscillation data exceeds a preset oscillation threshold, triggering a timing adjustment, and calculating an initial switching timing and a timing adjustment amount of the current oscillation data to obtain timing adjustment data;

[0033] Inputting the timing adjustment data into a pre-built impedance and current relationship model for processing, outputting the capacitor discharge data of the DC ice melting device, and processing the capacitor discharge data using a numerical integration method to obtain a voltage change trend;

[0034] The voltage variation trend is compared with a preset voltage variation range to determine DC bus voltage drop data of the DC ice melting device.

[0035] Specifically, because the impedance and fault current data of the commutation path are fundamental to monitoring the operational status of the entire DC de-icing device control system, the present invention uses high-precision current transformers and voltage transformers to collect the corresponding fault current and voltage data. The sampling frequency can reach above 10 kHz to ensure that high-frequency oscillation characteristics are captured. The impedance of the commutation path is determined by injecting a 1 kHz test current and measuring the voltage response. High-precision impedance sensors, such as those based on the four-terminal impedance measurement method, can also be used to collect the impedance of the commutation path. The commutation path in a DC de-icing device refers to the path by which current flows from the DC power source through components such as the converter and bridge arm buffer capacitors, ultimately to the transmission line. This path ensures smooth current flow, thereby achieving de-icing. The collected impedance and fault current data are constructed into a raw data set and processed using a fast Fourier transform to reveal the system's oscillation characteristics, resulting in current oscillation data for the DC de-icing device, including oscillation amplitude and oscillation frequency.

[0036] It should be noted that the selection of the preset oscillation threshold is crucial for judging the stability of the system. The present invention sets the oscillation amplitude threshold in the preset oscillation threshold to 8% of the rated current, and the oscillation frequency threshold in the preset oscillation threshold is set to 1.5kHz. When the current oscillation data exceeds these thresholds, the system will trigger the timing adjustment process and calculate the initial switching timing and timing adjustment amount of the current oscillation data based on a predefined empirical formula to obtain the timing adjustment data; wherein, the empirical formula is obtained by fitting the historical oscillation data: the initial switching timing contained in the timing adjustment data is set to 1 / 4 of the cycle, and the timing adjustment amount contained in the timing adjustment data is a linear function based on the oscillation amplitude, that is, every 1% increase in the oscillation amplitude corresponds to a timing delay of 0.1ms.

[0037] The impedance current model is key to predicting system behavior. This paper establishes a mathematical model of the relationship between impedance and current based on the circuit topology and component characteristics of the DC ice-melting device. By considering factors such as the dynamic characteristics of the switching tube and the nonlinear effects of the DC bus capacitor, a differential equation or difference equation is established to describe the relationship between impedance and current. Timing adjustment data is input into the constructed impedance current model for processing, and capacitor discharge data of the DC ice-melting device is output, including curves showing the change of capacitor voltage and capacitor current over time, thereby providing a reliable basis for subsequent voltage drop analysis. Since the numerical integration method has significant advantages in calculating the DC bus voltage, the present invention determines the integration interval based on the start time and end time of the capacitor discharge data. The start time is usually a certain moment after the timing adjustment, and the end time is the time when the capacitor voltage discharges to a specific value (the specific moment and specific value can be determined based on historical data or human experience). The capacitor discharge data is then integrated using the trapezoidal method or the Simpson method. The integration step can be selected to be 10μs. This can reduce the computational burden while ensuring accuracy, so as to obtain the change in capacitor voltage, combine it with the initial voltage value of the capacitor, and then calculate the change trend of the capacitor voltage over time, that is, the voltage change trend.

[0038] The preset voltage variation range is determined based on the design parameters and normal operating requirements of the DC ice-melting device. For example, if the device design requires that the DC bus voltage fluctuation range during normal operation does not exceed ±5%, the preset voltage variation range can be set to ±5% of the rated voltage. When the voltage variation trend exceeds the preset voltage variation range, the start and end times of the range are recorded, and the voltage drop amplitude is calculated to serve as the DC bus voltage drop data of the DC ice-melting device. The voltage drop amplitude can be calculated as the difference between the voltage values corresponding to the start and end times.

[0039] The present invention triggers timing adjustment by setting a preset oscillation threshold, which can quickly respond to abnormal current oscillations occurring during device operation, avoid further deterioration of current oscillations, protect the DC ice melting device from potential damage, and improve the reliability and safety of device operation; the impedance-current relationship model combined with the numerical integration method can predict the voltage change trend of future power frequency cycles, thereby reducing errors and accurately obtaining specific information on voltage drops, thereby optimizing the device's operation control strategy, improving the operating efficiency and performance of the DC ice melting device, and making it better adapt to actual work needs.

[0040] S2. Processing the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching devices in the DC ice melting device, and processing the stress distribution data using a Kalman filter algorithm to generate comprehensive stress distribution data to determine a dynamic correction coefficient for the bridge arm switching timing;

[0041] In one embodiment, the use of a support vector regression algorithm to process the DC bus voltage drop data to obtain a voltage stress surge trend to determine stress distribution data of a switching device in the DC ice melting device includes:

[0042] Processing the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend, and quantifying a pressure sudden increase rate of the DC ice melting device according to the voltage stress sudden increase trend;

[0043] If the pressure sudden increase rate exceeds a preset sudden increase threshold, obtaining status data of a switch component in the DC ice melting device to determine a protection threshold adjustment requirement;

[0044] Based on the protection threshold adjustment requirement, characteristic curve data is calculated using voltage characteristic data and current characteristic data of the DC ice melting device, and a distribution estimation value is generated using the characteristic curve data to determine stress distribution data of the switching device.

[0045] Specifically, the present invention processes DC bus voltage drop data using a support vector regression algorithm to identify stress spike trends. The SVR algorithm employs a radial basis function (RBF) kernel and trains the SVR model using historical voltage drop data and corresponding voltage stress data. During training, model parameters are adjusted through methods such as cross-validation to optimize performance. For example, if collected voltage drop data shows a sudden 10% drop from the rated value within a certain period, SVR can fit these data points to predict a stress trend curve over time, i.e., the voltage stress spike trend. The SVR algorithm captures the nonlinear variations in DC bus voltage drop data, providing a reliable basis for subsequent analysis. The slope of the trend curve is then used to directly calculate the pressure spike rate of the DC de-icing device. Analysis of multiple data sets reveals that the rate distribution is typically concentrated in high-load switching scenarios.

[0046] It should be noted that the determination of the preset surge threshold (preferably 45% / ms) helps to judge whether the system is close to the edge of instability. If the pressure surge rate exceeds the preset surge threshold, it is necessary to determine the need for adjusting the protection threshold based on the status data of the switching device. For example, if the temperature of the switching device rises from 50°C to 80°C, and the current data exceeds the rated value by 20%, it indicates that the current protection threshold is insufficient to cope with the stress shock. In this case, it is necessary to determine the threshold adjustment need based on the status data of the switching device and the size of the pressure surge rate. For example, a temperature threshold and a pressure surge rate threshold are set based on historical data. When the actual temperature exceeds When the temperature threshold is exceeded and the pressure surge rate exceeds the rate threshold, the protection threshold is lowered to the first ratio. An operating duration threshold is set based on historical data. When the operating duration is less than the operating duration threshold and the pressure surge rate is near the rate threshold, the protection threshold is raised to the second ratio. The first and second ratios can also be determined based on historical data or manual experience, but the adjustment range is limited to ±10%. This adjustment is based on an assessment of the switching device's tolerance to ensure safe operation under high stress. The necessity of adjustment can also be further confirmed by real-time monitoring of the switching device's operating status, such as on-time and off-frequency. Switching devices in DC ice-melting devices control the on-off flow of current. By controlling the on-off state of the switching devices, precise control of the ice-melting process can be achieved. Common switching devices include thyristors and IGBTs.

