Fault current suppression method and system for direct current deicing device

Abstract

The invention relates to the technical field of information, and discloses a fault current suppression method and system for a direct-current deicing device, fault current data of the direct-current deicing device are analyzed through fast Fourier transform, and direct-current bus voltage drop data are obtained based on an analysis result and are processed through a support vector regression algorithm, so that a fault current suppression result is obtained. Obtaining a voltage stress sudden increase trend and further determining stress distribution data of a switching device in the device; processing the stress distribution data by a Kalman filtering algorithm to obtain a processing result so as to determine a dynamic correction coefficient of a bridge arm switching time sequence, and processing the dynamic correction coefficient and element response delay data of the device by a particle swarm optimization algorithm to generate a bridge arm switching time sequence so as to determine a real-time adjustment parameter influencing the discharge rate of a buffer capacitor in the device; and comparing voltage oscillation characteristics before and after matching to determine an impedance compensation value of a commutation path for execution, thereby realizing effective suppression of current oscillation in the DC deicing device.

Description

Technical Field

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

[0002] With the frequent occurrence of climate change and extreme weather, the role of DC ice melting technology in suppressing fault current and improving system reliability has become increasingly prominent. As a key technology for dealing with line icing in the power system, the research on DC ice melting devices is of great importance for ensuring the safe and stable operation of the power grid. However, most traditional DC ice melting devices rely on a single topology structure or fixed control strategy, and it is difficult to effectively cope with the voltage stress mutation and current oscillation problems during the arm reconstruction process.

[0003] Therefore, how to deal with the voltage stress mutation and current oscillation of the existing DC ice melting device during the arm reconstruction process has become an urgent technical problem for those skilled in the art. Summary of the Invention

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

[0005] To solve the above technical problems, a first aspect of the present invention provides a method for suppressing fault current of a DC ice melting device, including: Real-time collecting the commutation path impedance and fault current data of the DC ice melting device, and analyzing through fast Fourier transform to obtain current oscillation data, which is input into a pre-constructed impedance-current relationship model for processing to generate DC bus voltage drop data; Processing the DC bus voltage drop data by using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine the stress distribution data of the switching devices in the DC ice melting device, and processing the stress distribution data by using a Kalman filter algorithm to generate comprehensive stress distribution data to determine the dynamic correction coefficient of the arm switching timing; Obtaining the component response delay data of the DC ice melting device and combining it with the dynamic correction coefficient, and processing through a particle swarm optimization algorithm to obtain an arm switching timing sequence to determine the real-time adjustment parameter that affects the discharge rate of the buffer capacitor in the DC ice melting device; 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 to execute, so as to suppress the current oscillation in the DC ice melting device.

[0006] The second aspect of the present invention provides a fault current suppression system for a DC ice melting device, including: A current data processing module, configured to collect the commutation path impedance and fault current data of the DC ice melting device in real time, analyze them through fast Fourier transform to obtain current oscillation data, and input the current oscillation data into a pre-constructed impedance-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 by using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine the stress distribution data of the switching devices in the DC ice melting device, and process the stress distribution data through a Kalman filter algorithm to generate comprehensive stress distribution data to determine the dynamic correction coefficient of the bridge arm switching timing; An adjustment parameter determination module, configured to obtain the component response delay data of the DC ice melting device and combine it with the dynamic correction coefficient, and process it through a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence to determine the real-time adjustment parameters that affect the discharge rate of the buffer capacitor in the DC ice melting device; A current oscillation suppression module, configured to extract the real-time data of the DC bus voltage in the DC ice melting device and combine it with the real-time adjustment parameters, perform adaptive impedance matching processing on the capacitor discharge circuit, and determine the impedance compensation value for the commutation path by comparing the voltage oscillation characteristics before and after impedance matching to execute, so as to suppress the current oscillation in the DC ice melting device.

[0007] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) By collecting the commutation path impedance and fault current data in real time and combining the fast Fourier transform to extract the current oscillation characteristics, it can dynamically identify high-frequency harmonic components and specifically suppress the oscillation of specific frequencies; using the support vector regression algorithm to perform non-linear modeling on the bus voltage drop data to predict the voltage stress sudden increase trend, effectively avoiding overvoltage damage to the switching devices; based on the bridge arm switching timing sequence generated by the particle swarm optimization and combining the component response delay data, dynamically adjusting the discharge rate of the buffer capacitor, thereby reducing the voltage oscillation amplitude; (2) Through adaptive impedance compensation, the commutation path impedance is matched with the capacitor discharge circuit to reduce the voltage spike caused by the reflected wave; multiple algorithms such as fast Fourier transform, support vector regression, Kalman filter, and particle swarm optimization are coordinated to 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. Description of the Drawings

[0008] To more clearly illustrate the technical solution of the present invention, the accompanying drawings required for the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0009] Figure 1 is a flowchart of a method for suppressing fault current of a DC ice melting device provided by an embodiment of the present invention; Figure 2 is a structural diagram of an electronic device provided by an embodiment of the present invention; Reference numerals: Among them, 10, current data processing module; 20, correction factor generation module; 30, adjustment parameter determination module; 40, current oscillation suppression module. Detailed implementation manners

[0010] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some of the 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0011] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0012] In the description of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" 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, and it can be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the system or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0013] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0014] Currently, for the suppression of the DC-side fault current in a DC ice melting device, the arm reconstruction technology is usually adopted. However, when the arm topology switches from a full-bridge structure to a half-bridge structure, the impedance mutation of the commutation loop in the DC ice melting device will cause an increase in the amplitude of current oscillation, an instantaneous drop in the DC bus voltage, and an instantaneous increase in the voltage stress borne by the switching device. Then, how to dynamically adjust the arm switching timing and the discharge rate of the buffer capacitor to effectively cope with the voltage stress mutation and current oscillation during the arm reconstruction process of the existing DC ice melting device has become an urgent problem to be solved.

[0015] Based on this, in one embodiment, as Figure 1 shown, the first aspect of the present invention provides a method for suppressing fault current for a DC ice melting device, including: S1. Real-time collect the commutation path impedance and fault current data of the DC ice melting device, and analyze them through fast Fourier transform to obtain current oscillation data, which is input into a pre-constructed impedance-current relationship model for processing to generate DC bus voltage drop data; In one embodiment, step S1 includes: Real-time collect the commutation path impedance and fault current data of the DC ice melting device to construct an original data set, and process the original data set by using fast Fourier transform to obtain the current oscillation data of the DC ice melting device; When the current oscillation data exceeds a preset oscillation threshold, trigger timing adjustment, and calculate the initial switching timing and timing adjustment amount of the current oscillation data to obtain timing adjustment data; Input the timing adjustment data into a pre-constructed impedance-current relationship model for processing, output the capacitor discharge data of the DC ice melting device, and use numerical integration to process the capacitor discharge data to obtain the voltage change trend; Compare the voltage change trend with a preset voltage change range to determine the DC bus voltage drop data of the DC ice melting device.

