Dynamic load test method and system for direct-current deicing device

Abstract

The invention relates to the technical field of direct-current deicing device tests, and provides a dynamic load test method and system for a direct-current deicing device. Comprising the following steps: generating a load sudden change sequence comprising ice layer falling positions, ice layer falling thicknesses and load impedance fluctuation time sequence data corresponding to a plurality of load sudden change time periods according to operation state data of a direct current ice melting device in an ice layer non-uniform melting scene; according to a preset dynamic load response mechanism comprising an adaptive adjustment power protection trigger threshold value and a control system time delay threshold value, bus voltage response simulation is carried out on each load abrupt change time period in the load abrupt change sequence in sequence to obtain a direct current bus response time sequence data set; and carrying out anti-disturbance capability analysis on the direct-current ice melting device by combining a preset anti-disturbance capability evaluation threshold value to obtain an anti-disturbance capability evaluation result. Based on dynamic simulation of load mutation caused by uneven melting of an ice layer and influence of the load mutation on bus voltage response, comprehensiveness and reliability of disturbance rejection evaluation of the direct-current ice melting device are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of direct current ice melting device testing, and in particular to a dynamic load testing method and system for a direct current ice melting device. Background Art

[0002] DC ice-melting devices can effectively remove ice from transmission lines and prevent line breakage due to ice overload. However, the actual ice-melting process is relatively complex. To ensure the anti-disturbance capability and operational stability of DC ice-melting devices in actual applications, it is usually necessary to conduct relevant ice-melting scenario tests on the DC ice-melting devices before they are put into use.

[0003] Existing DC ice-melting device testing methods primarily analyze the device's anti-disturbance capability through static load simulation, assuming uniform ice shedding. Their load simulation adjustment mechanism is relatively simple, neglecting the impact of changes in load characteristics on the DC bus voltage response during dynamic ice shedding. Consequently, they lack the ability to quickly respond to dynamic load changes. However, during actual ice-melting, the physical properties of ice at different line locations vary. When ice melts unevenly and partially sheds, it can cause current surges and voltage fluctuations, potentially triggering the DC ice-melting device's power protection. This scenario is difficult to replicate in existing static load simulation tests, and the load simulation adjustment mechanism used in these tests also limits the validation of the DC ice-melting device's power protection control. This leads to deviations between test results and actual operating conditions, making it impossible to comprehensively and reliably assess the device's true anti-disturbance capability and provide a reliable basis for power protection configuration in practical applications, increasing the risk of false trips. Therefore, there is an urgent need to provide a DC ice melting device test method that can dynamically simulate the load mutation characteristics caused by uneven melting of the ice layer, so as to comprehensively and effectively evaluate the anti-disturbance capability of the DC ice melting device and provide reliable guarantee for the operational stability and adaptability of the DC ice melting device in practical applications. Summary of the Invention

[0004] The purpose of the present invention is to provide a dynamic load test method for a DC ice-melting device. By dynamically simulating and analyzing the load mutation caused by uneven melting of the ice layer and the impact of the load mutation on the bus voltage response, the power protection optimization and anti-disturbance capability analysis of the DC ice-melting device are simultaneously realized. This method can effectively improve the comprehensiveness and reliability of the anti-disturbance capability evaluation of the DC ice-melting device, thereby providing reliable guarantees for the operational stability and adaptability of the DC ice-melting device in practical applications.

[0005] In order to achieve the above objectives, it is necessary to provide a dynamic load test method and system for a DC ice melting device in response to the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a dynamic load test method for a DC ice melting device, the method comprising the following steps: Obtaining operating status data of a DC ice melting device in a scenario of uneven ice melting, and generating a load mutation sequence based on the operating status data; the operating status data includes time series data of current change rate, voltage sag depth, and voltage harmonic content; the load mutation sequence includes time series data of ice shedding positions, ice shedding thickness, and load impedance fluctuation corresponding to multiple load mutation time periods; Performing a bus voltage response simulation for each load mutation period in the load mutation sequence in sequence according to a preset dynamic load response mechanism, obtaining a DC bus response time series data set; the preset dynamic load response mechanism includes adaptively adjusting a power protection trigger threshold and a control system delay threshold of the DC de-icing device; The anti-disturbance capability of the DC ice melting device is analyzed according to the DC bus response time series data set and a preset anti-disturbance capability evaluation threshold to obtain a corresponding anti-disturbance capability evaluation result.

[0007] Furthermore, the step of generating a load mutation sequence according to the operating status data includes: According to the voltage sag depth time series data and the voltage harmonic content time series data, a load impedance fluctuation prediction sequence and a bus impedance fluctuation prediction sequence are obtained based on a support vector machine regression model; Traversing the current change rate at each moment in the current change rate time series data and determining whether it is greater than a preset change rate threshold; If so, the duration of the change rate exceeding the threshold is obtained, and the corresponding mutation cause is obtained based on the random forest algorithm according to the subsequence corresponding to the duration of the change rate exceeding the threshold in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence; the mutation cause includes non-ice shedding and ice shedding; When the cause of the sudden change is ice shedding, obtaining the ice shedding position and ice shedding thickness during the corresponding load sudden change period according to the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence; According to the ice shedding position and ice shedding thickness in each load mutation period, the subsequences of the corresponding time period in the load impedance fluctuation prediction sequence are segmented and marked to obtain the load mutation sequence.

[0008] Furthermore, the current change rate threshold is adaptively adjusted based on temperature and wind speed; the current change rate threshold is expressed as: Where, Indicates the current change rate threshold; represents the temperature proportionality coefficient; Indicates ice melting reference temperature; Indicates the real-time ambient temperature; It represents the wind speed pressure coefficient; Indicates real-time wind speed.

[0009] Furthermore, the step of obtaining the ice shedding position and ice shedding thickness corresponding to the load mutation period according to the subsequence in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence corresponding to the duration of the change rate exceeding the threshold value includes: Based on the relationship between ice thickness and line impedance and the segmented superposition effect of line impedance, an equation correlating load impedance and bus impedance fluctuation is established. The equation correlating load impedance and bus impedance fluctuation includes: Where, and Respectively represent bus impedance fluctuation data and load impedance fluctuation data; represents the influence coefficient of ice layer on impedance; Indicates the thickness of ice shedding; Indicates the load line length; Indicates the location of ice shedding; Substituting the load impedance fluctuation data and the bus impedance fluctuation data at each moment in the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence into the load impedance and bus impedance fluctuation correlation equation respectively for solving, thereby obtaining an estimated value of the ice shedding position and an estimated value of the ice shedding thickness at the corresponding moment; The estimated values ​​of ice shedding positions and ice shedding thicknesses at each moment in the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence are weighted averaged based on the noise standard deviation of the load impedance fluctuation data at the corresponding moment to obtain the ice shedding positions and ice shedding thicknesses in the corresponding load mutation period.

[0010] Furthermore, the DC bus response time series data set includes DC bus response time series data of each load mutation period; The step of sequentially simulating the bus voltage response for each load mutation period in the load mutation sequence according to a preset dynamic load response mechanism to obtain a DC bus response time series data set includes: Initializing a preset DC ice-melting power system simulation model according to a preset voltage drop threshold and the load mutation sequence, and simulating the load impedance fluctuation time series data of the first load mutation period in the load mutation sequence to obtain initial DC bus voltage time series data; Determine whether the initial time series data of the DC bus voltage contains voltage change data exceeding the preset voltage drop threshold; if so, perform time domain analysis on the initial time series data of the DC bus voltage to obtain a DC bus voltage drop depth and a DC bus voltage recovery time corresponding to the load mutation moment; determining whether the DC bus voltage drop depth exceeds a preset voltage drop depth threshold; if so, adjusting the power protection trigger threshold and the control system delay threshold of the DC ice melting device according to the DC bus voltage drop depth and the DC bus voltage recovery time; According to the adjusted power protection trigger threshold and control system delay threshold, the DC bus response timing data of the first load mutation period in the load mutation sequence is re-simulated and obtained, and the subsequent load mutation periods are simulated in turn until the DC bus response timing data of all load mutation periods are obtained.