[0047] Based on the protection threshold adjustment requirements, characteristic parameters are extracted from the voltage characteristic data (such as the DC bus voltage and the voltage across the switching device) and current characteristic data (such as the commutation path current and the switching device current) of the DC ice melting device. The extracted characteristic parameters must be within the protection threshold adjustment requirements. That is, key indicators such as peak voltage and peak current are fitted using quadratic interpolation to generate characteristic curve data to reflect the overall stress change. Based on the characteristic curve data and the status data of the switching device, a large number of random samples are generated using the Monte Carlo simulation method. For each random sample, the corresponding voltage stress is calculated based on the physical model and characteristic curve of the switching device. The voltage stress S of the switching device can be expressed as S=f(V, I), where f is the stress calculation function, which is related to the type and structure of the switching device. V and I are the voltage and current of the switching device, respectively. Key data such as the maximum, minimum, average, or standard deviation of the stress are extracted from the estimated value of the stress distribution as the stress distribution data of the switching device. In addition, when generating distribution estimates through curve data, statistical methods can also be used to determine the stress distribution range of the switching device. For example, when the stress estimate shows that 90% of the switching devices have stress concentrations within 1.2 times the rated value, while the remaining part is close to 1.5 times, this distribution estimate can be verified through multiple sampling to ensure the reliability of the results.

[0048] The present invention quantifies the pressure surge rate through a support vector regression algorithm, enabling early detection of abnormal stress changes that may occur during device operation, providing early warning for timely protective measures, and preventing damage to switching devices due to excessive voltage stress, thereby ensuring the safe and stable operation of the DC ice melting device. When the pressure surge rate exceeds a preset surge threshold, the switching device status data is acquired to determine the protection threshold adjustment requirement. The protection threshold can be dynamically adjusted based on the actual operating status of the device, making the protection measures more accurate and effective. This not only protects the switching device in a timely manner in dangerous situations, but also avoids malfunctions caused by improper protection threshold settings, thereby improving the operational reliability of the device. Based on the protection threshold adjustment requirement, the voltage characteristic data and the current characteristic data are combined to calculate characteristic curve data and generate a distribution estimate, thereby determining the stress distribution data of the switching device. This allows for a more accurate understanding of the stress distribution of the switching device under different operating conditions, providing a scientific basis for the selection, layout, and optimization of the switching device, and helping to improve the overall performance and life of the device.

[0049] In one embodiment, the process of processing the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching device in the DC ice melting device further includes:

[0050] A voltage drop range is constructed based on the DC bus voltage drop data, and the voltage drop range is input into a nonlinear mapping model generated by a support vector regression algorithm for processing, and a stress spike value is output; the nonlinear mapping model is used to characterize the mapping relationship between the DC bus voltage drop range and the voltage stress of the switching device;

[0051] quantifying a trend change distribution according to the stress spike value, and determining a protection threshold adjustment amplitude by using the acquired state data of the switching device when the trend change distribution exceeds a preset trend change threshold;

[0052] The state data of the switching device is adjusted according to the protection threshold adjustment range to determine the stress distribution data of the switching device.

[0053] Specifically, the present invention constructs a voltage drop range based on DC bus voltage drop data. The voltage drop range can be constructed through extreme value analysis of sequence data. For example, if the voltage drops from 1000V to 900V within a certain period of time, then its drop range is 10%. The determination of this range relies on the continuity of the data to ensure that instantaneous changes are captured. The calculated voltage drop range is input into a nonlinear mapping model generated using a support vector regression algorithm for processing, and a stress spike value is output. When the support vector regression algorithm is used to generate the nonlinear mapping model, the historical sequence data containing timestamps corresponding to the DC bus voltage drop data can be used as a training set. For example, 5000 data points within the past 1 second are input. The support vector regression algorithm outputs a stress spike value by fitting the relationship between voltage drop and time. The stress spike value is the instantaneous power increase of the device caused by the voltage drop. Preferably, the algorithm can also be adjusted through a kernel function to enhance its adaptability to nonlinear fluctuations. It can be understood that this mapping model is used to characterize the mapping relationship between the DC bus voltage drop range and the voltage stress of the switching device, which can reveal hidden stress variation patterns.

[0054] When calculating the sudden increase trend based on a stress spike, the slope of the stress change within a time window can be used to characterize it. In one embodiment, if the stress increases from 100W to 130W within 1ms, the trend slope is 30W / ms. The trend change distribution is statistically derived from multiple data sets, for example, ranging from 20-40W / ms. It should be noted that this distribution reflects the stress response characteristics of the system under different loads. If the trend change distribution exceeds a preset trend change threshold (25W / ms), the protection threshold adjustment range is determined based on the switching device status data. For example, based on the switching device status data, if the temperature rises to 75°C and the current exceeds the rated value by 15%, it can indicate that the current threshold is insufficient to cope with the shock, and the protection threshold adjustment needs to be adjusted. The adjustment range can be determined based on the degree of the threshold exceeding the threshold. For example, if the current threshold is insufficient to cope with the shock, the protection threshold can be increased by 10% to increase the device's safety margin.

[0055] When adjusting the switching device status data based on the protection threshold adjustment range, control unit parameters can be updated in real time, such as shortening the on-time from 3ms to 2.5ms to avoid overload, to determine the stress distribution data of the switching device. After adjusting the switching device status data, the nonlinear mapping model is used to calculate the stress value of the switching device again. The calculated stress values are statistically analyzed, and a stress distribution histogram or probability density function curve is plotted to determine the stress distribution data of the switching device. Furthermore, the determined stress distribution data can be compared with simulation data to evaluate the effectiveness of the adjustment. If any deviation is found, the protection threshold or switching device status data can be further adjusted until the stress distribution data meets the requirements.

[0056] By constructing a voltage drop range and using a nonlinear mapping model generated by a support vector regression algorithm, the present invention can accurately characterize the complex relationship between the DC bus voltage drop range and the voltage stress of the switching device, providing a reliable basis for subsequent analysis and improving the accuracy of the prediction of the stress change of the switching device; when the trend change exceeds the threshold, a timely response can be given, and abnormal stress changes that may occur during the operation of the device can be quickly discovered, avoiding damage to the switching device due to a sudden increase in stress, and ensuring the safe operation of the device; the protection threshold adjustment amplitude is determined according to the stress trend change, and the switching device status data is adjusted accordingly to achieve dynamic optimization of the protection strategy, which can not only effectively protect the switching device, but also avoid malfunction caused by improper setting of the protection threshold, thereby improving the operating efficiency and reliability of the device.