[0016] Specifically, since the impedance of the commutation path and the fault current data are the basis for monitoring the operating state of the entire control system of the DC ice melting device, the present invention uses high-precision current transformers and voltage transformers to collect the corresponding fault current data and fault voltage data, and its sampling frequency can reach more than 10 kHz to ensure capturing the high-frequency oscillation characteristics, and measures the voltage response by injecting a 1 kHz test current to determine the impedance of the commutation path. High-precision impedance sensors can also be selected, such as impedance measurement sensors based on the four-terminal method, to collect the impedance of the commutation path; among them, the commutation path in the DC ice melting device refers to the path where the current flows from the DC power supply through components such as converters and arm buffer capacitors and finally flows to the transmission line. This path can ensure the smooth flow of current, thereby achieving the ice melting effect. The collected impedance data and fault current data are constructed into an original data set and processed by fast Fourier transform to reveal the oscillation characteristics of the system and obtain the current oscillation data of the DC ice melting device, which includes the oscillation amplitude and oscillation frequency.

[0017] It should be noted that the selection of the preset oscillation threshold is crucial for the stability judgment of the system. In the present invention, the oscillation amplitude threshold in the preset oscillation threshold is set to 8% of the rated current, and the oscillation frequency threshold in the preset oscillation threshold is set to 1.5 kHz. 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; among them, this empirical formula is obtained by fitting historical oscillation data: the initial switching timing included in the timing adjustment data is set at 1 / 4 of the cycle, and the timing adjustment amount included in the timing adjustment data is a linear function of the oscillation amplitude, that is, every 1% increase in the oscillation amplitude corresponds to a 0.1 ms timing delay.

[0018] The impedance current model is the key to predicting system behavior. According to the circuit topology and component characteristics of the DC ice melting device, the present invention establishes a mathematical model of the relationship between impedance and current. By considering factors such as the dynamic characteristics of the switching tube and the non-linear effect of the DC bus capacitor, differential equations or difference equations are established to describe the relationship between impedance and current. The time series adjustment data is input into the constructed impedance current model for processing, and the capacitor discharge data of the DC ice melting device is output, which includes the change curve of the capacitor voltage over time and the change curve of the capacitor current over time, thus 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 according to the start time and end time of the capacitor discharge data. The start time is usually a certain moment after time series adjustment, and the end time is the time when the capacitor voltage discharges to a certain specific value (the specific moment and specific value can be determined according to historical data or human experience). Then, the trapezoidal method or Simpson's method is used to integrate the capacitor discharge data, and the integration step size can be selected as 10 μs. This can reduce the calculation burden while ensuring accuracy to obtain the change amount of the capacitor voltage, and combine it with the initial voltage value of the capacitor to calculate the change trend of the capacitor voltage over time, that is, the voltage change trend.

[0019] Determine the preset voltage change range according to the design parameters and normal operation requirements of the DC ice melting device. For example, if the device design requires that the fluctuation range of the DC bus voltage during normal operation does not exceed ±5%, the preset voltage change range can be set to ±5% of the rated voltage. When the voltage change trend exceeds the preset voltage change range, record the start time and end time of the out-of-range situation, and calculate the voltage drop amplitude as the DC bus voltage drop data of the DC ice melting device; among them, the voltage drop amplitude can be calculated by the difference between the voltage values corresponding to the start time and the end time.

[0020] The present invention can quickly respond to abnormal current oscillation situations that occur during the operation of the device by setting a preset oscillation threshold to trigger time series adjustment, avoid the further deterioration of current oscillation, protect the DC ice melting device from potential damage, and improve the reliability and safety of the device operation; the impedance current relationship model combined with the numerical integration method can predict the voltage change trend in the future power frequency cycle, thereby reducing errors, and can more accurately obtain the specific information of voltage drop, so as to optimize the operation control strategy of the device, improve the operation efficiency and performance of the DC ice melting device, and make it better adapt to the actual work requirements.

[0021] S2. Process the DC bus voltage drop data by using the support vector regression algorithm to obtain the voltage stress sudden increase trend to determine the stress distribution data of the switching devices in the DC ice melting device, and process the stress distribution data by using the Kalman filter algorithm to generate comprehensive stress distribution data to determine the dynamic correction coefficient of the bridge arm switching timing; In one embodiment, processing the DC bus voltage dip data by using a support vector regression algorithm to obtain a voltage stress sudden increase trend so as to determine stress distribution data of switching devices in the DC ice melting device includes: Processing the DC bus voltage dip data by 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, acquiring status data of switching devices in the DC ice melting device to determine a protection threshold adjustment requirement; Based on the protection threshold adjustment requirement, calculating characteristic curve data through voltage characteristic data and current characteristic data of the DC ice melting device, and generating a distribution estimation value through the characteristic curve data to determine the stress distribution data of the switching devices.

[0022] Specifically, when the present invention processes DC bus voltage dip data by using a support vector regression algorithm to obtain a stress sudden increase trend, the support vector regression algorithm adopts a radial basis function (RBF) kernel function, and uses historical voltage dip data and corresponding voltage stress data to train a support vector regression model. During the training process, parameters of the model are adjusted by methods such as cross-validation to obtain optimal model performance. For example, the collected voltage dip data shows a sudden 10% drop from the rated value within a certain period, then the support vector regression can fit these data points to predict a trend curve of stress changing with time, that is, the voltage stress sudden increase trend. The present invention captures the non-linear change characteristics in the DC bus voltage dip data by using a support vector regression algorithm, providing a reliable basis for subsequent analysis. Subsequently, the slope of the trend curve is directly used to calculate the pressure sudden increase rate of the DC ice melting device. Through analysis of multiple groups of data, it is found that the rate distribution usually concentrates in high-load switching scenarios.

[0023] It should be noted that the determination of the preset surge threshold (preferably 45% / ms) helps to determine 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 protection threshold adjustment requirement through the status data of the switch device. For example, the temperature of the switch device rises from 50°C to 80°C, and the current data exceeds the rated value by 20%, indicating that the current protection threshold is insufficient to cope with the stress shock. In this case, it is necessary to determine the threshold adjustment requirement based on the status data of the switch 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 and the pressure sudden increase rate exceed the rate threshold, the protection threshold is reduced to the first ratio; a working time threshold is set according to historical data, and when the working time is less than the working time threshold and the pressure sudden increase rate is near the rate threshold, the protection threshold is increased to the second ratio. The first ratio and the second ratio can also be determined according to historical data or manual experience, but the adjustment range is limited to ±10%. This adjustment is based on the evaluation of the tolerance of the switching device to ensure that it can still operate safely under high stress; the necessity of adjustment can also be further confirmed by real-time monitoring of the working state of the switching device, such as the on-time and the off-frequency. Among them, the switching device plays the role of controlling the on and off of the current in the DC ice melting device. By controlling the switching state of the switching device, the precise control of the ice melting process can be achieved. Common switching devices include thyristors, IGBTs, etc.