[0011] Furthermore, the step of adjusting the power protection trigger threshold and the control system delay threshold of the DC ice melting device according to the DC bus voltage drop depth and the DC bus voltage recovery time includes: Comparing and analyzing the DC bus voltage drop depth, the DC bus voltage recovery time, and a preset voltage response threshold, to obtain a voltage response deviation; the voltage response deviation includes a voltage drop depth deviation and a voltage recovery time deviation; According to the voltage sag depth deviation in the voltage response deviation, the power protection trigger threshold of the DC ice melting device is adaptively updated based on a preset power protection trigger threshold update expression; the preset power protection trigger threshold update expression is expressed as: Where, and Respectively represent the power protection triggering threshold before and after the update; Indicates the power adjustment factor; Indicates voltage drop depth deviation; According to the voltage recovery time deviation in the voltage response deviation, the preset control system delay threshold of the DC ice melting device is adaptively updated based on a preset control system delay threshold update expression; the preset control system delay threshold update expression is expressed as: Where, and Respectively represent the control system delay thresholds before and after the update; Indicates the delay adjustment coefficient; Indicates the voltage recovery time deviation.

[0012] Furthermore, the preset anti-disturbance capability evaluation threshold includes a preset wire vibration period threshold, a preset period stability threshold and a preset recovery time threshold; The step of performing anti-disturbance capability analysis of the DC ice melting device according to the DC bus response time series data set and a preset anti-disturbance capability evaluation threshold to obtain a corresponding anti-disturbance capability evaluation result includes: Performing time domain analysis and frequency domain analysis on the DC bus response time series data of each load mutation period in the DC bus response time series data set, respectively, to obtain bus voltage fluctuation period and main frequency component time series data corresponding to the load mutation period; The bus voltage fluctuation period in each load mutation period is averaged to obtain the average bus voltage fluctuation period, and a period stability index is obtained based on the main frequency component time series data of each load mutation period; the period stability index includes the frequency fluctuation range and the frequency fluctuation rate; Comparing and analyzing the busbar average voltage fluctuation period with a preset conductor vibration period threshold to obtain a corresponding periodicity comparison result, and comparing and analyzing the periodicity stability index with a preset periodicity stability threshold to obtain a corresponding periodicity stability analysis result; Analyze the DC bus response time series data of each load mutation period based on the preset voltage drop threshold, and average the bus voltage recovery time of each load mutation period to obtain the average bus voltage recovery time; Comparing and analyzing the average bus voltage recovery time with a preset recovery time threshold to obtain a corresponding recovery characteristic analysis result; The anti-disturbance capability evaluation result is obtained by performing a fusion analysis based on the periodic comparison result, the periodic stability analysis result and the recovery characteristic analysis result.

[0013] Furthermore, the method further comprises: When the anti-disturbance capability evaluation result does not meet the anti-disturbance capability target, the power protection triggering threshold of the DC ice melting device is optimized according to the real-time environmental change data.

[0014] Furthermore, the real-time environmental change data includes temperature change data, humidity change data and wind speed change data; The step of optimizing the power protection triggering threshold of the DC ice melting device according to the real-time environmental change data includes: According to the temperature change data, the humidity change data, and the wind speed change data, ice layer physical property change data is obtained based on a preset ice layer physical property model; the preset ice layer physical property model is constructed by performing a multivariate regression analysis on historical winter environmental change data and the corresponding ice layer physical property change data; the ice layer physical property change data includes ice layer density change data and ice layer thickness change data; Obtain corresponding impedance change data according to the temperature change data and a preset temperature influence load impedance model; the preset temperature influence load impedance model is constructed based on fitting of historical winter temperature change data and conductor impedance change data; If the impedance change data exceeds a preset impedance change threshold, the power protection triggering threshold of the DC ice melting device is optimized according to the ice layer physical property change data.

[0015] In a second aspect, an embodiment of the present invention provides a dynamic load test system for a DC ice melting device, the system comprising: A data acquisition module is configured to acquire operating status data of a DC ice melting device in a scenario of uneven ice melting, and generate a load mutation sequence based on the operating status data; the operating status data includes time series data of current change rate, voltage drop depth, and voltage harmonic content; the load mutation sequence includes time series data of ice shedding position, ice shedding thickness, and load impedance fluctuation corresponding to multiple load mutation time periods; a simulation analysis module configured to sequentially simulate bus voltage responses for each load mutation period in the load mutation sequence according to a preset dynamic load response mechanism, thereby obtaining a DC bus response time series data set; the preset dynamic load response mechanism includes adaptively adjusting a power protection trigger threshold and a control system delay threshold of the DC de-icing device; The anti-disturbance evaluation module is used to analyze the anti-disturbance capability of the DC ice melting device according to the DC bus response time series data set and a preset anti-disturbance capability evaluation threshold, and obtain a corresponding anti-disturbance capability evaluation result.

[0016] The present invention provides a dynamic load test method and system for a DC ice melting device. The method achieves the goal of obtaining operating status data of the DC ice melting device under an uneven ice melting scenario, including current change rate time series data, voltage drop depth time series data, and voltage harmonic content time series data. After generating a load mutation sequence including ice shedding positions, ice shedding thicknesses, and load impedance fluctuation time series data corresponding to multiple load mutation time periods based on the operating status data, a bus voltage response simulation is sequentially performed for each load mutation time period in the load mutation sequence according to a preset dynamic load response mechanism including adaptively adjusting a power protection trigger threshold and a control system delay threshold of the DC ice melting device to obtain a DC bus response time series data set. The anti-disturbance capability of the DC ice melting device is analyzed based on the DC bus response time series data set and a preset anti-disturbance capability evaluation threshold to obtain a corresponding anti-disturbance capability evaluation result. Compared with the existing technology, the dynamic load test method of the DC ice melting device is based on the dynamic simulation analysis of the load mutation caused by uneven melting of the ice layer and the impact of the load mutation on the bus voltage response. It can simultaneously realize the power protection optimization and anti-disturbance capability analysis of the DC ice melting device, and can effectively improve the comprehensiveness and reliability of the anti-disturbance capability evaluation of the DC ice melting device, thereby providing reliable protection for the operational stability and adaptability of the DC ice melting device in actual application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 1 is a flow chart of a dynamic load test method for a DC ice melting device according to an embodiment of the present invention; Figure 2 2 is a schematic structural diagram of a dynamic load test system for a DC ice melting device according to an embodiment of the present invention; Reference numerals: Among them, 1. Data acquisition module; 2. Simulation analysis module; 3. Anti-disturbance evaluation module. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] In one embodiment, Figure 1 As shown, a dynamic load test method for a DC ice melting device is provided, comprising the following steps: S11. Obtain operating status data of the DC ice melting device in a scenario of uneven ice melting, and generate a load mutation sequence based on the operating status data. The operating status data can be understood as electrical parameter data corresponding to the operation of the DC ice melting device in the scenario of uneven ice melting, obtained by analyzing and processing current time series data and voltage time series data collected by pre-deployed relevant sensors during the DC ice melting process. To ensure the reliability of the generation of the load mutation sequence, this embodiment preferably configures the operating status data to include current change rate time series data, voltage sag depth time series data, and voltage harmonic content time series data.