[0057] In one embodiment, the processing of the stress distribution data by a Kalman filter algorithm to generate comprehensive stress distribution data to determine a dynamic correction coefficient of the bridge arm switching timing includes:

[0058] Acquiring thermal stress distribution data of the DC ice melting device, and fusing the stress distribution data with the thermal stress distribution data using a Kalman filter algorithm to output comprehensive stress distribution data;

[0059] Determine the stress peak value, its duration and change rate based on the comprehensive stress distribution data to calculate the initial change trend of the bridge arm switching timing;

[0060] If it is determined that the initial change trend exceeds a preset change trend threshold, calculating an offset of the initial change trend relative to the preset change trend threshold to determine a timing adjustment requirement;

[0061] According to the timing adjustment requirement, stress peak data is extracted from the comprehensive stress distribution data to determine the dynamic correction coefficient of the bridge arm switching timing.

[0062] Specifically, the present invention uses a temperature sensor to collect thermal stress distribution data from a DC ice-melting device. It then employs a Kalman filter algorithm to fuse the stress distribution data from the switching device with the thermal stress distribution data, effectively integrating the dynamic characteristics of the two data types. This allows the Kalman filter to smooth out noise effects through prediction and update steps, ultimately outputting a comprehensive stress distribution estimate. For example, assuming an initial estimate of device stress shows a peak value of 1.3 times the rated value, and thermal stress data indicates a temperature fluctuation range of 60°C to 90°C, the algorithm fusion results in a stable comprehensive stress peak value of 1.25 times the rated value, smoothing the temperature effect to approximately 75°C. This demonstrates the use of state estimation to optimize multi-source data.

[0063] By comprehensively analyzing the stress distribution data, the stress peak value, duration and change rate are extracted as key indicators. A linear regression model or a time series model can be used to establish an initial change trend model of the bridge arm switching timing based on the stress peak value, its duration and change rate. The parameters of the trend model are estimated using the least squares method. Based on the estimated parameters, the initial change trend of the bridge arm switching timing can be calculated.

[0064] Based on the design requirements of the DC ice-melting device, the performance parameters of the switching device, and safety standards, a reasonable preset trend threshold (which can also be set based on experience) is set and compared with the calculated initial trend. When the initial trend exceeds the preset trend threshold, the offset of the initial trend relative to the preset trend threshold is calculated. For example, if the switching sequence is originally set to switch every 5ms, characteristic parameter analysis reveals that the stress peak causes the timing to shift 0.5ms earlier. This trend can reflect the operating characteristics of the switching device under high stress. It should be noted that when determining whether the switching sequence exceeds the preset threshold, the preset trend threshold can be set to an offset of no more than 0.3ms. The calculated offset is 0.5ms, which clearly exceeds the threshold, indicating that the timing needs to be adjusted, triggering a timing adjustment request.

[0065] Stress peak data is extracted from the comprehensive stress distribution data, and the peaks that have a greater impact on the bridge arm switching timing are screened out based on the size and direction of the offset. For example, if the offset indicates that the stress rises too quickly, the peaks with a larger rise rate are screened out to extract the characteristics of the screened stress peaks, such as peak size, duration, change rate and other related stress peak data. A table lookup method is used to establish a mapping relationship between stress peak characteristics and dynamic correction coefficients of bridge arm switching timing. The table lookup method is to pre-establish a corresponding table of stress peak characteristics and correction coefficients based on historical data or simulation results, and then look up the corresponding correction coefficient in the table according to the actual extracted peak characteristics. Then, the dynamic correction coefficient of the bridge arm switching timing is determined based on the established mapping relationship.

[0066] The present invention fuses stress distribution data with thermal stress distribution data through the Kalman filtering algorithm, which can effectively remove noise and interference in the data and generate more accurate comprehensive stress distribution data, providing a reliable basis for the dynamic correction of subsequent bridge arm switching timing, and helping to improve the control accuracy and stability of the DC ice melting device; based on the comprehensive stress distribution data, the stress peak value and its duration and change rate are determined, and the initial change trend of the bridge arm switching timing is calculated, so that abnormal stress changes that may occur during the operation of the device can be discovered in time; by calculating the offset of the initial change trend relative to the preset threshold, the timing adjustment requirement is determined, and the stress peak value data is extracted from the comprehensive stress distribution data to determine the dynamic correction coefficient of the bridge arm switching timing, thereby realizing precise adjustment of the bridge arm switching timing, optimizing the switching timing according to the actual operating state of the device, and improving the operating efficiency and reliability of the device.

[0067] S3. Obtaining component response delay data of the DC ice-melting device and combining it with the dynamic correction coefficient, processing the data using a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence to determine a real-time adjustment parameter affecting the discharge rate of the buffer capacitor in the DC ice-melting device;

[0068] In one embodiment, the step of obtaining the component response delay data of the DC ice melting device, combining the data with the dynamic correction coefficient, and processing the data using a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence includes:

[0069] Acquiring component response delay data of the DC ice melting device to fuse with the dynamic correction coefficient, and processing the data through a particle swarm optimization algorithm to output a preliminary timing adjustment value;

[0070] Extracting the delay offset and switching frequency of the bridge arm switching timing according to the preliminary timing adjustment value to determine the fluctuation range of the preliminary timing adjustment value;

[0071] Comparing the fluctuation range with a preset fluctuation threshold, and when the fluctuation range is greater than the preset fluctuation threshold, optimizing the preliminary timing adjustment value based on normal distribution characteristic data of the component response delay data to obtain an optimized timing sequence;

[0072] According to the optimized timing sequence, the operating state parameters of the bridge arm switching timing sequence are extracted to determine the stability trend of the bridge arm switching, and the dynamic correction coefficient is updated based on the stability trend to obtain the bridge arm switching timing sequence.

[0073] Specifically, the response delay data of various components (such as switches and sensors) in the DC ice-melting device are measured using oscilloscopes and high-speed data acquisition cards. For example, the sensor's response time for voltage signals is 2ms, while the delay for current signals is 1.8ms. This data forms the basis of the initial delay dataset. The component response delay data in the initial delay dataset is used as input parameters, and the dynamic correction coefficient is used as an adjustment factor. A set of particles is randomly generated, each representing a possible timing adjustment value. Each particle has two attributes: position (timing adjustment value) and velocity (adjustment step size). Using system performance indicators (such as stress level and switching stability) based on the timing adjustment as the fitness function, a particle swarm algorithm is used to iteratively update particle positions and output the optimal timing adjustment value as the preliminary timing adjustment value. For example, if the initial delay is 2ms and the dynamic correction coefficient is initially set to 1.0, the preliminary timing adjustment value obtained after particle swarm optimization is 2.1ms. This demonstrates the use of swarm intelligence to balance multivariate relationships.

[0074] The delay offset of the bridge arm switching timing, i.e., the difference between the actual switching time and the ideal switching time, is extracted from the preliminary timing adjustment value. The bridge arm switching frequency, i.e., the number of switches per unit time, is determined based on the preliminary timing adjustment value. A statistical analysis is performed on the delay offset and switching frequency to calculate statistical quantities such as their mean, variance, and standard deviation. Based on these statistics, the fluctuation range of the preliminary timing adjustment value is determined. For example, the fluctuation range can be defined as the mean ± 3 times the standard deviation (covering 99.7% of the data). In one embodiment, the preliminary timing adjustment value is 2.1ms, the switching frequency is 200Hz, and the characteristic parameters indicate a delay offset of 0.1ms. Therefore, through the characteristic parameter analysis, it can be determined that the fluctuation range of the timing sequence is between 2.0ms and 2.2ms.