[0024] Based on the protection threshold adjustment requirements, characteristic parameters are extracted from the voltage characteristic data (such as DC bus voltage, voltage across the switch device, etc.) and current characteristic data (such as commutation path current, switch device current, etc.) 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 with characteristic curve data using quadratic interpolation to reflect the overall picture of stress changes; based on the characteristic curve data and the state data of the switch 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 according to the physical model and characteristic curve of the switch device. The voltage stress S of the switch 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 switch device; V and I are the voltage and current of the switch device, respectively; key data such as the maximum value, minimum value, average value or standard deviation of the stress are extracted from the estimated value of the stress distribution as the stress distribution data of the switch 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.

[0025] The present invention quantifies the pressure sudden increase rate through the support vector regression algorithm, can discover in advance the possible abnormal stress change situation during the operation of the device, provides early warning for taking protection measures in time, avoids the damage of the switching device due to excessive voltage stress, and ensures the safe and stable operation of the DC ice melting device; when the pressure sudden increase rate exceeds the preset sudden increase threshold, obtains the state data of the switching device to determine the protection threshold adjustment requirement, can dynamically adjust the protection threshold based on the actual operation state of the device, makes the protection measures more accurate and effective, can not only protect the switching device in time in dangerous situations, but also avoid misoperation caused by improper setting of the protection threshold, and improves the operation reliability of the device; based on the protection threshold adjustment requirement, combines the voltage characteristic data and the current characteristic data to calculate the characteristic curve data and generate a distribution estimate value, thereby determining the stress distribution data of the switching device, can more accurately understand the stress distribution situation of the switching device under different operation conditions, provides a scientific basis for the selection, layout and optimization of the switching device, and helps to improve the overall performance and service life of the device.

[0026] In one embodiment, the processing of the DC bus voltage drop data by using the support vector regression algorithm to obtain the voltage stress sudden increase trend to determine the stress distribution data of the switching device in the DC ice melting device further includes: Construct a voltage drop range based on the DC bus voltage drop data, and input it into a non-linear mapping model generated by using the support vector regression algorithm for processing to output a stress sudden increase value; the non-linear mapping model is used to characterize the mapping relationship between the voltage drop range of the DC bus and the voltage stress of the switching device; Quantify the trend change distribution according to the stress sudden increase value, and when the trend change distribution exceeds the preset trend change threshold, determine the protection threshold adjustment amplitude through the obtained state data of the switching device; Adjust the state data of the switching device according to the protection threshold adjustment amplitude to determine the stress distribution data of the switching device.

[0027] Specifically, the present invention constructs a voltage drop range based on DC bus voltage drop data. The voltage drop range can be constructed through the 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 depends on the continuity of the data to ensure the capture of instantaneous changes. The calculated voltage drop range is input into a non-linear mapping model generated by using the support vector regression algorithm for processing, and a stress sudden increase value is output. Among them, when using the support vector regression algorithm to generate a non-linear mapping model, the historical sequence data including timestamps corresponding to the DC bus voltage drop data can be used as the training set. For example, 5000 data points within the recent 1 second are input. The support vector regression algorithm outputs a stress sudden increase value by fitting the relationship between voltage drop and time. The stress sudden increase value is the instantaneous power increment of the device caused by the voltage drop. Preferably, the algorithm can also be adjusted through the kernel function to enhance the adaptability to non-linear fluctuations. It can be understood that this mapping model is used to characterize the mapping relationship between the voltage drop range of the DC bus and the voltage stress of the switching device, and can reveal the hidden stress change law.

[0028] When calculating the sudden increase trend based on the stress sudden increase value, it can be characterized by the slope of the stress change within the time window. In one embodiment, if the stress increases from 100W to 130W within 1ms, then the trend slope is 30W / ms. The trend change distribution is obtained through the statistics of multiple groups of data. For example, the distribution range is between 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 the preset trend change threshold (25W / ms), then the adjustment amplitude of the protection threshold needs to be determined according to the state data of the switching device. Exemplarily, through the state data of the switching device, such as the temperature rising to 75°C and the current exceeding the rated value by 15%, it can be indicated that the current threshold is insufficient to cope with the impact, and the adjustment requirement of the protection threshold needs to be adjusted. The adjustment amplitude can be determined according to the degree of exceeding the threshold. For example, when the current threshold is insufficient to cope with the impact, the protection threshold is increased by 10% to improve the safety margin of the device.

[0029] When adjusting the state data of the switching device according to the adjustment amplitude of the protection threshold, the control unit parameters can be updated in real time. For example, the conduction time is shortened from 3ms to 2.5ms to avoid overload, so as to determine the stress distribution data of the switching device. After adjusting the state data of the switching device, the stress value of the switching device is calculated again by using the non-linear mapping model. Statistical analysis is performed on the calculated stress value, and a stress distribution histogram or probability density function curve is drawn to determine the stress distribution data of the switching device. In addition, the determined stress distribution data can be compared and verified with the simulation data to evaluate the adjustment effect. If there is a deviation, the protection threshold or the state data of the switching device can be further adjusted until the stress distribution data meets the requirements.

[0030] Through constructing a voltage sag range and using a non - linear mapping model generated by the support vector regression algorithm, the present invention can accurately characterize the complex relationship between the DC bus voltage sag range and the voltage stress of the switching device, providing a reliable basis for subsequent analysis and improving the accuracy of predicting the stress change of the switching device. When the trend change exceeds the threshold, it can respond in a timely manner, quickly detect possible abnormal stress change situations during the operation of the device, avoid damage to the switching device due to sudden increase in stress, and ensure the safe operation of the device. According to the stress trend change, the adjustment amplitude of the protection threshold is determined, and based on this, the state data of the switching device is adjusted to realize the dynamic optimization of the protection strategy, which can not only effectively protect the switching device, but also avoid misoperation caused by improper setting of the protection threshold, and improve the operation efficiency and reliability of the device.

[0031] In one embodiment, the processing of the stress distribution data by the Kalman filter algorithm to generate comprehensive stress distribution data for determining the dynamic correction coefficient of the arm switching timing includes: Obtaining the thermal stress distribution data of the DC ice - melting device, and using the Kalman filter algorithm to fuse the stress distribution data and the thermal stress distribution data, and outputting comprehensive stress distribution data; Based on the comprehensive stress distribution data, determining the stress peak value, its duration and change rate to calculate the initial change trend of the arm switching timing; If it is determined that the initial change trend exceeds the preset change trend threshold, then calculate the offset of the initial change trend relative to the preset change trend threshold to determine the timing adjustment requirement; According to the timing adjustment requirement, extract the stress peak value data from the comprehensive stress distribution data to determine the dynamic correction coefficient of the arm switching timing.