[0020] In practical applications, after cleaning the current and voltage time series data, data change analysis can be performed on the cleaned current and voltage time series data based on a preset time window to obtain the required current change rate time series data and voltage drop depth time series data. For example, at a certain moment, the current change rate is 2A / s and the voltage drop depth is 5%. At the same time, the voltage time series data is frequency-decomposed using a fast Fourier transform algorithm to obtain the required voltage harmonic content time series data. For example, when the voltage time series data is subjected to frequency domain analysis, the frequency components include a fundamental wave accounting for 96%, a fifth harmonic accounting for 3%, and a seventh harmonic accounting for 1%, then the corresponding voltage harmonic content is 4%. It should be noted that cleaning the current and voltage time series data, performing data change analysis on the cleaned current and voltage time series data based on a preset time window, and frequency decomposing the voltage time series data using a fast Fourier transform algorithm can all be implemented by referring to relevant existing technologies and will not be described in detail here.

[0021] The load mutation sequence in this embodiment can be understood as dynamic load mutation data generated by identifying and analyzing load mutations caused by uneven melting and shedding of ice contained in the operating status data, and can be used to reliably evaluate the anti-disturbance capability of the DC ice melting device. Correspondingly, the load mutation sequence includes ice shedding positions, ice shedding thickness, and load impedance fluctuation time series data corresponding to multiple load mutation time periods. Specifically, the step of generating the load mutation sequence based on the operating status data includes: According to the voltage sag depth time series data and the voltage harmonic content time series data, a load impedance fluctuation prediction sequence and a bus impedance fluctuation prediction sequence are obtained based on a support vector machine regression model; wherein, the process of obtaining the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence can be understood as first standardizing the voltage sag depth time series data and the voltage harmonic content time series data, processing outliers and aligning the time series, and then analyzing and processing the voltage sag depth time series data and the voltage harmonic content time series data based on the pre-trained support vector machine regression model, and outputting the load impedance fluctuation prediction value and the bus impedance fluctuation prediction value corresponding to each moment; it is necessary It is noted that the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence need to be predicted separately using the corresponding support vector machine regression model, and the support vector machine regression model used to predict the load impedance fluctuation prediction sequence is constructed based on the voltage drop depth time series data, the voltage harmonic content time series data and the corresponding load impedance fluctuation data training, and the support vector machine regression model used for the bus impedance fluctuation prediction sequence is constructed based on the voltage drop depth time series data, the voltage harmonic content time series data and the corresponding bus impedance fluctuation data training; the specific support vector machine regression model construction method can refer to the relevant existing technology implementation, and will not be described in detail here.

[0022] The current change rate at each moment in the current change rate time series data is traversed and a determination is made as to whether it is greater than a preset change rate threshold; wherein the preset change rate threshold can be understood as an indicator threshold for identifying a sudden load change during ice melting. Considering that the ambient temperature and wind speed in the actual ice melting process may affect the impedance of the ice-covered transmission line, thereby affecting the load current change, in order to ensure the reliability of identifying a sudden load change during ice melting, this embodiment preferably adaptively adjusts the current change rate threshold based on the ambient temperature and wind speed. Specifically, the current change rate threshold is adaptively adjusted based on the temperature and wind speed. That is, by comprehensively considering the enhancing effect of the ambient temperature on the ice melting demand and the influence of the wind speed on the heat dissipation capacity, an adaptive adjustment expression for the current change rate threshold is designed based on the thermal balance principle. The current change rate threshold is expressed as: Where, Indicates the current change rate threshold, which indicates the maximum value of the normal current change rate; Indicates the temperature proportionality coefficient, which can be pre-calibrated based on the wire material properties (resistivity, heat capacity) and cross-sectional area, etc., and is not limited here. The unit is ; Indicates the reference temperature for ice melting, usually the melting point of ice; Indicates the real-time ambient temperature. The lower the temperature, the more likely it is that the current required to melt the ice will increase. It represents the wind speed pressure coefficient, reflecting the degree of enhancement of the heat dissipation capacity by wind speed. It can be calibrated through experiments or simulations and is not limited here. The unit is ; Indicates the real-time wind speed. The higher the wind speed, the better the heat dissipation of the conductor, and the threshold can be appropriately increased.

[0023] In this embodiment, the adaptive adjustment design of the current change rate threshold provided by considering the temperature dependence of the actual ice melting process and wind speed compensation can effectively improve the accuracy of load mutation identification while taking into account the safety and efficiency of ice melting.

[0024] If so, the duration of the change rate exceeding the threshold is obtained, and the corresponding mutation cause is obtained based on the random forest algorithm according to the subsequence corresponding to the duration of the change rate exceeding the threshold in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence; wherein, the duration of the change rate exceeding the threshold can be understood as the duration from the moment when the current change rate is identified to be greater than the preset change rate threshold in the current change rate time series data to the time when the current change rate is reduced to the preset change rate threshold, which is used to extract subsequences from the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence according to the load mutation period; in actual applications, the load for analysis can be selected from the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence according to the duration of the change rate exceeding the threshold. The load impedance fluctuation prediction subsequence and the bus impedance fluctuation prediction subsequence are then feature extracted from the load impedance fluctuation prediction subsequence and the bus impedance fluctuation prediction subsequence respectively to obtain corresponding statistical features including the mean, variance, skewness, kurtosis and duration of the impedance fluctuation. The statistical features corresponding to the load impedance fluctuation prediction subsequence and the bus impedance fluctuation prediction subsequence are then input into a pre-trained random forest model to perform load mutation cause analysis to identify whether the load mutation is caused by ice shedding, that is, the mutation cause includes non-ice shedding and ice shedding. It should be noted that the random forest model can be trained through a data set constructed by load impedance fluctuations and bus impedance fluctuations in ice shedding and non-ice shedding scenarios actually collected during the ice melting process, which will not be described in detail here.

[0025] When the cause of the load mutation is ice shedding, the ice shedding position and thickness corresponding to the load mutation period are obtained based on the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence. The load mutation period can be understood as the negative mutation time range corresponding to the duration of the change rate exceeding the threshold value. Since the load mutation is related to the corresponding ice shedding position and thickness, in order to facilitate the subsequent accurate simulation of the load mutation caused by uneven ice melting, this embodiment preferably introduces load impedance fluctuation and bus impedance fluctuation to identify the ice shedding position and thickness that trigger the load mutation, and introduces them when constructing the load mutation sequence. Specifically, the step of obtaining the ice shedding position and thickness corresponding to the load mutation period based on the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence includes: Based on the relationship between ice thickness and line impedance and the segmented superposition effect of line impedance, an equation is established to correlate load impedance and bus impedance fluctuations. The relationship between ice thickness and line impedance can be understood as the effect of ice on line resistance: ice cover increases line resistance, while ice shedding reduces line impedance. The segmented superposition effect of line impedance can be understood as the linear superposition of impedance changes between ice-covered and uncovered sections of the line. Each impedance change affects the load and bus, and can reflect the direct impact of ice shedding locations on impedance distribution. To better align with the physical characteristics of actual ice-covered lines and ensure the accuracy of identifying ice shedding locations and ice shedding thickness, this embodiment preferably sets the equation to correlate load impedance and bus impedance fluctuations to include: Where, and Respectively represent bus impedance fluctuation data and load impedance fluctuation data; Indicates the influence coefficient of ice layer on impedance, which can be set based on actual application requirements. Its unit is , represents the impedance increment caused by unit ice thickness (m) on the line per unit length (m) ( ); Indicates the thickness of ice shedding; Indicates the load line length, that is, the length of the transmission line where the DC ice melting device is used for ice melting; Indicates the ice shedding location, i.e. the location of the ice shedding point on the ice-melting treated transmission line; Substituting the load impedance fluctuation data and bus impedance fluctuation data at each moment in the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence into the load impedance and bus impedance fluctuation correlation equation for solving, the ice shedding position estimate and ice shedding thickness estimate at the corresponding moment are obtained; wherein the ice shedding position estimate and ice shedding thickness estimate can be expressed as: Where, and Respectively represent the duration of the change rate exceeding the threshold Estimated ice shedding position and ice shedding thickness at the moment; and Respectively represent the duration of the change rate exceeding the threshold Load impedance fluctuation data and bus impedance fluctuation data at each moment.