[0075] By comparing the fluctuation range of the preliminary timing adjustment value with the preset fluctuation threshold, the adjusted switching timing data can be determined. For example, if the preset fluctuation threshold is a fluctuation of no more than 0.15ms, but the actual fluctuation is 0.2ms, further adjustment is required. The normal distribution parameters (mean and variance) of the component response delay data are fitted using the maximum likelihood estimation method. Based on the characteristics of the normal distribution, the mean or variance of the timing adjustment value is adjusted to ensure that the fluctuation range of the delay offset and switching frequency falls within the preset threshold. This optimizes the preliminary timing adjustment value and obtains the optimized timing sequence.

[0076] The operating state parameters of the bridge arm switching sequence, such as average delay and maximum offset, are extracted from the optimized timing sequence. The changing trends of these operating state parameters are analyzed to determine the stability trend of the bridge arm switching. A gradual decrease in the maximum offset indicates improved switching stability. A dynamic correction coefficient update strategy is then developed based on the stability trend. The coefficient range can be adjusted based on the offset change. For example, if the offset decreases by 0.05, the dynamic correction coefficient is adjusted from 0.9 to 0.95, and the switching interval is adjusted from 5ms to 5.05ms, thus obtaining the bridge arm switching timing sequence.

[0077] The present invention combines component response delay data and dynamic correction coefficients and utilizes a particle swarm optimization algorithm for processing, thereby being able to more accurately calculate the preliminary timing adjustment value of the bridge arm switching and reduce the timing deviation caused by component delay. By extracting the delay offset and switching frequency of the bridge arm switching timing, determining the fluctuation range of the preliminary timing adjustment value, and comparing it with a preset fluctuation threshold, it is possible to ensure that the timing adjustment value is within a safe range and avoid system instability caused by excessive timing fluctuations. When the fluctuation range exceeds the preset threshold, the preliminary timing adjustment value is optimized based on the normal distribution characteristics of the component response delay data, which can effectively smooth the timing sequence, reduce unnecessary fluctuations, and improve the stability of the switching process. The operating status parameters are extracted according to the optimized timing sequence, the stability trend of the bridge arm switching is determined, and the dynamic correction coefficient is updated based on the trend, so that the system can adaptively adjust the timing and improve overall performance and reliability.

[0078] In one embodiment, determining a real-time adjustment parameter affecting a discharge rate of a buffer capacitor in the DC ice melting device includes:

[0079] Extracting the distribution characteristics of the fault current data based on the bridge arm switching timing sequence to determine current characteristic parameters, and determining the adjustment logic of the switching device based on the current characteristic parameters to obtain logic distribution data;

[0080] Adjusting the logic distribution data according to the change characteristics of the fault current data to obtain a logic adjustment parameter, and extracting the real-time change of the discharge rate of the buffer capacitor according to the logic adjustment parameter to determine the state parameter of the buffer capacitor;

[0081] The stability trend of the DC ice melting device is determined based on the state parameter of the buffer capacitor, and the logic adjustment parameter is updated according to the stability trend of the DC ice melting device to obtain a real-time adjustment parameter for the discharge rate of the buffer capacitor.

[0082] Specifically, the present invention extracts the distribution characteristic parameters of the fault current data, such as mean, variance, peak value, valley value, rise time, and fall time, based on the bridge arm switching timing sequence, and extracts the current peak value and duration as current characteristic parameters; based on the current characteristic parameters, formulates the adjustment logic rules of the switching device, for example, when the current peak value exceeds a certain threshold, increases the conduction time of the switching device to speed up the discharge rate of the buffer capacitor; when the current change rate is large, turns on the switching device in advance to cope with the rapid change of current; the deviation between the peak value and the normal current can also be analyzed to obtain the adjustment direction of the switching logic, such as the normal current peak value is 30A, while it is 50A during a fault, with a deviation of 20A, indicating that the switching logic needs to be adjusted in the direction of early closing, and then the adjustment logic rules are converted into logical distribution data in the form of tables, state machines, etc., which includes the adjustment strategies of the switching devices under different combinations of current characteristic parameters.

[0083] The logic distribution data is adjusted based on the changing characteristics of the fault current data, namely, the change in the current slope. For example, if the fault current rising slope is 100A / ms, the logic distribution data can be set to an upper limit of 80A / ms. If this is exceeded, the switch logic parameters are adjusted to shorten the switching delay from 1ms to 0.8ms. The parameter adjustment rate is the ratio of the rising slope to the upper limit of the slope to suppress current overshoot. In addition, if the current fluctuates frequently, the adjustment threshold of the switching device can be appropriately relaxed to avoid frequent switching. If the current shows an upward trend, the on-time of the switching device can be adjusted in advance to prevent capacitor overvoltage. It is also possible to establish an association table using historical adjustment data and historical current change characteristics, and then use a table lookup method to determine the specific adjustment amount. Similarly, a neural network model can be used to establish a correlation between historical adjustment data and historical current change characteristics to determine the specific adjustment amount. The specific process will not be elaborated here. The adjusted logic distribution data is then converted into logic adjustment parameters to reflect the dynamic changes in the adjustment logic of the switching device. When calculating the real-time changes in the discharge rate based on the switch logic adjustment parameters, it can be determined by the rate of decrease of the capacitor voltage. For example, if the adjusted switch delay is 0.8ms and it takes 2ms for the capacitor voltage to drop from 100V to 90V, then the discharge rate is 5V / ms. The state parameters of the buffer capacitor are determined based on the real-time changes in its discharge rate. By calculating the mean, variance, maximum value, minimum value, etc. of the discharge rate, the stability and change trend of the discharge rate can also be analyzed to reflect the working status and health of the buffer capacitor, that is, the state parameters of the buffer capacitor. For example, if the discharge rate is around 5V / ms and the voltage fluctuation period is 0.1ms, it indicates that the buffer capacitor is slightly aged.

[0084] The fluctuation range of the buffer capacitor voltage or the fluctuation coefficient of its discharge rate is used as the stability indicator of the DC ice melting device. Time series analysis methods, such as the moving average method and exponential smoothing method, are used to predict and analyze the state parameters of the buffer capacitor to determine whether the stability is increasing, decreasing, or maintaining a stable trend, thereby obtaining a stability trend. Based on the stability trend, an update strategy for the logic adjustment parameters is formulated. If the stability trend decreases, it means that the current logic adjustment parameters are not well adapted to the system state and it is necessary to increase the adjustment strength, that is, adjust the logic adjustment parameters to make the adjustment of the switching device more sensitive. If the stability trend increases, the adjustment strength can be appropriately reduced to avoid over-adjustment. The specific adjustment amount can be determined using a lookup table method or a neural network model method, which will not be elaborated here. The logic adjustment parameters are then updated according to the update strategy to obtain new logic adjustment parameters, which are then integrated with the state parameters of the buffer capacitor to obtain real-time adjustment parameters for the buffer capacitor discharge rate. These parameters contain the adjustment strategy of the switching device and the working state information of the buffer capacitor and can be directly used to control the discharge process of the buffer capacitor.