[0032] Specifically, the present invention is based on temperature sensors to collect the thermal stress distribution data of the DC ice - melting device, and uses the Kalman filter algorithm to fuse the stress distribution data of the switching device and the thermal stress distribution data to effectively integrate the dynamic characteristics of the two types of data, so that the Kalman filter smooths the noise influence through prediction and update steps and outputs the comprehensive stress distribution estimate. For example, assuming that the initial estimate of the device stress shows that the peak value is 1.3 times the rated value, and the thermal stress data indicates that the temperature fluctuation range is from 60 °C to 90 °C, after the algorithm fusion, the comprehensive stress peak value is stabilized at 1.25 times, and the temperature influence is smoothed to about 75 °C, that is, using state estimation to optimize multi - source data.

[0033] By extracting the stress peak value, duration, and change rate as key indicators from the comprehensive stress distribution data, a linear regression model or a time series model can be used to establish an initial change trend model of the arm switching timing based on the stress peak value, its duration, and the change rate. The least squares method is used to estimate the parameters of the trend model. According to the estimated parameters, the initial change trend of the arm switching timing can be calculated.

[0034] According to the design requirements of the DC de-icing device, the performance parameters of the switching device, and the safety standards, a reasonable preset change trend threshold is set (it can also be set according to manual experience) and compared with the calculated initial change trend. When the initial change trend exceeds the preset change trend threshold, calculate the offset of the initial change trend relative to the preset change trend threshold. Exemplarily, if the switching timing was originally set to switch every 5 ms, through characteristic parameter analysis, it is found that the stress peak causes the timing to advance by 0.5 ms. This trend can reflect the operating characteristics of the switching device under high stress. It should be noted that when judging whether the switching timing exceeds the preset threshold, the preset change trend threshold can be set to an offset not exceeding 0.3 ms, and the calculated offset is 0.5 ms. Obviously, it exceeds the threshold, indicating that the timing needs to be adjusted, triggering a timing adjustment requirement.

[0035] Extract the stress peak value data from the comprehensive stress distribution data, and screen out the peaks that have a greater impact on the arm switching timing according to the magnitude and direction of the offset. For example, if the offset indicates that the stress rises too fast, then screen out the peaks with a larger rising rate to extract the characteristics of the selected stress peak values, such as peak size, duration, change rate, and other relevant stress peak value data. Use the look-up table method to establish a mapping relationship between the stress peak value characteristics and the dynamic correction coefficient of the arm switching timing. The look-up table method is to pre-establish a corresponding table of stress peak value characteristics and correction coefficients according to historical data or simulation results, look up the corresponding correction coefficient in the table according to the actually extracted peak value characteristics, and then determine the dynamic correction coefficient of the arm switching timing according to the established mapping relationship.

[0036] The present invention fuses stress distribution data and thermal stress distribution data through the Kalman filtering algorithm, which can effectively remove noise and interference in the data, generate more accurate comprehensive stress distribution data, provide a reliable basis for the subsequent dynamic correction of the arm switching timing, and contribute to improving the control accuracy and stability of the DC ice melting device; based on the comprehensive stress distribution data, the stress peak, its duration and change rate are determined, and the initial change trend of the arm switching timing is calculated, which can timely detect abnormal stress change situations that may occur during the operation of the device; by calculating the offset of the initial change trend relative to the preset threshold, the timing adjustment requirement is determined, and stress peak data is extracted from the comprehensive stress distribution data to determine the dynamic correction coefficient of the arm switching timing, realizing the precise adjustment of the arm switching timing, optimizing the switching timing according to the actual operation state of the device, and improving the operation efficiency and reliability of the device.

[0037] S3. Obtain the component response delay data of the DC ice melting device, combine it with the dynamic correction coefficient, and process it through the particle swarm optimization algorithm to obtain an arm switching timing sequence to determine the real-time adjustment parameters affecting the discharge rate of the buffer capacitor in the DC ice melting device; In one embodiment, the obtaining the component response delay data of the DC ice melting device, combining it with the dynamic correction coefficient, and processing it through the particle swarm optimization algorithm to obtain an arm switching timing sequence includes: Obtain the component response delay data of the DC ice melting device to fuse it with the dynamic correction coefficient, and process it through the particle swarm optimization algorithm to output a preliminary timing adjustment value; According to the preliminary timing adjustment value, extract the delay offset and switching frequency of the arm switching timing to determine the fluctuation range of the preliminary timing adjustment value; Compare the fluctuation range with a preset fluctuation threshold, and when the fluctuation range is greater than the preset fluctuation threshold, optimize the preliminary timing adjustment value based on the normal distribution characteristic data of the component response delay data to obtain an optimized timing sequence; According to the optimized timing sequence, extract the operation state parameters of the arm switching timing to determine the stability trend of the arm switching, and update the dynamic correction coefficient based on the stability trend to obtain an arm switching timing sequence.

[0038] Specifically, response delay data of each component (such as switching devices, sensors, etc.) in the DC ice melting device is measured and obtained through devices such as oscilloscopes and high-speed data acquisition cards. For example, the response time of the sensor to monitor the voltage signal is 2 ms, and the delay of the current signal is 1.8 ms. These data form the basis of the initial delay data set. Taking the component response delay data in the initial delay data set as input parameters and the dynamic correction coefficient as an adjustment factor, a group of particles is randomly generated. Each particle represents a possible timing adjustment value, and each particle has two attributes: position (timing adjustment value) and velocity (adjustment step size). Using the system performance indicators (such as stress level, switching stability, etc.) after timing adjustment as the fitness function, the particle swarm algorithm is used to iteratively update the particle positions, and the timing adjustment value with the optimal fitness is output as the preliminary timing adjustment value. For example, the initial delay is 2 ms and the initial value of the dynamic correction coefficient is 1.0. After particle swarm optimization, the preliminary timing adjustment value is obtained as 2.1 ms, that is, using swarm intelligence to balance the multi-variable relationship.

[0039] Extract the delay offset of the arm switching timing from the preliminary timing adjustment value, that is, the difference between the actual switching time and the ideal switching time, and determine the arm switching frequency according to the preliminary timing adjustment value, that is, the number of switches per unit time; conduct statistical analysis on the delay offset and switching frequency, calculate statistical quantities such as their mean, variance, and standard deviation, and determine the fluctuation range of the preliminary timing adjustment value according to the statistical quantities. 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 2.1 ms, the switching frequency is 200 Hz, and the characteristic parameter shows that the delay offset is 0.1 ms. Then, through characteristic parameter analysis, it can be judged that the fluctuation range of the timing sequence is between 2.0 ms and 2.2 ms.

[0040] By comparing the fluctuation range of the preliminary timing adjustment value with the preset fluctuation threshold, the switched timing data after adjustment can be determined. For example, if the preset fluctuation threshold is that the fluctuation does not exceed 0.15 ms and the actual fluctuation is 0.2 ms, further adjustment is required. Use the maximum likelihood estimation method to fit the normal distribution parameters (mean and variance) of the component response delay data. According to the characteristics of the normal distribution, by adjusting the mean or variance of the timing adjustment value, the fluctuation ranges of the delay offset and switching frequency are made to fall within the preset threshold to optimize the preliminary timing adjustment value and obtain the optimized timing sequence.