[0026] The estimated ice shedding position and ice shedding thickness at each moment in the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence are weighted averaged based on the noise standard deviation of the load impedance fluctuation data at the corresponding moment to obtain the ice shedding position and ice shedding thickness during the corresponding load mutation period; wherein the noise standard deviation of the load impedance fluctuation data can be understood as an indicator for quantifying the uncertainty of the load impedance fluctuation data at each moment in the load impedance fluctuation prediction sequence, and its acquisition process includes: first, using a high-pass filter to separate the high-frequency noise of the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence to obtain a noise sequence, and then, by selecting a time window near each moment, calculating the standard deviation of the noise subsequence in the time window to obtain the noise standard deviation corresponding to each moment. After determining the noise standard deviation of the load impedance fluctuation data at each moment, the weight coefficient is set based on the principle that the larger the noise standard deviation, the smaller the weight. The estimated ice shedding position and ice shedding thickness at each moment during the duration of the change rate exceeding the threshold are weighted averaged to obtain the ice shedding position and ice shedding thickness corresponding to the load mutation period, which can be expressed as: Where, in, and They represent the ice shedding position and ice shedding thickness during the load mutation period corresponding to the duration of the change rate exceeding the threshold value; Indicates the duration of the change rate exceeding the threshold The noise standard deviation of the load impedance fluctuation data at each moment; Indicates the duration of the change rate exceeding the threshold The weight coefficients of the estimated ice shedding position and ice shedding thickness at the moment; n represents the total number of moments during which the rate of change exceeds the threshold.

[0027] The above method and steps can realize reliable identification and analysis of each load mutation period in the operating status data of the DC ice melting device under the scenario of uneven ice melting, and obtain the corresponding ice shedding position, ice shedding thickness and load impedance fluctuation time series data.

[0028] The subsequences of the corresponding time periods in the load impedance fluctuation prediction sequence are segmented and labeled according to the ice shedding positions and ice shedding thicknesses in each load mutation time period to obtain the load mutation sequence; that is, the load mutation sequence includes the ice shedding position, ice shedding thickness, and load impedance fluctuation time series data corresponding to at least one load mutation time period. It should be noted that, considering that actual load mutation data has a time series correlation, in order to ensure the reliability of the subsequent anti-disturbance capability assessment of the DC ice melting device based on the load mutation sequence, this embodiment preferably sets the load mutation sequence as a load impedance fluctuation time series obtained by labeling the subsequences corresponding to each load mutation time period in the load impedance fluctuation prediction sequence with the ice shedding position and ice shedding thickness.

[0029] S12. Perform bus voltage response simulations in sequence for each load mutation period in the load mutation sequence according to a preset dynamic load response mechanism to obtain a DC bus response time series data set; the preset dynamic load response mechanism includes adaptively adjusting the power protection trigger threshold and control system delay threshold of the DC ice melting device; wherein, the bus voltage response simulation can be understood as a power system operation simulation (using a DC ice melting device) of a power system whose DC bus voltage is affected by load mutations based on a power system simulation model constructed based on existing simulation platforms such as MATLAB, Simulink and PSCAD. The simulation process includes a dynamic load mutation scenario simulation based on the ice shedding position, ice shedding thickness and load impedance fluctuation time series data for each load mutation period in the load mutation sequence, so as to obtain the DC bus response time series data under the corresponding scenario as the data basis for the subsequent evaluation of the anti-disturbance capability of the DC ice melting device.

[0030] In this embodiment, the DC bus response time series data set includes DC bus response time series data for each load mutation period. Specifically, the step of sequentially simulating the bus voltage response for each load mutation period in the load mutation sequence according to a preset dynamic load response mechanism to obtain the DC bus response time series data set includes: According to the preset voltage drop threshold and the load mutation sequence, the preset DC ice-melting power system simulation model is initialized, and the load impedance fluctuation time series data of the first load mutation period in the load mutation sequence is simulated to obtain the initial time series data of the DC bus voltage; wherein, the preset voltage drop threshold can be understood as an indicator for judging whether there is a drop in the DC bus voltage, which can be set as a percentage of the steady-state interval voltage mean; the preset DC ice-melting power system simulation model can be understood as a simulation model that can simulate the function of the DC ice-melting device and the operation mechanism of the power system, and is used to realize the simulation of the association between the DC ice-melting device and the DC bus. The specific construction process can be implemented with reference to the relevant existing technology and will not be described in detail here. The process of initializing a preset DC de-icing power system simulation model based on a preset voltage sag threshold and load mutation sequence includes setting power supply parameters (setting the DC power supply's rated voltage, maximum output current, and internal resistance, etc.), initializing the transmission line model (inputting line length, resistance per unit length, inductance, resistance increment caused by icing, initial ice thickness, ambient temperature, and wind speed, etc.), setting load parameters (initial load impedance or power demand, configuring timing trigger conditions based on the load mutation sequence, etc.), configuring the voltage sag threshold and protection logic, and configuring the de-icing control strategy and dynamic response parameters. After completing the initialization of the preset DC de-icing power system simulation model, the simulation program can be started. The load impedance fluctuation time series data during the first load mutation period in the load mutation sequence is used as the simulation model operation input, and the DC bus voltage change time series data corresponding to the load mutation period (the initial DC bus voltage time series data) is monitored and obtained.

[0031] A determination is made as to whether the initial time series data of the DC bus voltage contains voltage change data exceeding a preset voltage drop threshold. If so, a time domain analysis is performed on the initial time series data of the DC bus voltage to obtain a DC bus voltage drop depth and a DC bus voltage recovery duration corresponding to a load mutation moment. Specifically, when a voltage drop triggers a preset voltage drop threshold in the initial time series data of the DC bus voltage, the initial time series data of the DC bus voltage is first de-noised using a low-pass filter. The de-noised voltage time series data is then traversed to find a time when the voltage first falls below the preset voltage drop threshold as the drop starting time. A backward search is then performed from the drop starting time to find a time when the voltage recovers to the corresponding voltage recovery threshold as the drop recovery time. After finding a minimum voltage value within the interval between the drop starting time and the drop recovery time, the DC bus voltage drop depth is obtained based on a difference between a voltage mean value in the steady-state interval and the minimum voltage value. Simultaneously, the DC bus voltage recovery duration is obtained based on a difference between the drop recovery time and the drop starting time. Of course, if the initial timing data of the DC bus voltage does not contain voltage change data that exceeds the preset voltage drop threshold, it is considered that the power protection mechanism of the DC ice melting device under the current load mutation scenario can ensure the stable operation of the system, and there is no need to optimize and adjust the response of the DC bus under the load mutation scenario. That is, after obtaining the required DC bus response timing data, the simulation can be directly performed based on the load impedance fluctuation timing data of the next load mutation period.