[0085] In one embodiment, the stability trend of the DC ice-melting device can also be determined based on the degree of rate dispersion. For example, if the discharge rate mean is 5V / ms and the variance is 0.2V / ms, it indicates a relatively concentrated distribution and the DC ice-melting device is likely stable. If the variance increases to 0.5V / ms, the capacitor health status needs to be checked. When using the DC ice-melting device's stability trend to update the threshold range of the logical distribution, the threshold can be adjusted based on the variance change. For example, if the variance decreases from 0.2V / ms to 0.1V / ms, the threshold range can be narrowed from 4-6V / ms to 4.5-5.5V / ms, dynamically adjusting a certain percentage to improve control accuracy. In one possible implementation, the threshold can be dynamically optimized based on historical trends. When outputting the real-time adjustment parameter for the final buffer capacitor discharge rate, it can also be determined based on the combined rate mean and threshold range to ensure a smooth discharge process. The buffer capacitor in the DC ice-melting device is used to stabilize current and voltage, reducing the impact of sudden current changes on the system. It can provide a certain degree of energy storage and release to ensure a smooth ice-melting process.

[0086] The present invention determines the current characteristic parameters by extracting the distribution characteristics of the fault current data, and then clarifies the adjustment logic of the switching device. It can more accurately adjust the discharge rate of the buffer capacitor in real time according to the current situation, avoid over-discharge or under-discharge, and improve the operating efficiency and stability of the DC ice melting device; adjust the logical distribution data according to the change characteristics of the fault current data, so that the system can adapt to the dynamic changes of the current, ensure that the discharge rate of the buffer capacitor always matches the actual needs, and enhance the adaptive ability of the system; determine its state parameters by extracting the real-time changes of the buffer capacitor discharge rate, and judge the stability trend of the DC ice melting device based on these parameters, so as to timely detect the abnormal state of the buffer capacitor and update the logical adjustment parameters to ensure the stable operation of the buffer capacitor and extend its service life; adjust the discharge rate of the buffer capacitor in real time, so as to reduce system failures caused by abnormal capacitor discharge, improve the overall reliability of the DC ice melting device, and ensure the stable operation of the device during the ice melting process.

[0087] S4. Extracting real-time DC bus voltage data from the DC ice-melting device and combining it with the real-time adjustment parameter, performing adaptive impedance matching on the capacitor discharge circuit, and determining an impedance compensation value for the commutation path by comparing voltage oscillation characteristics before and after impedance matching, thereby suppressing current oscillation in the DC ice-melting device.

[0088] In one embodiment, extracting the real-time DC bus voltage data in the DC ice melting device and combining it with the real-time adjustment parameter, performing adaptive impedance matching on the capacitor discharge circuit, and determining the impedance compensation value for the commutation path by comparing voltage oscillation characteristics before and after impedance matching, includes:

[0089] Extracting real-time data of the DC bus voltage in the DC ice melting device to fuse with the real-time adjustment parameter to obtain the discharge characteristics of the capacitor discharge circuit;

[0090] performing adaptive impedance matching processing on the capacitor discharge circuit according to the discharge characteristics to determine an adjustment direction of impedance matching and generate matching parameters;

[0091] The impedance matching is performed by comparing the change trend of the voltage oscillation of the DC ice melting device before and after the impedance matching is adjusted by the matching parameter to determine the impedance compensation value of the commutation path.

[0092] Specifically, a high-precision voltage sensor is installed on the DC bus of the DC ice melting device to ensure that the real-time value of the DC bus voltage, that is, the real-time data of the DC bus voltage, can be accurately measured, and integrated with the real-time adjustment parameters obtained from the buffer capacitor discharge rate adjustment process (such as the conduction time of the switching device, the adjustment logic, etc.). By establishing a mathematical model or using a data fusion algorithm, the two are combined to analyze the discharge characteristics of the capacitor discharge circuit, such as the discharge current, discharge time, and discharge energy. The discharge characteristics of the capacitor discharge circuit can also be determined from the dynamic changes in the real-time DC bus voltage data. Assuming a DC bus voltage acquisition frequency of 5kHz, the collected voltage data is 500V during normal operation and drops to 480V during abnormal conditions, with a drop duration of 1ms. This voltage drop distribution reflects the initial behavior of capacitor discharge. By processing the voltage signal with a low-pass filter to remove high-frequency interference and extract the true discharge trend, the discharge characteristics of the capacitor discharge circuit can be determined. The capacitor discharge circuit is used to release the energy stored in the bridge arm buffer capacitor to the transmission line, thereby melting ice. By controlling the capacitor discharge circuit's on / off function, the discharge time and energy release amount can be precisely controlled, thereby optimizing the ice melting effect.

[0093] Based on the circuit structure of the DC ice-melting device, an equivalent circuit model of the capacitor discharge loop is established. The model should include buffer capacitors, switching devices, commutation paths, and related resistors, inductors, and capacitors. Circuit analysis software (such as PSpice, MATLAB / Simulink, etc.) is used for modeling and simulation to more accurately analyze the impedance characteristics of the circuit. The parameter values of each component in the equivalent circuit model are determined through experiments or simulations. The actual impedance of the circuit can also be measured using equipment such as an impedance analyzer, or estimated based on the technical specifications of the components and circuit design parameters. With the goal of conjugate matching the input impedance of the discharge circuit with the output impedance of the DC bus and achieving maximum power transfer, the established impedance model and real-time data are used, using complex number operations to consider the impedance characteristics of the circuit's resistors, inductors, and capacitors at different frequencies. The current input impedance of the capacitor discharge circuit is calculated. The difference between the current impedance and the target impedance is then compared to determine the impedance matching adjustment direction. If the real part of the current impedance is less than the real part of the target impedance, the resistance in the discharge circuit is increased. If the imaginary part of the current impedance has an opposite sign to that of the target impedance, the inductor or capacitor is adjusted. Based on the determined adjustment direction, an optimization algorithm (such as a genetic algorithm or particle swarm optimization algorithm) is used to search for the optimal matching parameters, such as the resistance, inductance, or capacitance values that need to be increased or decreased, to meet the impedance matching goal. Finally, the calculated matching parameters are substituted into the impedance model for simulation verification to check whether the impedance matching effect meets the requirements. If the effect is unsatisfactory, the optimization algorithm parameters can be adjusted or the target can be reset to recalculate the matching parameters.

[0094] In another embodiment, when determining the direction of impedance matching adjustment, the relationship between the voltage drop rate and the system impedance can also be considered. If the voltage drop rate is 20V / ms, it may indicate that the impedance is too low, resulting in excessive discharge. However, based on historical data comparison, the normal drop rate is 15V / ms, so the adjustment direction may tend to increase the impedance. Preferably, impedance matching can be achieved by adding series resistors or adjusting the commutation path. The matching parameters can also be obtained by fitting using a lookup table method or a neural network model method.