[0041] Extract the operating state parameters of the arm switching timing sequence from the optimized timing sequence, such as the average delay and the maximum offset, analyze the change trend of the operating state parameters, and judge the stability trend of the arm switching. If the maximum offset gradually decreases, it indicates that the switching stability has improved. Then, formulate an update strategy for the dynamic correction coefficient according to the stability trend, and adjust the coefficient range according to the change of the offset. 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 5 ms to 5.05 ms, so as to obtain the arm switching timing sequence.

[0042] By combining the component response delay data and the dynamic correction coefficient and using the particle swarm optimization algorithm for processing, the present invention can more accurately calculate the preliminary timing adjustment value of the arm switching, reduce the timing deviation caused by component delay; by extracting the delay offset and switching frequency of the arm switching timing sequence, determine the fluctuation range of the preliminary timing adjustment value, and compare it with the preset fluctuation threshold, which can ensure that the timing adjustment value is within a safe range and avoid system instability caused by excessive timing fluctuation; when the fluctuation range exceeds the preset threshold, optimize the preliminary timing adjustment value 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; extract the operating state parameters according to the optimized timing sequence, determine the stability trend of the arm switching, and update the dynamic correction coefficient based on this trend, so that the system can adaptively adjust the timing, improve the overall performance and reliability.

[0043] In one embodiment, determining the real-time adjustment parameter that affects the discharge rate of the buffer capacitor in the DC ice melting device includes: Extract the distribution characteristics of the fault current data based on the arm switching timing sequence to determine the current characteristic parameters, and determine the adjustment logic of the switching device based on the current characteristic parameters to obtain the logical distribution data; Adjust the logical distribution data according to the change characteristics of the fault current data to obtain the logical adjustment parameter, and extract the real-time change of the discharge rate of the buffer capacitor according to the logical adjustment parameter to determine the state parameter of the buffer capacitor; Judge the stability trend of the DC ice melting device based on the state parameter of the buffer capacitor, and update the logical adjustment parameter according to the stability trend of the DC ice melting device to obtain the real-time adjustment parameter for the discharge rate of the buffer capacitor.

[0044] Specifically, based on the switching timing sequence of the bridge arm, the present invention extracts distribution characteristic parameters such as the mean value, variance, peak value, valley value, rise time, and fall time of the fault current data, and extracts the current peak value and duration as current characteristic parameters; based on the current characteristic parameters, adjustment logic rules for the switching device are formulated. For example, when the current peak value exceeds a certain threshold, the conduction time of the switching device is increased to accelerate the discharge rate of the buffer capacitor; when the current change rate is large, the switching device is turned on in advance to cope with the rapid change of the current; the deviation between the peak value and the normal current can also be analyzed to obtain the adjustment direction of the switching logic. For example, the normal current peak value is 30A, while the fault current is 50A, and the deviation is 20A, indicating that the switching logic needs to be adjusted in the direction of closing in advance. Then, the adjustment logic rules are transformed into logical distribution data in the form of tables, state machines, etc., which includes the adjustment strategies of the switching device under different combinations of current characteristic parameters.

[0045] According to the change characteristics of the fault current data, that is, the change of the current slope, the logical distribution data is adjusted. For example, if the rising slope of the fault current is 100A / ms, the slope upper limit of the logical distribution data can be set to 80A / ms. When it exceeds, the switching logic parameters are adjusted, and the switching delay is shortened from 1ms to 0.8ms. The parameter adjustment rate is the ratio of the rising slope to the slope upper limit to suppress the 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 conduction time of the switching device is adjusted in advance to prevent capacitor overvoltage. An association table can also be established using historical adjustment data and historical current change characteristics, and then the look-up table method is used to determine the specific adjustment amount. Similarly, a neural network model can be used to establish the association relationship between historical adjustment data and historical current change characteristics, and then the specific adjustment amount is determined. The specific process will not be elaborated here. Then, the adjusted logical distribution data is transformed into logical adjustment parameters to reflect the dynamic change of the switching device adjustment logic. When calculating the real-time change of the discharge rate according to the switching logic adjustment parameters, it can be determined by the falling speed of the capacitor voltage. Exemplarily, if the adjusted switching delay is 0.8ms and the capacitor voltage drops from 100V to 90V in 2ms, then the discharge rate is 5V / ms; and the state parameters of the buffer capacitor are determined according to the real-time change of the discharge rate of the buffer capacitor. By calculating the mean value, 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 state and health degree 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 has slight aging.

[0046] Taking the fluctuation range of the buffer capacitor voltage or the fluctuation coefficient of its discharge rate as the stability index of the DC ice melting device, using time series analysis methods such as the moving average method and the exponential smoothing method to predict and analyze the state parameters of the buffer capacitor, judge whether the stability is rising, falling or remaining stable, and obtain the stability trend; according to the stability trend, formulate an update strategy for the logical adjustment parameters: if the stability trend is decreasing, it means that the current logical adjustment parameters are not very suitable for the system state, and it is necessary to increase the adjustment strength, that is, adjust the logical adjustment parameters to make the adjustment of the switching device more sensitive; if the stability trend is rising, the adjustment strength can be appropriately reduced to avoid over-adjustment. The specific adjustment amount can be determined by the look-up table method or the neural network model method, which will not be elaborated here; then update the logical adjustment parameters according to the update strategy to obtain new logical adjustment parameters to be integrated with the state parameters of the buffer capacitor to obtain real-time adjustment parameters for the discharge rate of the buffer capacitor, which includes 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.

[0047] In one embodiment, the stability trend of the DC ice melting device can also be judged according to the degree of dispersion of the rate. For example, when the mean discharge rate is 5V / ms and the variance is 0.2V / ms, it indicates that the distribution is relatively concentrated and the DC ice melting device may be relatively stable. If the variance increases to 0.5V / ms, the health state of the capacitor needs to be checked. When updating the threshold range of the logical distribution using the stability trend of the DC ice melting device, the threshold can be adjusted according to the variance change. Exemplarily, 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, and a certain proportion is dynamically adjusted to improve the control accuracy. In one possible implementation, the threshold can be dynamically optimized in combination with the historical trend. When outputting the real-time adjustment parameters of the final buffer capacitor discharge rate, it can also be determined according to the comprehensive rate mean and the threshold range to ensure a smooth discharge process. Among them, the buffer capacitor in the DC ice melting device is used to stabilize the current and voltage, reduce the impact of current mutation on the system, and it can provide a certain energy storage and release function to ensure the smooth progress of the ice melting process.