[0032] Determine whether the DC bus voltage drop depth exceeds a preset voltage drop depth threshold. If so, adjust the power protection trigger threshold and the control system delay threshold of the DC ice melting device according to the DC bus voltage drop depth and the DC bus voltage recovery time. The preset voltage drop depth threshold can be understood as an indicator for determining whether the DC bus voltage has an abnormal drop, and can be set according to actual application requirements. That is, when the DC bus voltage drop depth exceeds the preset voltage drop depth threshold, it is considered that the control logic inside the DC ice melting device cannot respond well to the sudden load change, resulting in an excessive drop in the DC bus voltage, and the power protection trigger threshold and the control system delay threshold of the DC ice melting device need to be adjusted accordingly.

[0033] In order to ensure the scientific and reasonable regulation of the power protection trigger threshold and the control system delay threshold, this embodiment preferably adjusts the DC bus voltage drop depth and the DC bus voltage recovery time based on the deviation between the corresponding target response threshold. Specifically, the step of adjusting the power protection trigger threshold and the control system delay threshold of the DC de-icing device based on the DC bus voltage drop depth and the DC bus voltage recovery time includes: The DC bus voltage drop depth, the DC bus voltage recovery time and the preset voltage response threshold are compared and analyzed to obtain a voltage response deviation; wherein the preset voltage response threshold includes a voltage drop depth target threshold and a voltage recovery time target threshold; correspondingly, the voltage response deviation includes a voltage drop depth deviation and a voltage recovery time deviation, and the voltage drop depth deviation is the deviation between the DC bus voltage drop depth and the voltage drop depth target threshold, and the voltage recovery time deviation is the deviation between the DC bus voltage recovery time and the voltage recovery time target threshold.

[0034] Based on the voltage sag depth deviation in the voltage response deviation, the power protection trigger threshold of the DC ice melting device is adaptively updated based on a preset power protection trigger threshold update expression. The voltage sag depth deviation reflects abnormal changes in the system voltage (the magnitude of the voltage drop exceeds expectations). When the voltage sag is large, it may mean that the system is unstable and the protection threshold needs to be adjusted to avoid overload or other faults. To enable the power protection threshold to be dynamically adjusted according to the real-time system status and improve the timeliness and accuracy of the protection action, this embodiment preferably introduces the voltage sag depth deviation as a feedback quantity to adaptively update the power protection trigger threshold. Specifically, the preset power protection trigger threshold update expression is expressed as: Where, and Respectively represent the power protection triggering threshold before and after the update; Represents the power adjustment coefficient, which is used to quantify the influence of voltage sag depth deviation on the protection threshold. Its value needs to balance sensitivity and stability. The specific value can be set based on actual application requirements. When it is set to a larger value, it will be more sensitive to voltage sag and suitable for scenarios with fast fault response (such as short-circuit protection). When it is set to a smaller value, it can avoid false triggering caused by noise or instantaneous fluctuations and is suitable for long-term overload protection. The unit is ; Indicates the voltage sag depth deviation.

[0035] According to the voltage recovery time deviation in the voltage response deviation, the preset control system delay threshold of the DC ice melting device is adaptively updated based on the preset control system delay threshold update expression; wherein, the voltage recovery time deviation reflects the speed of system response. If the voltage recovery time is longer than expected, it means that the system response is slow, and the delay threshold needs to be relaxed to allow the system more time to adjust to avoid triggering protection actions too frequently, which may cause system instability. Conversely, if the recovery time is shorter than expected, the delay threshold can be tightened to speed up the system response. In order to enable the control system delay to be dynamically associated with the real-time system response capability, so that the delay threshold can be dynamically modified according to the real-time control effect to effectively balance the response speed and stability, this embodiment preferably introduces the voltage recovery time deviation as a feedback quantity to adaptively update the control system delay threshold; specifically, the preset control system delay threshold update expression is expressed as: Where, and Respectively represent the control system delay thresholds before and after the update; Represents the delay adjustment coefficient, which is used to quantify the influence of voltage recovery time deviation on the delay threshold. Its value needs to balance sensitivity and robustness. The specific value can be set based on actual application requirements. Setting a larger value can make the delay threshold more sensitive to changes in recovery time, which is suitable for fast response scenarios (such as high-frequency load fluctuations). Setting a smaller value can suppress the influence of noise or transient disturbances, which is suitable for scenarios with high stability requirements. The unit is ; Indicates the voltage recovery time deviation.

[0036] The adaptive adjustment mechanism for the power protection trigger threshold and control system delay threshold of the DC ice melting device provided in this embodiment not only enables the DC ice melting device to dynamically optimize the protection strategy when the voltage is abnormal, effectively balancing operating efficiency and safety, but also enables the DC ice melting device to dynamically optimize the control delay under complex operating conditions, achieving a balance between efficiency and stability.

[0037] Based on the adjusted power protection trigger threshold and control system delay threshold, the DC bus response timing data of the first load mutation period in the load mutation sequence is re-simulated and obtained, and the subsequent load mutation periods are simulated in sequence until the DC bus response timing data of all load mutation periods are obtained. That is, after the power protection trigger threshold and the control system delay threshold are updated through the above method steps, the relevant parameters in the preset DC de-icing power system simulation model can be reconfigured according to the power protection trigger threshold and the control system delay threshold, and then the corresponding load mutation scenario simulation is re-performed based on the load impedance fluctuation timing data of the first load mutation period, and the DC bus response timing data of the corresponding load mutation period is collected to facilitate the subsequent rationality verification of the power protection control logic of the DC de-icing device. It should be noted that when simulating other subsequent load mutation periods in the load mutation sequence, the above-mentioned implementation method of simulating the first load mutation period can also be used to obtain the required DC bus response timing data, which will not be repeated here.

[0038] S13. Perform anti-disturbance capability analysis on the DC ice melting device according to the DC bus response time series data set and the preset anti-disturbance capability evaluation threshold to obtain a corresponding anti-disturbance capability evaluation result; wherein the preset anti-disturbance capability evaluation threshold can be understood as an indicator threshold for evaluating the anti-disturbance capability of the DC ice melting device; in order to ensure the comprehensiveness and reliability of the anti-disturbance capability analysis, this embodiment preferably sets the preset anti-disturbance capability evaluation threshold to include a preset conductor vibration period threshold, a preset period stability threshold, and a preset recovery time threshold, and the preset conductor vibration period threshold is used to judge the consistency between the voltage fluctuation period and the transmission line vibration period, the preset period stability threshold is used to judge the stability of the voltage fluctuation period, and the preset recovery time threshold is used to judge the voltage drop recovery rate; specifically, the step of performing anti-disturbance capability analysis on the DC ice melting device according to the DC bus response time series data set and the preset anti-disturbance capability evaluation threshold to obtain a corresponding anti-disturbance capability evaluation result includes: The DC bus response timing data of each load mutation period in the DC bus response timing data set are subjected to time domain analysis and frequency domain analysis respectively to obtain the bus voltage fluctuation period and main frequency component timing data of the corresponding load mutation period; wherein, the process of performing time domain analysis on the DC bus response timing data of the load mutation period to obtain the bus voltage fluctuation period refers to the relevant description of the aforementioned DC bus voltage recovery time acquisition process, the difference being that the starting moment of calculating the bus voltage fluctuation period is the moment when the voltage starts to fluctuate abnormally, and the ending moment is the moment when the abnormal voltage fluctuation stops, which will not be described in detail here; performing frequency domain analysis on the DC bus response timing data of the load mutation period to obtain the main frequency component timing data can be understood as performing frequency decomposition on the DC bus response timing data using a fast Fourier transform algorithm, and extracting the main frequency component from the decomposed frequency component. The specific implementation process can be referred to the relevant existing technology implementation, which will not be described here.