[0095] Before performing impedance matching, record the voltage oscillation data of the DC ice melting device during the capacitor discharge process. Use an oscilloscope or data acquisition system to record the DC bus voltage and the voltage waveforms of key nodes in the capacitor discharge circuit, analyze the recorded voltage oscillation data, and extract characteristic parameters such as the frequency, amplitude, and decay time of the voltage oscillation. Adjust the impedance matching of the capacitor discharge circuit based on the generated matching parameters, and then record the voltage oscillation data during the capacitor discharge process again. Similarly, analyze the voltage oscillation data after impedance matching to extract characteristic parameters such as the frequency, amplitude, and decay time of the voltage oscillation. Compare the changing trends of the voltage oscillation characteristics before and after impedance matching. , such as whether the amplitude of voltage oscillation is reduced, whether the decay time is shortened, etc.: If the voltage oscillation is effectively suppressed after impedance matching, it means that the matching parameters are effective; if the voltage oscillation is not significantly improved or even worsens, further analysis of the cause is required; and based on the comparison results of the voltage oscillation characteristics, the impedance compensation value of the commutation path is determined. If the amplitude of the voltage oscillation is still large, the damping resistance in the commutation path needs to be increased; if the frequency of the voltage oscillation changes, the inductance or capacitance value in the commutation path needs to be adjusted. Specifically, the feedback control method in control theory is used to calculate the impedance compensation value based on the error signal of the voltage oscillation to achieve dynamic adjustment of the commutation path impedance.

[0096] Furthermore, when comparing the changing trends of voltage oscillations based on matching parameters and calculating the impedance compensation value for the commutation path, the amplitude of voltage fluctuations can also be observed. For example, if the voltage oscillations decrease from ±10V to ±5V after impedance adjustment, this indicates that the oscillations have been suppressed. Based on historical experience, the impedance compensation value can be estimated to be 1Ω. It should be noted that this comparative analysis requires the use of multiple sampling data to ensure the reliability of the trend. The rationality of the compensation value can be confirmed by continuously monitoring the voltage changes for 10 cycles.

[0097] By extracting real-time DC bus voltage data and combining it with real-time adjustment parameters, the present invention can more accurately determine the discharge characteristics of the capacitor discharge circuit, thereby performing adaptive impedance matching processing, effectively reducing energy loss during the capacitor discharge process, improving discharge efficiency, and enabling the DC ice melting device to more efficiently utilize electrical energy during the ice melting process. The adaptive impedance matching processing can reduce the impedance mismatch between the capacitor discharge circuit and the commutation path, reducing the occurrence of voltage oscillations. By comparing the voltage oscillation characteristics before and after impedance matching, the impedance compensation value of the commutation path can be accurately determined, further suppressing voltage oscillations, improving the stability and reliability of the system, and avoiding damage to the equipment caused by voltage oscillations. The impedance matching of the capacitor discharge circuit is automatically adjusted according to the dynamic changes of the real-time adjustment parameters and the DC bus voltage, enabling the system to adapt to different working conditions and fault conditions, with strong adaptive capabilities, ensuring that the DC ice melting device can operate stably in various environments. By reducing voltage oscillations and energy loss, the working stress and heat generation of the equipment are reduced, which helps to extend the service life of various components in the DC ice melting device and reduce the maintenance cost and failure rate of the equipment.

[0098] In one embodiment, extracting the real-time DC bus voltage data of the DC ice melting device and combining it with the real-time adjustment parameter, performing adaptive impedance matching on the capacitor discharge circuit, and determining the impedance compensation value for the commutation path by comparing voltage oscillation characteristics before and after impedance matching, includes:

[0099] When the stress distribution data exceeds a preset stress threshold, triggering a protection mechanism to determine timing adjustment data of the switching device;

[0100] Adjusting the switching device according to the timing adjustment data and obtaining a discharge distribution characteristic of the buffer capacitor;

[0101] Based on the discharge distribution characteristics, the impedance compensation value is updated by a recursive least square method to obtain an impedance adjustment value and control the execution of the commutation path to achieve suppression of current oscillation in the DC ice melting device.

[0102] Specifically, the present invention sets a preset stress threshold for stress distribution based on the design parameters and safety requirements of the DC ice-melting device. This threshold should comprehensively consider factors such as the material's mechanical strength, electrical properties, and thermal stability, and can also be determined based on manual experience. The actual stress distribution data is compared with the preset stress threshold. If the stress data exceeds the threshold stress range, a protection mechanism is immediately triggered. The current operating timing of the switching devices is analyzed to identify operational steps that may cause excessive stress. Based on the analysis results, the operating timing of the switching devices is adjusted, such as by delaying the on-time of certain switching devices or prematurely shutting off certain switching devices, to reduce stress concentration and uneven distribution. The adjusted operating timing of the switching devices is then recorded as timing adjustment data for subsequent control operations to prevent further stress increase and damage to the equipment. Furthermore, when activating the protection mechanism, the voltage trend of the capacitor discharge can also be used as a starting point. For example, if the DC bus voltage is 500V during normal operation and the preset stress threshold is 490V, if the monitored voltage drops to 485V, it indicates that the stress exceeds the range. In this case, the protection mechanism can respond by partially disconnecting the load or adjusting the switch state. In a possible implementation, the system may compare the duration of the voltage drop with a preset time threshold to determine whether to activate protection based on the comparison result.

[0103] Based on the timing adjustment data, a corresponding drive control signal is generated to drive the operation of the switching device. The drive control signal should precisely control the on and off times of the switching device to ensure that the adjusted timing can be accurately executed. The drive control signal is then sent to the driver circuit of the switching device to perform the timing adjustment operation. Simultaneously, the discharge current and voltage data of the buffer capacitor are collected to determine its discharge distribution characteristics. The adjustment direction of the switching timing can also be determined by the relationship between the voltage drop rate and the switching action. In one embodiment, if the voltage drop rate is 10V / ms, indicating that the switch is closing too quickly, the adjustment direction can be to delay the closing time. The original switching period of 5ms can be extended to 6ms to observe whether the voltage drop is alleviated. Preferably, this adjustment direction can also refer to the change in current peak value to ensure overall coordination.

[0104] An impedance model of the commutation path in a DC ice-melting device is established, and the impedance compensation value is used as a model parameter to reflect the relationship between the impedance compensation value and the discharge distribution characteristics. The parameters of the recursive least squares method, including the covariance matrix and the parameter estimation value, are initialized. After each discharge process, the new discharge distribution characteristic data is input into the recursive least squares method, the impedance compensation value estimate is updated, and the impedance adjustment value is obtained to generate a corresponding adjustment control signal for adjusting the impedance of the commutation path. The adjustment control signal can achieve impedance adjustment by changing the resistance, inductance or capacitance value in the commutation path; the adjustment control signal is sent to the actuator of the commutation path (such as an adjustable resistor, a variable inductor, etc.) to perform the impedance adjustment operation.

[0105] The present invention monitors stress distribution data in real time and triggers a protection mechanism when it exceeds a preset threshold. It can timely identify potential system failures or overload conditions, avoid damage to switching devices due to excessive stress, and thus improve the safety of the entire DC ice melting device; according to the protection mechanism triggered by the stress distribution data, the timing adjustment data of the switching device is determined, which can optimize the operating sequence and timing of the switching device and reduce current shocks and voltage oscillations caused by improper switch operation; by obtaining the discharge distribution characteristics of the buffer capacitor, the discharge process of the capacitor can be more accurately controlled, unnecessary energy loss can be reduced, and the utilization efficiency of the capacitor can be improved; based on the discharge distribution characteristics, the impedance compensation value is updated by the recursive least squares method, which can dynamically adjust the impedance of the commutation path, effectively suppress current oscillation, and improve the stability and reliability of the system; the application of the recursive least squares method enables the system to dynamically update the impedance compensation value according to real-time data, enhances the system's adaptability, and enables it to maintain optimal performance under different working conditions.