[0048] 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, which 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 operation efficiency and stability of the DC ice melting device; adjusts the logical distribution data according to the change characteristics of the fault current data, enables the system to adapt to the dynamic change of the current, ensures that the discharge rate of the buffer capacitor always matches the actual demand, and enhances the self-adaptive ability of the system; determines its state parameters by extracting the real-time change of the discharge rate of the buffer capacitor, and judges the stability trend of the DC ice melting device based on these parameters, can timely detect the abnormal state of the buffer capacitor, and update the logic adjustment parameters to ensure the stable operation of the buffer capacitor and extend its service life; adjusts the discharge rate of the buffer capacitor in real time, can reduce the system faults 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.

[0049] S4. Extract the 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 processing on the capacitor discharge circuit, and determine the impedance compensation value for the commutation path by comparing the voltage oscillation characteristics before and after impedance matching, and execute it to achieve the suppression of the current oscillation in the DC ice melting device; In one embodiment, the extracting the real-time data of the DC bus voltage in the DC ice melting device and combining it with the real-time adjustment parameters to perform 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 includes: Extract the real-time data of the DC bus voltage in the DC ice melting device and fuse it with the real-time adjustment parameters to obtain the discharge characteristics of the capacitor discharge circuit; Perform adaptive impedance matching processing on the capacitor discharge circuit according to the discharge characteristics to determine the adjustment direction of impedance matching and generate matching parameters; Compare the change trend of the voltage oscillation of the DC ice melting device before and after the impedance matching is adjusted by the matching parameters to determine the impedance compensation value of the commutation path.

[0050] Specifically, a high-precision voltage sensor is installed on the DC bus of the DC ice melting device to ensure accurate measurement of the real-time value of the DC bus voltage, that is, the real-time data of the DC bus voltage, and it is fused with the real-time adjustment parameters (such as the conduction time of the switching device, adjustment logic, etc.) obtained from the buffer capacitor discharge rate adjustment process. By establishing a mathematical model or using a data fusion algorithm, the two are combined to analyze the discharge characteristics of the discharge current, discharge time, discharge energy, etc. of the capacitor discharge circuit. In addition, the discharge characteristics of the capacitor discharge circuit can also be determined from the dynamic changes of the real-time data of the DC bus voltage. Assuming that the DC bus voltage acquisition frequency is 5 kHz, the voltage data collected is 500 V during normal operation and drops to 480 V during abnormal conditions, and the voltage drop process lasts for 1 ms. This distribution characteristic of the voltage drop reflects the initial behavior of the capacitor discharge. By processing the voltage signal through a low-pass filter to filter out high-frequency interference and extract the real discharge trend, the discharge characteristics of the capacitor discharge circuit can be obtained; among them, the capacitor discharge circuit is used to release the energy stored in the arm buffer capacitor to the transmission line to achieve the melting of ice. By controlling the switch of the capacitor discharge circuit, the discharge time and the amount of energy released can be accurately controlled, thereby optimizing the ice melting effect.

[0051] According to the circuit structure of the DC ice melting device, an equivalent circuit model of the capacitor discharge circuit is established. The model should include buffer capacitors, switching devices, commutation paths, and related resistors, inductors, and capacitor elements, and 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. Through experiments or simulations, the parameter values of each element in the equivalent circuit model are determined. 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 the 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, according to the established impedance model and the real-time collected data, complex number operation methods are used, considering the impedance characteristics of the resistors, inductors, and capacitor elements in the circuit at different frequencies, to calculate the current input impedance of the capacitor discharge circuit, compare the difference between the current impedance and the target impedance, and determine the adjustment direction of impedance matching: if the real part of the current impedance is less than the real part of the target impedance, increase the resistance in the discharge circuit; if the imaginary part of the current impedance has the opposite sign to the imaginary part of the target impedance, adjust the value of the inductor or capacitor, and according to the determined adjustment direction, use an optimization algorithm (such as a genetic algorithm, particle swarm optimization algorithm, etc.) to search for the optimal matching parameters, such as the resistance value, inductor value, or capacitor value that needs to be increased or decreased, to meet the goal of impedance matching. 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 not ideal, the parameters of the optimization algorithm can be adjusted or the target can be reset, and the matching parameters can be recalculated.

[0052] In another embodiment, when determining the adjustment direction of impedance matching, the relationship between the rate of voltage drop and the system impedance can also be considered. If the voltage drop rate is 20 V / ms, it may indicate that the impedance is low, resulting in too fast discharge. By comparing with historical data, the normal drop rate is 15 V / ms, then the adjustment direction can tend to increase the impedance. Preferably, impedance matching can be achieved by connecting a resistor in series or adjusting the commutation path, and the matching parameters can also be obtained by fitting through a look-up table method or a neural network model method.

[0053] Before performing impedance matching, record the voltage oscillation data of the DC de-icing device during capacitor discharge. An oscilloscope or a data acquisition system can be used to record the voltage waveforms of the DC bus voltage and the key nodes in the capacitor discharge circuit, and analyze the recorded voltage oscillation data to extract characteristic parameters such as the frequency, amplitude, and decay time of the voltage oscillation; according to the generated matching parameters, adjust the impedance matching of the capacitor discharge circuit, and then record the voltage oscillation data during capacitor discharge 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 change trends of the voltage oscillation characteristics before and after impedance matching, such as whether the amplitude of the voltage oscillation decreases and whether the decay time shortens, etc.: If the voltage oscillation is effectively suppressed after impedance matching, it indicates that the matching parameters are effective; if the voltage oscillation has no obvious improvement or even deteriorates, the reason needs to be further analyzed; and according to the comparison result of the voltage oscillation characteristics, determine the impedance compensation value of the commutation path. If the amplitude of the voltage oscillation is still large, a damping resistor 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, use the feedback control method in control theory to calculate the impedance compensation value according to the error signal of the voltage oscillation to achieve dynamic adjustment of the commutation path impedance.

[0054] In addition, when calculating the impedance compensation value of the commutation path by comparing the change trend of the voltage oscillation according to the matching parameters, the change in the amplitude of the voltage fluctuation can also be observed. For example, after adjusting the impedance, if the voltage oscillation decreases from ±10 V to ±5 V, it indicates that the oscillation is suppressed. At this time, according to historical experience, the impedance compensation value is estimated to be 1 Ω. It should be noted that this comparative analysis needs to combine multiple sampling data to ensure the reliability of the trend. By continuously monitoring the voltage changes in 10 cycles, the rationality of the compensation value is confirmed.

[0055] By extracting the real-time data of the DC bus voltage and combining with the 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 the energy loss during the capacitor discharge process, improving the discharge efficiency, and enabling the DC ice melting device to more efficiently utilize electric 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, and reduce the occurrence of voltage oscillation. 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 the voltage oscillation, improving the stability and reliability of the system, and avoiding damage to the equipment caused by voltage oscillation; according to the dynamic changes of the real-time adjustment parameters and the DC bus voltage, the impedance matching of the capacitor discharge circuit is automatically adjusted, enabling the system to adapt to different working conditions and fault situations, with strong adaptability, ensuring the stable operation of the DC ice melting device in various environments; by reducing voltage oscillation and energy loss, the working stress and heating of the equipment are reduced, which helps to extend the service life of each component in the DC ice melting device, and reduce the maintenance cost and failure rate of the equipment.