[0039] The bus voltage fluctuation periods of each load mutation period are averaged to obtain the bus average voltage fluctuation period, and the periodic stability index is obtained based on the main frequency component time series data of each load mutation period; the periodic stability index includes the frequency fluctuation range and the frequency fluctuation rate, and the frequency fluctuation range can be understood as the fluctuation interval corresponding to the maximum frequency and the minimum frequency in the main frequency component time series data, and the frequency fluctuation rate can be understood as the frequency fluctuation standard deviation calculated based on the main frequency component time series data.

[0040] The busbar average voltage fluctuation period is compared and analyzed with the preset conductor vibration period threshold to obtain a corresponding periodic comparison result, and the periodic stability index is compared and analyzed with the preset periodic stability threshold to obtain a corresponding periodic stability analysis result; wherein, the periodic comparison result can be understood as a periodic matching score given based on the comparison between the busbar average voltage fluctuation period and the preset conductor vibration period threshold. For example, if the busbar average voltage fluctuation period exceeds the preset conductor vibration period threshold, it is considered that the periodic stability is unqualified. The corresponding periodic matching score can be further determined based on the degree of deviation between the busbar average voltage fluctuation period and the preset conductor vibration period threshold, according to the principle that the greater the deviation, the lower the periodic matching degree. When the busbar average voltage fluctuation period is less than the preset conductor vibration period threshold, it can be determined that the periodic stability is qualified, and the corresponding periodic matching score is determined according to the principle that the smaller the deviation, the higher the periodic matching degree.

[0041] The preset periodic stability thresholds in this embodiment include a frequency fluctuation range threshold and a frequency volatility threshold. The corresponding periodic stability analysis results can be understood as periodic stability scores determined based on the size matching relationship between the actual frequency fluctuation range and frequency volatility and the corresponding preset periodic stability threshold. For example, the fluctuation range score is obtained based on the comparison result between the frequency fluctuation range and the frequency fluctuation range threshold, and the volatility score is obtained based on the comparison result between the frequency volatility and the frequency volatility threshold. The final periodic stability score is then obtained based on the weighted average of the fluctuation range score and the volatility score. It should be noted that the calculation process of the fluctuation range score and the volatility score can refer to the above-mentioned periodic matching score acquisition process, which will not be repeated here.

[0042] The DC bus response timing data of each load mutation period is analyzed based on the preset voltage drop threshold, and the bus voltage recovery time of each load mutation period is averaged to obtain the average bus voltage recovery time; among which, the process of obtaining the bus voltage recovery time of each load mutation period can refer to the relevant description of processing the DC bus voltage initial timing data in the aforementioned bus voltage response simulation process to obtain the DC bus voltage recovery time, which will not be repeated here.

[0043] The average bus voltage recovery time is compared and analyzed with the preset recovery time threshold to obtain the corresponding recovery characteristic analysis result; wherein, the recovery characteristic analysis result can be understood as a recovery characteristic score determined based on the size matching relationship between the average bus voltage recovery time and the preset recovery time threshold. The specific recovery characteristic score calculation process can also refer to the above-mentioned periodic matching score acquisition process, which will not be repeated here.

[0044] The anti-disturbance capability assessment result is obtained by performing a fusion analysis based on the periodic comparison results, the cyclic stability analysis results, and the recovery characteristics analysis results. The anti-disturbance capability assessment result can be understood as a comprehensive anti-disturbance score obtained by weighted summing the periodic comparison results, the cyclic stability analysis results, and the recovery characteristics analysis results according to a preset weighting coefficient. After obtaining the comprehensive anti-disturbance score, it can be compared with a preset anti-disturbance capability compliance score. If the score is greater than the anti-disturbance capability compliance score, the anti-disturbance capability assessment result is considered to meet the anti-disturbance capability target requirement; otherwise, it is determined that the anti-disturbance capability target is not met.

[0045] Embodiments of the present invention provide a technical solution for acquiring operating status data of a DC ice melting device in a scenario of uneven ice melting, including time series data of current change rate, voltage sag depth, and voltage harmonic content. Based on the operating status data, a load mutation sequence is generated, including time series data of ice shedding locations, ice shedding thickness, and load impedance fluctuation corresponding to multiple load mutation periods. Bus voltage response simulation is then performed for each load mutation period in the load mutation sequence according to a preset dynamic load response mechanism that includes adaptively adjusting a power protection trigger threshold and a control system delay threshold of the DC ice melting device to obtain a DC bus response time series dataset. The DC ice melting device's anti-disturbance capability is then analyzed based on the DC bus response time series dataset and a preset anti-disturbance capability assessment threshold to obtain a corresponding anti-disturbance capability assessment result. Based on the dynamic simulation analysis of load mutations caused by uneven ice melting and their impact on bus voltage response, power protection optimization and anti-disturbance capability analysis of the DC ice melting device can be simultaneously achieved. This effectively improves the comprehensiveness and reliability of the DC ice melting device's anti-disturbance capability assessment, thereby providing reliable assurance for the operational stability and adaptability of the DC ice melting device in practical applications.

[0046] Furthermore, considering that in actual applications, environmental data may have a certain degree of impact on the operating performance of the DC ice melting device, in order to effectively improve the anti-disturbance capability of the DC ice melting device, this embodiment preferably optimizes the power protection triggering threshold of the DC ice melting device based on real-time environmental change data when the DC ice melting device's anti-disturbance capability is insufficient based on dynamic load mutation verification. Specifically, in one embodiment, the method further includes: When the anti-disturbance capability assessment result does not meet the anti-disturbance capability target, the power protection trigger threshold of the DC ice melting device is optimized based on the real-time environmental change data. The real-time environmental change data can be understood as the working environment data of the DC ice melting device that affects the change data of the physical properties of the ice layer. To ensure the comprehensiveness and reliability of the optimization and adjustment of the power protection trigger threshold based on the environmental data, this embodiment preferably sets the real-time environmental change data to include temperature change data, humidity change data, and wind speed change data. Specifically, the step of optimizing the power protection trigger threshold of the DC ice melting device based on the real-time environmental change data includes: According to the temperature change data, the humidity change data and the wind speed change data, the ice layer physical property change data is obtained based on a preset ice layer physical property model; wherein, the preset ice layer physical property model is constructed based on a multiple regression analysis of historical winter environmental change data and the corresponding ice layer physical property change data, that is, the temperature change data, humidity change data and wind speed change data in the collected historical winter environmental change data are used as independent variables, and the corresponding ice layer physical property change data are used as dependent variables to perform a multiple regression analysis to obtain a mapping relationship model characterizing the environmental change data and the ice layer physical property change data; the ice layer physical property change data includes ice layer density change data and ice layer thickness change data, both of which will affect the actual ice melting effect.

[0047] According to the temperature change data and the preset temperature influence load impedance model, the corresponding impedance change data is obtained; wherein, the preset temperature influence load impedance model is constructed based on the fitting of historical winter temperature change data and conductor impedance change data, that is, the collected temperature change data is used as the independent variable, and the corresponding conductor impedance change data is used as the dependent variable for fitting analysis to obtain a model that ensures the mapping relationship between temperature change and conductor impedance change.