[0106] In the embodiment of the present application, based on how to deal with the problem of voltage stress mutation and current oscillation in the bridge arm reconstruction process of the existing DC ice melting device, a fault current suppression method for a DC ice melting device is designed. The method collects and analyzes the commutation path impedance and fault current data in real time, and generates DC bus voltage drop data based on these data, and then determines the stress distribution of the switching device and the dynamic correction coefficient of the bridge arm switching timing, and finally realizes compensation for the commutation path impedance, effectively suppresses current oscillation, ensures the stability of the DC ice melting device during operation, and reduces equipment failures and operational abnormalities caused by current oscillation; uses the support vector regression algorithm to process the DC bus voltage drop data to obtain the voltage stress sudden increase trend, and combines the Kalman filter algorithm to generate comprehensive stress distribution data. It can more accurately determine the stress distribution of switching devices, help optimize the working state of switching devices, extend their service life, and reduce equipment maintenance costs; combine the component response delay data with the dynamic correction coefficient, and use the particle swarm optimization algorithm to obtain the bridge arm switching timing sequence, which can dynamically adjust the bridge arm switching timing according to the actual operating conditions of the device, improve the operating efficiency and performance of the device, and ensure that the DC ice melting device can operate stably and efficiently under different working conditions; by extracting the real-time data of the DC bus voltage and combining it with real-time adjustment parameters to perform adaptive impedance matching on the capacitor discharge circuit, it can accurately control the discharge rate of the buffer capacitor according to the real-time operating status of the device, further improving the device's adaptability to different working conditions and ensuring the accuracy and reliability of the DC ice melting operation.

[0107] It should be noted that although the steps in the above flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0108] In another embodiment, if Figure 2 As shown, the second aspect of the present invention provides a fault current suppression system for a DC ice melting device, comprising:

[0109] The current data processing module 10 is used to collect the commutation path impedance and fault current data of the DC ice melting device in real time, and analyze them through fast Fourier transform to obtain current oscillation data for input into a pre-built impedance and current relationship model for processing to generate DC bus voltage drop data;

[0110] a correction coefficient generating module 20 for processing the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching devices in the DC ice melting device, and processing the stress distribution data using a Kalman filter algorithm to generate comprehensive stress distribution data to determine a dynamic correction coefficient for the bridge arm switching timing;

[0111] an adjustment parameter determination module 30 for acquiring component response delay data of the DC ice-melting device, combining the data with the dynamic correction coefficient, and processing the data using a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence to determine a real-time adjustment parameter affecting the discharge rate of the buffer capacitor in the DC ice-melting device;

[0112] The current oscillation suppression module 40 is configured to extract real-time data of the DC bus voltage in the DC ice-melting device and combine it with the real-time adjustment parameters to perform adaptive impedance matching on the capacitor discharge circuit. Furthermore, the module determines an impedance compensation value for the commutation path by comparing voltage oscillation characteristics before and after impedance matching, thereby suppressing current oscillations in the DC ice-melting device.

[0113] It should be noted that the various modules in the aforementioned fault current suppression system for a DC ice-melting device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor within a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module. For the specific definition of a fault current suppression system for a DC ice-melting device, refer to the definition of a fault current suppression method for a DC ice-melting device above. Both have the same functions and effects and are not further elaborated here.

[0114] In summary, the present invention relates to the field of information technology, and discloses a fault current suppression method and system for a DC ice melting device. The fault current data of the DC ice melting device is analyzed by fast Fourier transform, and the DC bus voltage drop data is obtained based on the analysis result, which is processed by a support vector regression algorithm to obtain the voltage stress surge trend and thus determine the stress distribution data of the switching device in the device; the stress distribution data is processed by a Kalman filter algorithm to obtain a processing result to determine the dynamic correction coefficient of the bridge arm switching timing, and the result is processed together with the component response delay data of the device by a particle swarm optimization algorithm to generate a bridge arm switching timing sequence to determine the real-time adjustment parameters that affect the discharge rate of the buffer capacitor in the device, and impedance matching is performed on the capacitor discharge circuit, and the voltage oscillation characteristics before and after the matching are compared to determine the impedance compensation value of the commutation path for execution, thereby achieving effective suppression of current oscillation in the DC ice melting device.

[0115] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A fault current suppression method for a DC ice melting device, characterized in that: include: Real-time acquisition of commutation path impedance and fault current data from the DC de-icing device. Fast Fourier transform analysis is performed to obtain current oscillation data, which is then fed into a pre-built impedance and current relationship model for processing, generating DC bus voltage drop data. The DC bus voltage drop data is processed using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching device in the DC ice melting device, and the stress distribution data is processed using a Kalman filter algorithm to generate comprehensive stress distribution data to determine a dynamic correction coefficient for the bridge arm switching timing; Obtaining component response delay data of the DC ice melting device and combining it with the dynamic correction coefficient, processing the data using a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence to determine a real-time adjustment parameter that affects the discharge rate of the buffer capacitor in the DC ice melting device; extracting real-time DC bus voltage data from the DC de-icing device and combining it with the real-time adjustment parameter, performing adaptive impedance matching on the capacitor discharge circuit, and determining an impedance compensation value for the commutation path by comparing voltage oscillation characteristics before and after impedance matching, thereby suppressing current oscillation in the DC de-icing device; The method of processing the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching device in the DC ice melting device includes: Processing the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend, and quantifying a pressure sudden increase rate of the DC ice melting device according to the voltage stress sudden increase trend; If the pressure sudden increase rate exceeds a preset sudden increase threshold, obtaining status data of a switch component in the DC ice melting device to determine a protection threshold adjustment requirement; Based on the protection threshold adjustment requirement, calculating characteristic curve data using voltage characteristic data and current characteristic data of the DC ice melting device, and generating a distribution estimation value using the characteristic curve data to determine stress distribution data of the switching device; The processing of the stress distribution data by a Kalman filter algorithm to generate comprehensive stress distribution data for determining a dynamic correction coefficient of a bridge arm switching timing sequence includes: Acquiring thermal stress distribution data of the DC ice melting device, and fusing the stress distribution data with the thermal stress distribution data using a Kalman filter algorithm to output comprehensive stress distribution data; Determine the stress peak value, its duration and change rate based on the comprehensive stress distribution data to calculate the initial change trend of the bridge arm switching timing; If it is determined that the initial change trend exceeds a preset change trend threshold, calculating an offset of the initial change trend relative to the preset change trend threshold to determine a timing adjustment requirement; According to the timing adjustment requirement, stress peak data is extracted from the comprehensive stress distribution data to determine the dynamic correction coefficient of the bridge arm switching timing.

2. A fault current suppression method for a DC ice melting device according to claim 1, characterized in that: The real-time acquisition of commutation path impedance and fault current data of the DC ice melting device and analysis thereof through fast Fourier transform obtains current oscillation data which is input into a pre-built impedance and current relationship model for processing to generate DC bus voltage drop data, including: collecting commutation path impedance and fault current data of the DC ice melting device in real time to construct an original data set, and processing the original data set using a fast Fourier transform to obtain current oscillation data of the DC ice melting device; When the current oscillation data exceeds a preset oscillation threshold, triggering a timing adjustment, and calculating an initial switching timing and a timing adjustment amount of the current oscillation data to obtain timing adjustment data; Inputting the timing adjustment data into a pre-built impedance and current relationship model for processing, outputting the capacitor discharge data of the DC ice melting device, and processing the capacitor discharge data using a numerical integration method to obtain a voltage change trend; The voltage variation trend is compared with a preset voltage variation range to determine DC bus voltage drop data of the DC ice melting device.