[0056] In one embodiment, after extracting the real-time data of the DC bus voltage in the DC ice melting device, combining with the real-time adjustment parameters, 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, it includes: When the stress distribution data exceeds the preset stress threshold, trigger a protection mechanism to determine the timing adjustment data of the switching device; Adjust the switching device according to the timing adjustment data, and obtain the discharge distribution characteristics of the buffer capacitor; Based on the discharge distribution characteristics, update the impedance compensation value by the recursive least squares method, obtain the impedance adjustment value and control the commutation path to execute, so as to suppress the current oscillation in the DC ice melting device.

[0057] Specifically, according to the design parameters and safety requirements of the DC ice melting device, a preset stress threshold for stress distribution is set. This threshold should comprehensively consider factors such as the mechanical strength, electrical performance, and thermal stability of the material, and can also be determined based on artificial experience. Then, the actual stress distribution data is compared with the preset stress threshold. If the stress data exceeds the threshold stress range, the protection mechanism is immediately triggered. At the same time, the operation timing of the current switching device is analyzed to identify the operation steps that may cause excessive stress. According to the analysis results, the operation timing of the switching device is adjusted. For example, the conduction time of some switching devices is delayed, and some switching devices are cut off in advance, etc., to reduce the problems of stress concentration and uneven distribution. Then, the adjusted operation timing of the switching device is recorded as timing adjustment data for subsequent control operations to prevent further stress increase from causing equipment damage. In addition, when activating the protection mechanism, it is also possible to start from the voltage change trend of capacitor discharge. Assume that 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. At this time, the protection mechanism can respond by cutting off some loads or adjusting the switch state. In a possible implementation, the system can compare the duration of voltage drop with a preset time threshold to determine whether to activate the protection according to the comparison result.

[0058] According to the timing adjustment data, corresponding drive control signals are generated to drive the operation of the switching device. Among them, the drive control signals should precisely control the conduction and cut-off times of the switching device to ensure that the adjusted timing can be accurately executed, and the drive control signals are sent to the drive circuit of the switching device to perform the timing adjustment operation. At the same time, the discharge current and voltage data of the buffer capacitor are collected to determine its discharge distribution characteristics. Also, when the adjustment direction of the switch timing can be obtained through the relationship between the voltage drop rate and the switch action, in one embodiment, if the voltage drop rate is 10V / ms, it indicates that the switch closes too quickly, and the adjustment direction can be to delay the closing time. The original switch period is 5ms, and it can be extended to 6ms to observe whether the voltage drop is alleviated. Preferably, this adjustment direction can also refer to the change in the current peak value to ensure overall coordination.

[0059] An impedance model of the commutation path in the 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 are initialized, including the covariance matrix and the parameter estimation value. After each discharge process, the new discharge distribution characteristic data is input into the recursive least squares method to update the estimation of the impedance compensation value, and an 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, variable inductor, etc.) to perform the impedance adjustment operation.

[0060] By monitoring the stress distribution data in real time and triggering a protection mechanism when it exceeds the preset threshold, the present invention can timely identify potential system failures or overload conditions, avoid damage to the 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 devices can be determined, which can optimize the operation sequence and timing of the switching devices, and reduce the current impact and voltage oscillation caused by improper switching operations; by obtaining the discharge distribution characteristics of the buffer capacitor, the discharge process of the capacitor can be controlled more precisely, reducing unnecessary energy loss and improving the utilization efficiency of the capacitor; 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 the 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, enhancing the adaptive ability of the system and enabling it to maintain the best performance under different working conditions.

[0061] In the embodiments of the present application, based on how to address the problems of voltage stress mutation and current oscillation during the bridge arm reconstruction process of the existing DC ice melting device, a fault current suppression method for the DC ice melting device is designed. It collects and analyzes the commutation path impedance and fault current data in real time, generates DC bus voltage drop data based on these data, and then determines the stress distribution of the switching devices and the dynamic correction coefficient of the bridge arm switching timing. Finally, it realizes the compensation of the commutation path impedance, effectively suppresses the current oscillation, ensures the stability of the DC ice melting device during operation, and reduces equipment failures and operation abnormalities caused by current oscillation; the support vector regression algorithm is used to process the DC bus voltage drop data to obtain the voltage stress sudden increase trend, and combined with the Kalman filter algorithm to generate comprehensive stress distribution data, which can more accurately determine the stress distribution of the switching devices, help optimize the working state of the switching devices, extend their service life, and reduce the equipment maintenance cost; by combining the component response delay data with the dynamic correction coefficient and using the particle swarm optimization algorithm to obtain the bridge arm switching timing sequence, the bridge arm switching timing can be dynamically adjusted according to the actual operation of the device, improving the operation efficiency and performance of the device, and ensuring 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 performing adaptive impedance matching processing on the capacitor discharge circuit in combination with the real-time adjustment parameters, the discharge rate of the buffer capacitor can be accurately controlled according to the real-time operation state of the device, further improving the adaptability of the device to different working conditions and ensuring the accuracy and reliability of the DC ice melting operation.

[0062] It should be noted that although the steps in the above flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0063] In another embodiment, as Figure 2 shown, the second aspect of the present invention provides a fault current suppression system for a DC ice melting device, including: A current data processing module 10, configured to collect the commutation path impedance and fault current data of the DC ice melting device in real time, analyze them through fast Fourier transform to obtain current oscillation data, and input the current oscillation data into a pre-constructed impedance-current relationship model for processing to generate DC bus voltage drop data; A correction coefficient generation module 20, configured to process the DC bus voltage drop data by using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine the stress distribution data of the switching devices in the DC ice melting device, and process the stress distribution data through a Kalman filter algorithm to generate comprehensive stress distribution data to determine the dynamic correction coefficient of the bridge arm switching timing; An adjustment parameter determination module 30, configured to obtain the component response delay data of the DC ice melting device and combine it with the dynamic correction coefficient, and process it through a particle swarm optimization algorithm to obtain a bridge arm switching timing sequence to determine the real-time adjustment parameters that affect the discharge rate of the buffer capacitor in the DC ice melting device; A current oscillation suppression module 40, configured to extract the real-time data of the DC bus voltage in the DC ice melting device and combine it with the real-time adjustment parameters, perform adaptive impedance matching processing on the capacitor discharge circuit, and determine the impedance compensation value for the commutation path by comparing the voltage oscillation characteristics before and after impedance matching to execute, so as to realize the suppression of the current oscillation in the DC ice melting device.

[0064] It should be noted that each module in the above fault current suppression system for a DC ice melting device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules. 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 in the above text. The two have the same functions and effects and will not be elaborated here.