[0048] If the impedance change data exceeds a preset impedance change threshold, the power protection triggering threshold of the DC ice melting device is optimized according to the ice layer physical property change data; wherein the preset impedance change threshold can be understood as a threshold used to determine whether it is necessary to optimize the power protection triggering threshold of the DC ice melting device based on the real-time environmental change data; if the impedance change data does not exceed the preset impedance change threshold, it is considered that the environmental change data is not the main factor affecting the operation effect of the DC ice melting device, and the design of the DC ice melting device can be optimized and adjusted from other aspects; otherwise, it is considered that the environmental change data is the main factor affecting the current operation effect of the DC ice melting device, and the real-time environmental change data can be used as the basis for optimizing the power protection triggering threshold of the DC ice melting device; the specific optimization process is as follows: 1) Based on the ice layer thermodynamic model, the target total ice melting power required to melt a unit volume of ice is calculated, which is expressed as: in, is the target total ice melting power, in W; A is the conductor surface area, in ; and represent ice density and ice thickness respectively; and represent the latent heat of melting and specific heat capacity of ice respectively; Indicates the difference between ice layer temperature and ambient temperature; Indicates the unit time for calculating the target ice melting power; Under the dual constraints of safety and efficiency, the power protection threshold is determined and expressed as: Where, Indicates the optimized power protection threshold; Indicates the current power protection threshold; It represents the safety factor, which is used to deal with ice parameter measurement errors or environmental changes, and is usually taken as 1.2~1.5. is the dimensionless proportionality coefficient; Indicates the maximum allowable power of the DC ice melting device.

[0049] The embodiments of the present invention can not only dynamically simulate and analyze the load mutation caused by uneven melting of the ice layer and the impact of the load mutation on the bus voltage response, but also optimize the power protection and analyze the anti-disturbance capability of the DC ice melting device. It can effectively improve the comprehensiveness and reliability of the anti-disturbance capability evaluation of the DC ice melting device, provide reliable protection for the operational stability and adaptability of the DC ice melting device in actual applications, and optimize power protection according to the environmental conditions of actual application scenarios, effectively balancing the ice melting efficiency and ice melting safety of the DC ice melting device under different weather conditions, so that the DC ice melting device after experimental adjustment can meet the application requirements of different extreme weather conditions.

[0050] It should be noted that although the steps in the above flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0051] In one embodiment, Figure 2 As shown, a dynamic load test system for a DC ice melting device is provided, the system comprising: Data acquisition module 1 is used to obtain operating status data of the DC ice melting device in the scenario of uneven ice melting, and generate a load mutation sequence based on the operating status data; the operating status data includes time series data of current change rate, voltage sag depth, and voltage harmonic content; the load mutation sequence includes time series data of ice shedding position, ice shedding thickness, and load impedance fluctuation corresponding to multiple load mutation time periods; Simulation and analysis module 2 is configured to sequentially simulate bus voltage responses for each load mutation period in the load mutation sequence according to a preset dynamic load response mechanism, thereby obtaining a DC bus response time series data set; the preset dynamic load response mechanism includes adaptively adjusting a power protection trigger threshold and a control system delay threshold of the DC de-icing device; The anti-disturbance evaluation module 3 is configured to analyze the anti-disturbance capability of the DC ice melting device according to the DC bus response time series data set and a preset anti-disturbance capability evaluation threshold, and obtain a corresponding anti-disturbance capability evaluation result.

[0052] In one embodiment, a dynamic load test system for a DC ice melting device is provided, the system further comprising: The power protection optimization module is configured to optimize the power protection triggering threshold of the DC ice melting device according to real-time environmental change data when the anti-disturbance capability evaluation result does not meet the anti-disturbance capability target.

[0053] The specific definitions of the dynamic load test system for a DC ice-melting device can be found in the definitions of the dynamic load test method for a DC ice-melting device described above. The corresponding technical effects are equivalent and are not further elaborated here. Each module in the dynamic load test system for a DC ice-melting device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0054] In summary, the embodiments of the present invention provide a dynamic load test method and system for a DC ice melting device. The dynamic load test method for a DC ice melting device can not only be based on a dynamic simulation analysis of the load mutation caused by uneven melting of the ice layer and the impact of the load mutation on the bus voltage response, but also realize the power protection optimization and anti-disturbance capability analysis of the DC ice melting device. It can effectively improve the comprehensiveness and reliability of the anti-disturbance capability evaluation of the DC ice melting device, provide reliable protection for the operational stability and adaptability of the actual application of the DC ice melting device, and optimize power protection according to the environmental conditions of the actual application scenario, effectively balance the ice melting efficiency and ice melting safety of the DC ice melting device under different weather conditions, so that the DC ice melting device after test adjustment can meet the application requirements of different extreme weather conditions and has high practical value.

[0055] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0056] The above-described embodiments merely represent several preferred implementations of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art can make several improvements and substitutions without departing from the technical principles of the present invention, and such improvements and substitutions should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the scope of protection of the claims.

Claims

1. A dynamic load test method for a DC ice melting device, characterized in that: The method comprises the following steps: Obtaining operating status data of a DC ice melting device in a scenario of uneven ice melting, and generating a load mutation sequence based on the operating status data; the operating status data includes time series data of current change rate, voltage sag depth, and voltage harmonic content; the load mutation sequence includes time series data of ice shedding positions, ice shedding thickness, and load impedance fluctuation corresponding to multiple load mutation time periods; Performing a bus voltage response simulation for each load mutation period in the load mutation sequence in sequence according to a preset dynamic load response mechanism, obtaining a DC bus response time series data set; the preset dynamic load response mechanism includes adaptively adjusting a power protection trigger threshold and a control system delay threshold of the DC de-icing device; The anti-disturbance capability of the DC ice melting device is analyzed according to the DC bus response time series data set and a preset anti-disturbance capability evaluation threshold to obtain a corresponding anti-disturbance capability evaluation result.

2. The dynamic load test method of a DC ice melting device according to claim 1, characterized in that: The step of generating a load mutation sequence according to the operating status data includes: According to the voltage sag depth time series data and the voltage harmonic content time series data, a load impedance fluctuation prediction sequence and a bus impedance fluctuation prediction sequence are obtained based on a support vector machine regression model; Traversing the current change rate at each moment in the current change rate time series data and determining whether it is greater than a preset change rate threshold; If so, the duration of the change rate exceeding the threshold is obtained, and the corresponding mutation cause is obtained based on the random forest algorithm according to the subsequence corresponding to the duration of the change rate exceeding the threshold in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence; the mutation cause includes non-ice shedding and ice shedding; When the cause of the sudden change is ice shedding, obtaining the ice shedding position and ice shedding thickness during the corresponding load sudden change period according to the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence; According to the ice shedding position and ice shedding thickness in each load mutation period, the subsequences of the corresponding time period in the load impedance fluctuation prediction sequence are segmented and marked to obtain the load mutation sequence.

3. The dynamic load test method of a DC ice melting device according to claim 2, characterized in that: The current change rate threshold is adaptively adjusted based on temperature and wind speed; the current change rate threshold is expressed as: Where, Indicates the current change rate threshold; represents the temperature proportionality coefficient; Indicates ice melting reference temperature; Indicates the real-time ambient temperature; It represents the wind speed pressure coefficient; Indicates real-time wind speed.