3. The fault current suppression method for a DC ice melting device according to claim 1, characterized in that: The method further includes: processing the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching device in the DC ice melting device; A voltage drop range is constructed based on the DC bus voltage drop data, and the voltage drop range is input into a nonlinear mapping model generated by a support vector regression algorithm for processing, and a stress spike value is output; the nonlinear mapping model is used to characterize the mapping relationship between the DC bus voltage drop range and the voltage stress of the switching device; quantifying a trend change distribution according to the stress spike value, and determining a protection threshold adjustment amplitude by using the acquired state data of the switching device when the trend change distribution exceeds a preset trend change threshold; The state data of the switching device is adjusted according to the protection threshold adjustment range to determine the stress distribution data of the switching device.

4. The method for suppressing fault current in a DC ice melting device according to claim 1, wherein: The step of obtaining the component response delay data of the DC ice melting device, combining the data with the dynamic correction coefficient, and processing the data using a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence includes: Acquiring component response delay data of the DC ice melting device to fuse with the dynamic correction coefficient, and processing the data through a particle swarm optimization algorithm to output a preliminary timing adjustment value; Extracting the delay offset and switching frequency of the bridge arm switching timing according to the preliminary timing adjustment value to determine the fluctuation range of the preliminary timing adjustment value; Comparing the fluctuation range with a preset fluctuation threshold, and when the fluctuation range is greater than the preset fluctuation threshold, optimizing the preliminary timing adjustment value based on normal distribution characteristic data of the component response delay data to obtain an optimized timing sequence; According to the optimized timing sequence, the operating state parameters of the bridge arm switching timing sequence are extracted to determine the stability trend of the bridge arm switching, and the dynamic correction coefficient is updated based on the stability trend to obtain the bridge arm switching timing sequence.

5. The method for suppressing fault current in a DC ice melting device according to claim 1, wherein: The method of determining the real-time adjustment parameter affecting the discharge rate of the buffer capacitor in the DC ice melting device includes: Extracting the distribution characteristics of the fault current data based on the bridge arm switching timing sequence to determine current characteristic parameters, and determining the adjustment logic of the switching device based on the current characteristic parameters to obtain logic distribution data; Adjusting the logic distribution data according to the change characteristics of the fault current data to obtain a logic adjustment parameter, and extracting the real-time change of the discharge rate of the buffer capacitor according to the logic adjustment parameter to determine the state parameter of the buffer capacitor; The stability trend of the DC ice melting device is determined based on the state parameter of the buffer capacitor, and the logic adjustment parameter is updated according to the stability trend of the DC ice melting device to obtain a real-time adjustment parameter for the discharge rate of the buffer capacitor.

6. The method for suppressing fault current in a DC ice melting device according to claim 1, wherein: The extracting of the real-time DC bus voltage data in the DC ice melting device and combining it with the real-time adjustment parameter, performing adaptive impedance matching processing on the capacitor discharge circuit, and determining the impedance compensation value for the commutation path by comparing the voltage oscillation characteristics before and after impedance matching include: Extracting real-time data of the DC bus voltage in the DC ice melting device to fuse with the real-time adjustment parameter to obtain the discharge characteristics of the capacitor discharge circuit; performing adaptive impedance matching processing on the capacitor discharge circuit according to the discharge characteristics to determine an adjustment direction of impedance matching and generate matching parameters; The impedance matching is performed by comparing the change trend of the voltage oscillation of the DC ice melting device before and after the impedance matching is adjusted by the matching parameter to determine the impedance compensation value of the commutation path.

7. The method for suppressing fault current in a DC ice melting device according to claim 1, wherein: After extracting the real-time data of the DC bus voltage in the DC ice melting device and combining it with the real-time adjustment parameter, performing adaptive impedance matching processing on the capacitor discharge circuit, and determining the impedance compensation value for the commutation path by comparing the voltage oscillation characteristics before and after impedance matching, the method includes: When the stress distribution data exceeds a preset stress threshold, triggering a protection mechanism to determine timing adjustment data of the switching device; Adjusting the switching device according to the timing adjustment data and obtaining a discharge distribution characteristic of the buffer capacitor; Based on the discharge distribution characteristics, the impedance compensation value is updated by a recursive least square method to obtain an impedance adjustment value and control the execution of the commutation path to achieve suppression of current oscillation in the DC ice melting device.

8. A fault current suppression system for a DC ice melting device, characterized in that: include: The current data processing module is used to collect the commutation path impedance and fault current data of the DC ice melting device in real time, and analyze them through fast Fourier transform to obtain current oscillation data. The data is then input into a pre-built impedance and current relationship model for processing to generate DC bus voltage drop data. a correction coefficient generation module, configured to process the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching devices in the DC ice melting device, and process the stress distribution data using a Kalman filter algorithm to generate comprehensive stress distribution data to determine a dynamic correction coefficient for the bridge arm switching timing; an adjustment parameter determination module, configured to obtain component response delay data of the DC ice-melting device, combine the data with the dynamic correction coefficient, and process the data using a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence to determine a real-time adjustment parameter affecting the discharge rate of the buffer capacitor in the DC ice-melting device; a current oscillation suppression module, configured to extract real-time data of the DC bus voltage in the DC ice-melting device and combine it with the real-time adjustment parameter, perform adaptive impedance matching on the capacitor discharge circuit, and determine an impedance compensation value for the commutation path by comparing voltage oscillation characteristics before and after impedance matching, thereby suppressing current oscillations in the DC ice-melting device; The method of processing the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switching device in the DC ice melting device includes: Processing the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend, and quantifying a pressure sudden increase rate of the DC ice melting device according to the voltage stress sudden increase trend; If the pressure sudden increase rate exceeds a preset sudden increase threshold, obtaining status data of a switch component in the DC ice melting device to determine a protection threshold adjustment requirement; Based on the protection threshold adjustment requirement, calculating characteristic curve data using voltage characteristic data and current characteristic data of the DC ice melting device, and generating a distribution estimation value using the characteristic curve data to determine stress distribution data of the switching device; The processing of the stress distribution data by a Kalman filter algorithm to generate comprehensive stress distribution data for determining a dynamic correction coefficient of a bridge arm switching timing sequence includes: Acquiring thermal stress distribution data of the DC ice melting device, and fusing the stress distribution data with the thermal stress distribution data using a Kalman filter algorithm to output comprehensive stress distribution data; Determine the stress peak value, its duration and change rate based on the comprehensive stress distribution data to calculate the initial change trend of the bridge arm switching timing; If it is determined that the initial change trend exceeds a preset change trend threshold, calculating an offset of the initial change trend relative to the preset change trend threshold to determine a timing adjustment requirement; According to the timing adjustment requirement, stress peak data is extracted from the comprehensive stress distribution data to determine the dynamic correction coefficient of the bridge arm switching timing.

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