[0065] In summary, the present invention relates to the field of information technology, and discloses a method and system for suppressing fault current in a DC ice melting device. The fault current data of the DC ice melting device is analyzed by fast Fourier transform, and based on the analysis results, the DC bus voltage drop data is processed by the support vector regression algorithm to obtain the sudden increase trend of voltage stress, and then the stress distribution data of the switching devices in the device is determined. The stress distribution data is processed by the Kalman filter algorithm to obtain the processing result to determine the dynamic correction coefficient of the bridge arm switching timing, and it is processed by the particle swarm optimization algorithm together with the element response delay data of the device to generate the 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 the impedance matching of the capacitor discharge circuit is performed. By comparing the voltage oscillation characteristics before and after the matching, the impedance compensation value for the commutation path is determined and executed, thereby effectively suppressing the current oscillation in the DC ice melting device.

[0066] Each embodiment in this specification is described in a progressive manner. For the parts that are the same or similar in each embodiment, they can be referred to each other. 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 above technical features of the embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the above technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0067] The above embodiments only represent several preferred embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art in this technical field, without departing from the technical principle of the present invention, several improvements and substitutions can still be made, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the protection scope of the claims.

Claims

1. A fault current suppression method for a DC ice melting device, characterized in that: include: The commutation path impedance and fault current data of the DC ice melting device are collected in real time, and analyzed through fast Fourier transform to obtain current oscillation data for input into the pre-built impedance and current relationship model for processing, thereby generating DC bus voltage drop data; The DC bus voltage drop data is processed by using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine stress distribution data of the switch device in the DC ice melting device, and the stress distribution data is processed by a Kalman filter algorithm to generate comprehensive stress distribution data to determine a dynamic correction coefficient of the bridge arm switching timing; Acquire the component response delay data of the DC ice melting device and combine it with the dynamic correction coefficient, process it through a particle swarm optimization algorithm, and 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; The real-time data of the DC bus voltage in the DC ice-melting device is extracted to be combined with the real-time adjustment parameters, adaptive impedance matching processing is performed on the capacitor discharge circuit, and the impedance compensation value for the commutation path is determined by comparing the voltage oscillation characteristics before and after the impedance matching, so as to suppress the current oscillation in the DC ice-melting device.

2. A fault current suppression method for a DC ice melting device according to claim 1, characterized in that: The real-time acquisition of the commutation path impedance and fault current data of the DC ice melting device and the analysis 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 includes: 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 by fast Fourier transform to obtain current oscillation data of the DC ice melting device; When the current oscillation data exceeds a preset oscillation threshold, a timing adjustment is triggered, and an initial switching timing and a timing adjustment amount of the current oscillation data are calculated to obtain timing adjustment data; Input the timing adjustment data into a pre-built impedance and current relationship model for processing, output the capacitor discharge data of the DC ice melting device, and process 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 the DC bus voltage drop data of the DC ice melting device.

3. A fault current suppression method for a DC ice melting device according to claim 1, characterized in that: The method of using a support vector regression algorithm to process the DC bus voltage drop data to obtain a voltage stress sudden increase trend to determine stress distribution data of the switch device in the DC ice melting device includes: The DC bus voltage drop data is processed by a support vector regression algorithm to obtain a voltage stress sudden increase trend, and the pressure sudden increase rate of the DC ice melting device is quantified 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 device in the DC ice melting device to determine a protection threshold adjustment requirement; Based on the protection threshold adjustment requirement, characteristic curve data is calculated through voltage characteristic data and current characteristic data of the DC ice melting device, and a distribution estimation value is generated through the characteristic curve data to determine stress distribution data of the switching device.

4. A fault current suppression method for a DC ice melting device according to claim 3, characterized in that: The method of using a support vector regression algorithm to process the DC bus voltage drop data to obtain a voltage stress sudden increase trend to determine the stress distribution data of the switch device in the DC ice melting device also includes: 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 voltage drop range of the DC bus and the voltage stress of the switching device; quantifying the trend change distribution according to the stress spike value, and determining the protection threshold adjustment amplitude by acquiring the 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 amplitude to determine the stress distribution data of the switching device.

5. The fault current suppression method for a DC ice melting device according to claim 1, characterized in that: 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 a bridge arm switching timing sequence includes: Acquire thermal stress distribution data of the DC ice melting device, and fuse 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 and 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, then 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.

6. A fault current suppression method for a DC ice melting device according to claim 1, characterized in that: The step of acquiring the component response delay data of the DC ice melting device and combining it with the dynamic correction coefficient, and processing it through 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 merge with the dynamic correction coefficient, and processing the data through a particle swarm optimization algorithm to output a preliminary timing adjustment value; According to the preliminary timing adjustment value, extracting the delay offset and the switching frequency of the bridge arm switching timing 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 the 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 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.

7. A fault current suppression method for a DC ice melting device according to claim 1, characterized in that: The method for 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 switch device based on the current characteristic parameters to obtain logic distribution data; The logic distribution data is adjusted according to the change characteristics of the fault current data to obtain a logic adjustment parameter, and the real-time change of the discharge rate of the buffer capacitor is extracted 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.

8. The fault current suppression method for a DC ice melting device according to claim 1, characterized in that: The extracting of the real-time data of the DC bus voltage in the DC ice melting device to combine with the real-time adjustment parameter, performing adaptive impedance matching processing on the capacitor discharge loop, and determining the impedance compensation value of the commutation path by comparing the voltage oscillation characteristics before and after the impedance matching, includes: Extracting real-time data of the DC bus voltage in the DC ice melting device to merge 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 loop 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 matching parameter adjustment to determine the impedance compensation value of the commutation path.

9. The fault current suppression method for a DC ice melting device according to claim 1, characterized in that: The method of extracting the real-time data of the DC bus voltage in the DC ice melting device to combine it with the real-time adjustment parameter, performing adaptive impedance matching processing on the capacitor discharge loop, and determining the impedance compensation value for the commutation path by comparing the voltage oscillation characteristics before and after impedance matching, comprises: When the stress distribution data exceeds a preset stress threshold, triggering a protection mechanism to determine the timing adjustment data of the switching device; Adjusting the switch 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.

10. 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 for input into the pre-built impedance and current relationship model for processing, so as to generate DC bus voltage drop data; A correction coefficient generation module is used to process the DC bus voltage drop data using a support vector regression algorithm to obtain a voltage stress sudden increase trend to determine the stress distribution data of the switch device in the DC ice melting device, and to process the stress distribution data using a Kalman filter algorithm to generate comprehensive stress distribution data to determine the dynamic correction coefficient of the bridge arm switching timing; An adjustment parameter determination module is used to obtain the component response delay data of the DC ice melting device and combine it with the dynamic correction coefficient, and process it through 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; A current oscillation suppression module is used to extract the real-time data of the DC bus voltage in the DC ice melting device to combine it with the real-time adjustment parameter, perform adaptive impedance matching processing on the capacitor discharge circuit, and determine the impedance compensation value for the commutation path by comparing the voltage oscillation characteristics before and after impedance matching to implement the suppression of current oscillation in the DC ice melting device.

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