4. The dynamic load test method of a DC ice melting device according to claim 2, characterized in that: The step of obtaining the ice shedding position and ice shedding thickness corresponding to the load mutation period according to the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence includes: Based on the relationship between ice thickness and line impedance and the segmented superposition effect of line impedance, an equation correlating load impedance and bus impedance fluctuation is established. The equation correlating load impedance and bus impedance fluctuation includes: Where, and Respectively represent bus impedance fluctuation data and load impedance fluctuation data; Indicates the influence coefficient of ice layer on impedance; Indicates the thickness of ice shedding; Indicates the load line length; Indicates the location of ice shedding; Substituting the load impedance fluctuation data and the bus impedance fluctuation data at each moment in the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence into the load impedance and bus impedance fluctuation correlation equation respectively for solving, thereby obtaining an estimated value of the ice shedding position and an estimated value of the ice shedding thickness at the corresponding moment; The estimated values ​​of ice shedding positions and ice shedding thicknesses at each moment in the subsequence corresponding to the duration of the change rate exceeding the threshold value in the load impedance fluctuation prediction sequence and the bus impedance fluctuation prediction sequence are weighted averaged based on the noise standard deviation of the load impedance fluctuation data at the corresponding moment to obtain the ice shedding positions and ice shedding thicknesses in the corresponding load mutation period.

5. The dynamic load test method of a DC ice melting device according to claim 1, characterized in that: The DC bus response time series data set includes DC bus response time series data of each load mutation period; The step of sequentially simulating the bus voltage response for each load mutation period in the load mutation sequence according to a preset dynamic load response mechanism to obtain a DC bus response time series data set includes: Initializing a preset DC ice-melting power system simulation model according to a preset voltage drop threshold and the load mutation sequence, and simulating the load impedance fluctuation time series data of the first load mutation period in the load mutation sequence to obtain initial DC bus voltage time series data; Determine whether the initial time series data of the DC bus voltage contains voltage change data exceeding the preset voltage drop threshold; if so, perform time domain analysis on the initial time series data of the DC bus voltage to obtain a DC bus voltage drop depth and a DC bus voltage recovery time corresponding to the load mutation moment; determining whether the DC bus voltage drop depth exceeds a preset voltage drop depth threshold; if so, adjusting the power protection trigger threshold and the control system delay threshold of the DC ice melting device according to the DC bus voltage drop depth and the DC bus voltage recovery time; According to the adjusted power protection trigger threshold and control system delay threshold, the DC bus response timing data of the first load mutation period in the load mutation sequence is re-simulated and obtained, and the subsequent load mutation periods are simulated in turn until the DC bus response timing data of all load mutation periods are obtained.

6. The dynamic load test method of a DC ice melting device according to claim 5, characterized in that: The step of adjusting the power protection trigger threshold and the control system delay threshold of the DC ice melting device according to the DC bus voltage drop depth and the DC bus voltage recovery time includes: Comparing and analyzing the DC bus voltage drop depth, the DC bus voltage recovery time, and a preset voltage response threshold, to obtain a voltage response deviation; the voltage response deviation includes a voltage drop depth deviation and a voltage recovery time deviation; According to the voltage sag depth deviation in the voltage response deviation, the power protection trigger threshold of the DC ice melting device is adaptively updated based on a preset power protection trigger threshold update expression; the preset power protection trigger threshold update expression is expressed as: Where, and Respectively represent the power protection triggering threshold before and after the update; Indicates the power adjustment factor; Indicates voltage drop depth deviation; According to the voltage recovery time deviation in the voltage response deviation, the preset control system delay threshold of the DC ice melting device is adaptively updated based on a preset control system delay threshold update expression; the preset control system delay threshold update expression is expressed as: Where, and Respectively represent the control system delay thresholds before and after the update; Indicates the delay adjustment coefficient; Indicates the voltage recovery time deviation.

7. The dynamic load test method of a DC ice melting device according to claim 1, characterized in that: The preset anti-disturbance capability evaluation threshold includes a preset wire vibration period threshold, a preset period stability threshold and a preset recovery time threshold; The step of performing anti-disturbance capability analysis of the DC ice melting device according to the DC bus response time series data set and a preset anti-disturbance capability evaluation threshold to obtain a corresponding anti-disturbance capability evaluation result includes: Performing time domain analysis and frequency domain analysis on the DC bus response time series data of each load mutation period in the DC bus response time series data set, respectively, to obtain bus voltage fluctuation period and main frequency component time series data corresponding to the load mutation period; The bus voltage fluctuation period in each load mutation period is averaged to obtain the average bus voltage fluctuation period, and a period stability index is obtained based on the main frequency component time series data of each load mutation period; the period stability index includes the frequency fluctuation range and the frequency fluctuation rate; Comparing and analyzing the busbar average voltage fluctuation period with a preset conductor vibration period threshold to obtain a corresponding periodicity comparison result, and comparing and analyzing the periodicity stability index with a preset periodicity stability threshold to obtain a corresponding periodicity stability analysis result; Analyze the DC bus response time series data of each load mutation period based on the preset voltage drop threshold, and average the bus voltage recovery time of each load mutation period to obtain the average bus voltage recovery time; Comparing and analyzing the average bus voltage recovery time with a preset recovery time threshold to obtain a corresponding recovery characteristic analysis result; The anti-disturbance capability evaluation result is obtained by performing a fusion analysis based on the periodic comparison result, the periodic stability analysis result and the recovery characteristic analysis result.

8. The dynamic load test method of a DC ice melting device according to claim 1, characterized in that: The method further comprises: When the anti-disturbance capability evaluation result does not meet the anti-disturbance capability target, the power protection triggering threshold of the DC ice melting device is optimized according to the real-time environmental change data.

9. The dynamic load test method for a DC ice melting device according to claim 8, characterized in that: The real-time environmental change data includes temperature change data, humidity change data and wind speed change data; The step of optimizing the power protection triggering threshold of the DC ice melting device according to the real-time environmental change data includes: According to the temperature change data, the humidity change data, and the wind speed change data, ice layer physical property change data is obtained based on a preset ice layer physical property model; the preset ice layer physical property model is constructed by performing a multivariate regression analysis on historical winter environmental change data and the corresponding ice layer physical property change data; the ice layer physical property change data includes ice layer density change data and ice layer thickness change data; Obtain corresponding impedance change data according to the temperature change data and a preset temperature influence load impedance model; the preset temperature influence load impedance model is constructed based on fitting of historical winter temperature change data and conductor impedance change data; If the impedance change data exceeds a preset impedance change threshold, the power protection triggering threshold of the DC ice melting device is optimized according to the ice layer physical property change data.

10. A dynamic load test system for a DC ice melting device, characterized in that: The system comprises: A data acquisition module is configured to acquire operating status data of a DC ice melting device in a scenario of uneven ice melting, and generate a load mutation sequence based on the operating status data; the operating status data includes time series data of current change rate, voltage drop depth, and voltage harmonic content; the load mutation sequence includes time series data of ice shedding position, ice shedding thickness, and load impedance fluctuation corresponding to multiple load mutation time periods; a simulation analysis module configured to sequentially simulate bus voltage responses for each load mutation period in the load mutation sequence according to a preset dynamic load response mechanism, thereby obtaining a DC bus response time series data set; the preset dynamic load response mechanism includes adaptively adjusting a power protection trigger threshold and a control system delay threshold of the DC de-icing device; The anti-disturbance evaluation module is used to analyze the anti-disturbance capability of the DC ice melting device according to the DC bus response time series data set and a preset anti-disturbance capability evaluation threshold, and obtain a corresponding anti-disturbance capability evaluation result.

Images